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Transcript of Out-of-pocket patient payments and vulnerable …...Out-of-pocket Patient Payments and Vulnerable...
Out-of-pocket patient payments
and vulnerable population groups
in Serbia
Jelena Arsenijevic
Out-of-pocket patient payments and vulnerable population groups in Serbia
© Jelena Arsenijevic, 2015
Cover page designed by Marko Milojevic
Printed by Ipskamp Drukkers, Enschede
Design by Legatron electronic Publishing, Rotterdam
All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted in any form or by any means, electronic, mechanical,
photocopying, recording or otherwise, without the written permission from the author.
ISBN: 978-94-6259-617-7
Out-of-pocket Patient Payments
and Vulnerable Population Groups in Serbia
Dissertation
to obtain the degree of Doctor at Maastricht University,
on the authority of the Rector Magnificus, Prof. Dr. L.L.G. Soete in
accordance with the decision of the Board of the Deans,
to be defended in public
on Wednesday 1 April 2015, at 14.00 hours
by
Jelena Arsenijevic
SupervisorProf. dr. Wim Groot
Co-supervisorDr. Milena Pavlova
Assessment CommitteeProf. dr. S.M.A.A. Evers (chair), University Maastricht
Prof. dr. H. Maassen van den Brink, University Maastricht
Prof. dr. E.K.A. van Doorslaer, Rotterdam University
Prof. dr. G.G. van Merode, University Maastricht
Prof. dr. S. Simic, Belgrade University
Acknowledgement of funding
The study is financed by the European Commission under the 7 th Framework P r o g r a m ,
Theme 8 Socio-economic Sciences and Humanities, Project ASSPRO CEE 2007 (Grant
Agreement no. 217431). The content of the publication is the sole responsibility of the
author and it in no way represents the views of the Commission or its services.
Contents
Chapter 1 General Introduction 7
Chapter 2 Measuring the Catastrophic and Impoverishing Effect of Household 27
Health Care Spending in Serbia
Chapter 3 Different Types of Out-of-pocket Payments for Health Care: 49
How do they Contribute to Impoverishing and Catastrophic Effects
among Serbian Households?
Chapter 4 Out-of-Pocket Payments for Public Health Care Services by Selected 73
Exempted Groups in Serbia During the Period of the Post-war
Health Care Reforms
Chapter 5 The Effects of Chronic Diseases on Poverty 103
Chapter 6 Shortcomings of maternity care in Serbia 125
Chapter 7 General Discussion 151
References 175
Appendix 187
Summary 191
Acknowledgements 199
Curriculum Vitae 205
CHAPTER 1
General Introduction
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Chapter 1
1.1 The scope of dissertation
During the period 1991-2000, Serbia faced a civil war that was accompanied with a severe
economic crisis. The infrastructure in the country was ruined, the government institutions
responsible for providing social protection, did no longer function adequately, while
the population was impoverished (Bajec et al., 2008). However, it was not possible to
estimate the extent of the impoverishment, since valid data were not available (Bogićević
et. al., 2002). After the civil war, in 2003, the Serbian government, in cooperation
with the World Bank, developed the first strategy to monitor and reduce poverty in the
country (World Bank, 2002). Related to this, the World Bank conducted the first Living
Standard Measurement Survey (LSMS) in Serbia in 2002, and subsequently in 2003 and
2007 (World Bank, 2011). The methodology for estimating the poverty level was also
provided by the World Bank (World Bank, 2003). The main goal was to measure and
decrease the level of poverty, and to assure social protection of vulnerable groups (Bajec
et al., 2008; Vukovic & Perisic, 2010). The social protection system was designed to
include not only financial assistance but also the provision of education and health care
services (Bajec et al., 2008; World Bank, 2003).
At the same time, in 2002, the Serbian government started reforms in the health care
system (Gajic-Stevanovic et al., 2010). In order to assure the financial sustainability of
the health care system, the government introduced a system of official co-payments in
the public health care sector (World Bank, 2003). Since compulsory health care insurance
already existed, the introduction of co-payments increased household spending on health
care (Bajec et al., 2008). In accordance with the strategy for poverty reduction and
social protection, the introduction of co-payments was accompanied with an exemption
mechanism (Gajic-Stevanovic, 2010). Nevertheless, the co-payments still present a risk
of financial burden not only for vulnerable groups but also for all health care users (Xu et
al., 2010). For example, frequent health care users, like chronically sick, are only partially
exempted from co-payments in Serbia and can experience high costs.
Impoverishment was not the only consequence of the civil war. Like in many other
post-conflict and transitional societies, corruption became a modus vivendi in the public
sector in Serbia (van Duyne, 2010). Since 2000, several policy documents were written in
order to propose a solution how to decrease corruption in the public sector (Bajec et al.,
2008; van Duyne, 2010; World Bank, 2003). Despite the pressure from the international
community and civil sector organizations, none of the Serbian governments applied any
restrictive policy measures towards corruption behavior in the public sector.
Corruptive behavior had a direct effect on health care consumers as well. In 2002,
when the official co-payments were introduced, different types of informal (under-the-
table) patient payments were already common practice in the health care system (CESID,
2011). Although anecdotal evidence suggests a high financial burden provoked by
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Introduction
9
informal patient payments (Blic, 2013), hitherto, there is no empirical study reporting
on their existence and their impact in the health care sector. Corruptive behavior in
health care, such as informal payments, has negative effects on health care users. The
vulnerable population groups are also affected by those negative effects. For example,
pregnant women are officially exempted from official co-payments but they might still
be asked to pay informally for maternity care (Stamenkovic, 2011). For pregnant women
from vulnerable groups, such extra payments might have catastrophic effects on their
household budget. In contrast to official co-payments, the government has no ability to
mitigate the negative effects of informal payments because they are not registered and
thus, outside of government control.
Using data collected by the World Bank, previous studies have reported on the
reduction of the poverty level among the general population, praising the effects of the
applied strategy mentioned above (Bajec et al., 2008; Krstic, 2008). However, only few
of these studies have addressed the financial burden provoked by patient payments in
health care and their possible effects on poverty (Bredenkamp et al., 2011; Bredenkamp
et al., 2008). Furthermore, the studies focused on the period from 2002 to2003. Also,
they did not address the type and effects of out-of-pocket patient payments on vulnerable
groups (Bredenkamp et al., 2011; Bredenkamp et al., 2008).
This dissertation is motivated by the fact that although anecdotal evidence indicates
a high financial burden provoked by out-of-pocket spending in Serbia, the empirical
evidence is limited. Moreover, there is no evidence how different types of payments
(formal and informal) contribute to this burden. Therefore, the dissertation focusses
on these issues. Previous studies that address financial burden were not focused on
vulnerable groups. The dissertation also outlines to what extent vulnerable groups are
protected by the current exemption mechanism, with a special focus on pregnant women
and chronically sick.
1.2 Social protection – beyond the theory
Social protection is traditionally defined as a set of policy measures to protect individuals
from income loss (Esping-Andersen, 1990). Defined in this way, social protection is a
concept mainly focused on income protection related to a certain period of life, like being
unemployed or becoming poor due to certain life events (becoming a widow, orphan, or
living in a large families) (Esping-Andersen 1990; Tanzi, 2000). Three mechanisms are
mostly used to achieve this type of social protection: social insurance, social assistance
and labor market regulations (Barrientos, 2011).
In welfare states, the concept of social protection has a broader definition (Tanzi,
2002). Social protection is defined there as a set of policy measures to protect individuals,
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especially the critically poor, from financial losses due to high risk events (Holzman &
Jorgsen, 2001). Risk or hazard events include environmental risks like natural disasters,
social risks like unemployment, war or unexpected health shocks (illness), and political
risks like discrimination of minorities in conflict zones (Barrientos, 2010; Estevez-
Abe, Iversen & Soskice, 1999; Holzman & Jorgsen, 2001). Different stakeholders like
governments, public organizations but also non-governmental organizations (NGO) and
international organizations (UNSD), provide interventions related to social protection
(Holzman & Jorgsen, 2001; World Bank, 2003). In this way, social protection represents
a set of interventions that help individuals, households and communities to manage
different risks factors, but also to prevent the risk of financial losses and to anticipate
their possible consequences (Holzman & Jorgsen, 2001).
For a successful implementation of a social protection policy, it is necessary to identify
the population groups that are most vulnerable to certain risks (Holzman & Jorgsen,
2001). Population groups that are most often exposed to certain risks and/or do not have
enough resources to cope with the consequences provoked by risks events are usually
described as vulnerable groups (Fiszbein, Kanbur & Yemtsov, 2013; Holzman, 2001;
Philip & Ryanan, 2004). However, the risk factors that make certain population groups
vulnerable and eligible for social protection differ between developing and developed
countries (de Haan & Sturm, 2000). Moreover, the targeting of eligible population
groups not only depends on the type of risk but also on social, economic and political
circumstances in the country (Slater, 2008). In times of natural disasters, like earthquake,
children and elderly are perceived as vulnerable groups, but in times of war, minorities can
be perceived as the more vulnerable groups (UNRISD, 2010). Some population groups
can receive social protection assistance on political grounds (e.g. free higher education for
all ethnical minorities) (UNRISD, 2010). However, often population groups eligible for
social protection are not necessary the vulnerable groups (Fraser and Gordon 1994, Slater,
2008; UNRISD, 2010). For example not all children younger than 15 are eligible to be
exempted from official co-payments or not all elderly people should be exempted from
official co-payments.
According to the definitions above, poor population groups are always perceived as
eligible for social protection benefits. Although poverty is perceived as a clear vulnerability,
it is sometimes difficult to determine who is poor (Alkire & Foster, 2011). In order to
identify the poor, traditional poverty measures first identify a suitable indicator of wealth
(such as income or consumption) and then set a cut-off point, i.e. a poverty line defined
as a minimum level below which one is consider to be poor (Ravallion, 1989; Sen, 1997).
Recent studies have shown that poverty is a multidimensional concept that includes
not only income deprivation, but also psychological and sociological deprivation
(Atkinson, 2003; Bourgouignon & Charkavatry, 2003; Kakwani & Silber, 2008).
Following the multidimensional concept of poverty, social policy goes beyond protection
1
Introduction
11
towards social inclusion. Social inclusion emphasizes the satisfaction of cultural needs,
active participation in social life and subjective well-being as important dimensions not
only for individuals but also for the population wealth (Coates et al., 2001). A well-
designed social policy that provides equal inclusion of different population groups in
society does not only reduce poverty but also increases economic progress (Fiszbein,
Kanbur & Yemtsov, 2013).
In terms of health, social protection includes protection against health risks (like
epidemiological risks), patient protection that is related to quality of care and financial
protection that aims to protect people from unexpected health care shocks (Knaul et
al., 2012). One of the ways to assure financial protection in the health care sector is to
introduce universal social health insurance (Li et al., 2012). However, when universal
health insurance cannot provide financial sustainability of the health care system, patient
charges are necessary. In this case, social protection can be achieved by the implementation
of an exemption mechanism (Bitran & Giedon, 2002). The successful implementation
of an exemption mechanism also requires identification of eligible groups. Previous
literature has described four main methods to identify the population groups eligible for
exemption namely individual identification, identification based on group characteristics,
self-identification and self-selection based on types of services (Bitron & Giedan, 2002).
Population groups that are usually exempted include children, elderly, unemployed, and
poor (Bitron & Giedan, 2002; Tambor et al, 2010). However, when social protection in
health care fails, different population groups can experience a financial burden provoked
by health care spending (Xu et al., 2010). The level of the financial burden can be
measured by three different approaches namely the impoverishing effects of health care
spending (the proportion of people who go below the poverty line after the health care
costs are subtracted), catastrophic health care expenditure (pre-defined proportion of
household income or consumption that is spend on health care is perceived as catastrophic
if it exceeds certain threshold) and subjective poverty (individuals’ perception of being
poor) (Wagstaff, 2008; Xu et al., 2010). While the first two approaches are based on real
expenditure and strongly depend on welfare indicators, the third approach represents the
subjective perception of the possible burden.
1.3 Social protection in Serbia
Social protection in Serbia has a long tradition (Lakicevic, 1995). The first Red Cross
was founded in 1786. The formal funds for social protection of orphans, people with
mental illness, poor and people injured in wars were also established in 1786. Anecdotal
evidence shows that although social protection provided by the state existed officially
since 1786, it did not work in reality (Lazarevic, 1882). During the period of the Balkan
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wars (1912-1913), several funds were established to protect orphans, single mothers and
people injured during these wars (Stojanovic, 2010). Although some of those funds were
supported by the Serbian government, most of them were established by wealthy citizens
and had a more informal character (Stojanovic, 2010).
The first formal system of social protection was established after the Second World
War. The new socialistic government emphasized at that time the importance of
solidarity with vulnerable groups. Every local community had a special unit (center)
for social protection called Centar za socialni rad (Golubovic, 1997). The centers were
authorized to provide financial assistance, accommodation but also social inclusion for
vulnerable groups (such as special arts activities for disabled children). The centers
employed different types of professionals like lawyers, health care workers, social workers
and psychologists. Disabled people, children with cognitive disabilities, orphans, elderly,
single mothers, pensioners but also people with life-treating injuries from the Second
World War were perceived as vulnerable groups. The centers were funded directly by the
government (Bajec et al., 2008).
During the period of Yugoslavia, the poverty level was not monitored. The common
attitude of the socialistic government was that there were no poor people, only people
with certain life events who may face temporary financial difficulties (Palairet, 2001).
However, in reality, poverty existed but it was hidden (Bajec et al., 2008).
In 1991, the system of social protection was abolished by the Constitution of the
Republic of Serbia (Bajec et al., 2008). Many of the existing centers were closed. The
reason was the lack of financial resources. After the major political changes in 2000, the
new government re-established the system of social protection. The current system of
social protection consists of: a social insurance scheme (pension and invalidity insurance,
unemployment insurance and health insurance), family and social benefits and services,
and education. The system of social protection is funded by compulsory contributions
and by the government budget. It is jointly governed by the Ministry of Labor and Social
Policy, the Ministry of Health, the Health Insurance Fund and the Pension and Invalidity
Institute (Government of Republic of Serbia, 2014). The current social protection system
consists of 12 different programs aimed to protect different vulnerable groups (Bajec et
al., 2008). The services are provided by local municipalities and the re-established Centar
za socijalni rad within each municipality. Several groups are defined as vulnerable, namely:
people with disabilities, children without parental care, the Roma population, refugees
and internally displaced people, victims of family abuse, unemployed, homeless persons,
war veterans, discriminated groups (people who are HIV positive). Social protection in
health care in Serbia is based on an exemption mechanism (Bajec et al., 2008). A large
number of population groups is exempted (Gajic-Stevanovic, 2010). As mentioned before,
as part of the social protection system, a strategy for monitoring and reducing poverty
was established in 2003 (World Bank, 2003). Since then, people with a monthly income
1
Introduction
13
below the national poverty line are also considered as a vulnerable group. Although
the normative legalization of social protection exists, the system is characterized by a
low level of efficiency (e.g. they do not provide support for all vulnerable groups, very
often all vulnerable groups are not targeted well). The inefficiency and limited financial
resources are the main reason for the difficulties in the provision of social services.
1.4 Health care system in Serbia
1.4.1 Historical overview of health care system in SerbiaThe first organized provision of health care in Serbia was reported at the beginning
of twenty century (Stojanovic, 2006). The health care system was organized through
two levels: the first level consisted of general practitioners (GPs), and the second
level consisted of several general hospitals. GPs were obliged to obtain a work permit
from the Ministry of Internal Affairs, Department of Health (Stojanovic, 2006). Local
municipalities gave them apartments and a small monthly allowance in order to keep
them in their municipalities. Their services were paid by out-of-pocket payments by
patients and were perceived as hardly affordable for the average citizen. Patients who
could not afford direct out-of-pocket payments paid by goods (eggs, sugar, etc.). Some of
them were using services of pseudo-doctors.
The first Serbian medical society was founded in 1878. In 1914, at the beginning of
the First World War, 534 doctors were registered in Serbia (Milicevic-Santric, 2009).
After the war, official state statistics reported one physician per 2716 citizens in the
Kingdom of Yugoslavia. Physicians were also acting as medical researchers. Organized
in a medical society, they annually published a statistical report with epidemiological
indicators.
The second level of the health care system included a network of general hospitals
situated mostly in big cities (Beograd, Sabac, Kragujevac, Pozarevac, Sombor, Subotica)
(Durlevic, 2012). They were founded and paid by local municipalities. Patients were
referred to hospitals by local physicians (Durlevic, 2012). There were no reported out-
of-pocket patient payments, but citizens were sent to hospitals only if they had a severe
illness or some of the contagious diseases.
In 1918, infant mortality during delivery in the Belgrade district was 5.5 per 1000
deliveries (Joksimovic, 1911), while the average life expectancy of men was 50.2 years
and for women 41.2 (Joksimovic, Statisticki Godisnjak, 1911). Those indicators were
similar to other countries in the Balkan Peninsula but much less favorable than those
in France for example (Stojanovic, 2006). The Serbian medical society also emphasized
problems of alcoholism, especially among young men in high school (26.2% of them were
drinking regularly every night) (Narodno Zdravlje, 1909). The Serbian state encouraged
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physicians to report and publish the data, but did not use the data to design actual policy
measures (Stojanovic, 2006).
After the Second World War the new state was founded, the Federal People’s Republic
of Yugoslavia and in 1963 the name is changed to Socialistic Federative Republic of
Yugoslavia (SFRJ). The state consisted of 6 federal units named republics (Slovenia,
Croatia, Bosnia and Herzegovina, Serbia, Montenegro and Macedonia) with the legal
right to act as independent units within the federation (National Constitution Act,
1943). The SFRJ was a country characterized by a unique type of socialism compared to
other socialist countries, so called “samoupravljanje” or self-managed society (Markovic,
1968). Officially, the country was “self-managed” by the citizens, but in reality, the
country was controlled by the communist elite organized in local, republic and federal
party levels (Perovic, 2013). The citizens of the SFRJ indicated that the master piece of
this unique type of socialism was the health care system.
According to officials, the health care system was a combination of the experience
from the UK and the USSR. The health care system was organized through primary,
secondary and tertiary care. Similarly to the UK, primary care included GPs who were
supposed to act as gate keepers towards secondary and tertiary care (Vukomanovic, 1972).
Also, similarly to the USSR model, primary care centers included specialists for maternal
care, occupational medicine and children care. In this way, citizens were able to visit those
specialists without any referral (Saric & Rodwin, 1993). Secondary care included general
hospitals, while tertiary care included state clinical centers and specialized hospitals.
The health care institutions were owned by the society (Saric & Rodwin, 1993). Private
practices were forbidden.
Contrary to the USSR health care model (the so called Semashko model implemented
in other socialist countries), the health care system in the SFRJ was funded by compulsory
health insurance contributions. Health insurance contributions were paid by all employed
citizens at 8% of their gross salaries. Non-employed citizens were insured by employed
family members or by the national insurance body (‘self-managing communities of
interest‘, so called the SIZ). Children, students and pensioners were exempted from paying
contributions but were still covered by the health insurance scheme. Individual farmers
were not allowed to join the compulsory health insurance until 1958, while after 1958,
they were contributing through general taxes. The allocation of collected funds and their
control was done by the SIZ. The SIZ was established as a quasi-insurance company. The
SIZ included two groups of elected members: the group of consumers (citizens, workers,
teachers, local party leaders) and the group of providers (physicians, hospital managers
some of them also local party leaders etc.). The two groups negotiated what should be
included in the compulsory health care package (Saric & Rodwin, 1993). The negotiation
process was facilitated by an administrative body, i.e. the general management of the
SIZ. The SIZ existed as a federal, republic and local body. The allocation of funds was
1
Introduction
15
done by the federal SIZ to the republics, and the republics’ government body allocated
money either directly to providers (a fixed annual budget for hospitals was negotiated
with the republics SIZ) or to the local SIZ (primary care was funded by the local SIZ)
(Letica, 1984). More developed republics like Slovenia, Croatia and Serbia allocated more
money and included more services in the compulsory health care package (for example in
those three republics spa treatment for 3 weeks was included in the compulsory package)
(Mastilica, 1990). If citizens from Slovenia decided to use any health service in Croatia,
the SIZ of Slovenia was obliged to reimburse the SIZ of Croatia for this particular service.
Similar regulations were applied to different municipalities within the same republics
(Saric & Rodwin, 1993). The SIZ of a particular republic also allocated funds for provider’s
salaries, but the decision who should be employed was done by the health care managers
in each institution.
During the 1970s, the system received a lot of attention from researchers from the
UK and the US (Himmelstein et al., 1984; Parmelee, 1979; Ward, 1973). They were
describing the health care system of the SFRJ as a “Swedish model in the Balkan”.
Although highly prized by citizens, authorities and foreign researchers, this system also
had several limitations (Letica, 1982; Saric & Rodwin, 1993).
1.4.2 Limitations of the health care system in the SFRJ Although, described as an “idealistic system”, the SFRJ health care system in practice
had several drawbacks (Letica, 1984). GPs were never acting as true gate keepers. The
majority of GPs were young physicians waiting for additional education. Therefore, they
had a tendency to refer citizens to the secondary level (Saric & Rodwin, 1993). The
geographical accessibility to secondary and tertiary care was not equal for the whole
country (Vukomanovic, 1972). In order to promote the constantly ongoing growth in
health care, the local the SIZ were stimulating an increase in the number of beds per
capita in secondary care and an increase in the number of physicians per capita (Saric
& Rodwin, 1993). Moreover, the highly developed republics allocated more money to
their SIZ provoking social and health inequalities in the country (Mastilica, 1990) (see
Figure 1.1). The differences in the allocated funds among different republics were the
first precursor for the occurrence of social and health inequalities. Social inequalities were
also reported within the same republic. For example, the infant mortality rate in 1981 in
the well-developed parts in Serbia, like Central Serbia and Vojvodina, was respectively
23.8 infant deaths per birth and 17.1infant deaths per birth while in Kosovo, it was 62.9
infant death per birth (Mastilica, 1990). The working urban class was favored within
the health care system, as they were using health care services more often and they were
better informed about the available services (Mastilica, 1990; Parmelee, 1979). Managers
and party leaders lived on average longer than non-educated workers or farmers. They
also reported fewer chronic diseases (Mastilica, 1990).
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Although officially funded by the SIZ, in practice, secondary and tertiary care was funded
directly by the republic governments. Very often, when the fixed annual budget was
exceeded before the end of the year, state companies gave so called “donations” (Palairet,
2001). Donations gave some extra rights to their workers (preventive use of diagnostic
services or spa resorts). The SIZ included consumers in the decision making process,
but in practice consumer representatives were loyal members of the communistic party
(Saric & Rodwin, 1993). In this way, the communist party controlled all the decisions
of the SIZ. Often, the SIZ management was asked to “borrow” money to ministry of
education or to some companies for education or some other service. Thus, the door to
“state corruption” was open.
In the beginning of 1980, it was clear that the shortage in resources could not be
overcome by the state and the SIZ introduced the first official co-payments (participacija)
for services like plastic surgery or alternative medicine. The decision had never been
approved by the central committee of the communist party and was highly criticized in
public (Palariet, 2001; Saric & Rodwin, 1993)
Figure 1.1: Infant mortality rates in SFRJ per republic (1954-1981), source: Mastilica, 1990.
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Introduction
17
1.4.3 Post-socialistic period and health care reforms in SerbiaIn 1992, two republics (Croatia and Slovenia) used their constitutional right to declare
independence from the SFRJ, provoking the beginning of a civil war (Black, 1993). The
civil war had a great impact on the health care system of the SFRJ (Mastilica, 2001;
Palairet, 2001). Although the republics had their own SIZs and independence in the
decision-making process, the pharmaceutical industry and the industry for disposable
materials were spread in different geographical areas (e.g. a part of the infusion systems
was produced in Croatia, and another part in Serbia) (Palairet, 2001). During the civil
war, the former cooperation between these areas ceased to exist. More than 40% of the
society-owned companies in Serbia were not able to function anymore because they could
not cooperate with their partners in other republic (Madzar, 1998). Beside the civil war,
Serbia was facing economic sanctions imposed by the United Nation Security Council
(UNSC). At the same time, monetary inflation reached its highest level (Nelson, 2003).
These developments had a devastating impact on the health care sector. Companies
that did not function, could not provide payments for compulsory health care insurance
and those that were functioning provided payments on an irregular basis (Palairet, 2001).
The UN sanctions were banning imports of goods including pharmaceuticals, medical
supplies, and disposable materials. The high monetary inflation depreciated the already
law salaries of physicians (Black, 1993).
At the same time, in 1992, Serbia introduced the new Law of Health Care, with the
objective to centralize all financial resources and management decisions related to the
Serbian SIZ, which later led to the creation of the Health Insurance Fund (HIF) (Bajec
et al., 2008). The health care system maintained the same structure, i.e. it was divided
into primary, secondary and tertiary care. The main change was related to primary care.
The local municipalities were no longer responsible for primary care (Bajec et al., 2008).
Contrary to the SFRJ period, where primary health care centers were praised, the new law
shifted the focus and financial resources towards secondary and tertiary care (Bajec et al.,
2008). Around 20 of the 150 small primary health care centers were closed (World Bank,
2009). All managers in the health care sector were then employed by the government.
The newly established HIF could not always assure basic medical supplies and
adequate healthcare services. This resulted in a lack of basic medical materials, supplies
and pharmaceuticals and led to irregular payment of salaries to health care professionals
(Black, 1993). Moreover, it was not possible to maintain the already existing infrastructure.
Often, physicians were asking patients and their families to bring to the hospital food
and necessary pharmaceuticals for their operations (Palairet, 2001). Also, some physicians
were asking patients to pay them extra directly otherwise they would not provide any
services (Black, 1993). Corruption was prevalent not only at the level of health care
provision but also at the state level. For example, it became known that during the period
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1990-1998, two ministers of health were transferring money from the HIF to outside the
country (Pieterel, 2001; van Duyne et al., 2010)
The civil war ended by NATO bombing of Serbia and Montenegro in 1999. According
to the NGO sector and international communities, in 1999, more than 47% of Serbian
population was living below the absolute poverty line (UNDP, 2006). The infrastructure
of the health care facilities was damaged and the system was collapsing. During the
period 1991-2000, many international organizations (UN, WHO, UNHCR, Red Cross)
were providing aid to Serbian hospitals to prevent a humanitarian crisis (Black, 1993;
WHO, 2005). Nevertheless, the level of contagious diseases was increasing as well as the
incidence of mental diseases (UNHCR, 1999). The high number of refugees and people
injured during the civil war increased the pressure on the already collapsing health care
system.
After the major political change in 2000, the Serbian government introduced health
care reforms. The main objective was to improve efficiency, service quality and equity in
health care (World Bank, 2009). As part of the health care reforms, in 2002, the Serbian
government introduced official co-payments for services covered by the compulsory health
insurance to improve the financial situation of the public health care system. The first co-
payments were with amounts ranging from almost symbolic 0.59 US dollars up to 30 US
dollars (World Bank, 2009). The introduction of official co-payments was accompanied
with an exemption mechanism. In 2003, the World Bank approved approximately 20
million US dollars to support the health care reforms. The initial planning included
the introduction of IT technologies in public primary health care centers and hospitals,
better prevention programs and financial reforms. In 2005, the Law of Health Care was
revised aiming to facilitate the financial health care reforms taking into account the
organization of the health care system. The same law allowed physicians who are full
time employed in the public sector, to work also in the private sector (Ministry of Health,
2005). In 2007, the additional Law of Health Care was introduced. The main goal was to
decentralize the health care system. According to this law, local municipalities were made
responsible for primary care and regional hospitals (Ministry of Health, 2007). Several
legal acts were introduced from 2008-2010, mostly defining the level of out-of-pocket
patient payments. Specifically, co-payments for physician services, laboratory diagnostic
radiography and ultrasound examination have increased twice during the period 2008-
2010.
1.4.4 The current funding of the health care system Today, the public health care system in Serbia is funded by resources collected through
compulsory health care insurance (controlled by the HIF and through resources provided
by the government (controlled by the Ministry of Health), as well as by out-of-pocket
payments. The compulsory health insurance covers 96.2% of the total population (71%
1
Introduction
19
are contributions from employees, 21% contributions from the Retirement Fund, and 1%
contribution from farmers) (Bajec et al., 2008; World Bank, 2009). For the unemployed
and those who are exempted, the government is paying for compulsory health care
insurance (12.3% of the minimum net salary in the country). The HIF also uses the
collected payments to reimburse the costs of sick leave and to reimburse the travel costs
for health care utilization. The fund is also responsible for the positive pharmaceutical
lists. The Ministry of Health is directly responsible for capital investments. Since 1992,
private health care providers exist in Serbia but they are not included in the compulsory
insurance system. Moreover private health care is not monitored by the Ministry of
Health.
As mentioned earlier, health care is provided through primary care facilities (outpatient
services) as well as secondary and tertiary care facilities (inpatient care in hospitals and
clinical centers). Regarding the payment mechanism in primary care facilities, a capitation
based payment system has been introduced (World Bank, 2009). Every medical doctor
is reimbursed at a standard rate for each patient. In order to encourage physicians to
provide services to their patients, additional fee for services are introduced. It should
be mentioned however that hitherto the capitation payments system is only partially
introduced. The reimbursement in secondary and tertiary care has remained unchanged
(through annual fixed budgets and salaries) although there are plans for the introduction
of a DRG-based system (diagnostic related groups or output related system) (World
Bank, 2009).
Out-of-pocket patient payments include official co-payments for public health care
services as well as payments for private health care. Official co-payments are paid for
diagnostic procedures, physician’s visits, pharmaceuticals and for so called-non-standard
procedures. The amount of the official co-payment varies between 1 USD (e.g. for a
referral) and 551 USD (e.g. for disposable materials). The maximum annual co-payment
by a patient (excluding payments for disposable materials and pharmaceuticals) may not
be higher than one third of a patient’s salary or patient’s average net income.
Next to the formal types of patient payments, there are also informal patient payments
(cash and gifts in kind given to the physician), as well as payments for “bought & brought
goods” (i.e. payments for goods brought by the patient to the health care facility such
as disposable materials and medicine). Informal patient payments are defined as every
direct patient’s contribution (in cash or in kind) for publicly funded services .This
includes informal payments to physicians and envelope payments to other medical staff.
They have spread in Serbia since the economic crisis in 1992. During the period of the
SFRJ, informal patient payments were present only in the form of gratitude payments or
gifts. Moreover, recent research shows an increasing problem of institutional corruption
in Serbia due to informal patient payments (CESID, 2011). Payments for “bought &
brought goods” have their roots in the times when the health care settings lacked medical
20
Chapter 1
materials, supplies, and pharmaceuticals due to the financial crisis (CESID, 2011). In
such circumstances, medical staff asks the patients and/or relatives to bring supplies and
pharmaceuticals that are necessary for the treatment that the hospital cannot provide due
to poor funding (Palairet, 2001).
In 2007, households’ out-of-pocket payments were estimated to be 34.8% of
total health expenditure, total health expenditure was 10.4% of GDP (WHO, 2012),
although these shares are slightly different in other sources (e.g. Bajec et al., 2008; Gajic-
Stevanovic, 2010; Markovic 2011; World Bank, 2009; WHO, 2012). The share of out-
of-pocket payments as a percentage of total health expenditure is similar to that in other
Western Balkan countries but much higher than in other EU countries (Bredenkamp et
al., 2008; Tomini et al., 2011). It should be mentioned however that this estimate of total
out-of-pocket payments does not include informal patient payments as well as payments
for “bought & brought goods”. Although official co-payments were introduced in 2002,
informal patient payments and payments for “bought & brought goods” still exist. There
is still no official strategy for their reduction. The existence of different types of out-of-
pocket patient payments can decrease the utilization of health care. They can also provoke
impoverishment among users with a low-social economic status.
In order to prevent the negative effects of official co-payments, an exemption
mechanism was introduced with the objective to assure equity in access to healthcare. It
concerns both outpatient and inpatient services. The Serbian Law on Health Insurance
defines several population groups that are exempted from patient fees: children younger
than 15 years, pregnant women, persons older than 65 years, disabled persons, HIV-
infected persons, monks, people with low family income, unemployed, chronically ill
people, military service servants, people registered as refuges and the Roma population
(HIF, 2010). According to the Serbian law, groups that are exempted from patient fees
should not be charged at all when they use healthcare services (Bajec et al., 2008).
1.5 Research gap
During the last twenty years, a lot of scientific research has been conducted on social
protection in health care systems in CEE countries (Alam et al., 2005). Most attention
was paid to health care systems that were based on the so called Semashko model like
Bulgaria, Hungary, Romania (Baji et al., 2012; Delceva, Balabanova & McKee, 1997;
Lewis, 2000) and the former Soviet republics (Georgia, Moldova, Russia, Tajikistan
and Ukraine) (Ensor & Savelyeva , 1998; Falkingham, 2004; Ferrer-i-Carbonell &van
Praag, 2001; Gotsadze et al., 2009; Stepurko et al.,2010). Health care systems based on
the Semashko model were characterized by free of charge services funded through tax-
based funding mechanisms (Ensor, 2004). However, those systems were not financially
1
Introduction
21
sustainable (Lewis, 2000). After the collapse of Soviet Union, the main change within
most of those health care systems was the development of the private health care sector
and the introduction of insurance-based system funding. In some of those systems, official
co-payments were also introduced (e.g. Bulgaria). Beside, most of those systems were also
characterized by the existence of informal patient payments (Lewis, 2000). The existence
of different types of out-of-pocket patient payments (formal and informal) provoked a
financial burden in CEE countries, reduced the accessibility of health care services and
influenced the quality of provided services. Previous studies related to CEE countries
have addressed those issues. Particular attention is paid to informal patient payments
(Lewis, 2000).
However, the ex-Yugoslavia health care system was financed by compulsory health
insurance already during the communist period. Nevertheless, the system was still not
sustainable. After the collapse of the communism, all ex-Yugoslavia republics inherited
a compulsory health insurance system, but followed different patterns regarding the
introduction of patient payments (Albreht & Klazinga, 2009). Slovenia, an EU member
since 2002, introduced additional private insurance instead of pure co-payments, while
other republics including Serbia introduced official patient payments accompanied by
exemption mechanisms (Albreht & Klazinga, 2009). Except Slovenia, in all ex-Yugoslavia
republics informal patient payments are widely spread. In Serbia, a newly designed social
protection system in health care was officially introduced in 2002 (Bajec et al., 2008).
Nevertheless, the scientific evidence about the effectiveness of the social protection
system in health care in Serbia is still lacking. Moreover, there is still no evidence about
the effectiveness of the social protection system in health care in Serbia in comparison
with other CEE countries. This dissertation intends to fill this gap by exploring the
extent and intensity of the financial burden provoked by different types of out-of-pocket
patient payments and their impact on vulnerable population groups. Furthermore,
this dissertation examines the effectiveness of the current exemption mechanism in the
Serbian health care system.
1.6 Research aim, objectives and data used in the dissertation
In the previous paragraphs we gave a broad overview of the historical development of the
social protection system in Serbia. We also gave an extended overview of the historical
development of the health care system in Serbia focusing on public health care services.
In this dissertation, we explore one aspect of social protection in the public health
care system in Serbia, namely the out-of-pocket payments. As mentioned earlier, the
out-of-pocket patient payments are a well-known trigger for economic burden in low
and middle income countries. In Serbia, the burden of out-of-pocket payments is only
22
Chapter 1
scantily studied. Therefore, the main aim of this dissertation is to examine the effects of
out-of-pocket patient payments on vulnerable population groups in Serbia. Serbia represents an interesting case where different forms of out-of-pocket patient
payments have co-existed with compulsory health insurance for more than a decade. In
this dissertation, attention is paid to the relation between different types of payments
and poverty. Moreover, the dissertation explores to what extent those payments affect
vulnerable groups like the chronically sick and pregnant women. Following the main
research aim, several research questions are addressed:
What is the financial burden provoked by out-of-pocket patient payments?
Out-of-pocket patient payments are well-known financial burden (Xu et al., 2010).
Several studies have examined the effects of out-of-pocket patient payments in developing
countries (Xu et al., 2010; van Doorslaer, 2007; Wagstaff, 2008). However, current
research still does not provide consensus on how the financial burden provoked by out-
of-pocket patient payments should be assessed. This research question focuses on the
measurement of the financial burden provoked by out-of-pocket patient payments in
Serbia. The main objective is to give an overview of the methodology used to measure the
financial burden provoked by out-of-pocket payments. We also estimate the level of the
financial burden provoked by out-of-pocket patient payments in Serbia using different
methodological approaches (e.g. Xu et al., 2010; Wagstaff, 2008).
How do different types of out-of-pocket patient payments contribute to the financial burden?
Like in other Central and Eastern European countries (CEE), in Serbia, the practice
of giving money and gifts in kind to health care professionals is common. Besides the
informal patient payments, previous studies have described different forms of payments
in CEE countries like quasi-formal and quasi-informal patient payments (Stepruko et
al., 2014). However, in Serbia scientific evidence on the different types of out-of-pocket
patient payments is lacking. Moreover it is not clear how different types of payments
contribute to the financial burden. In order to address this research question, we examine
the level of different types of out-of-pocket patient payments in Serbia. We provide an
overview of different types of payments: formal payments, informal payments and goods
that are brought to health care facilities. We also analyze the possible financial burden
provoked by each type of payments using different approaches (e.g. Xu et al., 2010;
Wagstaff, 2008).
1
Introduction
23
To what extent does the exemption mechanism protect vulnerable groups?
In health care systems that rely on out-of-pocket patient payments, exemption mechanisms
are often used to protect most vulnerable groups. However, previous literature suggests
that not all exemption mechanisms are effective (Lagarde & Palmer, 2008; Perkins et al.,
2009) .To assess this question, we provide an overview of possible pitfalls of the design
and implementation of an exemption mechanism (Lagarde & Palmer, 2008; Perkins et
al., 2009; Witter et al., 2010). We also describe how the exemption mechanism in Serbia
is designed and implemented. More precisely, we examine whether selected exempted
groups (older than 65 years, younger than 15 years, disabled, unemployed and people
with low family income status) pay official copayments when they are supposed to be
exempted from such fees.
What is the relation between chronic diseases and poverty?
Previous literature reports on the growing prevalence of chronic diseases in Serbia
(Jankovic, Simic & Marinkovic, 2010). Patients diagnosed with a chronic disease are
frequent users of health care. In a situation where different types of out-of-pocket patient
payments exist, patients can easily experience the financial burden and become poor.
Moreover, poverty can also be a trigger for developing a chronic disease (Alleyene et al.,
2013). Therefore, the relation between chronic diseases and poverty is characterized by
a complex joint causality (Bonu et al., 2005; Engelgaou, Karan & Mahal, 2012; Xu et
al., 2003). To assess this joint causality in Serbia, we apply an instrumental variable (IV)
approach (Geneau et al., 2010; Khandker, Koolwal & Samad, 2010).
To what extent are pregnant women protected within the health care system in Serbia?
Maternity care is usually one of the most sensitive parts in the health care systems over
the world. During the transition period in CEE countries, informal patient payments for
maternity care and for surgery were the highest when compared with informal payments
for other services (Stepurko et al., 2013). However, previous research that has examined
maternity care in CEE countries mostly focused on macro indicators like the mortality
rate during the birth or the presence of skilled persons during the birth. Data related
to the financial and other aspects of social protection in maternity wards specifically in
Serbia, are limited. In order to assess the question above, we do not only examine the
financial protection mechanisms, but also several other aspects of maternity care like
accessibility to maternity care, quality of care and policy regulations (Ronsmans, 2001).
We combine data from three different sources: online collected
24
Chapter 1
questionnaires, literature review and data obtained from official and hospital guidelines
and institutional websites (Jick, 1979).
1.6 Data used in the analyses
For this dissertation, we use different data. To provide an answer to the first four research
questions, we use data from the LSMS carried out in 2002, 2003 and 2007 by The World
Bank group. The Serbian LSMS data for 2002 consists of 19725 participants living in
6386 households, for 2003 the total number of participants is 8027 living in 2548
households. Data for 2007 consists of 17,375 participants living in 5,557 households.
Although, the intention was to have panel data for year 2002 and 2003, current datas are
cross-sectional.
The data consists of nine different modules including a health module. The health
module includes variables regarding various types of health care spending of household
members for hospitalization, pharmaceuticals, and diagnostics in public inpatient units
during the last 12 months and variables on household spending on physician visits,
pharmaceuticals, and diagnostics in outpatient public health care units during the
preceding month. The data also contain information about the demographic structure,
household conditions, agriculture, health care, education, labor activities and social
welfare programs in Serbia. A standardized questionnaire is used during the survey. The
survey and the questionnaire were designed by the World Bank, and the collection of data
was conducted by the Statistical Office of the Republic of Serbia. The data were collected
directly from the respondents by trained interviewers. For children under the 15 years,
parents were giving the answers (World Bank, 2011). Detailed information about the
LSMS data for Serbia can be found on the World Bank website (http://go.worldbank.
org/8XI2AXPP00.
To provide an answer to the last research question, we use data collected through
online questionnaire by the Serbian NGO “Mother Courage”, as well as data obtained
from the World Health Organization (WHO), World Bank and United Nations High
Commissioner for Refugees (UNHCR).
1.7 General structure of the dissertation
After this general introduction chapter, in Chapter 2, the dissertation describes different
approaches to measure the financial burden provoked by out-of pocket patient payments
and analyzes the burden of
1
Introduction
25
these payments in Serbia. Then, in Chapter 3, we examine the financial burden of different
types of out-of-pocket patient payments. In Chapter 4, we also describe the effects of the
exemption policy on several vulnerable groups: older than 65 years, younger than 15
years, disabled, unemployed and people with low family income status. Also, we examine
the effects of out-of pocket patient payments on chronically sick in Chapter 5. In Chapter
6, we outline how the pregnant women are protected in the health care system in Serbia.
A general discussion of the key research findings is provided in Chapter 7.
CHAPTER 2
Measuring the Catastrophic and Impoverishing Effect of Household Health Care Spending in Serbia
Published as:
Arsenijevic, J., Pavlova, M., & Groot, W. (2013). Measuring the catastrophic and impoverishing effect of household health care spending in Serbia. Social Science & Medicine, 78, 17-25.
28
Chapter 2
Abstract
Introduction: Out-of-pocket patient payments can impose a catastrophic burden on
households. This problem may not only affect poor but also wealthy households who
need to use health care frequently. The available literature offers no consensus on how to
measure poverty and how to measure the effects of out-of-pocket payments on household
budgets. The objective of this chapter is to contribute to current research in this area
by comparing results across different approaches. In particular, the chapter examines
the catastrophic and impoverishing effects of health care spending in Serbia applying
different types of thresholds used in previous research. The application of various
approaches allows us to analyze the robustness and convergent validity of the results. We
also include the subjective poverty approach in our examination.
Method: We use household data collected in LSMS in 2007. The Serbian LSMS data
offers data for 2007 and consists of 17,375 participants living in 5,557 households.
Results: Our results indicate that irrespective of the approach applied, out-of-pocket
patient payments have a catastrophic effect on poor households in Serbia. Moreover, some
households that are above the absolute, relative and subjective poverty lines respectively,
after the subtraction of out-of-pocket payments fall below these poverty lines. The
probability of catastrophic out-of-pocket patient payments is higher in rural areas, and
among chronically sick household members (namely, people with diabetes and mental
diseases, as well cardiology diseases in some instances). Perceived health status also
appears to be a significant indicator.
Conclusions: Policy in Serbia should aim to protect vulnerable groups, especially
chronically sick patients and people from rural areas.
2
Financial burden and out-of-pocket patient payments
29
2.1 Introduction
The catastrophic impact of out-of-pocket patient payments on the household budget is
a frequent problem in low- and middle-income countries in Asia (Falkingham, 2004;
Limwattananon et al., 2007; Yardim et al., 2010), Africa (Ekman, 2007; Xu et al., 2006;
van Doorslaer et al., 2006), South America (Baeza & Packard, 2006) and South-Eastern
Europe (Bredenkamp et al., 2010; Gotsadze et al., 2009; Habicht et al., 2006) but also in
OECD countries, e.g. Portugal, Greece, the UK and the US (Xu et al., 2003;Wagstaff &
van Doorslaer, 2003). In low- and middle-income countries, even small medical costs can
become a considerable burden for poor households and may discourage the use of health
care services (Falkingham et al., 2010; Habicht et al., 2006; Skarbinski et al., 2002;
Szende & Culyer, 2006; Roberts et al., 2004; Xu et al., 2003; Vian et al., 2006). When
out-of-pocket patient payments are a major source of health care financing, they can
push even wealthy households into poverty (Bredenkamp et al., 2010; Xu et al., 2006).
Most households find it difficult to recover from such financial shock, especially if they
are exposed to health costs over a longer period of time, for example in case of chronic
diseases (Abegunde & Stanciole, 2008; Hwang et al., 2001; McIntyre et al., 2006).
A number of studies have examined the problem of poverty caused by out-of pocket
patient payments (Flores at al., 2008; Russel, 2004; Xu et al., 2003; van Doorslaer et al.,
2007; Wagstaff & van Doorslaer, 2003). However, studies apply a variety of methods.
In particular, some studies estimate the burden of health expenditure on households
(catastrophic expenditure approach) while other studies estimate the impact of this
expenditure on poverty levels (impoverishing effects approach).Within each approach,
different indicators of household wealth (income and consumption) and different poverty
thresholds based on these indicators are applied (Xu et al., 2010; van Doorslaer et al.,
2006), see Figure 2.1. Overall, there is no clear consensus about the poverty measures
that should be used in empirical analyses (Xu et al., 2003; Xu et al., 2010).
The aim of this chapter is to contribute to the current research in this area by comparing
the effects of out-of-pocket patient payments on individual’s budget estimated by the
application of different approaches, different wealth indicators and different poverty
thresholds. Based on Wagstaff and van Doorslaer (2003) and Xu et al. (2003), we first
provide an overview of the different wealth indicators, different approaches and different
poverty thresholds within each approach. Then, we apply these approaches to an identical
data for Serbia. This allows us to analyze the robustness and convergent validity of the
results. In addition to earlier studies, we apply a third approach based on subjective
poverty measures (Marks, 2005). The data for the analysis are taken from the LSMS in
2007 (see Chapter 1, Section 1.6).
Our analysis is relevant not only to research but also to future health care reforms.
LSMS data for several Western Balkan’s countries, namely Albania, Bosnia and
30
Chapter 2
Herzegovina, Montenegro, Kosovo and Serbia have already been used in a comparative
study on the measurement of the poverty effects of out-of-pocket payments (Bredenkamp
et al., 2010). The results confirm the growing incidence and intensity of catastrophic
health expenditures in this region. However, the characteristics of potentially vulnerable
households and household members have not been identified before. Since these countries
are going through continuous health care reforms, our research can contribute to a more
evidence-based policy debate on this issue.
What poverty effects: catastrophic or impoverishing effects?
What wealth measure: expenditure/consumption orincome (objective or subjective)?
What level: household or individual level?
Poverty measure selected
What sample: those who used health care or total sample?
Figure 2.1: Selection of approaches related to financial burden
2
Financial burden and out-of-pocket patient payments
31
2.2 Background
2.2.1 Indicators of household wealthPoverty measures are related to direct indicators of household wealth such as income (total
amount of money received by the households), or consumption and expenditure (the total
amount of money that households spend). Although consumption and expenditure are
not identical by definition, they are used interchangeably (also in this chapter) because
in practice they do not considerably differ (O’Donnell et al., 2008). However, household
income can be significantly different from household consumption (and expenditure).
For example, some households can achieve a higher consumption level than their
income allows, by mobilizing additional resources (e.g. borrowing money and/or selling
assets) to pay for various goods/services (not only health care). In this case, income is
on average less than consumption. When income is used as a wealth indicator, these
additional resources are ignored (Flores et al., 2008; Kim & Jang, 2010; van Doorslaer et
al., 2006). However, when consumption is taken as a wealth indicator, wealthy households
who do not have to borrow money or sell assets and poor households that rely on such
coping strategies to survive, are treated similarly.
Also, consumption includes the consumption value of long-term assets (such as a
house), which is difficult to measure. If average values for the country are used as proxies,
the consumption level of individuals outside expensive cities is frequently artificially
increased. These assets cannot be easily sold and transferred into money (Haughton &
Khandker, 2009; O’Donnell et al, 2008) especially in times of an economic crisis. Thus,
the fact that individuals have a high consumption level does not necessarily mean that
they have a high ability to pay. In contrast, income represents direct household command
over resources, and is a better indicator of the household’s ability to pay for health care.
However, the accurate measurement of income can be questioned in case of a large
informal economy where a large share of income remains unregistered/unreported and
where goods and services are directly exchanged (e.g. exchanging life stock and agricultural
products for services). This can also be a reason that household income measured is lower
than household consumption. In cases where a large part of the households’ income is
generated by informal sources, the use of household consumption and expenditure data
is advised (see e.g. Deaton, 1997; Xu et al., 2003; Wagstaff & van Doorslaer, 2003;
Wagstaff, 2008; World Bank, 2008).
Overall, the literature (Haughton & Khandker, 2009; O’Donnell et al. 2008)
concludes that all indicators of wealth have their pros and cons, and there is no “best”
indicator of wealth. Based on this insight, we use both wealth indicators in our study.
32
Chapter 2
2.2.2 Approaches to measure the poverty effects of out-of-pocket patient paymentsThree main approaches are described in the literature to measure the poverty effects of
out-of-pocket payments for health care: catastrophic health expenditure, impoverishing
effects, and subjective poverty. Since these approaches all have their limitations, we
employ all three of them and compare their results. An outline of these three approaches
is provided below.
Catastrophic health care expenditure
Catastrophic health care expenditure defines out-of-pocket patient spending as
catastrophic if it exceeds a certain threshold in a given period (Wagstaff, 2008). The
threshold represents a pre-defined proportion of household income or consumption.
The threshold is arbitrary and can vary from 5 up to 40% of total income/consumption
(Habicht et al., 2006; Gotsazde et al., 2009; Knaul et al., 2006; Limwattananon et al.,
2007; Shakarishvili, 2006; Xu et al., 2003; Yardim et al., 2010). The threshold of 40%
has received wide application in empirical research (Xu et al., 2003; Xu et al., 2007). Xu
et al. (2003) propose to subtract the food expenditures from total household consumption
when applying the catastrophic expenditure approach. This requires the assumption that
food and health care expenditure are not substitutes (Wagstaff, 2008). Since we consider
both food and health expenditures as necessities, we do not subtract food expenditure
from total consumption. The catastrophic expenditure approach has been widely used in
previous research to examine the incidence of catastrophic health care expenditure, the
influence of policy institutions and insurance on it, as well as the characteristics of the
most vulnerable households (Wagstaff, 2008; Wagstaff & Pradhan, 2005). Studies suggest
that households in lower income groups are more likely to experience catastrophic health
care expenditure, but if the threshold is set lower, catastrophic health care expenditures
are also observed within richer households (Wagstaff & van Doorslaer, 2003). Also,
households with elderly, unemployed and chronically sick members are more likely to
experience a catastrophic financial burden (Kawabata et al., 2002).
Impoverishment due to health care spending
The impoverishing effect of health care spending is measured by the proportion of
households that goes below the poverty line after health care spending is subtracted from
total income or consumption (Wagstaff, 2008). It is based on the comparison between
the incidence of poverty before and after the subtraction of health care spending by the
household. For the calculation of the impoverishing effect of out-of-pocket payments,
absolute (Ravallion, 1998) and relative (Foster, 1998) poverty lines are used as thresholds
(Wagstaff & van Doorslaer, 2003). In low- and middle-income countries, poverty
measures are mostly based on consumption (Xu et al., 2006). However, it is also possible
2
Financial burden and out-of-pocket patient payments
33
to define poverty lines on household income such as a specific minimum level household
income or by using average household income. Previous research has reported a higher
incidence of poverty after subtracting health care costs among the chronically sick, in
particular among people on who are on chemotherapy and renal dialysis (Knaul et al.,
2006; Wagstaff & van Doorslaer, 2003).
Subjective poverty
Subjective poverty is a measure based on the respondents’ perception of being poor
(Ferrer-i-Cabonell & van Praag, 2001; Marks, 2005). Although, there are several ways of
measuring subjective poverty, they are all based on an assessment of personal wellbeing.
Most of the measures are constructed as a one item scale, which makes them easy to
apply. Subjective poverty is the only measure that includes the psychological component
of poverty (Ferrer-i-Cabonell & van Praag, 2001; Marks, 2005). Previous research has
shown that men are more likely to perceive themselves as subjectively poor. Subjective
poverty is higher among single parents’ households and among single people. In contrast
to the other two approaches, subjective poverty is not necessarily related to age, education
or health status. However, a higher income is related to a less frequent perception of
being poor. The analysis of the relation between subjective poverty and out-of-pocket
payments for health care can show whether increased out-of-pocket spending leads to a
more frequent perception of being poor. Since subjective poverty is informative in this
respect, we have also incorporated it in our research.
2.2.3 A framework to compare the performance of different approachesThe two approaches – catastrophic health care expenditure and impoverishing effects –
identify the share of households/individuals who experience an economic burden due to
out-of-pocket patient payments. We use this as a framework to compare the performance
of these approaches. To avoid overestimation of poverty levels, we focus our comparison
on the absolute poverty line for the impoverishing effects approach and the 40% level
for the catastrophic expenditure approach. We compare the incidence of the economic
burden caused by out-of-pocket patient payments indicated by the two approaches using
the two poverty thresholds mentioned above and the two wealth indicators – income
and consumption. Thus, we compare not only the approaches, but also the wealth
indicators. We do not directly compare the subjective poverty approach with the other
two approaches. We only use the subjective poverty approach for further explanation of
the catastrophic and impoverishing effects. However, we compare factors associated with
impoverishing and catastrophic effects with factors associated with subjective poverty.
34
Chapter 2
2.3 Methods
In this chapter we use LSMS data collected for 2007 by World Bank. A detailed
description of the data is provided in Chapter 1.The data contains several poverty
measures, including household consumption per household member as well as absolute
and relative poverty lines (for detailed technical information see World Bank, 2011). The
absolute poverty line is based on the monetary value of the minimum food basket plus
other goods that households with a minimum basket food consumption, are supposed to
spend. The absolute poverty line for 2007 is set to be 8883 CSD (≈ 100 euro) per adult
per month. Based on this poverty line, all respondents in the Serbian LSMS data for 2007
are classified into two consumption-based categories: poor and non-poor. The relative
poverty line is calculated as 60% of the median of total household consumption (11283
CSD per person per month).
To measure the impoverishing effect of health care spending based on income, we also
create income-based poverty categories. For this purpose, we first create a variable that
indicates total household income, which includes an extensive list of income categories
included in the Serbian LSMS data (for details see World Bank, 2011). Total income per
household per year is then divided by the number of household’s members in order to
obtain income per household member. We did not use any adjustment for economies of
scale related to household size since such adjustments are based on scales that are fairly
arbitrary, developed mostly for high income countries (e.g. the Oxford scale) and not very
well applicable to former-socialist countries (Ferrer-i-Carbonell & van Praag, 2001) like
Serbia. Haughton and Khandker (2009) show that there are no significant differences
in results when there is some correction for potential economies of scales compared to a
straightforward use of income per capita.
Based on this, we created an additional categorical variable, which indicates whether
income per household member per month is:
– less than the absolute poverty line of 8883 CSD,
– between the absolute poverty line of 8883 CSD and relative poverty line of 11283
CSD,
– between the relative poverty line of 11283 CSD and average net income of 36610
CSD,
– more than the average net income of 36610 CSD.
All poverty lines listed above, are multiplied by 12 to obtain an annual estimation.
To calculate the catastrophic effects of health care spending, we use the variable
indicating household consumption, available in the Serbian LSMS for 2007. We also
use data about household health care expenditure provided in the data, which includes
both formal and informal payments for out-patient and hospital care. Since dental care
is predominantly provided in the private sector and is not included in the compulsory
2
Financial burden and out-of-pocket patient payments
35
insurance package, it is not included in our analysis. Data related to out-patient care are
based on a re-call period of one month and the data for hospital care use a re-call period
of 12 months. Therefore, we have multiplied the payments for out-patient care by 12
to obtain an annual estimate comparable to the data for hospital care (possible over- or
under-estimation might have occurred). We then divide health care expenditure first by
total household expenditure and then by total household income. Thus, we obtain two
new variables that indicate the percentage of total health care expenditure spent on health
care and the percentage of total household income spent on health care respectively.
We use two additional items from the LSMS data in order to measure subjective
poverty. The first item uses the following question to measure satisfaction with the
household’s financial status: “How would you rate the current financial status of your
household?” The answer can range from 1 (bad) to 5 (very good). We use this answer
as an indicator of subjective poverty. The second item asks for the minimum amount
that would satisfy household needs. We compare the answer to this question with
actual household income and divide respondents into two categories: subjectively poor
(when the income perceived as necessary is higher than actual household income), and
subjectively non-poor (when this condition does not apply).
To assess the impoverishing effect of health care spending, we calculate the percentage
of respondents (health care users and total sample) who are poor based on three types
of thresholds: income, consumption and subjective poverty respectively. We use
descriptive statistics. We repeat the analysis after subtracting health care expenditures.
We also calculate pre-payment and post-payment poverty headcounts and poverty index
using different types of poverty lines. We also calculate the catastrophic health care
expenditure ratio. As indicators of wealth, we use both income and consumption (based
on expenditure). We present different poverty thresholds for this ratio ranging from 10%
to 40% of household income and consumption respectively.
We perform regression analysis to explore the variations within the social-demographic
groups. Ordered probit regression is performed using the following dependent variables:
pre-payment income category (from 0 = below the absolute poverty line to 3 = above the
average net income), change in income category after subtracting health care spending
(from 0 = no change to -2 = two categories lower), catastrophic health care expenditure
based on consumption (from 1 = up to 10% burden to 5 = up to 40% burden) and
perceived-income categories (from 1= bad to 3 = good). We also perform binary logistic
regression with the following dependent variables: pre-payment consumption category
(0 = non-poor, 1 = poor) and subjective poverty category (0 = non-poor, 1 = poor). In all
regressions, we include two groups of independent variables: socio-demographic variables
(gender, age, marital status, education, residence place, nationality, employments and
household size) and health status (perceived health status, existence of chronic diseases
36
Chapter 2
like asthma and bronchial diseases, cardiology diseases, cancer, diabetes). All data are
analyzed using statistical package SPSS (version 16).
2.4 Results
Table 2.1: presents the consumption and income characteristics of all households included
in our study.
Table 2.1: Household income, consumption and expenditure per income- and consumption-based
quintiles (all households)
Income per household per
month[CSD] a
Consumption per household per
month[CSD] a
Households reporting lower
income than consumptionN (% within
quintile)
Consumption of food per
household per month[CSD] a
Health care expenditure per household per
month[CSD] a
Consumption-based quintiles
Poorest 17075.36 22998.80 950(17.1) 4344.39 359.41
2 24359.02 35862.78 865(15.6) 6011.35 609.60
3 29398.51 47021.51 870(15.6) 7128.60 802.53
4 34867.76 60453.09 857(15.4) 8751.54 1049.87
Richest 45450.23 62686.31 900(16.2) 11675.28 1970.48
Income-based quintiles
Poorest 3227.76 36174.08 1105(19.9) 6684.57 804.97
2 13058.12 33612.13 1041(18.7) 6774.46 838.93
3 22882.12 45020.53 901(16.21) 7214.83 927.88
4 36798.31 57465.34 792 (14.2) 7795.11 990.35
Richest 71718.41 80308.61 603(10.8) 8623.43 1069.35a 1 CSD = 0.0125 Euro
2
Financial burden and out-of-pocket patient payments
37
Tab
le 2
.2: C
atas
trop
hic
heal
th c
are
expe
ndit
ure
per
inco
me-
and
con
sum
ptio
n-ba
sed
quin
tile
s (a
ll r
espo
nden
ts)
Shar
e of
hou
seh
old
exp
end
itu
re o
n h
ealt
h
care
exp
ress
ed p
er c
onsu
mp
tion
-bas
ed q
uin
tile
Poo
rest
23
4R
ich
est
Tot
al
N (%
wit
hin
q
uin
tile
)N
(% w
ith
in
qu
inti
le)
N (%
wit
hin
q
uin
tile
)N
(% w
ith
in
qu
inti
le)
N (%
wit
hin
q
uin
tile
)N
(% w
hol
e sa
mp
le)
No
heal
th c
are
expe
ndit
ure
1717
(49.
4)13
89 (4
0.0)
1308
(37.
7)14
66 (4
2.2)
1568
(45.
1)74
48(4
2.9)
Mor
e th
an 0
% u
p to
10%
1492
(42.
9)17
95 (5
1.7)
1861
(53.
6)17
96 (5
1.6)
1677
(48.
3)86
21(4
9.6)
Mor
e th
an 1
0% u
p to
20%
212
(6.1
)19
9 (5
.7)
183
(5.3
)12
3 (3
.5)
158(
4.5)
875(
5.0)
Mor
e th
an 2
0% u
p to
30%
27 (0
.8)
44 (1
.3)
69 (2
.0)
34 (1
.0)
42(1
.3)
216(
1.2)
Mor
e th
an 3
0% u
p to
40%
5 (0
.1)
16(0
.5)
11(0
.3)
30(0
.9)
17(0
.5)
79(0
.5)
Mor
e th
an 4
0%23
(0.7
)31
(0.9
)42
(1.2
)29
(0.8
)11
(0.3
)13
6(0.
8)
Shar
e of
hou
seh
old
inco
me
spen
t on
hea
lth
ca
re e
xpre
ssed
per
inco
me-
bas
ed q
uin
tile
Poo
rest
23
4R
ich
est
Tot
al
N (%
wit
hin
q
uin
tile
)N
(% w
ith
in
qu
inti
le)
N (%
wit
hin
q
uin
tile
)N
(%
qu
inti
le)
N (%
q
uin
tile
)N
(% w
hol
e sa
mp
le)
No
heal
th c
are
expe
ndit
ure
2373
(69.
0)13
89(3
9.6)
1403
(40.
5)14
09 (4
0.8)
1556
(44.
6)81
59(4
7.0)
Mor
e th
an 0
% u
p to
10%
542
(15.
8)14
09 (4
0.2)
1585
(45.
8)16
79 (4
8.6)
1690
(48.
5)69
05(3
9.7)
Mor
e th
an 1
0% u
p to
20%
220
(6.4
)40
2 (1
1.5)
253
(7.3
)25
0(7.
2)17
2(4.
9)12
97(7
.5)
Mor
e th
an 2
0% u
p to
30%
61 (1
.8)
127(
3.6)
100
(2.9
)48
(1.4
)33
(0.9
)36
9(2.
1)
Mor
e th
an 3
0% u
p to
40%
87 (2
.5)
66 (1
.9)
37(1
.1)
31(0
.9)
22(0
.6)
243(
1.4)
Mor
e th
an 4
0%15
8 (5
.6)
111
(3.2
)85
(2.5
)35
(1.0
)13
(0.4
)40
2(2.
3)
38
Chapter 2T
able
2.3
: Inc
ome
grou
ps, c
onsu
mpt
ion
grou
ps, a
nd s
ubje
ctiv
e in
com
e gr
oups
bef
ore
and
afte
r th
e su
btra
ctio
n of
hea
lth
care
spe
ndin
g
Var
iab
leIn
com
e ca
tego
ries
a, b
Con
sum
pti
on-b
ased
cat
egor
ies
(hea
lth
car
e u
sers
)C
ateg
orie
s b
ased
on
su
bje
ctiv
e in
com
e(h
ealt
h c
are
use
rs) c
Tot
al
sam
ple
d
Poo
r e
Non
poo
r e
Tot
al
use
rsB
adN
eith
er b
ad
nei
ther
goo
dG
ood
Tot
al
use
rs
Average income perhousehold member
≤ ab
solu
te p
over
ty li
ne22
3(4.
5)22
77(4
5.9)
2500
(50.
3)11
90(2
4.2)
938(
19.1
)34
7(7.
1)24
75(5
0.4)
8255
(47.
6)
> a
bsol
ute
pove
rty
line
≤
rela
tive
pov
erty
line
12(0
.2)
479(
9.6)
491(
9.9)
231(
4.7)
174(
3.5)
77(1
.6)
482(
9.8)
1607
(9.3
)
> r
elat
ive
pove
rty
line
≤
aver
age
net
inco
me
40(0
.8)
1336
(26.
9)13
76(2
7.7)
561(
11.4
)53
2(10
.8)
269(
5.5)
1362
(27.
7)50
81(2
9.3)
> a
vera
ge n
et in
com
e9(
0.2)
590(
11.9
)59
9(12
.1)
285(
5.8)
185(
3.8)
125(
2.5)
595(
12.1
)24
03(1
3.9)
Var
iab
leIn
com
e ca
tego
ries
a, b
Con
sum
pti
on-b
ased
cat
egor
ies
(hea
lth
car
e u
sers
)C
ateg
orie
s b
ased
on
su
bje
ctiv
e in
com
e (h
ealt
h c
are
use
rs) c
Tot
al
sam
ple
d
Poo
r e
Non
poo
rT
otal
u
sers
Bad
Nei
ther
bad
n
eith
er g
ood
Goo
dT
otal
u
sers
Average income per household member
after subtracting the household health care
spending
≤ ab
solu
te p
over
ty li
ne22
4(4.
5)25
68(5
1.7)
2792
(56.
2)13
29(2
7.0)
1035
(21.
0)40
1(8.
2)27
65(5
6.3)
8587
(49.
4)
> a
bsol
ute
pove
rty
line
≤
rela
tive
pov
erty
line
11(0
.2)
366(
7.4)
377(
7.6)
171(
3.5)
132(
2.7)
66(1
.3)
369(
7.5)
1486
(8.6
)
> r
elat
ive
pove
rty
line
≤
aver
age
net
inco
me
40(0
.8)
1199
(24.
1)12
39(2
4.9)
496(
10.1
)50
1(10
.2)
229(
4.7)
1226
(24.
9)49
19(2
8.3)
> a
vera
ge n
et in
com
e9(
0.2)
549(
11.1
)55
8(11
.2)
271(
5.5)
161(
3.3)
122(
2.5)
554(
11.3
)23
54(1
3.5)
a A
bsol
ute p
over
ty li
ne o
f 888
3 C
SD p
er h
ouse
hold
mem
ber
per
mon
th, 1
CSD
= 0
.012
5 E
uro
b R
elat
ive p
over
ty li
ne o
f 112
83 C
SD p
er h
ouse
hold
mem
ber
per
mon
thc N
umbe
rs in
bra
cket
s pre
sent
the p
erce
ntag
e of h
ealt
h ca
re u
sers
d Num
bers
in b
rack
ets p
rese
nt th
e per
cent
age o
f tot
al sa
mpl
ee B
ased
on
cons
umpt
ion-
base
d po
vert
y li
ne
2
Financial burden and out-of-pocket patient payments
39
As suggested by the table 2.1, about 80% of the households report a higher consumption
level than income. The share of households where consumption exceeds income is the
highest in the poorest quintiles (based on both income and consumption).
Table 2.2 presents the incidence of catastrophic health care expenditure by income
quintiles and consumption quintiles calculated for the entire sample (per household
member). When we compare the results related to the 40% threshold, based on
income as a wealth indicator, 2.3% respondents experience the burden, while based on
consumption, this share is 0.8%. When catastrophic health care expenditure is calculated
using income, the poorest quintiles seem most affected, even when we look at the 40%
threshold, but when the calculation is based on consumption, the 40% burden is heaviest
among the middle quintiles.
As indicated in Table 2.3, within the group of health care users, 50.3% of the
respondents are below the absolute poverty line based on income before the subtraction
of household health care spending, and 56.2% are below that line after the subtraction
of health care spending. This indicates an increase of 5.9 percentage points (including
individuals who are non-poor based on consumption, and mostly subjectively poor based
on subjective income). For the total sample, these percentages are 47.6% and 49.4%
respectively, which indicates an increase of about 2 percentage points.
We use both income and consumption, to calculate the pre-payment and post-payment
headcount and poverty index (see Table 2.4). We provide results for the entire sample as
well as for health care users. For health care users, the differences between pre-payment
and post-payment headcounts for the absolute poverty line is 5.9 percentage points for
income-based measures and 2.0 percentage points for consumption-based measures. For
the total sample, these differences are 2.4 percentage points and 1.1 percentage points
respectively.
Table 2.5 presents our regression results (see methods section). The results suggest
that respondents who are married, live in big cities, work and have higher education
are less likely to be classified as poor. Respondents with a mental health disease and
from larger households have a higher probability to be classified as poor. Impoverishing
effects of health care spending is more likely for respondents who report chronic diseases
(like diabetes) and some progressive illnesses (like cancer), as well as for respondents
who perceive their health as bad. Household size also appears significant. Being married,
having a job and living in rural areas are protective characteristics of poverty caused by
health care.
40
Chapter 2T
able
2.4
: Pov
erty
hea
dcou
nt a
nd p
over
ty g
ap w
ith
diff
eren
t de
finit
ion
of p
over
ty li
ne a
nd d
iffe
rent
mea
sure
of w
ealt
h
Pov
erty
mea
sure
s u
sin
g co
nsu
mp
tion
as
a m
easu
re o
f w
ellb
ein
gP
over
ty m
easu
res
usi
ng
inco
me
as a
mea
sure
of
wel
lbei
ng
Ab
solu
te p
over
ty li
ne a
Rel
ativ
e p
over
ty li
ne
bA
bso
lute
pov
erty
lin
e a
Rel
ativ
e p
over
ty li
ne
b
Tot
alsa
mp
leH
ealt
h c
are
use
rsT
otal
sam
ple
Hea
lth
car
e u
sers
Tot
alsa
mp
leH
ealt
h c
are
use
rsT
otal
sam
ple
Hea
lth
car
e u
sers
Pre
-pay
men
t po
vert
y he
adco
unt
7.5%
7.0%
16.5
%15
.1%
47%
50.3
%57
.0%
60.0
%
Pos
t-pa
ymen
t po
vert
y he
adco
unt
8.6%
9.0%
18.6
%18
.7%
49.4
%56
.2%
58.0
%63
.0%
Per
cent
age
poin
ts c
hang
e1.
1 2
.02.
13.
62.
45.
91.
03.
0
Pre
-pay
men
t po
vert
y di
ffer
ence
s 21
260.
2417
375.
6329
833.
6325
908.
7559
188.
8058
152.
9476
101.
5675
160.
07
Pos
t-pa
ymen
t po
vert
y di
ffer
ence
s22
457.
5120
234.
4230
811.
3728
495.
1563
761.
5175
181.
1181
093.
5989
703.
58
Pre
-pay
men
t po
vert
y ga
p in
dex
0.01
50.
009
0.03
60.
029
0.26
0.28
0.32
0.33
Pos
t-pa
ymen
t po
vert
y ga
p in
dex
0.02
30.
015
0.04
30.
039
0.30
0.39
0.35
0.42
Per
cent
age
poin
ts c
hang
e0.
80.
60.
70.
14.
011
.03.
09.
0a A
bsol
ute p
over
ty li
ne o
f 888
3 C
SD p
er h
ouse
hold
mem
ber
per
mon
th, 1
CSD
= 0
.012
5 E
uro
b R
elat
ive p
over
ty li
ne o
f 112
83C
SD p
er h
ouse
hold
mem
ber
per
mon
th
2
Financial burden and out-of-pocket patient payments
41
Tab
le 2
.5: R
esul
ts o
f the
reg
ress
ion
anal
ysis
Exp
lan
ator
y va
riab
les
Dep
end
ent
vari
able
s
Pov
erty
cat
egor
y(f
rom
0 =
bel
ow a
bso
lute
pov
erty
li
ne
to 3
= a
bov
e av
erag
e n
et in
com
e)
Ch
ange
in p
over
ty
cate
gory
aft
er
sub
trac
tin
g h
ealt
h
care
sp
end
ing
(fro
m
0 to
-2
cate
gori
es)
Bel
ow a
bso
lute
p
over
ty li
ne
afte
r su
btr
acti
ng
hea
lth
ca
re s
pen
din
g (0
= a
bov
e;
1 =
bel
ow)
Cat
astr
oph
ic h
ealt
h
care
exp
end
itu
re
cate
gory
(fro
m 1
= u
p t
o 10
%
to 5
= m
ore
than
40
%)
Ind
icat
or o
f su
bje
ctiv
e p
over
ty(0
= s
ub
ject
ivel
y n
on-p
oor;
1 =
su
bje
ctiv
ely
poo
r)
Per
ceiv
ed in
com
e(f
rom
1 =
bad
to
3 =
goo
d)
coef
fici
ent
SEco
effi
cien
tSE
coef
fici
ent
SEco
effi
cien
tSE
coef
fici
ent
SEco
effi
cien
tSE
Thr
esho
ld 0
-0.9
30.
15-2
.54
0.34
-3.4
70.
56-0
.37
0.15
2.16
0.37
0.45
0.22
Thr
esho
ld 1
-0.5
80.
15-1
.56
0.34
--
0.09
0.15
--
1.54
0.22
Thr
esho
ld 2
0.66
0.15
--
--
0.37
0.15
--
--
Thr
esho
ld 3
--
--
--
0.60
0.15
--
--
Gen
der
2 =
fem
ale
/ 1 =
mal
e0.
050.
040.
380.
63-0
.13
0.14
-0.0
20.
04-0
.00
0.09
0.05
0.06
Mar
ital
sta
tus
1 =
mar
ried
/ 0
= n
ot m
arri
ed-0
.09*
*0.
010.
81**
0.21
-.01
*0.
44-0
.03*
0.01
0.05
0.0.
30.
030.
02
Age
age
in y
ears
0.00
0.00
0.00
-0.0
0-0
.00
0.00
0.00
0.01
-0.0
1*0.
00-0
.00
0.02
Edu
cati
on le
vel
from
1 =
bas
ic t
o 9
= h
ighe
st le
vel
0.12
**0.
00-0
.07*
*0.
010.
060.
030.
010.
00-0
.08*
0.02
0.05
**0.
02
Wor
k st
atus
1 =
wor
king
/ 0
= n
ot w
orki
ng0.
11**
0.04
0.07
0.23
-0.0
10.
150.
010.
04-0
.28*
0.09
-0.0
60.
06
Nat
iona
lity
1= S
erbi
an /
0 =
oth
er0.
31**
0.06
-0.0
8-0
.09
0.12
0.20
-0.0
90.
05-0
.06
0.13
-0.0
10.
09
Hou
seho
ld s
ize
num
ber
of h
ouse
hold
mem
bers
-0.4
9**
0.01
0.07
**0.
020.
020.
04-0
.01
0.06
-0.0
20.
270.
010.
02
Urb
an1
= c
ity
/ 0 =
rur
al0.
27**
0.04
-0.1
7**
0.07
0.21
0.14
0.04
0.04
-0
.06
0.09
0.19
**0.
06
Per
ceiv
ed h
ealt
hfr
om 1
= v
ery
good
to
4 =
ver
y ba
d-0
.11*
*0.
02-0
.09*
-0.0
40.
29*
0.09
0.22
*0.
020.
17*
0.06
-0.0
1*0.
04
* p
< 0
.05;
**
p <
0.0
1
42
Chapter 2T
able
2.5
: Res
ults
of t
he r
egre
ssio
n an
alys
is (
cont
inue
d)
Exp
lan
ator
y va
riab
les
Dep
end
ent
vari
able
s
Pov
erty
cat
egor
y(f
rom
0 =
bel
ow a
bso
lute
pov
erty
li
ne
to 3
= a
bov
e av
erag
e n
et in
com
e)
Ch
ange
in p
over
ty
cate
gory
aft
er
sub
trac
tin
g h
ealt
h
care
sp
end
ing
(fro
m
0 to
-2
cate
gori
es)
Bel
ow a
bso
lute
p
over
ty li
ne
afte
r su
btr
acti
ng
hea
lth
ca
re s
pen
din
g (0
= a
bov
e;
1 =
bel
ow)
Cat
astr
oph
ic h
ealt
h
care
exp
end
itu
re
cate
gory
(fro
m 1
= u
p t
o 10
% t
o 5
= m
ore
than
40%
)
Ind
icat
or o
f su
bje
ctiv
e p
over
ty(0
= s
ub
ject
ivel
y n
on-p
oor;
1 =
su
bje
ctiv
ely
poo
r)
Per
ceiv
ed in
com
e(f
rom
1 =
bad
to
3 =
goo
d)
coef
fici
ent
SEco
effi
cien
tSE
coef
fici
ent
SEco
effi
cien
tSE
coef
fici
ent
SEco
effi
cien
tSE
Ast
hma
bron
chos
pasm
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed0.
000.
07-0
.00
-0.1
9-0
.45
0.26
0.08
0.07
-0.0
50.
17-0
.15
0.10
Car
diov
ascu
lar
dise
ase
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed0.
010.
04-0
.03
0.06
0.00
0.14
0.04
0.03
-0.0
40.
090.
18**
0.06
Abd
omen
dis
ease
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed-0
.04
0.05
-0.1
4*-0
.07
0.30
0.16
0.13
0.05
0.03
0.13
-0.1
80.
06
Dia
bete
s1
= d
iagn
osed
/ 0
=no
t di
agno
sed
0.13
*0.
06-0
.20*
*0.
080.
37*
0.18
0.07
0.06
0.00
0.14
0.18
*0.
09
Epi
leps
y1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.22
0.19
0.18
-0.3
0-1
.14
1.02
-0.1
20.
19-0
.45
0.46
-0.0
00.
03
Pro
gres
sive
dis
ease
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed0.
090.
09-0
.27*
*0.
120.
58*
0.27
0.12
0.
10-0
.12
0.23
0.14
0.15
Rhe
umat
olog
y di
seas
e1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
-0.0
80.
06-0
.02
0.07
0.12
0.17
0.03
0.
350.
190.
140.
050.
08
Legs
or
feet
dis
ease
1 =
dia
gnos
ed /
0= n
ot d
iagn
osed
0.03
0.06
-0.0
60.
080.
090.
190.
10
0.06
-0.1
80.
150.
010.
09
Bec
k an
d ne
ck
dise
ase
1 =
dia
gnos
ed /
0 =
not
diag
nose
d-0
.06
0.06
-0.1
7*0.
080.
230.
190.
04
0.06
0.03
0.14
0.07
0.09
Oph
thal
mol
ogy
dise
ase
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed
0.01
0.05
-0.2
2**
0.07
0.64
*0.
16-0
.43
0.06
0.19
0.14
0.02
0.08
All
ergi
es1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.04
0.12
-0.1
90.
150.
190.
350.
41
0.13
-0.3
10.
270.
080.
18
Men
tal i
llne
ss1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
-0.1
7**
0.07
0.18
*0.
10-0
.32
0.24
-0.0
1 0.
060.
.20
0.18
0.14
0.10
* p<
0.0
5;
** p
< 0
.01
2
Financial burden and out-of-pocket patient payments
43
Exp
lan
ator
y va
riab
les
Dep
end
ent
vari
able
s
Pov
erty
cat
egor
y(f
rom
0 =
bel
ow a
bso
lute
pov
erty
li
ne
to 3
= a
bov
e av
erag
e n
et in
com
e)
Ch
ange
in p
over
ty
cate
gory
aft
er
sub
trac
tin
g h
ealt
h
care
sp
end
ing
(fro
m
0 to
-2
cate
gori
es)
Bel
ow a
bso
lute
p
over
ty li
ne
afte
r su
btr
acti
ng
hea
lth
ca
re s
pen
din
g (0
= a
bov
e;
1 =
bel
ow)
Cat
astr
oph
ic h
ealt
h
care
exp
end
itu
re
cate
gory
(fro
m 1
= u
p t
o 10
% t
o 5
= m
ore
than
40%
)
Ind
icat
or o
f su
bje
ctiv
e p
over
ty(0
= s
ub
ject
ivel
y n
on-p
oor;
1 =
su
bje
ctiv
ely
poo
r)
Per
ceiv
ed in
com
e(f
rom
1 =
bad
to
3 =
goo
d)
coef
fici
ent
SEco
effi
cien
tSE
coef
fici
ent
SEco
effi
cien
tSE
coef
fici
ent
SEco
effi
cien
tSE
Hea
ring
-spe
ech
prob
lem
s1
= d
iagn
osed
/ 0
–not
dia
gnos
ed-0
.13
0.09
0.08
0.12
-0.2
70.
270.
16
0.09
-0.0
10.
210.
050.
13
Oth
er d
isea
ses
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed-0
.07
0.05
-0.1
5*0.
070.
160.
170.
20 *
0.
05-0
.06
0.13
-0.1
50.
08
N49
7649
7646
7546
7546
7549
76
-2LL
(chi
-squ
are)
8639
.14
(202
0.24
*)24
50.6
(137
.85*
)19
27.9
(79.
39)
1088
2(10
.852
.17)
3500
.4(6
8.02
)89
93.2
8 (1
11.8
*)
Nag
el. R
²0.
400
0.07
00.
050.
060.
030.
031
* p
< 0
.05;
**
p <
0.0
1
Tab
le 2
.5: R
esul
ts o
f the
reg
ress
ion
anal
ysis
(co
ntin
ued)
44
Chapter 2
Participants, who live in urban areas and have a higher education, are more satisfied with
their financial status (less likely to report subjective poverty) than other groups. Having
diabetes or heart diseases is also a significant indicator of satisfaction with financial status.
In all regression models, being married and perceiving one’s own health as good are
associated with a lower financial burden of out-of-pocket payments.
In addition to those who paid for health care and suffer impoverishing or catastrophic
effects, there were also individuals who forwent the use of health care services due to
payments. Our data show that 12.2 % of the respondents needed health care but could
not afford it because it was too expensive.
2.5 Discussions and conclusions
According to our results, 47.6% of all respondents can be classified as poor using income
as the indicator of wealth and absolute poverty line as the poverty threshold (47% pre-
payment poverty headcount). The official Serbian statistics suggest a poverty level of
7.5% of total population (Statistical Office of the Republic of Serbia, 2011) but based on
consumption (World Bank, 2009). When we use consumption as a measure of wealth, our
results confirm the official statistics on poverty (7.5% pre-payment poverty headcount),
as well as previous studies (Bajec et al., 2008).
This huge discrepancy between the pre-payment poverty levels using different
indicators of wealth is related to the nature of the wealth indicators and the way how
the wealth indicators are measured (see background section). As observed in our analysis,
about 80% of the households report a higher consumption level than income, which
results in lower poverty levels based on consumption than those based on income. This
discrepancy can be attributed to additional resources mobilized by the households by
borrowing money and/or selling assets. Another reason for this discrepancy can be that
in the Serbian LSMS, the consumption value of houses is calculated as a regional average
(World Bank, 2009). Thus, the consumption of persons outside expensive cities (such
as Belgrade), is artificially increased. The existence of an informal economy in Serbia
is another reason for the differences between consumption-based and income-based
estimations in our study.
Despite the huge difference in the pre-payment poverty levels discussed above, the
poverty effects of the health care spending are less diverse across the wealth indicators:
2-2.4% of individuals experiencing catastrophic and/or impoverishing effects based on
income-based poverty measures versus 0.8-1.1% based on consumption-based poverty
measures. Nevertheless, in relative terms, this difference means a two times higher
catastrophic and impoverishing effect based on income than based on consumption.
The discrepancy between the poverty measures based on income and those based on
2
Financial burden and out-of-pocket patient payments
45
consumption confirms that the use of multiple wealth indicators provides a better insight
in the prevalence of poverty.
When we compare the different approaches while keeping the wealth indicators
constant (e.g. using the income-based poverty measures indicated by the different
approaches), we observe a similarity: 2% of the respondents go below the absolute
poverty line (impoverishing effects) and 2.3% of the respondents spend more than 40%
of their total income on health care (catastrophic effects). The comparability between
these findings is an indication of the robustness of the two approaches.
However, when we apply the impoverishing effects approach, the burden is higher
for families who are already poor or close to the absolute poverty line. This is consistent
with the idea that the impoverishing effects reflect the poverty situation in society and
emphasized the poverty effects among vulnerable groups (Adhikiri et al., 2009). On
the other hand, the catastrophic health care expenditure approach shows a burden of
health care spending among the poorest quintiles but also among middle quintiles.
This phenomenon is well described in the literature (Adhikiri et al, 2009; Wagstaff &
van Doorslaer, 2003) and we also observe it in our results when we use consumption-
based poverty measures. However, in case of income-based poverty measures, the poorest
quintiles are most affected by catastrophic health care spending. This implies that the
choice of the wealth indicator (see background section) might be the reason for the
differences across catastrophic and impoverishing effects reported in previous studies.
Both approaches – the impoverishing effects and catastrophic health care expenditure
– are widely used in country comparison studies (van Doorslaer et al., 2006; Xu et al.,
2010). However, recent studies show that they both have limitations in capturing the
financial burden provoked by out-of-pocket patient payments (Flores & O’Donnell, 2013).
Both approaches focus on those people who actually experience the financial burden,
while those who forgo the use of health care services because they cannot afford it are
not observed by those approaches (O’Donnell et al., 2008; . Flores & O’Donnell, 2013).
One way to overcome those limitations is to examine the different coping strategies
that households use to overcome the financial burden provoked by out-of-pocket patient
payments (Flores et al., 2008). Such strategies include cutting in short term consumption
of other goods or selling assets and borrowing money, or just do not use health care
services (Flores et al., 2008). Households that forgo the use of health care services can
face higher medical expenditure later in time or income loss and wealth deprivation (if
one of the household members do not work in order to provide care to other member)
(Flores & O’Donnell, 2013). The use of longitudinal data that measures different coping
strategies is considered as a desirable way to overcome the limitations of those approaches
(Wagstaff, 2006). Another approach is based on measuring the downside risks provoked
by medical expenditure. This approach estimates the level of risk that households can
bear conditional on their preferences. Moreover, this approach estimates the potential risk
46
Chapter 2
before the household uses the health care services and is based on an ex-ante perspective
(Flores & O’Donnell, 2013).
In this study, we use regression analysis to examine socio-demographic factors
associated with catastrophic health care expenditure, impoverishments effects and
subjective poverty. When we use variables related to impoverishment effects as dependent
variables, we identify a higher number of independent variables that are statistically
significant than when we use variables related to catastrophic health expenditure.
One of the reasons can be that in calculating dependent variables, we use income for
impoverishment, while when we calculate categories related to catastrophic health care
expenditure we use consumption. Results related to subjective poverty are consistent
with results on the impoverishing effects based on income. Previous research results show
a high correlation between income and subjective poverty (Deaton, 2008).
Also, our regression results indicate several population groups at risk of poverty based
on both real and subjective income, as well as based on the change in the income category
due to health care spending.
This includes groups with higher education, living in urban areas, having poor health
and diagnosed with diabetes. Respondents, who are not-married, and have certain chronic
diseases are at risk of impoverishing (i.e. changing their poverty group) due to health
care spending. Our results are consistent with previous research, although it should be
mentioned that only few studies (Falkingham, 2004) recognize perceived health as an
important indicator. In contrast to previous research however, our results do not indicate
statistically significant differences by age and employment status.
There are several possible reasons in the current health care system in Serbia that can
trigger the poverty effect of health care spending measured in our study. These include
the monopoly position of the HIF, the existence of informal patient payments together
with official co-payments, the complex and still not fully applied exemption mechanism,
as well as the lack of an adequate provider payment mechanism (such as DRG system in
hospitals and/or capitation based reimbursement mechanism), which is a reason for poor
monitoring of the money flow within the health care system.
Special attention should be paid to our findings for chronically sick persons. These
persons are recognized as a vulnerable group when it comes to health care spending. This
group needs to use health care frequently and the accumulated out-of-pocket payments
can easily become unbearable. Our findings suggest that people with diabetes and
progressive chronicle diseases are especially vulnerable to out-of-pocket payments. From
equity perspective, reduced or no patient charges should be considered for these groups.
In addition to the poverty effects of out-of-pocket payments, the problem of forgoing
health care due to payments (reported by 12% of our sample) also requires the policy
attention in Serbia.
2
Financial burden and out-of-pocket patient payments
47
Our cross-sectional data do not allow us to estimate the long-term poverty effects of
out-of-pocket patient payments. Since the out-of-pocket patient payments are enforced,
and usually non-discretionary shocks (Wagstaff & van Doorslaer, 2003) and even small
but frequent health care payments can produce persistent financial burden (Gertler &
Gruber, 2002), panel data would suit better in exploring their long term effects and the
variation of these effects across approaches and wealth indicators. Panel data can also help
to examine the mechanisms that are used in financing out-of-pocket patient payments
(O’Donnell et al., 2005). Furthermore, future research should apply different approaches
such as catastrophic medical expenditure risks to give a more comprehensive picture
regarding the burden provoked by out-of-pocket patient payments in Serbia.
CHAPTER 3
Different Types of Out-of-pocket Payments for Health Care: How do they Contribute to Impoverishing and Catastrophic Effects among Serbian Households?
Submitted as:
Arsenijevic, J., Pavlova, M., & Groot, W. (2015). Out-of-pocket payments for health care in Serbia.
50
Chapter 3
Abstract
Introduction: This study focuses on the impoverishing and catastrophic effects of
different types of out-of-pocket payments for health care. In contrast to previous poverty
studies, we distinguish three types of out-of-pocket patient payments: official co-
payments, informal (under-the-table) payments and payments for “bought & brought
goods” (i.e. payments for health care goods brought by the patient to the health care
facility).
Methods: We examine the impoverishing and catastrophic effects of each type of out-
of-pocket payments on household budgets in Serbia. For this purpose, we use data from
the LSMS data carried out in 2007. Out-of-pocket patients payments for both outpatient
and inpatient health care are included. Consumption-based indicators to measure and
compare the impoverishing and catastrophic effects of the three types of out-of-pocket
payments are used. We also explore the socio-demographic determinants of different
types of payments.
Results: Our results show that total out-of-pocket patient payments in Serbia create
a substantial burden on households. All three types of out-of-pocket patient payments
may provoke impoverishing and catastrophic effects for Serbian households. Regarding
the regression results, users with an income below the poverty line, those from rural
areas and who are not married are more likely to report payments for “bought & brought
goods, while young and more educated users are more likely to report informal patient
payments.
Conclusions: The distinction between different types of out-of-pocket payments is
essential in assessing impoverishing and catastrophic effects. Serbian policymakers need
to consider different strategies to deal with informal payments and eliminate the practice
of “bought & brought goods”. These will be important to decrease the overall burden of
out-of-pocket payments in Serbia.
3
Different types of out-of-pocket payments
51
3.1. Introduction
Most of previous studies have looked at out-of-pocket payments as a univalent concept,
without making a distinction between different types of payments. In particular, patient
payments in some countries include not only official co-payments regulated by official
policy arrangements but also informal (under-the-table) payments (Falkingham, 2004;
Kutzin et al., 2009; Lewis, 2000). Recent evidence suggests that the presence of informal
patient payments is not unique to low income countries but is also observed in some
high-income countries (Greece, Italy, France, Austria) (Stepurko et al., 2010; Tambor et
al., 2014). The distinction between official co-payments and informal patient payments
is important since the strategies for dealing with their catastrophic and impoverishing
effects differ. In case of official co-payments, these negative effects can be diminished by
an adequately designed exemption mechanism (Xu et al., 2010) while in case of informal
payments; these effects are better mitigated by applying strategies for their elimination.
In CEE countries, out-of-pocket patient payments became an important part of health
care financing during the 1990s (Moreno-Serra & Wagstaff, 2010; Thompson & Witter,
2000). Their catastrophic and impoverishing effects on households have been clearly
shown in recent studies (Bredenkamp et al., 2011; Gotsadze, Zoidze, & Rukhadze,
2009; Habicht et al., 2006). A significant number of studies also confirm the existence
of informal patient payments in this region (Balabanova & McKee, 2002; Falkingham,
2004; Kutzin et al., 2009; Lewis, 2000, Shishkin, 2003; Szende & Culyer, 2006; Vian et
al., 2006). However, studies have so far exclusively focused on the description of informal
payments and the reasons for their existence. Hitherto, there is no research on whether
these payments have significant catastrophic and impoverishing effects on households.
This chapter aims to analyze this issue from the perspective of the Serbian public
health care system. This system presents an interesting case due to the parallel existence
of official co-payments, informal patient payments (cash and gifts in kind given to the
physician), as well as payments for “bought & brought goods” (i.e. payments for goods
brought by the patient or their families to the health care facility such as disposable
materials and pharmaceuticals) (Hubrecht & Najman, 2005). The latter type of out-of-
pocket payments has rarely been studied before. Thus, their scope and scale are largely
unknown, as well as their effects on poverty. We study the variation in catastrophic and
impoverishing effects of these types of payments across socio-demographic groups in
Serbia. We focus on the public health care system excluding payments in the private
health care sector. Detailed information about the Serbian public health care system can
be found in Chapter 1.
Similarly, to Chapter 2, to study the catastrophic and impoverishing effects of the
tree types of out-of-pocket payments in the Serbian public health care system, we use
household-level data from the LSMS for Serbia carried out in 2007. The data are analyzed
52
Chapter 3
using quantitative methods (statistical packages STATA 8). After this introductory
section, we first outline the operational definition of out-of-pocket payments applied in
our analysis. In the next step, we present the method section describing the analytical
framework of the analyses and data that are used. Based on this, results are presented. The
chapter concludes by a discussion and conclusions for policy and research.
3.2. Background – different types of out-of-pocket patient payments
Informal patient payments are widely studied (Lewis, 2007; Stepurko et al., 2010) but
there is still no consensus about the definition of informal payments (Gaal et al., 2006).
For example, the definition given by Lewis, states that any payment given in kind or in
cash to public health providers outside the official channels as well as any purchase that
should be covered by the health care system is an informal payment. This definition
emphasizes the unregistered and unaudited nature of informal patient payments.
However, Gaal et al.(2006) point out that not all types of informal patient payments
are unregistered and unaudited. They use the example of medical pharmaceuticals that
are bought by patients for their treatment. In order to provide a more comprehensive
definition that includes all types of informal payments, Gaal et al. (2006) define informal
patient payments as every direct patient’s contribution (in cash or in kind) for the services
that should be provided free of charge. This includes informal payments to physicians,
envelope payments and gifts but also payments for “bought & brought goods”. In
hospital care, “bought & brought goods” payments can include payments to purchase
pharmaceuticals, medical materials, even hospital equipment or meals that patients are
required to bring to the hospital although patients are entitled to get these for free (Gaal
et al. 2006). In outpatient care, “bought & brought goods” can include pharmaceuticals
that are on the positive lists but are not available in state pharmacies so patients have to
pay for them in a private pharmacy.
A drawback of the above definitions is that they do not separate payments for goods
brought by patients to the health care facilities (i.e. the “bought & brought goods”
payments) from pure informal payments. We find it essential to make a distinction
between these two types of patient payments because they differ in nature (see Figure 3.1).
While informal patient payments (such as gifts to the physicians) remain unregistered,
payments for “bought & brought goods” (e.g. for pharmaceuticals bought in pharmacy
and brought to the hospital) are officially registered at the point of purchase but usually
not visible in the financial flows of the institution that provides the services. Also,
payments for “bought & brought goods” are usually essential for the treatment, while
informal payments (gifts and money given to physicians) are not necessary related to
patient treatment. The motivation for informal patient payments can also vary from
3
Different types of out-of-pocket payments
53
a request by the medical staff to voluntary patient payments to obtain better care or
to express pure gratitude (Balabanova & McKee, 2002; Balabanova et al., 2004;
Bredenkamp et al., 2011; HIF, 2011; Stepurko et al., 2010). In contrast, the payments
for “bought & brought goods” always result from a request of the provider and they
are frequently necessary in the curative process (World Bank, 2005). Informal patient
payments are provided before, during or after the curative process, while payments for
“bought & brought goods” occur during the curative process and outside the health care
setting where the service is provided, sometimes within a particularly short period of
time (Lewis, 2007).
Given their registered but shadow nature, patient payments for “bought & brought
goods” represent an important problem in the health care system. Policy makers may
easily overlook this type of out-of-pocket payments as long as the purchase of goods
by patients outside the health care setting does not breach any laws and regulations.
Patients may also underreport their out-of-pocket expenditures related to a treatment if
they are not specifically asked about goods that they brought to the health care setting
(TNS Media Gallup, 2010). Nevertheless, if payments for “bought & brought goods” are
frequent, they might substantially increase the burden of out-of-pocket payments to the
patient and their household.
Although some poverty studies (Flores et al., 2008; Habicht et al., 2006; Thompson
& Witter, 2000) have taken the complex nature of out-of-pocket payments into account
by making a distinction between payments for pharmaceuticals and for health care
services, or between payments by different groups of health care users (e.g. chronically
sick patients), hitherto, no distinction has been made between the catastrophic and
impoverishing effects of informal payments and official co-payments. Needless to say,
there is no study that has specifically focused on the catastrophic and impoverishing
effects of patient payments for “bought & brought goods” distinct from the pure informal
payments (as defined above). Therefore, in this chapter, we study the catastrophic and
impoverishing effects of these three different types of out-of-pocket patient payments
separately.
54
Chapter 3
Figure 3.1: Types of out-of-pocket payments is Serbia
3.3 Methods and data description
3.3.1 Data descriptionAs in Chapter 2, we use the LSMS data for Serbia collected under the supervision of
the World Bank in 2007. Detailed information about the LSMS data for Serbia can be
found in Chapter 1. In this chapter, we use data related to official co-payments and
informal payments for both outpatient and inpatient care, and out-of-pocket payments
for “bought & brought goods” in case of hospitalization only. Data for payments for
“bought & brought goods” for outpatient care are not available. Informal and “bought &
brought goods” payments of a household member for other individuals are also registered.
Specifically, some users of outpatient services in our data who were not hospitalized also
report informal payments and payments for “bought & brought goods” for inpatient care.
Since Serbian patients are not supposed to bring goods for their hospitalization, we
assume that payments for “bought & brought goods” have a quasi-informal nature as
defined by Stepurko (2013). This means that the goods are officially purchased by the
patients or their families but the fact that they are brought to the hospital is against
official regulation. Direct payments to health care providers for goods that should be
provided for free, are treated as informal payments.
The data does not provide any indication of which particular pharmaceutical or
diagnostic procedure has been paid for. The data includes information regarding the
private health care sector, which we do not analyze. Based on variables in the health
module of the data, we have created four variables that specify respectively the total
3
Different types of out-of-pocket payments
55
out-of-pocket patient payments, total official co-payments, total informal payments and
total payments for “bought & brought goods” per household member during the last
12 months. The data for outpatient care are based on a re-call period of one month and
the data for inpatient care use are based on a re-call period of 12 months. Therefore,
we have multiplied the payments for out-patient care by 12 to obtain an annual
estimate comparable to the data for inpatient care. We do not exclude the possibility
that the method used for annualizing the costs can lead to over- or under-estimation.
However, this is still a widely advised and applied method to make figures comparable
(Linden & Samuels, 2013). Although annualized costs can lead to overestimation, they
are still widely advised and applied (Linden & Samuels, 2013). Total out-of-pocket
patient payments include both direct (official, informal and “bought & brought goods”
payments) and indirect (transport costs and extra accommodation costs) medical costs
for health care. Official co-payments include the payments related to physician visits,
nurse intervention, laboratory tests, ultrasounds, referrals, hospital services and other
services. Informal payments include the amounts that are given to physicians and/or
nurses on their request or as a gift. The payments for “bought & brought goods” include
the payments for pharmaceuticals and/or disposable and orthopedic materials that the
patient brought to the health care institution and that should be provided for free by the
institution.
3.3.2. Analytical framework for assessing the catastrophic and impoverishing effects We estimate the effects of different types of out-of-pocket patient payments on households’
wealth using two approaches: catastrophic health care expenditure and impoverishing
effects of out-of-pocket payments. In Chapter 2 of this dissertation, both approaches have
been described in detail. Furthermore, we also described the disadvantages related to
both approaches (Chapter 2). Here, we outline how we calculate the indicators related to
impoverishment and catastrophic effects. We first choose a wealth indicator. Although,
there is no consensus about the “best” indicator of wealth, consumption is the most often
used indicator of wealth in low-and middle-income countries (such as Serbia). Since these
countries frequently have a low tax morale and a large informal economy, it is difficult
to measure individual or household income accurately (Knaul et al., 2006). Therefore,
in this chapter, we take consumption as an indicator of wealth. The LSMS data contains
consumption per household as well as per adult equivalent. We have also calculated
consumption per household member. However, there is no statistically significant
difference between consumption per adult equivalent and per household member.
Following previous studies, we use consumption per household member.
We calculate the impoverishment due to a given type of out-of-pocket payments as
the proportion of households that end up below the poverty line after the annual amount
56
Chapter 3
of that type of out-of-pocket payments is subtracted from their total annual consumption
(Knaul et al., 2006). In our analysis, we included the absolute and relative poverty lines
(for detailed technical information see Chapter 2). The absolute poverty line is equal to
8883 CSD per adult equivalent per-month, which is about 5.40 US per person per day or
approximately 150 US dollars per adult equivalent per-month. The relative poverty line
is defined as 60% of median consumption per adult. This amount was 11283 CSD (about
207.40 US dollars) per adult equivalent per-month in 2007 (Gajic-Stevanovic et al.,
2010). Both poverty lines have been explained in detail in Chapter 2 of this dissertation.
Using the absolute poverty line as a threshold, we calculate the poverty headcount (the
incidence of people who go below the poverty line after health care spending is subtracted
from their total consumption) and the poverty gap index (the mean distance separating
the population from the poverty line, where the non-poor are considered to have the
distance zero). We repeat the calculations using the relative poverty line as a threshold.
We use descriptive statistics to present the above impoverishing indicators and thus, to
summarize the impoverishing effect of the three types of out-of-pocket payments as well
as of the total out-of-pocket payments.
As in Chapter 2, we assume that a given type of out-of-pocket payments has
catastrophic effects on household budgets when the annual amount of that type of
out-of-pocket payments exceeds a certain share of the annual household consumption
(catastrophic threshold). Since there is no clear consensus regarding the threshold, in this
study we use a threshold of 10% especially because we only look at one component of the
out-of-pocket payments (Xu et al., 2010). In order to examine the catastrophic effects of
the three types of out-of-pocket payments, we divide the annual amount of each type of
payment per person per year by the total annual household expenditure per household
member. As explained above, we assume that a situation is catastrophic for the household
budget when the type of payment exceeds 10% of household expenditure. The percentage
of respondents for whom the threshold is exceed, is known as the catastrophic payment
headcount. We also calculate the catastrophic payment gap (which is analogue to the
poverty gap index) and the concentration index (i.e. the variation of the catastrophic
effects across the consumption quintiles). The catastrophic payment gap shows the depth
of poverty, while the concentration index shows how the proportion of those who exceeds
the thresholds varies across different consumption quintiles (van Doorslaer et al., 2007).
We calculate the catastrophic and impoverishing effects of different types of payments
and total out-of-pocket patient payments for the total sample and health care users in our
sample.
3
Different types of out-of-pocket payments
57
3.3.3. The analytical framework for analyzing the probability of different types of out-of-pocket patient paymentsWe examine the probability of payment and the amounts paid for official co-payments,
payments for “bought & brought goods” and informal patient payments. For this purpose,
we run three sample selection models for the three separate types of payments: official
co-payments, payments for “bought & brought goods” and informal patient payments.
The first part of the model also known as the selection equation, uses a binary outcome
variable (e.g. official co-payments yes = 1; no = 0), while the second part uses a linear
regression to model the amount paid officially, if the binary outcome variable is higher
than 0. The selection equation assumes that the probability to pay is determined by a
latent variable. We use the variable living in an urban/rural area as identifying variables
for all three types of out-of-pocket patient payments. We apply analogous sample-
selection models for informal patient payments and payments for “bought & brought
goods”. As independent variables, based on previous studies (Ensor, 2004; Stepurko
et al., 2010; Stepurko et al., 2013), we include socio-demographic variables (gender,
education, marital status, settlement, work status and household size) as well as binary
variables regarding the presence of different chronic diseases (asthma, cardio-vascular
diseases, diabetes mellitus, hear and speech disorders etc.). We expect that people with
chronic diseases (more frequent users of health care services) report more often all three
types of payments (Gordeev et al., 2014). Also, we expect that people living in urban
areas, women and people with good perceived health have a lower probability to report
any types of out-of-pocket patient payments (Tambor et al., 2014).
In order to solve the problem of a skewed data distribution, we use a logarithmic
transformation for variables related to paid amounts for all three types of payments.
We also present results from OLS regression related to the amount paid for all three
types of out-of-pocket patient payments.
3.4 Results
In this study among the 17,375 respondents in the sample, we identify 22.8% who
report official co-payments, 16.4% who report “bought & brought “payments and 2.9%
report informal patient payments. If we look from the perspective of health care users,
we identify 4,976 (28.6% of the household members, i.e. the sample) health care users
of public health care services (those in our sample who report using outpatient and/
or inpatient health care services during the last 12 months). As indicated in Table 3.1,
among the health care users, 93.9% respondents report some type of payments for public
health care services (i.e. official co-payments, informal payments and/or “bought &
brought goods” payments) during the last 12 months.
58
Chapter 3
The majority of health care users 84.7% report official co-payments. However, a
considerable number of health care users 61.1% also report “bought & brought goods”
payments, whereas only 5.7% health care users declare that they have paid informally.
Also, 191 (3.8%) of health care users reported all three types of payments. Among payers,
the average amount that is paid for “bought & brought goods” per year is higher than the
official payments and informal payments. As mentioned above, only a small number of
respondents report informal payments.
Table 3.1 also separately presents data for payments for outpatient care and payments
for inpatient care. As suggested by the table 3.1, informal payments are more frequent
in case of inpatient services (10.9% of all payers for inpatient care reported informal
payments) than in case of payments for outpatient care (≈2% of all payers for outpatient
care reported informal payments). Payments for “bought & brought goods” for outpatient
services are not measured in the Serbian LSMS 2007.
The results regarding the catastrophic effects of each type of payment for public
health care services are presented in Table 3.2. We present the catastrophic effects of
each type of payments for different consumption quintiles. Using the 10% threshold, we
observe catastrophic effects for all types of out-of-pocket payments. However, this effect is
stronger for payments for “bought & brought goods” payments and official co-payments
than for informal payments. Also, we observe that the burden of official co-payments for
the 10% threshold is highest among the third quintile, while for “bought & brought
goods” payments the burden is highest among the second quintile. The concentration
index shows that the catastrophic effects of official co-payments are stronger among the
poorest quintiles, while informal patient payments impose a higher burden for non-poor
respondents.
3
Different types of out-of-pocket payments
59
Tab
le 3
.1: D
escr
ipti
ve s
tati
stic
s pe
r ty
pes
of p
aym
ents
a
Typ
es o
f p
aym
ents
% o
f u
sers
re
por
tin
g th
e p
aym
ent
typ
e
Pay
men
t si
ze
bas
ed o
n u
sers
% o
f p
ayer
s re
por
tin
g th
e p
aym
ent
typ
e
Pay
men
t si
ze
bas
ed o
n p
ayer
s
Mea
n (S
D)
Min
.M
ax.
Mea
n (S
D)
Min
.M
ax.
Pay
men
ts f
or o
utp
atie
nt
and
inp
atie
nt
care
Offi
cial
co-
paym
ents
84
.7%
4394
.8(1
0913
.1)
014
4720
(90.
1%)
5188
.8(1
1683
.8)
614
4720
Pay
men
ts fo
r “b
ough
t &
bro
ught
goo
ds”
61.1
%80
42.3
(185
69.3
)0
2916
00(6
5.0%
)11
739.
1(21
446.
6)20
2916
00
Info
rmal
pat
ient
pay
men
ts5.
7%28
1.5(
3980
.1)
012
8000
(6.1
)49
14.4
(159
57.7
)25
1280
00
Pay
men
ts f
or o
utp
atie
nt
care
Offi
cial
co-
paym
ents
85.3
%45
15.7
(111
40.9
)0
1447
20(9
0.5%
)52
93.7
(118
90.6
)24
1447
20
Offi
cial
co-
paym
ents
for
phys
icia
ns v
isit
59.7
%42
.3(1
47.8
)0
3000
(63.
3%)
42.2
(147
.8)
2030
00
Offi
cial
co-
paym
ents
for
phar
mac
euti
cals
45
.2%
105.
1(31
7.9)
050
00(4
7.9%
)23
2.6(
440.
5)10
5000
Offi
cial
co-
paym
ents
for
labo
rato
ry a
naly
ses
16.4
%17
1.6(
805.
5)0
2000
0(1
7.4%
)10
44.7
(174
3.9)
20
2000
0
Offi
cial
co-
paym
ents
for
disp
osab
le m
ater
ials
19.8
%10
4.3(
499.
4)0
9000
(11.
5%)
961.
7(12
15.7
)20
9000
Offi
cial
co-
paym
ents
for
tran
spor
t23
.7%
121.
9(42
2.7)
012
000
(25.
1%)
515.
0(74
3.3)
2012
000
Info
rmal
pat
ient
pay
men
ts1.
7%18
.8(5
06.7
)0
3200
0(1
.8%
)10
60.1
(367
6.7)
2532
000
Mon
ey r
eque
sted
by
med
ical
sta
ff0.
23%
2.96
(106
.4)
050
00(0
.24%
)12
56.8
(188
7.5)
2550
00
Gif
ts g
iven
to
med
ical
sta
ff1.
55%
15.9
(495
.5)
032
000
(1.6
%)
1030
.1(3
886.
4)50
3200
0
a Mea
sure
d in
200
7 in
CSD
, 1 C
SD =
0.0
125
Eur
o or
0.0
1 U
SD
60
Chapter 3 T
able
3.1
: Des
crip
tive
sta
tist
ics
per
type
s of
pay
men
ts a (
cont
inue
d)
Typ
es o
f p
aym
ents
% o
f u
sers
re
por
tin
g th
e p
aym
ent
typ
e
Pay
men
t si
ze
bas
ed o
n u
sers
% o
f p
ayer
s re
por
tin
g th
e p
aym
ent
typ
e
Pay
men
t si
ze
bas
ed o
n p
ayer
s
Mea
n (S
D)
Min
.M
ax.
Mea
n (S
D)
Min
.M
ax.
Pay
men
ts f
or in
pat
ien
t ca
re
Offi
cial
co-p
aym
ents
72.8
%37
08.9
(807
09.2
)0
1020
00(7
7.1%
)50
93.7
(985
6.2)
6.
1020
00
Offi
cial
co-
paym
ents
for
hosp
ital
izat
ion
48.2
%18
88.6
(640
5.5)
010
0000
(51.
0%)
3918
.8(8
789.
3)15
1000
00
Offi
cial
co-
paym
ents
for
phar
mac
euti
cals
22.1
%52
8.9(
2434
.3)
050
000
(23.
3%)
2398
.7(4
738.
9)50
5000
0
Offi
cial
co-
paym
ents
for
labo
rato
ry s
ervi
ces
12.1
%33
5.8(
1851
.6)
040
000
(12.
8%)
2781
.8(4
662.
7)25
4000
0
Offi
cial
co-
paym
ents
for
disp
osab
le m
ater
ials
6.8%
584.
1(36
74.8
)0
5000
0(7
.2%
)85
34.9
(114
49.3
)40
5000
0
Offi
cial
co-
paym
ents
for
tran
spor
t38
.2%
371.
4(10
95.7
)0
2000
0(4
0.4%
)97
1.8(
1600
.5)
2020
000
Pay
men
ts fo
r “b
ough
t & b
roug
ht”
good
s 22
.7%
526.
3(26
99.8
)0
5500
0(2
4.1%
)23
16.5
(529
3.8)
6055
000
Pay
men
ts fo
r ph
arm
aceu
tica
ls b
roug
ht b
y pa
tien
t19
.932
2.1(
1451
.6)
030
000
(21.
0%)
1621
.1(2
920.
9)60
3000
0
Pay
men
ts fo
r di
spos
al m
ater
ials
br
ough
t by
pat
ient
1.9%
90.6
(118
5.9)
025
000
(2.1
1%)
4540
.5(7
259.
1)15
025
000
Pay
men
ts fo
r or
thop
aedi
c m
ater
ials
br
ough
t by
pat
ient
2.
8%11
3.6(
1425
.9)
030
000
(2.9
)41
20.3
(769
5.3)
100
3000
0
Info
rmal
pat
ient
pay
men
ts10
.4%
525.
4(36
63.7
)0
8000
0(1
0.9%
)50
71.1
(103
61.5
)50
8000
0
Mon
ey r
eque
sted
by
med
ical
sta
ff0.
4%72
.2(1
463.
3)0
4000
0(0
.4%
)19
000(
1645
2)40
0040
000
Gif
ts t
o m
edic
al s
taff
10.1
%45
3.2(
3313
.9)
080
000
(10.
7%)
4497
.6(9
568.
7)50
8000
0
a M
easu
red
in 2
007
in C
SD, 1
CSD
= 0
.012
5 E
uro
or 0
.01
USD
3
Different types of out-of-pocket payments
61
Tab
le 3
.2: S
hare
of h
ouse
hold
hea
lth
care
exp
endi
ture
per
con
sum
ptio
n-ba
sed
quin
tile
s fo
r to
tal s
ampl
e an
d he
alth
car
e us
ers
Poo
rest
qu
inti
le2
34
Ric
hes
t q
uin
tile
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al
sam
ple
Hea
lth
ca
re u
sers
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
Ave
rage
con
sum
ptio
n pe
r qu
inti
le
1114
09.6
111
4794
.616
7159
.21
1670
89.4
2157
72.8
021
5902
.128
1617
.63
2824
14.5
4789
72.9
148
2530
.425
0964
.325
5221
.8
For
tot
al p
aym
ents
:
Ave
rage
am
ount
pai
d (m
ean)
a30
16.3
859
66.8
850
94.6
482
12.9
469
62.4
511
982.
7177
44.0
314
945.
4810
910.
6121
922.
7667
39.5
1271
7.9
No
heal
th c
are
expe
ndit
ure
1717
(49.
4)76
(8.4
)13
89 (4
0.0)
58(5
.7)
1308
(37.
7)49
(4.7
)14
66 (4
2.2)
61(6
.1)
1573
(45.
3)58
(5.7
)74
53(4
2.9)
302(
6.1)
Mor
e th
an 0
% u
p to
10
%14
92 (4
2.9)
696(
76.7
)17
95 (5
1.7)
813(
80.3
)18
61 (5
3.6)
832(
80.0
)17
96 (5
1.6)
810(
80.8
)16
72 (4
8.1)
814(
80.4
)86
16(4
9.6)
3965
(79.
7)
Mor
e th
an 1
0% (p
over
ty
head
coun
t)26
7(7.
7)13
5(14
.9)
290(
8.4)
142(
14.0
)30
5(8.
8)15
9(15
.3)
216(
6.2)
132(
13.2
)22
8(6.
5)14
1(13
.9)
1306
(7.5
)70
9(14
.2)
Pos
t-pa
ymen
t po
vert
y ga
p in
dex
*0.
070.
07
Con
cent
rati
on in
dex*
*
For
offic
ial c
o-pa
ymen
ts:
Ave
rage
am
ount
pai
d (m
ean)
a22
97.4
226
43.6
730
32.3
131
25.4
142
59.6
546
96.2
256
16.7
262
56.2
174
61.4
189
14.7
922
97.9
4394
.2
No
heal
th c
are
expe
ndit
ure
1882
(54.
1)15
6(17
.2)
1661
(47.
8)16
2(16
.0)
1549
(44.
6)14
5(13
.9)
1686
(48.
5)14
4(14
.4)
1804
(51.
9)15
9(15
.7)
8582
(49.
4)76
6(15
.4)
Mor
e th
an 0
% u
p to
10%
1533
(44.
1)71
7(79
.1)
1755
(50.
5)82
4(81
.3)
1840
(53.
0)84
8(81
.5)
1729
(49.
7)81
9(81
.7)
1623
(46.
7)82
1(81
.0)
8480
(48.
8)40
29(8
1.0)
Mor
e th
an 1
0% (p
over
ty
head
coun
t)61
(1.7
)34
(3.7
)58
(1.7
)27
(2.7
)85
(2.4
)47
(4.5
)63
(1.9
)40
(4.0
)46
(1.3
)33
(3.3
)61
(1.8
)18
1(3.
6)
Pov
erty
gap
inde
x0.
060.
06
Con
cent
rati
on in
dex
-0.0
02a M
easu
red
in C
SD, 1
CSD
= 0
.012
5 E
uro
62
Chapter 3T
able
3.2
: Sha
re o
f hou
seho
ld h
ealt
h ca
re e
xpen
ditu
re p
er c
onsu
mpt
ion-
base
d qu
inti
les
for
tota
l sam
ple
and
heal
th c
are
user
s (c
onti
nued
)
Poo
rest
qu
inti
le2
34
Ric
hes
t q
uin
tile
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al
sam
ple
Hea
lth
ca
re u
sers
Tot
al s
amp
leH
ealt
h
care
use
rs
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
N
(% w
ith
in
qu
inti
le)
For
info
rmal
pay
men
ts:
Ave
rage
am
ount
pai
d (m
ean)
a
4171
.11
2458
.00
1700
.67
1814
.78
1962
.74
1941
.46
1369
.99
1431
.88
8698
.47
1050
8.38
145.
328
1.5
No
heal
th c
are
expe
ndit
ure
3428
(98.
6)88
2(97
.2)
3377
(97.
2)96
7(95
.5)
3358
(96.
7)99
4(95
.6)
3331
(95.
8)93
7(93
.4)
3279
(94.
4)91
2(90
.0)
1677
3(96
.5)
4692
(94.
3)
Mor
e th
an 0
% u
p to
10%
44(1
.3)
24(2
.6)
97(2
.8)
46(4
.5)
116(
3.3)
46(4
.4)
147(
4.2)
66(6
.6)
188(
5.4)
97(9
.6)
592(
3.4)
279(
5.6)
Mor
e th
an 1
0% (p
over
ty
head
coun
t)4(
0.1)
1(0.
1)0(
0.0)
0(0.
0)0
(0.0
)0(
0.0)
0(0.
0)0(
0.0)
6(0.
2)4(
0.1)
10(0
.1)
5(0.
1)
Pov
erty
gap
inde
x0.
170.
14
Con
cent
rati
on in
dex
0.2
For
“b
rou
ght
& b
ough
t” p
aym
ents
:
Ave
rage
am
ount
pai
d (m
ean)
a
3701
.26
3701
.26
5431
.94
5431
.94
6793
.22
6793
.22
8132
.65
8132
.65
1321
5.70
1321
5.70
4296
.380
42.3
No
heal
th c
are
expe
ndit
ure
2948
(84.
8)37
9(41
.8)
2846
(81.
9)38
5(38
.0)
2820
(81.
2)38
6(37
.1)
2877
(82.
7)40
2(40
.1)
2856
(82.
2)39
6(39
.1)
1434
7(82
.6)
1948
(39.
1)
Mor
e th
an 0
% u
p to
10%
501(
14.4
)50
1(55
.2)
581(
16.7
)58
1(57
.4)
612(
17.6
)61
2(58
.8)
571(
16.4
)57
1(56
.9)
576(
16.6
)57
6(56
.9)
2841
(16.
4)28
41(5
7.1)
Mor
e th
an 1
0%
(pov
erty
hea
dcou
nt)
27(0
.8)
27(3
.0)
47(1
.4)
47(4
.6)
42(1
.4)
42(4
.3)
30(0
.9)
30(3
.0)
41(1
.1)
41(4
.1)
188(
1.1)
188(
3.7)
Pos
t-pa
ymen
t po
vert
y ga
p in
dex
0.07
00.
07
Con
cent
rati
on in
dex
0.00
4a M
easu
red
in C
SD, 1
CSD
= 0
.012
5 E
uro.
3
Different types of out-of-pocket payments
63
Table 3.3 presents the descriptive results regarding the poverty groups before and after
the subtraction of different types of payments for public health care services. Payments
for “bought & brought goods” provoke a higher burden in terms of impoverishing
effects than the other two types of payments. If we only consider users of health care, the
percentage difference between the post-payment and pre-payment poverty headcount
for “bought & brought goods” payments is similar to those for official co-payments.
Since informal patient payments are not frequently reported in our sample, the poverty
headcount and poverty gap index for those payments is close to zero.
Table 3.4 presents the results of the selection model (known as the Heckman model)
for three types of out-of-pocket patient payments. The probability to report official co-
payments and payments for “bought & brought goods” is higher among participants
with a chronic disease (asthma, cardiovascular diseases, diabetes and progressive diseases).
Participants with lower perceived health report more often all three types of payments.
On the other side, participants living in urban areas (0.19; p≤ 0.05) have a higher
probability to report official co-payments while participants living in rural areas (-0.08;
p≤ 0.05) have a higher probability to report payments for “bought & brought goods”.
The probability to report informal patient payments is higher among those who are
better educated, younger, living in urban areas and working. Results from the second
stage regression show that among those who paid officially, higher amounts are reported
by those who are unemployed. In case of participants who report payments for “bought
&brought goods”, higher amounts are paid by patients diagnosed with progressive
diseases and higher educated. Amounts paid informally are lower in larger households,
while higher amounts are reported among those diagnosed with asthma. Respondents
from non-poor households more frequently report all three types of payments.
Table 3.5 presents the results of the three OLS models related to the amounts paid
for three types of payments. The regression analysis includes participants who paid for
certain type of services. Our results show that payers with lower perceived health, report
higher amounts for official co-payments and payments for “bought & brought goods”.
Higher amounts for informal patient payments are reported by payers with diagnosed
asthma. Results from OLS are similar to those of the sample selection models.
.
64
Chapter 3T
able
3.3
: Pov
erty
hea
dcou
nt a
nd p
over
ty g
ap w
ith
diff
eren
t de
finit
ion
of p
over
ty li
ne a
nd d
iffe
rent
typ
es o
f pay
men
ts
Pov
erty
mea
sure
s re
late
d
to t
otal
pay
men
tsP
over
ty m
easu
res
rela
ted
to
offi
cial
co-
pay
men
tsP
over
ty m
easu
res
rela
ted
to
info
rmal
pay
men
tsP
over
ty m
easu
res
rela
ted
to
“ b
ough
t &
bro
ugh
t go
ods”
p
aym
ents
Ab
solu
te
pov
erty
lin
e a
Rel
ativ
e
pov
erty
lin
e b
Ab
solu
te
pov
erty
lin
e a
Rel
ativ
e
pov
erty
lin
e b
Ab
solu
te
pov
erty
lin
e a
Rel
ativ
e
pov
erty
lin
e b
Ab
solu
te
pov
erty
lin
e a
Rel
ativ
e
pov
erty
lin
e b
Tot
al s
amp
le
Pre
-pay
men
t po
vert
y he
adco
unt
7.5%
16.5
%2.
9%7.
5%0.
00%
0.00
2%0.
8%2.
4%
Pos
t-pa
ymen
t po
vert
y he
adco
unt
8.6%
18.6
%3.
2%8.
2%0.
00%
0.00
2%1.
0%3.
0%
Per
cent
age
poin
ts c
hang
e1.
1%2.
1%0.
3%0.
7%0.
00%
0.00
%0.
2%0.
6%
Pre
-pay
men
t po
vert
y di
ffer
ence
s 21
260.
2429
833.
6315
395.
5525
071.
3748
18.9
113
766.
3816
267.
7724
620.
05
Pos
t-pa
ymen
t po
vert
y di
ffer
ence
s22
457.
5130
811.
3716
735.
0025
420.
5714
605.
2318
092.
7617
060.
6925
791.
22
Pre
-pay
men
t po
vert
y ga
p in
dex
0.01
50.
036
0.00
40.
014
0.00
0.00
0.00
10.
004
Pos
t-pa
ymen
t po
vert
y ga
p in
dex
0.02
30.
043
0.00
50.
015
0.00
0.00
0.00
10.
006
Hea
lth
car
e u
sers
Pre
-pay
men
t po
vert
y he
adco
unt
7.0%
15.1
%4.
68%
12.2
7%0.
00%
0.40
%3.
05%
8.68
%
Pos
t-pa
ymen
t po
vert
y he
adco
unt
9.0%
18.7
%5.
00%
13.4
0%0.
00%
0.40
%3.
78%
10.0
0%
Per
cent
age
poin
ts c
hang
e2.
0%3.
6%0.
32%
1.13
%0.
00%
0.00
0.73
%1.
32%
Pre
-pay
men
t po
vert
y di
ffer
ence
s17
375.
6325
908.
7516
896.
1925
529.
0948
17.9
112
326.
5516
267.
7724
620.
05
Pos
t-pa
ymen
t po
vert
y di
ffer
ence
s20
234.
4228
495.
1518
223.
0726
217.
8311
028.
0514
946.
1117
060.
3125
791.
22
Pre
-pay
men
t po
vert
y ga
p in
dex
0.00
90.
029
0.00
70.
029
0.00
0.00
0.00
10.
014
Pos
t-pa
ymen
t po
vert
y ga
p in
dex
0.01
50.
039
0.01
00.
026
0.00
0.00
0.00
50.
019
a A
bsol
ute p
over
ty li
ne o
f 888
3 C
SD p
er h
ouse
hold
mem
ber
per
mon
th, 1
CSD
= 0
.012
5 E
uro
b R
elat
ive p
over
ty li
ne o
f 112
83C
SD p
er h
ouse
hold
mem
ber
per
mon
th
3
Different types of out-of-pocket payments
65
Tab
le 3
.4: R
esul
ts o
f the
Hec
kman
sel
ecti
on m
odel
, pay
ers
Exp
lan
ator
y va
riab
les
Offi
cial
co
-pay
men
tsP
aym
ents
for
“b
ough
t &
bro
ugh
t go
ods”
Info
rmal
pat
ien
t
pay
men
ts
Sele
ctio
n
mod
elA
mou
nt
1 p
aid
(Ln
)Se
lect
ion
m
odel
Am
oun
t 1
pai
d (L
n)
Sele
ctio
n
mod
elA
mou
nt
1 p
aid
(Ln
)
BSE
BSE
BSE
BSE
BSE
BSE
Gen
der
2 =
fem
ale
/ 1 =
mal
e-0
.04*
0.21
0.0
30.
050.
14*
0.03
-0.0
20.
06 0
.14
0.04
0.0
20.
15
Mar
ital
sta
tus
1 =
mar
ried
/ 0
= n
ot m
arri
ed 0
.09
0.23
0.0
30.
060.
040.
030.
060.
05 0
.14*
0.04
0.3
30.
18
Age
age
in y
ears
-0.0
20.
01-0
.01
0.01
0.01
0.01
-0.0
020.
002
-0.0
1*0.
001
-0.0
3*0.
01
Ed
uca
tion
al le
vel
Uni
vers
ity
degr
ee1=
yes
/ 0=
no-0
.07
0.06
0.2
5*0.
11 0
.03
0.06
0.5
6*0.
12 0
.14*
0.08
0.4
6**
0.33
Up
to h
igh
scho
ol1=
yes
/ 0=
no-0
.06
0.03
-0.2
60.
06-0
.02
0.03
-0.2
20.
06-0
.17*
0.05
-0.3
2**
0.19
Wor
k st
atus
1 =
wor
king
/ 0
= n
ot w
orki
ng-0
.02
0.03
-0.1
6*0.
06-0
.04
0.03
-0.0
80.
06 0
.08*
0.05
0.5
7*0.
20
Nat
iona
lity
1= S
erbi
an /
0 =
oth
er 0
.03
0.04
0.0
20.
07 0
.03
0.04
-0.0
60.
07 0
.07
0.06
0.3
10.
23
Hou
seho
ld s
ize
num
ber
of h
ouse
hold
mem
bers
-0.0
3*0.
01-0
.01
0.02
-0.0
10.
08 0
.01
0.02
0.0
20.
01-0
.25*
0.05
Urb
an1
= c
ity
/ 0 =
rur
al 0
.19*
0.02
-0.0
8*0.
03 0
.11*
0.03
Per
ceiv
ed h
ealt
h go
od-0
.91*
0.04
0.0
30.
15-0
.81*
0.04
-0.1
50.
19-0
.18*
0.06
-0.7
6*0.
25
Per
ceiv
ed h
ealt
h ba
d 0
.40*
0.03
0.3
6*0.
07 0
.40*
0.03
0.2
9*0.
09 0
.12*
*0.
05 0
.78*
0.27
Exe
mpt
ed g
roup
s1
= e
xem
pted
/ 0
= n
ot-e
xem
pted
-0.0
10.
04 0
.18*
0.06
-0.0
40.
03 0
.01
0.06
-0.0
10.
06 0
.34
0.25
Con
sum
ptio
n pe
r pe
rson
1= m
ore
than
abs
olut
e po
vert
y li
ne 0
.54*
0.05
0.6
60.
12 0
.53*
0.06
0.6
8*0.
14 0
.82*
0.18
3.3
0*0.
84
1 All
am
ount
s are
pre
sent
ed a
s nat
ural
loga
rith
m tr
ansf
orm
atio
ns
*p≤
0.05
** p
≤ 0.
10
66
Chapter 3T
able
3.4
: Res
ults
of t
he H
eckm
an s
elec
tion
mod
el, p
ayer
s (c
onti
nued
)
Exp
lan
ator
y va
riab
les
Offi
cial
co
-pay
men
tsP
aym
ents
for
“b
ough
t &
bro
ugh
t go
ods”
Info
rmal
pat
ien
t
pay
men
ts
Sele
ctio
n
mod
elA
mou
nt
1 p
aid
(Ln
)Se
lect
ion
m
odel
Am
oun
t 1
pai
d (L
n)
Sele
ctio
n
mod
elA
mou
nt
1 p
aid
(Ln
)
BSE
BSE
BSE
BSE
BSE
BSE
Ast
hma
& b
ronc
hosp
asm
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.27*
0.07
0.0
80.
09 0
.26*
0.06
0.0
30.
09 0
.09
0.10
0.9
3*0.
04
Car
diov
ascu
lar
dise
ase
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.56*
0.04
-0.0
10.
07 0
.49*
0.03
0.2
70.
09 0
.05
0.06
-0.3
40.
21
Abd
omen
dis
ease
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.32*
0.05
0.1
3*0.
07 0
.22*
0.04
0.0
50.
07 0
.04
0.08
0.1
90.
28
Dia
bete
s1
= d
iagn
osed
/ 0
=no
t di
agno
sed
0.3
6*0.
06 0
.18*
0.08
0.3
9*0.
05-0
.04
0.09
0.1
20.
09 0
.56
0.39
Epi
leps
y1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.2
50.
02.
-0.2
50.
29 0
.34*
0.18
-0.0
70.
29 0
.23
0.28
-0.7
30.
90
Pro
gres
sive
dis
ease
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.32*
0.05
0.1
4*0.
08 0
.37*
0.04
0.2
5*0.
09 0
.07
0.08
0.1
90.
33
Rhe
umat
olog
y di
seas
e1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.0
70.
05 0
.05
0.07
0.0
60.
04 0
.05
0.07
0.0
20.
08 0
.41
0.34
Legs
or
feet
dis
ease
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.00
0.06
-0.0
10.
08 0
.06
0.05
0.0
90.
08 0
.07
0.09
0.2
10.
39
Bec
k an
d ne
ck d
isea
se1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.0
70.
05 0
.06
0.08
0.0
40.
05 0
.04
0.08
0.0
40.
08 0
.21
0.31
Oph
thal
mol
ogy
dise
ase
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed
-0.0
50.
04 0
.06*
0.07
0.1
5*0.
04 0
.05
0.07
0.1
20.
08 0
.38
0.36
Men
tal i
llne
ss1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.0
20.
06 0
.11
0.09
-0.0
40.
05-0
.23*
0.09
-0.1
60.
11-0
.64
0.05
Hea
ring
-spe
ech
diffi
cult
ies
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed-0
.27*
0.08
0.1
40.
11-0
.03
0.06
-0.0
20.
11-0
.02
0.12
0.3
50.
45
Con
stan
t-3
.16*
0.15
9.0
4*0.
03-3
.7*
0.14
7.3
10.
89-3
.60.
36 6
.07*
2.1
N s
elec
ted
3690
2845
504
Ath
rho
-0.0
7-0
.01
1.7*
LR t
est
of in
dep.
eqn
s. (c
hi2 )
0.39
0.10
18.2
7*
Wal
d ch
i2 (w
hole
mod
el)
130.
24*
129.
0890
.23
1 All
am
ount
s are
pre
sent
ed a
s nat
ural
loga
rith
m tr
ansf
orm
atio
ns
*p≤
0.05
** p
≤ 0.
10
3
Different types of out-of-pocket payments
67
Tab
le 3
.5: R
esul
ts o
f the
thr
ee li
near
reg
ress
ions
, pay
ers
Exp
lan
ator
y va
riab
les
Dep
end
ent
vari
able
s (L
n t
ran
sfor
mat
ion
)
Offi
cial
co
-pay
men
tsP
aym
ents
for
“b
ough
t an
d b
rou
ght
good
s”In
form
al
pat
ien
t p
aym
ents
BSE
BSE
BSE
Gen
der
2 =
fem
ale
/ 1 =
mal
e 0
.04
0.05
0.0
20.
05-0
.11
0.15
Mar
ital
sta
tus
1 =
mar
ried
/ 0
= n
ot m
arri
ed 0
.06
0.05
0.0
80.
05-0
.09
0.17
Age
age
in y
ears
-0.0
1*0.
02-0
.007
*0.
002
-0.0
060.
007
Ed
uca
tion
leve
l
Uni
vers
ity
degr
ee1=
yes
/ 0=
no 0
.22
0.11
0.6
0*0.
12 0
.52*
*0.
28
Up
to h
igh
scho
ol1=
yes
/ 0=
no-0
.22*
0.06
-0.2
2*0.
06 0
.05
0.02
Wor
k st
atus
1 =
wor
king
/ 0
= n
ot w
orki
ng 0
.18
0.30
0.6
4*0.
15-0
.65
0.78
Nat
iona
lity
1= S
erbi
an /
0 =
oth
er 0
. 04
0.08
-0.0
60.
07 0
.18
0.23
Hou
seho
ld s
ize
num
ber
of h
ouse
hold
mem
bers
-0.0
10.
02 0
.01
0.02
-0.2
60.
05
Urb
an1
= c
ity
/ 0 =
rur
al 0
.16
0.05
0.0
60.
05-0
.03
0.06
Per
ceiv
ed h
ealt
hR
efer
ence
cat
egor
y=fa
ir
Per
ceiv
ed h
ealt
h1=
good
/ 0=
othe
rs-0
.11
0.08
-0.1
50.
09-0
.19
0.18
Per
ceiv
ed h
ealt
h1=
bad/
0=ot
her
0.4
1*0.
06 0
.30*
0.06
0.5
5*0.
22
Exe
mpt
ed g
roup
s1
= e
xem
pted
/ 0
= n
ot-e
xem
pted
0.2
2*0.
07 0
.10
0.07
0.1
40.
26
Con
sum
ptio
n pe
r pe
rson
1=
mor
e th
an a
bsol
ute
pove
rty
line
0.6
7*0.
11 0
.65*
*0.
11 1
.27
0.81
*p≤
0.05
** p
≤ 0.
10
68
Chapter 3T
able
3.5
: Res
ults
of t
he t
hree
line
ar r
egre
ssio
ns, p
ayer
s (c
onti
nued
)
Exp
lan
ator
y va
riab
les
Dep
end
ent
vari
able
s (L
n t
ran
sfor
mat
ion
)
Offi
cial
co
-pay
men
tsP
aym
ents
for
“b
ough
t an
d b
rou
ght
good
s”In
form
al
pat
ien
t p
aym
ents
BSE
BSE
BSE
Ast
hma
& b
ronc
hosp
asm
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.08
0.09
0.0
20.
08 0
.73*
*0.
35
Car
diov
ascu
lar
dise
ase
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.01
0.05
-0.0
30.
05-0
.29
0.21
Abd
omen
dis
ease
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.14*
0.07
0.0
60.
07 0
.26
0.21
Dia
bete
s1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.0
18*
0.08
0.1
20.
07 0
.22
0.32
Epi
leps
y1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
-0.2
60.
29-0
.02
0.27
0.0
70.
97
Pro
gres
sive
dis
ease
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.15*
0.07
0.2
50.
07-0
.46
0.27
Rhe
umat
olog
y di
seas
e1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.0
60.
07 0
.08
0.07
0.1
90.
29
Legs
or
feet
dis
ease
1 =
dia
gnos
ed /
0= n
ot d
iagn
osed
0.0
20.
08 0
.12
0.08
0.0
40.
33
Bec
k an
d ne
ck d
isea
se1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.0
60.
08-0
.04
0.08
0.2
60.
31
Oph
thal
mol
ogy
dise
ase
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed
0.0
70.
08 0
.05
0.07
-0.3
20.
29
Men
tal i
llne
ss1
= d
iagn
osed
/ 0
= n
ot d
iagn
osed
0.1
00.
09-0
.22
0.09
-0.3
10.
42
Hea
ring
-spe
ech
diffi
cult
ies
1 =
dia
gnos
ed /
0 =
not
dia
gnos
ed 0
.13
0.11
-0.0
20.
10 0
.32
0.45
Con
st 5
.50.
49 5
.32*
6.3
3*2.
03
N39
6028
4550
4
R²
0.06
0.07
0.14
*p≤
0.05
** p
≤ 0.
10
3
Different types of out-of-pocket payments
69
3.5 Discussion and conclusions
In this study, we distinguish three types of out-of-pocket patient payments in the Serbian
public health care sector: official co-payments, payments for “bought & brought goods”
and informal payments. We analyse the level of these types of payments among different
socio-demographic groups in Serbia. Given the available data, we operationalise patient
payments for “bought & brought goods” as the costs for pharmaceuticals and/or disposable
materials and nonmedical goods that the patient or the relatives brought to the hospital.
Our results show that official co-payments and payments for “bought & brought
goods” present a relatively significant proportion of out-of-pocket payments in Serbia
while pure informal payments are somewhat less frequent (only 5.7% of all health care
users). Furthermore, the share of payments for “bought & brought goods“ are higher than
the share of informal and official co-payments in the annual household consumption.
The explanation for the high share of patient payments for “bought & brought goods” in
Serbia can be found in the nature of these payments. Goods that are requested by medical
doctors are very often necessary for medical treatment, and it is less possible to ignore
bringing these goods than to pay the informal patient payments. Also, both patients
and providers might perceive payments for “bought & brought goods” differently from
informal payments, for example view them as less problematic from an ethical point
of view. One of the reason that patient do not perceive “bought & brought goods”
payments problematic from ethical point of you, is that the payments for “bought &
brought goods” have their roots in the times when the health care settings lacked medical
materials, supplies, and pharmaceuticals due to a financial crisis (Gaal et al., 2010). In
such circumstances, medical staff would ask the patient and/or relatives to bring supplies
and pharmaceuticals that are necessary for the treatment but the hospital cannot provide
due to poor funding (Kutzin et al., 2009). From the perspective of patients and their
families, this request is perceived as an act of cooperation and extra attention on the
side of the health care provider rather than corruption. Furthermore, patients and their
families might expect that by buying and bringing goods like medicine that are not
available in the hospital, they will secure better quality of care (Garfield, 2001; Stepurko
et al., 2013;Stepurko et al., 2015). At first glance, such request is not interwoven with
benefits to the health care providers and at the same time it is important for the curative
process. In practice however, “bought & brought goods” can generate additional benefits
for health care providers in several manners. For example, hospital staff may still declare
the use of supplies and pharmaceuticals (even though these are brought by the patient)
and can sell the “saved” medical goods on the black market, or use the “saved” goods in
their private practices, or simply divide the money claimed for these goods in the form of
an extra bonus. Such situations have been mentioned in research articles but the evidence
for their existence is sparse and therefore, anecdotal (CESID, 2011).Additionally, our
70
Chapter 3
regression results show that participants with a higher education have a higher probability
of paying informally. Apparently, these health care users are more aware of the common
practice of paying additionally for health services that should be provided for free. They
apply the so called “do-it-yourself” approach described in the literature as alternative
politics (Cohen, 2012). These alternative politics refer to situation when people are
dissatisfied with current government policy, they take unilateral initiatives. This means
that they obtain the desired services but in a way that is different from the way defined
by government policy, i.e. in a semi-private way (Cohen, 2012). Thus, better educated
people pay informally because they anticipate that government policy does not work and
if they do not pay informally, they will not get adequate service.
The limitations of this study are mostly related to the data. We use an existing dataset
collected by others, which provides no information about the type of treatment or type of
pharmaceuticals that patients pay for. Also, we do not have information about the obstacles
related to the utilisation of health care. More precisely, we do not know if patients forego
using services that they need because they cannot afford them. Furthermore, a recall
period of 12 months is rather long and may lead to recall bias. Information regarding
“bought & brought goods” payments is only available for inpatient care and we do not
know which particular pharmaceuticals or type of disposable material has been brought
and bought into hospital. Moreover, payments for “bought & brought goods” are reported
not only by inpatient health care users, but also by household members. The latter group
made payments for “bought & brought goods “for others. Despite these limitations, the
dataset provides a representative sample and information on official co-payments and
informal payments for health care, as well as on payments for “bought & brought goods”,
which makes it particularly useful for our study.
Our results show that there are respondents who report all three types of payments.
These findings indicate that the current health care policy regarding official co-payments
is not efficient. Health care users who pay officially do not have a guarantee that they will
receive the services which they officially pay for. In order to obtain adequate health care
service, health care users are often forced to bring necessary goods and pay informally.
Official co-payments were introduced to provide better financial sustainability in addition
to premiums from the Republic Health Insurance Fund (RHIF)(Bajec et al., 2008;Gajic-
Stevanovic, 2010; Vukovic &Perisic, 2011). Recent studies (Gavrilovic & Trmcic, 2013;
Stosic et al., 2014) emphasize that official co-payments are very low in nominal amounts
and therefore do not contribute to the financial sustainability of the health care system.
Since they are part of the official health policy, we do not question their existence here.
However, Serbian policymakers should better regulate the system of patient charges. In
particular, policymakers need to consider strategies to deal with informal payments and
eliminate the practice of “bought & brought goods”. These will be important policy
measures to decrease the overall burden of out-of-pocket payments in Serbia.
3
Different types of out-of-pocket payments
71
Despite the high share of payments for “bought & brought goods” (as indicated by
our results), they are often neglected by both researchers and policy-makers (Garfield,
2001; McCarthy, 2007). Moreover, recent results in other CEE countries show that
even when the probability of informal patient payments has decreased, the purchase of
medical supplies and pharmaceuticals that should be provided for free, continuous to
exist (Stepurko et al., 2015). The reason can be seen in the fact that in many countries
pure informal patient payments are strictly forbidden, while payments for “bought &
brought goods” are not fully regulated (Stepurko et al., 2015). Since the public health
care services in those countries are still poorly funded, payments for “bought & brought
goods” can be a valuable source for additional funding. Thus, the distinction between
the two types of unofficial payments in empirical research is important. Future research
should use longitudinal data to analyse the evolution and dynamics of these payments at
different time points. Furthermore, using qualitative data can give better insight in the
determinants of these payments. In some countries, health care users are more willing to
pay informally for public health care services than officially in private facilities (Stepurko
et al., 2015). It would be useful to examine willingness to buy and bring foods in public
services instead of paying for them in private facilities.
CHAPTER 4
Out-of-Pocket Payments for Public Health Care Services by Selected Exempted Groups in Serbia During the Period of the Post-war Health Care Reforms
Published as:
Arsenijevic, J., Pavlova, M., & Groot, W. (2013). Out-of-pocket payments for public healthcare services by selected exempted groups in Serbia during the period of post-war healthcare reforms. The International Journal of Health Planning and Management. DOI: 10.1002/hpm.2188
74
Chapter 4
Abstract
Background: This chapter focuses on the exemption mechanism that accompanies
patient co-payments for outpatient and inpatient hospital care in Serbia. The objective
was to investigate the level and dynamics of out-of-pocket payments for this type of
services by exempted groups (older than 65 years, younger than 15 years, unemployed,
disabled and individuals with low family income) compared with that by other groups.
Methods: For this purpose, we use household data collected in the LSMS carried out in
2002, 2003 and 2007. These years correspond to the start of the recent reforms in the
Serbian healthcare sector and 1 and 5 years after the start of the reform.
Results: Our results show that people who belong to exempted groups were paying
for healthcare in 2002, 2003 and 2007. They report different types of out-of-pocket
payments for outpatient and inpatient hospital care.
Conclusions: Thus, despite the ambition of the Ministry of Health in Serbia to promote
equity in healthcare as a leading aim of the reforms, the implementation of the exemption
mechanism fails to protect the targeted groups. Future exemption mechanisms should be
pro-poor oriented but should also exempt those whose health status requires a frequent
healthcare use.
4
Exemption mechanism
75
4.1 Introduction
Systems of patient co-payments are often accompanied by exemptions in order to protect
equity in health care financing and access (Blas & Limbabala, 2001; Gilson, 1997; van
Doorslaer & Wagstaff, 1992). Children, elderly, unemployed and disabled people are often
eligible for exemptions from paying co-payments because these groups are thought to be
unable to pay for health care or use health care frequently (Chapter 1). If these groups are
not exempted from co-payments, they incur high health care expenses (taken as a share
of their household income), which might have catastrophic effects on their household
budgets (Bitran & Giedion, 2003; James et al., 2006; Kruk et al., 2008; Perkins et
al., 2009; Sepehri & Chernomas, 2001). Alternatively, vulnerable groups might forgo
or delay the use of essential health care services if co-payments are high (Deninger &
Mpunga, 2005; Kruk et al., 2008; Nynator & Kutzin, 1999; Perkins et al., 2009).
As we mentioned in Chapter 1, two main reasons are put forward for the failure
of exemption mechanisms: an inadequate design of the exemption mechanism and the
inability of policy-makers to implement it in practice (Gilson et al., 1995; Masiye et
al., 2010; Russell, 2004; Sepehri & Chernomas, 2001). For example, if the design of
the exemption mechanism is based on individual identification (e.g. identifying persons
who are poor), the assessment of individual-specific eligibility criteria (e.g. being poor)
may be difficult in practice. Alternatively, group targeting that identifies eligible
groups (e.g. elderly, children, monks, civil servants etc.) may neglect the importance of
health status and ability to pay (Hanson et al., 2007; Mkandawire, 2005; Quayyum et
al., 2009; van Adams & Harnett, 1996). Individuals belonging to such groups do not
necessarily use health care frequently and are not necessarily unable to pay for health
care. At the same time, groups who actually need the exemption may be easily left out
of the exemption scheme (Quayyum et al., 2009; Tambor et al., 2010). Instead, self-
targeting gives exemption rights to everyone, but makes those rights more attractive
for a target population (e.g. allowing patients who are able to pay to skip the queue
while providing free-of-charge care for those who cannot pay and have to wait). However,
self-targeting can impact on egalitarian issues (Hanson et al., 2007; Mkandawire, 2005).
The legislation may also fail to clarify who will compensate health care providers for
the revenue loss due to the exemptions – the users who are able to pay, donors, the
government, or others (Bitran & Giedion, 2003). If the exemption policy is designed
to directly include health care providers in the process of co-payments collection, the
actual exemption of eligible patients depends on the providers’ willingness to grant the
exemption. Also, when the exemption policy only addresses direct patient payments
for medical costs (hospitalization, laboratory analysis, etc.), vulnerable groups although
exempted, may forego health care use because of high indirect costs like costs of transport
and accommodation (Kruk et al., 2008; Perkins et al., 2009; Quayyum et al., 2010;
76
Chapter 4
Russell et al., 2004). Those indirect costs are sometimes even a higher burden than the
co-payments themselves (Kruk et al., 2008; Perkins et al., 2009; Sepehri & Chernomas,
2001).
Even when the exemption mechanism is adequately designed, its implementation
might still fail in practice (James et al., 2006). The failure of the exemption mechanism
can be caused by a lack of appropriate dissemination of information about its existence
(Deininger & Mpuga, 2005; Gilson et al., 2001) or the social stigma associated with
exemptions (Messen et al., 2006). Providers’ reluctance to grant the exemption and a
negative attitude among medical staff towards exempted groups may also play a role
(Nyonator & Katzin, 1999; Perkins et al., 2009). As a result, individuals who are entitled
to an exemption, might still be asked to pay the formal co-payment or even to pay
informally either in cash or as a gift in kind (Blas & Limbabala, 2001; Borgi et al.,2003;
Nyonator & Kutzin, 1999; Perkins et al., 2009; Witter et al., 2007). The existence of
informal patient payments should be addressed as part of an overall anti-corruption policy,
but also during the implementation of exemptions from official co-payments (Kruk et al.,
2008). The adequate implementation of the exemption policy should include additional
strategies like improving the quality of care in public health care facilities and an equal
distribution of skilled health care providers in different geographical areas (Kruk et al.,
2008; Lagarde & Palmer, 2008; Perkins et al., 2009; Witter et al., 2010). If the quality
of health care provided for free is low, exempted groups may need to pay for better service
or to use alternative non-medical care (Honda et al., 2011; Kruk et al., 2008; Lagarde &
Palmer, 2006; Mkandawire, 2005; Perkins et al., 2009; Quayyum et al., 2010; Witter et
al., 2010).
The sparse evidence on the implementation of exemption mechanisms pertains only
to African and Asian countries (Jacobs, Price & Oeun, 2007; Kruk et al., 2008; Lagarde &
Palmer, 2006; Lagarde & Palmer, 2008; Messen et al., 2006; Mkandawire, 2005; Sepehri
& Chernomas, 2005). As suggested by these studies, due to the reasons described above,
only in a few cases, an adequate exemption policy is successfully implemented. Still,
there are many middle-income countries in Central and Eastern Europe with ongoing
health care reforms. These reforms include, among other measures, the implementation
of patient co-payments accompanied by exemption mechanisms. Hitherto, there is
no evidence from these countries on the effectiveness of their exemption mechanisms
(Balabanova, 2007; Polleti et al., 2007).
This chapter focuses on the exemption mechanism that accompanies patient co-
payments in Serbia, one of the Eastern European countries. Our aim is to review the
problems with the exemption mechanism in Serbia reported in the literature, and to
investigate the level and dynamics of out-of-pocket payments for outpatient and inpatient
hospital care by selected exempted groups. We are specifically interested in whether
exempted groups pay official co-payments when they are supposed to be exempted from
4
Exemption mechanism
77
such fees. For this purpose, we perform quantitative analyses using the Serbian Living
Standard Measurement Study (see World Bank, 2011) carried out in 2002, 2003 and
2007 (i.e. when co-payments were implemented in Serbia, and 1 and 5 years after their
implementation).
4.2 Background
4.2.1 The context of the exemption mechanism in Serbia As we outlined in Chapter 1, during 2002, the first post-war health care reforms were
launched in Serbia. The health care reforms, launched by the Ministry of Health, have
focused on the renovation of medical equipment and the improvement of physicians’ skills
(World Bank, 2009). The health care financial mechanism based on compulsory health
insurance, also required changes since it was unable to assure resources for an adequate
service provision (Gajic-Stevanovic, 2010). The reforms of the health care financial
mechanism were coordinated by the HIF. The introduction of patient co-payments in
2002 was one of the first measures to improve the financial sustainability of the already
“poor” health care system.
At present, the Ministry of Health emphasizes the importance of technical equipment
innovation, re-building of the current infrastructure and staff education HIF, on the
other side, focuses on financial reforms, more particularly on reforms of the official co-
payments. Thus, the two main stakeholders have a different focus regarding the reform
path. However, neither of these stakeholders directly focuses on the barriers to access,
such as indirect and informal payments, but also the long waiting times in the Serbian
health care system. Although these factors are marked as a problem to equity in health
care access (Bajec et al., 2008), there is still no systematic approach to solve it.
4.2.2. The design of the exemption mechanism in Serbia and design-related problems The system of patient co-payments in Serbia (introduced in 2002) is accompanied by
an exemption mechanism with the objective to assure equity in access to health care. It
concerns both outpatient and inpatient services. The Serbian Law on Health Insurance
defines several population groups that are exempted from patient co-payments: children
younger than 15 years, pregnant women, persons older than 65 years, disabled persons,
HIV infected persons, monks, people with low family income, unemployed, chronically
ill people, military service servants, monks people registered as refuges and the Roma
population (Chapter 1). According to the Serbian law, groups that are exempted from
patient co-payments should not be charged at all when they use health care services (Bajec
et al., 2008). A detailed definition of those groups is available in guidelines (Official
78
Chapter 4
Gazette of RS, n. 1/2007, 52/2007, 99/2007, 14/2008, 20/2008, 7/2009, 82/2009 &
23/2010). About 1.2 million people (16% of the total population) have the status of
exempted individuals, i.e. belonging to at least one of the exempted groups (Institute
for health Insurance of Serbia, 2010). The share of the officially exempted population
is similar to that in many developing countries (Bitran & Giedion, 2003; Palmer et
al., 2004; Poletti et al., 2007) but relatively low compared to some European countries
(Tambor et al., 2011). For example, patient co-payments apply only to 60% of the
population in Italy and to 50% in Portugal (Tanner, 2008).
It should be noted however that not all groups included in the exemption list are
unable to pay or need health care frequently (e.g. monks, children and elderly as mentioned
above). At the same time, some of the groups that cannot afford to pay co-payments are
not clearly defined, even though from the point of view of the legislation, they have in
principle the right to an exemption. The reason for this is the fact that the description
of exemptions in guidelines are not clear and that they can change considerably over a
short period of time. This is the case of people with low family income. In Serbia, people
with an income below the minimum net income, should be exempted from regular
patient co-payments (Official Gazette of RS, 11/2010). The threshold of the minimum
net income is calculated by the Serbian Statistical Office and it is officially used by the
Serbian government (Statistical Office of the Republic of Serbia, 2010). However, that
income threshold has changed almost every year (even twice a year) and so has the right to
an exemption for people with low family income. The HIF adjusts the threshold every six
months. This creates confusion among patients and health care providers. Although the
definition of low family income status has been regulated for a certain period of time by
special policy documents (Official Gazette of RS, n. 1/2007, 52/2007, 99/2007, 14/2008,
20/2008, 7/2009, 82/2009 & 23/2010), this information is not readily available for the
individual patient.
Also, guidelines are not clear enough for the majority of the population. They have
been written in strict law-centered language. In conclusion, the procedures described in
guidelines need to be simplified and made available for the most of the users. Moreover,
private outpatient and inpatient hospital services in Serbia are not included in the system
of compulsory health insurance (Gajic-Stevanovic, 2010). Therefore, private health care
providers are not obliged to follow the exemption policy and may still charge patients
belonging to exempted groups. The lack of an exemption mechanism in private health
care institutions can also lead to the failure of the exemption mechanisms especially if
the patient needs quick access to health care for which he/she has to wait at the public
hospitals (Lagarde & Palmer, 2008). For example, due to access-related problems (e.g.
long waiting times), Serbian patients often use private health care services even when
they have a referral to specialized care in the public health care sector.
4
Exemption mechanism
79
4.2.3 The implementation of the exemption policy and current implementation problemsBesides the design, the successful implementation of exemption policy requires clear
agreements among the main stakeholders. According to the Serbian legislation related
to exemptions, the health care costs of the exempted groups are covered by the Ministry
of Health. For this purpose, the ministry provides 12.3% of the annual net revenue of
HIF. However, the amount that the institute spends on exempted groups is much lower
than that percentage and moreover, it is decreasing (Chapter 1). In 2004, the institute
spent 3.14% of the total revenue for exempted groups, and in 2009, this percentage was
only 1.88% (Bajec et al., 2008). What is more, in 2007, the Ministry of Health did not
transfer the necessary amount for the exempted groups to HIF (World Bank, 2009). One
of the reasons that the Ministry of Health sends money irregularly is the lack of resources
from the central budget. The other reason is the lack of clear responsibility regarding the
implementation of the exemption mechanism between HIF and Ministry of Health. In
particular, there is no policy document that defines the role of the main stakeholders and
providers regarding the exempted groups (Bajec et al., 2008). Thus, there is no single
institution responsible for the application of the entire exemption mechanism. Also, the
increased level of corruption among the main stakeholders can be an additional reason
for irregular money transfers (Chapter 1). Anecdotal evidence describes “unwritten”
agreements between HIF and Ministry of Health for not-paying the costs for exempted
groups to the providers or choosing the providers that will be paid (Stamenkovic, 2011).
During the period 2007-2010, new documents were provided by the Ministry of
Health as “guidelines” for health institutions how to apply the legislation concerning
exemptions (Official Gazette of Republic Serbia, 2007, 2008, 2009, 2010). Despite
these new guidelines, the definition of some groups that are eligible for exemptions (e.g.
people with low family income) is still unclear. Moreover, the procedure of obtaining
an exemption status is rather complicated. For example, if a person with low family
income applies for the exemption, he has to provide several different documents (e.g.
a confirmation of the person’s individual income, confirmation of property), and bring
them to the nearest department of HIF.
Then, HIF decides whether to grant the exemption and in case of a positive decision,
issues an official confirmation (so called Obrazac UP-2) to the person. This confirmation
is valid only with the person’s health card (the substitution for the health insurance
card in Serbia). For some of the exempted groups (such as people with low income), the
exemption status is granted by HIF, for others (such as people diagnosed by HIV), it
needs to be confirmed by a GP. When the person visits the GP in primary health care,
the administrative staff puts a stamp in the health care card that confirms the status of
the exempted person. Overall, it is hard and time-consuming for a patient to obtain
all administrative documents and to establish whether one is entitled to an exemption.
80
Chapter 4
Recently, with the aim to enhance the implementation of capitation in primary health
care, HIF started introducing electronic health cards. Since each insured person will have
such health card, this can be used for a more rational targeting of exempted groups.
However, the new health care cards can be a possible solution for avoiding visible
notification of being exempted/not exempted directly on the card.
In addition to this, the issues concerning the social stigma associated with the
exemptions and possible providers’ reluctance to grant the exemption or to induce
informal payments (the latter is commonly reported in Eastern European countries; see
Stepurko et al., 2010), raises the question whether the exemption mechanism in Serbia is
actually effective. In other words: did exempted groups pay for health care services that
should be provided free-of-charge to them?
Hitherto, there are no official data about the effectiveness of the exemption mechanism
that was introduced in 2002. It is only known (Bajec et al., 2008) that the exemption
mechanism has never been fully implemented in Serbia due to reasons described above
(mainly the lack of clear responsibility of the main stakeholders, and the lack of necessary
financial resources). To examine the effectiveness of the Serbian exemption mechanism,
in this study, we investigate the out-of-pocket payments for outpatient and inpatient
hospital services by selected exempted groups compared to the out-of-pocket payments
by other groups.
4.3 Methods
As in Chapter 2 and Chapter 3, for our analysis, we use the LSMS data for Serbia. For
the purpose of this study, we use the data collected at three time points: 2002, 2003
and 2007 among 19725, 8027 and 17375 respondent respectively (The World Bank
group, 2011). The three samples are representative for Serbia (World Bank, 2011). As we
mentioned in Chapter 1, the attempt was to have panel data for 2002 and 2003 but this
was not achieved. As a result, the number of hospital users who participated in both 2002
and 2003 survey is very low (less than 2%) and prohibits any separate panel analysis.
The data collected for 2007 are not connected to the previous two years. Therefore, we
consider the data as cross-sectional.
As explained above, we focus on both outpatient and inpatient hospital care. We
examine five exempted groups: older than 65 years, younger than 15 years, disabled,
unemployed and people with low family income status. These are the exempted groups
that we could identify based on the data. Moreover, the ability to pay of these exempted
groups is often questioned in Serbia and their utilization of health care services is often
intensive. The questionnaire that was used in the LSMS survey, does not allow us to
identify respondents who belong to other exempted categories, like pregnant women
4
Exemption mechanism
81
and HIV infected persons. The identification of another relevant group – the group of
individuals with low family income – is also not straightforward because (as mentioned
earlier) the poverty line is continuously changing even during one year. Nevertheless,
considering the importance of this group for our analysis (they have been identified as
vulnerable by Serbian guidelines), we took the lowest poverty line for each year (2002,
2005 and 2007) to identify individuals belonging to this exempted group.
Based on the LSMS data, we compare data on the probability of paying and the amount
of out-of-pocket payments for outpatient and inpatient hospital services across the five
exempted groups that we identified. We also compare the out-of-pocket payments for
outpatient and inpatient hospital services paid by the five exempted groups with that of
other population groups (non-exempted and exempted group that we could not identify).
We present data for all five exempted groups together, for each of the exempted groups
separately, and for other population groups.
We divide the data on out-of pocket payments for inpatient hospital care available in
the LSMS data into four payment categories:
– Official co-payments that should not apply to the exempted groups. This group
incorporates official co-payments for physician visits, hospitalizations, pharmaceuticals
prescribed on behalf of HIF, laboratory analyses, disposal material (like surgical).
– Indirect patient payments like transport costs that are not included in the exemption
scheme. Those data are reported for health care users but also for accompanying
households’ members.
– Payments for goods bought & brought by the patient in case of hospitalization. This
group consists of payments for medical goods (including pharmaceuticals, disposal
materials and orthopedic materials) that should be provided by the hospital to any
hospitalized patient for free, but the patient is required to bring to the hospital.
Data on payments for goods bought & brought by patients in outpatient were not
collected.
– Informal payments. This group incorporates one part of the informal payments that
were requested by the medical staff either in cash or in kind, and payments that were
given as a gift.
Two types of analyses are performed (SPSS 17.00) to analyze the variation in the out-
of-pocket payments – comparative descriptive analysis and regression analysis. First,
we compare the five exempted groups and the rest of the respondents with regard to
the propensity to pay (I’ve paid, I didn’t pay) officially, by buying and bringing goods,
informally and indirectly. We use χ²-tests for the comparison of this first set of dependent
variables given their binary nature. Then, we compare the amounts reported by the
five exempted groups and the rest of the respondents. We compare this second set of
dependent variables using ANOVA, more precisely Bonferroni correction as post hoc
82
Chapter 4
test. Furthermore, we explore the differences between the selected exempted groups and
other population groups at different time points, namely in 2002 (the introduction of the
exemption mechanism), in 2003 (one year after) and in 2007 (5 years after).
Second, for both outpatient and inpatient hospital services, we perform logistic and
linear regressions. We perform a set of logistic regression analyses with the first set of
dependent variables indicating the propensity to pay (I’ve paid, I didn’t pay) for different
types of payments. Then, we run a set of linear regressions with the amounts paid (the
second set of dependent variables), using only the subset of respondents who paid for
hospital services. As a basic year, we take 2002, providing two dummy variables for
2003 and 2007. The dummy variables are entered in the same block. An indicator for
the exempted groups (taken together) is also included as a dummy variable. Interactions
between the dummy for exempted groups and each dummy for the year are also included
in the model. We also include basic social demographic characteristics such as gender
and marital status. We do not include age and income because they are already used to
define the respective exempted groups. Thus, we obtain two sets of regression models
(related to the propensity and the amounts respectively) with the same predictors (social
demographic variables, two dummy variables for the two years – 2003 and 2007 and an
indicator for exempted groups). We also include relevant interactions between variables.
All amounts are expressed in Serbian dinars (Serbian national currency; 1 USD ≈ 88.3
CSD) and they have been corrected by the Consumer Price Index (CPI index) for the
inflation rate (for a definition of CPI index see OECD, 2010). Since the analysis is on a
micro level, weighting is not used.
4.4 Results
4.4.1 Descriptive statistics and comparisonsIn 2002, 2003 and 2007, the five exempted groups analyzed in our study, reported
payments for outpatient and inpatient hospital services (see Table 4.1) while these persons
should not have paid any official co-payments according to the Serbian exemption policy.
The payments by the five exempted groups include payments of official fees, indirect
payments, payments for “bought & brought goods”, and informal payments.
Our results for outpatient care (see Table 4.2) show that from the five exempted
groups, outpatient care was most frequently used by elderly people. In outpatient care,
exempted groups most often paid official fees for visiting a physician (representing 8.9%,
6.8% and 29.6% of all users in 2002, 2003 and 2007 respectively, (see Table4.1). Official
fees for pharmaceuticals were also relatively often paid by the five exempted groups.
However, the highest amounts for outpatient care were paid in 2007 for disposable
materials by persons with two or more exemption criteria (see Table 4.2).
4
Exemption mechanism
83
For all types of outpatient payments (official and informal), ANOVA models are not
statistically significant. Thus, for outpatient payments, we do not observe statistically
significant differences across the five exempted groups and when we compare the five
exempted groups to other population groups (i.e. non-exempted and exempted groups
that we could not identify based on our data).
Inpatient hospital care was also most frequently used by elderly people compared to the
other four exempted groups (see Table 4.3). If we look at all five exempted groups together
(see Table 4.1), in inpatient care, they most often paid for transport and pharmaceuticals
(both official fees for pharmaceuticals and payments for pharmaceuticals brought to the
hospital). The payment of official fees for hospitalization by the five exempted groups was
also frequent (representing 9.6%, 9.8% and 26.5% of all users in 2002, 2003 and 2007
respectively, see Table 4.1). If we look through different exempted groups, the amount
paid for hospitalizations varies from 808.00 CSD in 2003 (9.1 USD) to 5255.00 CSD
(59.4 USD) in 2007 (see Table 4.3). The highest amounts for hospitalization were paid in
2003 by the group of older than 65 and the lowest amounts were reported by the group
younger than 15 (also in 2003).
84
Chapter 4T
able
4.1
: The
dis
trib
utio
n of
out
-of-
pock
et p
atie
nt p
aym
ents
am
ong
the
sele
cted
exe
mpt
ed g
roup
s an
d ot
her
popu
lati
on g
roup
s
Ou
t-of
-poc
ket
pat
ien
t p
aym
ents
rep
orte
d in
20
02O
ut-
of-p
ock
et p
atie
nt
pay
men
ts r
epor
ted
in
2003
Ou
t-of
-poc
ket
pat
ien
t p
aym
ents
rep
orte
d in
20
07
Sele
cted
exe
mp
ted
gr
oup
sO
ther
pop
ula
tion
gr
oup
sSe
lect
ed e
xem
pte
d
grou
ps
Oth
er p
opu
lati
on
grou
ps
Sele
cted
exe
mp
ted
gr
oup
sO
ther
pop
ula
tion
gr
oup
s
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Ou
tpat
ien
t ca
reN
use
rs=
456
2N
use
rs=
170
3N
use
rs=
467
5
For
mal
pat
ien
t fe
es:
Phy
sici
an v
isit
s40
8(8.
9)19
45(4
2.6)
1179
(25.
8)91
0(19
.9)
115(
6.8)
792(
46.5
)43
5(25
.5)
361(
21.2
)13
86(2
9.6)
1157
(24.
7)14
12(3
0.2)
720(
15.4
)
Pha
rmac
euti
cals
286(
6.3)
1777
(38.
9)69
4(15
.2)
1279
(28.
1)10
5(6.
2)80
2(47
.1)
297(
17.4
)49
9(29
.3)
1179
(25.
2)13
64(2
9.2)
933(
20.0
)11
99(2
5.6)
Labo
rato
ry a
naly
ses
180(
3.9)
1866
(40.
9)41
2(9.
0)15
52(3
4.0)
72(4
.2)
835(
49.0
)16
2(9.
5)63
4(37
.2)
357(
7.6)
2186
(46.
8)41
1(8.
8)17
21(3
6.8)
Dis
posa
ble
mat
eria
ls30
(0.7
)19
91(4
3.6)
34(0
.7)
1860
(40.
7)10
(0.6
)89
7(52
.7)
10(0
.6)
786(
46.2
)58
(1.2
)24
85(5
3.2)
30(0
.6)
2102
(45.
0)
Info
rmal
pay
men
ts:
Mon
ey r
eque
sted
by
staf
f8(
0.2)
2124
(46.
6)9(
0.2)
2043
(44.
7)4(
0.2)
903(
53.0
)3(
0.2)
793(
46.6
)5(
0.1)
2538
(54.
3)6(
0.1)
2126
(45.
5)
Gif
ts t
o st
aff
51(1
.1)
2073
(45.
4)65
(1.4
)19
44(4
2.6)
11(0
.6)
896(
52.6
)10
(0.6
)78
6(46
.2)
33(0
.7)
2510
(53.
7)39
(0.8
)20
93(4
4.8)
Ind
irec
t p
aym
ents
:
Tran
spor
t76
4(16
.7)
1366
(29.
9)79
0(17
.3)
1202
(26.
3)31
5(18
.5)
592(
34.8
)28
0(16
.4)
516(
30.3
)59
9(12
.8)
1944
(41.
6)50
8(10
.9)
1624
(34.
7)
*Num
bers
in b
rack
ets p
rese
nt th
e per
cent
age o
f N u
sers
Not
e: D
ata
rela
ted
to o
utpa
tien
t car
e hav
e re-
call
per
iod
of o
ne m
onth
and
dat
a re
late
d to
inpa
tien
t car
e hav
e re-
call
per
iod
for
one y
ear;
pay
men
ts fo
r br
ough
t and
bou
ght g
oods
are
not
ava
ilab
le fo
r ou
tpat
ient
pa
ymen
ts.
4
Exemption mechanism
85
Tab
le 4
.1: T
he d
istr
ibut
ion
of o
ut-o
f-po
cket
pat
ient
pay
men
ts a
mon
g th
e se
lect
ed e
xem
pted
gro
ups
and
othe
r po
pula
tion
gro
ups
(con
tinu
ed)
Ou
t-of
-poc
ket
pat
ien
t p
aym
ents
rep
orte
d in
20
02O
ut-
of-p
ock
et p
atie
nt
pay
men
ts r
epor
ted
in
2003
Ou
t-of
-poc
ket
pat
ien
t p
aym
ents
rep
orte
d in
20
07
Sele
cted
exe
mp
ted
gr
oup
sO
ther
pop
ula
tion
gr
oup
sSe
lect
ed e
xem
pte
d
grou
ps
Oth
er p
opu
lati
on
grou
ps
Sele
cted
exe
mp
ted
gr
oup
sO
ther
pop
ula
tion
gr
oup
s
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
Rep
orte
d
pay
men
ts *
No
pay
men
ts
rep
orte
d *
N u
sers
= 1
002
N u
sers
= 3
89N
use
rs=
1052
For
mal
pat
ien
t fe
es:
Hos
pita
liza
tion
96 (9
.6)
433(
43.2
)18
6(18
.6)
287(
28.6
)38
(9.8
)19
1(49
.5)
61(1
5.8)
99(2
5.4)
279(
26.5
)28
4(26
.9)
228(
21.7
)26
1(24
.8)
Pha
rmac
euti
cals
116
(11.
6)36
0(36
.0)
136(
13.6
)30
1(30
.1)
61(1
5.8)
168(
43.4
)43
(11.
1)11
4(29
.5)
130(
12.4
)37
2(35
.4)
102(
9.7)
248(
23.6
)
Labo
rato
ry a
naly
ses
27 (2
.7)
440(
43.9
)49
(4.9
)37
2(37
.2)
11(2
.8)
218(
56.5
)19
(4.9
)13
8(35
.8)
62(5
.9)
351(
33.4
)65
(6.2
)24
3(23
.1)
Dis
posa
ble
mat
eria
ls43
(4.3
)42
9(42
.8)
60(6
.0)
365(
36.5
)14
(3.6
)21
5(55
.7)
16(4
.1)
141(
36.5
)41
(3.9
)26
5(25
.2)
31(2
.9)
204(
19.4
)
Bou
ght
& b
roug
ht g
oods
:
Pha
rmac
euti
cals
137(
13.7
)34
2(34
.2)
143(
14.2
)28
8(28
.8)
47(1
2.2)
182(
47.2
)40
(10.
4)11
7(30
.3)
136(
12.9
)10
2(9.
7)73
(6.9
)94
(8.9
)
Dis
posa
ble
mat
eria
ls38
(3.8
)43
5(43
.5)
38(3
.8)
382(
38.1
)9(
2.3)
220(
57.0
)11
(2.8
)14
6(37
.8)
8(0.
7)55
6(52
.8)
14(1
.3)
475(
45.2
)
Ort
hope
dics
16 (
1.6)
457(
14.6
)11
(1.1
)40
3(40
.3)
10(2
.6)
219(
56.7
)6(
1.6)
151(
39.1
)14
(1.3
)54
9(52
.2)
15(1
.4)
474(
45.1
)
Info
rmal
pay
men
ts:
Mon
ey r
eque
sted
by
staf
f8
(0.8
)48
0(48
.0)
14(1
.4)
420(
42.0
)3(
0.8)
226(
58.5
)1(
0.3)
156(
40.4
)1(
0.0)
75(7
.1)
3(0.
3)68
(6.5
)
Gif
ts t
o st
aff
71(7
.1)
408(
40.8
)79
(7.8
)35
6(35
.6)
30(7
.8)
199(
51.6
)18
(4.7
)13
9(36
.0)
32(3
.0)
75(7
.1)
59(5
.6)
68(6
.5)
Indi
rect
pay
men
ts:
Tran
spor
t21
4 (2
1.4)
271(
27.1
)21
2(21
.1)
224(
22.4
)91
(23.
6)13
8(35
.8)
62(1
6.1)
95(2
4.6)
234(
22.2
)16
9(16
.1)
168(
15.9
)10
5(9.
9)
*Num
bers
in b
rack
ets p
rese
nt th
e per
cent
age o
f N u
sers
Not
e: D
ata
rela
ted
to o
utpa
tien
t car
e hav
e re-
call
per
iod
of o
ne m
onth
and
dat
a re
late
d to
inpa
tien
t car
e hav
e re-
call
per
iod
for
one y
ear;
pay
men
ts fo
r br
ough
t and
bou
ght g
oods
are
not
ava
ilab
le fo
r ou
tpat
ient
pa
ymen
ts.
86
Chapter 4Ta
ble
4.2:
Out
-of-
pock
et p
aym
ents
for
outp
atie
nt s
ervi
ces
duri
ng t
he p
revi
ous
mon
th, a
mou
nts
in d
inar
s; 1
USD
≈ 8
8.3
dina
rs
For
mal
pat
ien
t fe
es f
or
ph
ysic
ian
vis
its
For
mal
pat
ien
t fe
es f
or
ph
arm
aceu
tica
lsF
orm
al p
atie
nt
fees
for
la
bor
ator
y an
alys
esF
orm
al p
atie
nt
fees
for
d
isp
osab
le m
ater
ials
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
Tot
al
sam
ple
(use
rs)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
N u
sed
(%to
tal)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
4036
(88.
5)17
03(1
00.0
)46
75(1
00.0
)40
30(8
8.3)
1687
(99.
00)
4675
(100
.0)
3915
(85.
8)16
67(9
7.9)
4675
(100
.0)
N p
aid
(%to
tal)
1587
(34.
8)55
0(3
2.3)
2798
(59.
9)98
0(2
1.4)
402
(23.
6)21
12(4
5.2)
592
(12.
9)23
4(1
3.7)
768
(16.
4)64
(1.4
)20
(1.2
)88
(1.9
)
Mea
n pa
id72
.477
.942
.34
127.
713
9.1
2398
.715
80.0
719.
710
44.7
5146
.219
05.5
3900
.6
SD19
8.95
220.
914
7.8
284.
842
0.9
4738
.925
07.3
2289
.317
43.9
1489
4.6
3490
.755
43.0
Low
-in
com
e an
d u
nem
plo
yed
N u
sed
(%to
tal)
284
(6.2
)94
(5.5
)15
4(3
.3)
242
(5.3
)94
(5.5
)15
4(3
.3)
247
(5.4
)94
(5.5
)15
4(3
.3)
242
(5.3
)94
(5.5
)15
4(3
.3)
N p
aid
(%to
tal)
119
(2.6
)29
(1.7
)81
(1.7
)45
(0.9
)16
(0.9
)72
(1.5
)50
(1.1
)15
(0.9
)23
(0.5
)3
(0.0
)1
(1.9
)3
(0.1
)
Mea
n pa
id67
.31
120.
051
.458
.949
7.2
216.
243
7.0
2032
.018
11.3
943.
313
800.
071
33.3
SD14
2.58
347.
282
.497
.517
35.6
402.
798
9.7
3299
.321
61.2
1275
.0-
1114
6.9\
Dis
able
d in
div
idu
als
N u
sed
(%to
tal)
60(1
.3)
20(1
.2)
386
(8.3
)49
(1.1
)20
(1.2
)38
6(8
.3)
49(1
.1)
20(1
.2)
386
(8.3
)43
(0.9
)-
386
(8.3
)
N p
aid
(%to
tal)
25(0
.5)
9(0
.5)
223
(4.8
)17
(0.4
)7
(0.4
)21
1(4
.5)
8(0
.2)
4(0
.2)
68(1
.5)
1(0
.0)
Mea
n pa
id27
2.0
28.9
66.2
91.2
68.6
316.
939
5.6
107.
513
96.6
9000
.0-
6545
.7
SD10
91.0
10.5
112.
112
0.1
50.1
589.
650
8.1
129.
919
27.1
--
8515
.3
Old
er t
han
65
year
s
N u
sed
(%to
tal)
1401
(30.
7)60
0(3
5.2)
1385
(29.
6)12
30(2
6.9)
600
(35.
2)13
85(2
9.6)
1231
(26.
9)60
0(3
5.2)
1385
(29.
6)12
03(2
6.4)
600
(35.
2)13
85(2
9.6)
N p
aid
(%to
tal)
227
(4.9
)74
(4.3
)92
9(1
9.9)
182
(3.9
)77
(4.5
)74
5(1
5.9)
111
(2.4
)52
(3.1
)21
4(4
.6)
19(0
.4)
8(0
.5)
Mea
n pa
id71
.075
.34
59.2
214.
516
9.5
238.
339
8.4
320.
078
5.7
3894
.716
17.5
1685
.1
SD13
2.0
158.
815
0.8
385.
329
6.5
418.
961
5.4
469.
811
04.7
6605
.731
96.7
1973
.1
4
Exemption mechanism
87
Tab
le 4
.2: O
ut-o
f-po
cket
pay
men
ts fo
r ou
tpat
ient
ser
vice
s du
ring
the
pre
viou
s m
onth
, am
ount
s in
din
ars;
1 U
SD ≈
88.
3 di
nars
(co
ntin
ued)
For
mal
pat
ien
t fe
es f
or
ph
ysic
ian
vis
its
For
mal
pat
ien
t fe
es f
or
ph
arm
aceu
tica
lsF
orm
al p
atie
nt
fees
for
la
bor
ator
y an
alys
esF
orm
al p
atie
nt
fees
for
d
isp
osab
le m
ater
ials
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
Tota
l sa
mpl
e(us
ers)
4562
(1
00.0
)17
03
(100
.0)
4675
(1
00.0
)45
62
(100
.0)
1703
(1
00.0
)46
75
(100
.0)
4562
(1
00.0
)17
03
(100
.0)
4675
(1
00.0
)45
62
(100
.0)
1703
(100
.0)
4675
(1
00.0
)
You
nge
r th
an 1
5 ye
ars
N u
sed
(%to
tal)
564
(12.
3)17
7 (1
0.4)
401
(8.6
)50
7 (1
1.1)
177
(10.
4)40
1 (8
.6)
500
(10.
9)17
7 (1
0.3)
401
(8.6
)49
7 (1
0.9)
177
(10.
4)40
1 (8
.6)
N p
aid
(%to
tal)
30
(0.6
)2
(0.1
)34
(0
.7)
37
(0.9
)4
(0.2
)34
(0
.7)
6 (0
.1)
1 (0
.1)
19
(0.4
)7
(0.1
)1
(0.1
)4
(0.1
)
Mea
n pa
id57
.340
.020
0.9
115.
635
.026
3.9
128.
350
0.0
1617
.417
07.1
300.
037
25.0
SD48
.528
.28
460.
917
9.8
19.2
399.
396
.01
-22
67.2
1132
..3-
4262
.5
Two
or m
ore
of t
he
abov
e ex
emp
tion
cri
teri
a
N u
sed
(%to
tal)
44
(0.9
)16
(0
.9)
217
(4.6
)35
(0
.8)
16
(0.9
)21
7 (4
.6)
39
(0.8
)-
217
(4.6
)-
-21
7 (4
.6)
N p
aid
(%to
tal)
7 (0
.1)
1 (0
.0)
119
(2.5
)5
(0.1
)1
(0.1
)11
7 (2
.5)
5 (0
.1)
-33
(0
.7)
--
10
(0.2
)
Mea
n pa
id37
.140
.068
.473
.060
.028
2.9
80.0
-30
9.7
--
6993
.0
SD24
.3-
99.1
34.5
-40
6.1
74.8
-40
5.2
--
8581
.7
Oth
er p
opu
lati
on g
rou
ps
N u
sed
(%to
tal)
2209
(4
8.4)
796
(46.
7)21
32
(45.
6)19
73
(43.
2)79
6 (4
6.7)
2132
(4
5.6)
1964
(4
3.0)
796
(46.
7)21
32
(45.
6)18
94
(41.
5)79
6 (4
6.7)
2132
(4
5.6)
N p
aid
(%to
tal)
1179
(2
5.8)
435
(25.
5)14
12
(30.
2)69
4 (1
5.2)
297
(17.
4)93
3 (2
0.0)
412
(9.0
)16
2 (9
.5)
411
(8.8
)34
(0
.7)
10
(0.6
)30
(0
.6)
Mea
n pa
id69
.276
.77
77.4
111.
311
5.3
202.
854
4.5
743.
011
11.0
4428
.82
1107
.144
63.7
SD15
0.7
222.
5621
1.4
265.
623
8.3
423.
919
26.3
2519
.119
51.3
1325
3.9
993.
652
23.4
88
Chapter 4 T
able
4.2
: Out
-of-
pock
et p
aym
ents
for
outp
atie
nt s
ervi
ces
duri
ng t
he p
revi
ous
mon
th, a
mou
nts
in d
inar
s; 1
USD
≈ 8
8.3
dina
rs (
cont
inue
d)
Pay
men
ts f
or t
ran
spor
tM
oney
req
ues
ted
by
med
ical
sta
ffG
ifts
to
med
ical
sta
ff
2002
2003
2007
2002
2003
2007
2002
2003
2007
Tota
l sa
mpl
e(us
ers)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
N u
sed
(%to
tal)
4122
(90.
3)17
03 (1
00.0
)46
75 (1
00.0
)41
84 (9
1.7)
1083
(63.
6)46
75 (1
00.0
)40
95 (8
9.8)
1667
(97.
9)46
75 (1
00.0
)
N p
aid
(%to
tal)
1554
(34.
1)59
5 (3
4.9)
1107
(23.
7)17
(0.4
)7
(0.4
)11
(0.2
)11
6 (2
.5)
21 (1
.2)
72 (1
.5)
Mea
n pa
id33
1.1
398.
051
4.9
2965
.925
25.7
1256
.861
1.9
729.
510
30.1
SD64
1.1
829.
974
3.3
7823
.655
24.1
1887
.516
70.0
1489
.338
86.4
Low
-in
com
e an
d u
nem
plo
yed
N u
sed
(%to
tal)
253
(5.5
)94
(5.5
)15
4 (3
.3)
257
(5.6
)94
(5.5
)15
4 (3
.3)
251
(5.5
)94
(5.5
)15
4 (6
.8)
N p
aid
(%to
tal)
98 (2
.1)
31 (1
.8)
41 (0
.9)
1 (0
.0)
1 (0
.1)
1 (0
.0)
7 (0
.1)
3 (0
.2)
2 (0
.0)
Mea
n pa
id34
0.3
563.
743
2.4
150.
015
00.0
200.
081
7.1
133.
330
0.0
SD47
0.4
946.
161
8.2
--
-10
48.5
144.
328
2.8
Dis
able
d in
div
idu
als
N u
sed
(%to
tal)
51 (1
.1)
20 (1
.2)
386
(8.3
)50
(1.1
)-
386
(8.3
)50
(1.1
)-
386
(8.3
)
N p
aid
(%to
tal)
21 (0
.5)
7 (0
.4)
102
(2.2
)1
(0.0
)-
1 (0
.0)
2 (0
.0)
-8
(0.2
)
Mea
n pa
id66
0.0
1814
.363
1.0
900.
0-
200.
027
5.0
-16
6.3
SD12
67.9
3627
.185
7.0
--
-
154.
9
Old
er t
han
65
year
s
N u
sed
(%to
tal)
1267
(27.
8)60
0 (3
5.2)
1385
(29.
6)12
98 (2
8.5)
-13
85 (2
9.6)
1271
(27.
8)60
0 (3
5.2)
1385
(29.
6)
N p
aid
(%to
tal)
484
(11.
7)21
9 (1
2.9)
347
(7.4
)4
(0.0
)-
1(0.
0)39
(0.9
)6
(0.4
)15
(0.3
)
Mea
n pa
id35
7.4
301.
545
3.9
362.
5-
5000
.026
9.5
511.
761
6.7
SD86
6.2
340.
181
2.2
170.
2-
541.
997
7.2
1303
.4
4
Exemption mechanism
89
Tab
le 4
.2: O
ut-o
f-po
cket
pay
men
ts fo
r ou
tpat
ient
ser
vice
s du
ring
the
pre
viou
s m
onth
, am
ount
s in
din
ars;
1 U
SD ≈
88.
3 di
nars
(co
ntin
ued)
Pay
men
ts f
or t
ran
spor
tM
oney
req
ues
ted
by
med
ical
sta
ffG
ifts
to
med
ical
sta
ff
2002
2003
2007
2002
2003
2007
2002
2003
2007
Tota
l sa
mpl
e(us
ers)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
4562
(100
.0)
1703
(100
.0)
4675
(100
.0)
You
nge
r th
an 1
5 ye
ars
N u
sed
(%to
tal)
519
(11.
4)17
7 (1
0.4)
401
(8.6
)52
7 (1
1.6)
177
(10.
4)40
1 (8
.6)
5 14
(11.
3)17
7 (1
0.4)
401(
8.6)
N p
aid
(%to
tal)
149
(3.6
)54
(3.2
)54
(1.2
)2
(0.0
)2
(0.1
)1
(0.0
)3
(0.0
)2
(0.1
)2
(0.0
)
Mea
n pa
id 3
20.4
293.
1544
7.4
925.
076
50.0
020
0.0
275.
030
50.0
527.
5
SD47
2.9
342.
649
9.8
601.
110
394.
5-
238.
541
71.9
668.
2
Two
or m
ore
of t
he
abov
e ex
emp
tion
cri
teri
a
N u
sed
(%to
tal)
40 (0
.9)
16 (0
.9)
217
(4.6
)-
16 (0
.9)
217
(4.6
)-
-21
7 (4
.6)
N p
aid
(%to
tal)
12 (0
.3)
4 (0
.2)
55 (1
.2)
-1
(0.1
)1
(0.0
)-
-6(
0.1)
Mea
n pa
id20
00.0
227.
559
9.0
-60
0.0
4800
--
251.
2
SD-
190.
761
8.1
--
--
-18
5.9
Oth
er p
opu
lati
on g
rou
ps
N u
sed
(%to
tal)
1992
(43.
6)79
6 (4
6.7)
2132
(45.
6)20
52 (4
4.9)
796
(46.
7)21
32 (4
5.6)
2009
(44.
0)79
6 (4
6.7)
2132
(45.
6)
N p
aid
(%to
tal)
790
(17.
3)28
0 (1
6.4)
508
(10.
9)9
(0.2
)3(
0.2)
6 (0
.1)
65 (1
.4)
10 (3
5.4)
39 (0
.8)
Mea
n pa
id30
8.9
442.
453
8.2
5118
.993
.357
0.8
821.
157
5.0
1549
.4
SD48
5.0
953.
571
0.1
1054
4.7
94.5
759.
721
45.3
1042
.551
89.8
90
Chapter 4
Tab
le 4
.3: O
ut-o
f-po
cket
pay
men
ts fo
r in
pati
ent
hosp
ital
ser
vice
s du
ring
the
last
12
mon
ths,
am
ount
s in
din
ars;
1 U
SD ≈
88.
3 di
nars
For
mal
pat
ien
t fe
es f
or
hos
pit
aliz
atio
nF
orm
al p
atie
nt
fees
for
p
har
mac
euti
cals
For
mal
pat
ien
t fe
es f
or
lab
orat
ory
serv
ices
For
mal
pat
ien
t fe
es f
or
dis
pos
able
mat
eria
lsP
aym
ents
for
tran
spor
t
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
Tota
l sa
mpl
e(us
ers)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
N u
sed
(%to
tal)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
823
(82.
1)38
6 (9
9.9)
852
(80.
9)88
8 (8
8.6)
380
(97.
7)72
1 (6
8.5)
897
(89.
5)37
8 (9
7.2)
541
(51.
4)92
1 (9
1.9)
386
(99.
9)67
6 (6
4.3)
N p
aid
(%to
tal)
282
(28.
1)99
(2
5.4)
507
(48.
2)25
2 (2
5.2)
104
(26.
9)23
2 (2
2.0)
76
(7.6
)30
(7
.7)
127
(12.
1)10
3 (1
0.3)
30
(7.7
)72
(6
.8)
426
(42.
5)15
3 (3
9.3)
402
(38.
2)
Mea
n pa
id44
83.5
3275
.939
18.8
3073
.420
59.2
2398
.715
80.0
2050
.027
81.8
3603
.152
58.0
8534
.910
78.1
800.
197
1.8
SD28
558.
512
003.
487
89.3
7336
.129
01.6
4738
.925
07.3
2427
.946
62.7
5682
.211
685.
911
449.
317
69.9
1470
.516
00.5
Low
-in
com
e an
d u
nem
plo
yed
N u
sed
(%to
tal)
55 (5
.5)
36 (9
.3)
33 (3
.1)
71 (7
.1)
36 (9
.3)
75 (7
.1)
75 (7
.5)
35 (8
.9)
57 (5
.4)
74 (7
.4)
36 (3
4.2)
37 (3
.5)
75 (7
.5)
36 (9
.8)
63 (5
.9)
N p
aid
(%to
tal)
34 (3
.4)
3 (0
.8)
16 (1
.5)
21 (2
.1)
5 (1
.3)
24 (2
.3)
9 (1
.0)
1 (0
.3)
14 (1
.3)
9 (1
.0)
4 (1
.0)
7 (0
.6)
35 (3
.8)
13 (3
.4)
42 (3
.9)
Mea
n pa
id13
706.
021
48.9
2452
.926
04.8
1916
.917
91.7
758.
232
6.7
2059
.347
07.7
2575
.015
42.9
*90
9.7
685.
945
1.7*
SD72
784.
048
29.5
5491
.131
97.1
2633
.221
36.1
668.
326
1.0
3538
.540
42.4
2335
.782
4.3
1493
.877
3.1
389.
3
Dis
able
d in
div
idu
als
N u
sed
(%to
tal)
10 (0
.9)
-10
3 (9
.8)
16 (1
.5)
5 (1
.3)
75 (7
.1)
16 (1
.6)
-67
(6.4
)16
(1.5
)-
44 (4
.2)
16 (1
.5)
5 (1
.3)
62 (5
.9)
N p
aid
(%to
tal)
4 (0
.4)
-55
(5.2
)4
(0.4
)1
(0.3
)19
(1.8
)2
(0.2
)-
10 (0
.9)
3 (0
.3)
-10
(0.9
)3
(0.3
)1
(0.3
)33
(3.1
)
Mea
n pa
id37
75.0
-33
37.5
1400
.021
66.7
5868
.4*
2250
.0-
3380
.050
0.0
-17
700.
0*66
6.7
1400
1805
.1*
SD27
88.5
-71
92.9
955.
711
93.0
1124
8.2
1767
.7-
3737
.143
5.9
-15
304.
728
8.7
-36
25.9
Old
er t
han
65
year
s
N u
sed
(%to
tal)
145
(14.
5)15
2 (3
9.1)
286
(27.
2)28
9 (2
8.9)
152
(39.
1)21
6 (2
0.5)
279
(27.
9)15
2 (3
9.1)
181
(17.
2)28
5 (2
8.5)
152
(39.
1)13
7 (1
3.2)
292
(29.
2)15
2 (3
9.1)
175
(16.
6)
N p
aid
(%to
tal)
41 (4
.1)
28 (7
.3)
161
(15.
3)68
(6.8
)44
(11.
4)61
(5.8
)13
(1.3
)9
(2.3
)29
(2.7
)25
(2.5
)10
(2.6
)19
(1.8
)12
8 (1
2.7)
62 (6
.1)
98 (9
.3)
Mea
n pa
id35
95.1
5950
.142
96.6
1721
.522
43.1
1727
.0*
1203
.811
40.0
1628
.28
4904
.044
75.0
6944
.791
9.1
405.
173
0.8*
SD59
26.8
2280
2.5
1059
6.9
2651
.931
40.6
2678
.615
03.9
2185
.919
22.6
7110
.642
44.2
9969
.916
08.3
456.
984
6.1
p<
0.0
5
4
Exemption mechanism
91
Tab
le 4
.3: O
ut-o
f-po
cket
pay
men
ts fo
r in
pati
ent
hosp
ital
ser
vice
s du
ring
the
last
12
mon
ths,
am
ount
s in
din
ars;
1 U
SD ≈
88.
3 di
nars
(co
ntin
ued)
For
mal
pat
ien
t fe
es f
or
hos
pit
aliz
atio
nF
orm
al p
atie
nt
fees
for
p
har
mac
euti
cals
For
mal
pat
ien
t fe
es f
or
lab
orat
ory
serv
ices
For
mal
pat
ien
t fe
es f
or
dis
pos
able
mat
eria
lsP
aym
ents
for
tran
spor
t
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
Tota
l sa
mpl
e(us
ers)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
You
nge
r th
an 1
5 ye
ars
N u
sed
(%to
tal)
39 (3
.9)
33 (8
.5)
80 (7
.6)
85 (8
.5)
33 (8
.5)
66 (6
.3)
82 (8
.2)
33 (8
.5)
51 (4
.8)
82 (8
.1)
-42
(3.9
)87
(8.7
)33
(8.5
)46
(4.4
)
N p
aid
(%to
tal)
15 (1
.4)
7 (1
.8)
11 (1
.0)
18 (1
.7)
7 (1
.8)
4 (0
.4)
2 (0
.2)
1 (0
.3)
3 (0
.3)
4 (0
.4)
-3
(0.3
)42
(4.1
)14
(3.6
)24
(2.3
)
Mea
n pa
id18
87.1
808.
052
55.4
533
73.5
1194
.084
5.0
1600
.018
00.0
2333
.365
5.0
-67
46.6
1695
.769
1.2
788.
33
SD41
01.5
627.
249
31.0
5731
.188
7.2
588.
919
70.9
-14
43.4
427.
5-
1147
8.0
2994
.095
9.3
983.
1
Two
or m
ore
of t
he
abov
e ex
emp
tion
cri
teri
a
N u
sed
(%to
tal)
8 (0
.9)
3 (0
.8)
61 (5
.8)
15 (1
.5)
3 (0
.8)
70 (6
.6)
15 (1
.5)
3 (0
.8)
57 (5
.4)
15 (1
.4)
3 (0
.8)
46 (4
.4)
15 (1
.4)
3 (0
.8)
57 (5
.4)
N p
aid
(%to
tal)
2 (0
.2)
3 (0
.8)
28 (2
.7)
5 (0
.5)
1 (0
.3)
22 (2
.0)
1 (0
.1)
1 (0
.3)
6 (0
.6)
2 (0
.2)
3 (0
.8)
2 (0
.2)
6 (0
.6)
1 (0
.3)
37 (3
.5)
Mea
n pa
id14
25.0
4772
.435
49.2
3230
.034
58.3
1175
.0*
400.
030
0.0
1458
.310
00.0
4666
.760
50.0
752.
512
86.5
988.
6
SD95
4.6
4955
.168
69.9
4452
.952
59.5
1330
.6-
-13
13.9
707.
128
86.8
8414
.096
2.9
2539
.112
62.9
Oth
er p
opu
lati
on g
rou
ps
N u
sed
(%to
tal)
275
(27.
5)15
7 (4
0.3)
489
(46.
5)34
7 (3
4.7)
157
(40.
3)35
0 (3
3.3)
421
(42.
1)15
7 (4
0.3)
308
(29.
3)42
5 (4
2.5)
157
(40.
3)23
5 (2
2.3)
436
(4
3.5)
157
(40.
3)27
3 (2
5.9)
N p
aid
(%to
tal)
186
(18.
6)53
(14.
6)22
8 (2
1.7)
134
(13.
4)35
(8.9
)10
2 (9
.7)
49 (4
.8)
17 (4
.4)
65 (6
.2)
60 (6
.0)
16 (4
.1)
31 (2
.9)
212
(21.
2)62
(16.
1)16
8 (1
5.9)
Mea
n pa
id30
11.9
2241
.041
27.7
3844
.815
57.6
2621
.6*
1848
.929
00.0
3502
.932
35.7
6509
.384
65.2
1102
.510
05.7
1101
.3
SD12
374.
144
12.9
8741
.994
84.1
1635
.144
76.5
2985
.525
79.0
5916
.356
23.7
1635
0.9
1128
3.0
1605
.516
41.6
1562
.2
p<0.
05
92
Chapter 4T
able
4.3
: Out
-of-
pock
et p
aym
ents
for
inpa
tien
t ho
spit
al s
ervi
ces
duri
ng t
he la
st 1
2 m
onth
s, a
mou
nts
in d
inar
s; 1
USD
≈ 8
8.3
dina
rs (
cont
inue
d)
For
mal
pat
ien
t fe
es f
or
hos
pit
aliz
atio
nF
orm
al p
atie
nt
fees
for
p
har
mac
euti
cals
For
mal
pat
ien
t fe
es f
or
lab
orat
ory
serv
ices
For
mal
pat
ien
t fe
es f
or
dis
pos
able
mat
eria
lsP
aym
ents
for
tran
spor
t
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
Tota
l sa
mpl
e(us
ers)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
N u
sed
(%to
tal)
910
(90.
81)
241
(61.
9)40
5 (3
8.5)
893
(89.
1)15
9 (4
0.9)
1052
(1
00.0
)88
7 (8
8.5)
106
(27.
2)10
52
(100
.0)
922
(92.
1)74
(19.
1)14
7 (1
3.9)
902
(90.
0)48
(12.
3)23
4 (2
2.2)
N p
aid
(%to
tal)
280
(27.
9)87
(22.
4)20
9 (1
9.8)
76 (7
.5)
14 (3
.6)
21 (1
.9)
27 (2
.7)
16 (4
.1)
29 (2
.8)
22 (2
.2)
4 (1
.0)
4 (0
.4)
150
(14.
8)48
(12.
3)91
(8.7
)
Mea
n pa
id26
53.5
4191
.716
21.0
3461
.826
90.0
4540
.576
22.6
3091
.941
20.3
1232
3.6
3700
.019
000.
022
21.5
1857
.344
97.5
SD98
15.1
2566
4.4
2920
.983
89.2
2999
.172
59.1
2082
9.1
6329
.476
95.3
3174
6.5
3897
.916
451.
949
51.0
2564
.095
68.7
Low
-in
com
e an
d u
nem
plo
yed
N u
sed
(%to
tal)
74 (7
.4)
27 (6
.9)
32 (3
.0)
76 (7
.5)
22 (5
.7)
33 (3
.1)
76 (7
.5)
15 (3
.8)
33 (3
.1)
76 (7
.5)
12 (3
.1)
11 (1
.0)
76 (1
.6)
5 (1
.3)
16 (1
.5)
N p
aid
(%to
tal)
17 (1
.7)
10 (2
.6)
17 (1
.6)
8 (0
.8)
5 (1
.3)
3 (0
.3)
4 (0
.4)
3 (0
.8)
1 (0
.1)
1 (0
.1)
1 (0
.3)
-13
(1.5
)5
(1.3
)5
(0.5
)
Mea
n pa
id14
84.2
2597
0.0*
2431
.712
00.0
1366
.728
3.3
4270
.047
00.0
300.
015
0000
.060
00.0
1406
.725
40.0
825.
0
SD12
16.1
7526
0.5
3511
.289
9.4
1001
.318
9.3
6533
.863
92.9
--
-15
97.4
1804
.968
3.9
Dis
able
d in
div
idu
als
N u
sed
(%to
tal)
16 (1
.5)
1 (0
.3)
39 (3
.7)
16 (1
.6)
1 (0
.3)
103
(9.8
)16
(1.6
)-
103
(9.8
)16
(1.6
)-
15 (1
.4)
16 (0
.3)
1 (0
.3)
21 (1
.9)
N p
aid
(%to
tal)
6 (0
.6)
-22
(2.1
)1
(0.1
)-
2 (0
.2)
--
2 (0
.2)
--
-3
(0.1
)1
(0.3
)6
(0.6
)
Mea
n pa
id28
66.7
-23
07.7
1500
.0-
7000
.0-
-32
50.0
--
-17
100.
020
00.0
3128
.3
SD36
61.5
-42
50.4
--
7071
.1-
-24
74.9
--
-24
329.
6-
3333
.7
Old
er t
han
65
year
s
N u
sed
(%to
tal)
288
(28.
6)89
(22.
5)10
9 (1
0.4)
283
(28.
3)61
(15.
7)28
6 (2
7.2)
284
(28.
4)41
(10.
5)28
6 (2
7.2)
293
(29.
2)28
(7.2
)29
(2.8
)28
7 (3
.9)
14 (3
.6)
42 (1
7.9)
N p
aid
(%to
tal)
93 (9
.3)
30 (2
.9)
67 (6
.4)
26 (2
.5)
2 (0
.5)
2 (0
.2)
8 (0
.8)
9 (2
.3)
10 (1
.0)
5 (0
.5)
1 (0
.3)
1 (0
.1)
40 (3
.9)
14 (3
.6)
14 (1
.3)
Mea
n pa
id18
64.4
11
01.8
1115
.852
98.1
1375
.022
50.0
8335
.037
57.8
5183
.326
40.0
300.
040
000
785.
612
67.9
3542
.9
SD35
22.2
1112
.311
43.8
1109
3.2
1108
.735
3.6
1701
4.7
7730
.982
46.3
2246
.8-
-99
5.4
2044
.557
41.7
p<0.
05
4
Exemption mechanism
93
Tab
le 4
.3: O
ut-o
f-po
cket
pay
men
ts fo
r in
pati
ent
hosp
ital
ser
vice
s du
ring
the
last
12
mon
ths,
am
ount
s in
din
ars;
1 U
SD ≈
88.
3 di
nars
(co
ntin
ued)
For
mal
pat
ien
t fe
es f
or
hos
pit
aliz
atio
nF
orm
al p
atie
nt
fees
for
p
har
mac
euti
cals
For
mal
pat
ien
t fe
es f
or
lab
orat
ory
serv
ices
For
mal
pat
ien
t fe
es f
or
dis
pos
able
mat
eria
lsP
aym
ents
for
tran
spor
t
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
2002
2003
2007
Tota
l sa
mpl
e(us
ers)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
1002
(1
00.0
)38
9 (1
00.0
)10
52
(100
.0)
You
nge
r th
an 1
5 ye
ars
N u
sed
(%to
tal)
86 (8
.6)
23 (5
.9)
21 (1
.9)
83 (8
.3)
11 (2
.8)
80 (7
.6)
82 (8
.2)
6 (1
.5)
80 (7
.6)
88 (8
.7)
4 (1
.0)
13 (1
.2)
87 (
1.5)
4 (1
.0)
17 (1
.6)
N p
aid
(%to
tal)
18 (1
.7)
4 (1
.0)
6 (0
.6)
3 (0
.3)
1 (0
.3)
1 (0
.1)
3 (0
.3)
-1
(0.1
)2
(0.2
)-
-15
(1.4
)4
(1.0
)4
(0.4
)
Mea
n pa
id11
90.0
90
0.0
2300
.010
50.0
400.
015
00.0
2700
.0-
1000
.030
00.0
--
2166
.121
75.0
3945
.7
SD10
42.9
496.
721
20.4
697.
8-
-30
11.6
--
2121
.3-
-22
84.8
1090
.571
91.2
Two
or m
ore
of t
he
abov
e ex
emp
tion
cri
teri
a
N u
sed
(%to
tal)
15(1
.4)
28(7
.2)
37(3
.5)
15(1
.5)
19(4
.9)
61(5
.8)
15(1
.5)
11(2
.8)
61(5
.8)
15(1
.5)
6(1.
5)8(
0.8)
1(0.
1)7(
1.8)
11(1
.0)
N p
aid
(%to
tal)
3(0.
3)10
(2.6
)24
(2.3
)-
1(0.
3)-
1(0.
1)1(
0.3)
2(0.
2)-
1(0.
3)-
1(0.
1)7(
1.8)
3(0.
3)
Mea
n pa
id77
2.5
1163
.013
92.9
-40
00.0
-20
00.0
500.
020
0.0
-80
00.0
-50
0.0
1614
.338
3.3
SD72
4.2
1502
.018
64.7
-35
35.5
--
-14
1.4
--
--
2457
.329
2.9
Oth
er p
opu
lati
on g
rou
ps
N u
sed
(%to
tal)
431
(43.
0)73
(18.
7)16
7 (1
5.8)
420
(42.
0)45
(11.
6)48
9 (4
6.5)
414
(41.
4)33
(8.5
)48
9 (4
6.5)
434
(43.
4)24
(6.2
)71
(6.7
)43
5 (7
.7)
17 (4
.4)
127
(12.
1)
N p
aid
(%to
tal)
143
(14.
2)33
(8.4
)73
(6.9
)38
( 3.
8)5
(1.3
)14
(1.2
)11
(1.1
)3
(0.7
)15
(1.4
)14
(1.4
)1
(0.3
)3
(0.3
)77
(7.7
)17
(4.4
)59
(5.6
)
Mea
n pa
id35
52.2
17
17.9
*17
08.2
3019
.445
28.6
5730
.810
177.
335
0.0
4617
.172
80.0
500.
012
000.
025
78.9
2158
.853
52.1
SD13
463
.027
62.8
3662
.976
09.2
4081
.186
55.8
2980
3.1
86.6
8934
.595
52.8
-10
583.
045
26.1
3466
.311
169.
2
p< 0
.05
94
Chapter 4
For 2002, the ANOVA analysis shows no statistically significant difference between the
official co-payments, indirect payments (transport), payments for ”bought & brought
goods” and informal payments reported by the five exempted groups, as well as between
these groups and other population groups. For 2003, the results are similar to those
for 2002. There are no statistically significant differences between the five exempted
groups for different types of payments except for “bought & brought goods” payments for
pharmaceuticals. The unemployed and people with low family income reported higher
payments (than other exempted groups examined in our study) for pharmaceuticals that
they brought to the hospital. This difference is also statistically significant when these
exempted groups are compared to other population groups.
For 2007, the ANOVA analysis shows an overall trend of increased payments. Both
exempted and others (non-exempted and exempted that we could not identify within
the data) paid more for officially co-payments, “bought & brought goods” payments
and informal payments for inpatient care compared to previous years (see Table 4.3).
Although the absolute amount is higher, there is no statistically significant difference
between the five exempted groups or compared to other population groups. It should
be noted however that the disabled paid a higher amount in official co-payments for
pharmaceuticals than other exempted groups examined in our study. They also paid more
in official co-payments for disposable materials. Both differences are significant. Overall,
the five exempted groups report more often “bought & brought goods” payments than
informal payments for inpatient care. For example, individuals in the five exempted
groups who report payments for pharmaceuticals brought and bought to the hospital
represent 12% of all users, while for informal payments in inpatient care this share is only
3%.
4.4.2 Regression results As we mentioned above, we perform logistic and linear regression for both outpatient
and inpatient services. The regression models include social demographic characteristics,
three dummy variables (indicators for belonging to one of the five exempted groups, for
2003 and for 2007 respectively) and two interaction variables (between the indicator
for belonging to one of the five exempted groups and the two year indicators). We use
the same set of independent variables for both outpatient and inpatient hospital care.
The regression models are not significant for any type of payment regarding outpatient
services. Those results are compatible with results of the ANOVA analyses. Therefore, we
do not present regression results for outpatient services.
Regarding inpatient services, both logistic and linear regression models are significant.
The results of the logistic regression are presented in Table 4.4. They indicate factors that
are associated with the propensity of paying out-of-pocket for inpatient care across the
five exempted groups and other population groups, as well as the differences across the
4
Exemption mechanism
95
years. This way, we examine if the exemption policy is implemented in practice. The
statistically significant results are described below.
Regarding official co-payments for hospitalization, the indicators for 2003 and 2007
are statistically significant. Overall, all respondents have a lower propensity to pay for
hospitalization in 2003 and 2007 than in 2002. The indicator for belonging to one of the
five exempted group is also a significant predictor indicating that these exempted groups
paid less often for hospitalization than other population groups. However, the interaction
between year 2007 and the five exempted groups show that exempted persons in these
groups pay more frequently for hospitalization in 2007. Being married and/or having
chronicle diseases also indicate lower payments for a hospitalization in 2002 than in 2003
and 2007.
With regard to official co-payments, for pharmaceuticals, disposable materials and
laboratory analyses for inpatient care significant predictors are the year 2007 and the
indicator of belonging to one of the five exempted groups. The probability to pay for
these services was less in 2007 and for the five exempted groups. For transport costs, the
propensity to pay in 2003 was significantly less than in 2002 for all respondents. Also,
respondents from the five exempted groups paid less frequently for these services than
other groups. Among the social demographic variables, significant predictors are being
married, presence of chronicle diseases and/or type of settlement.
For pharmaceuticals that respondents brought to the hospital, significant predictors
are the indicator for the five exempted groups, the interaction between year 2007, and
the indicator of belonging to one of the five exempted groups and the presence of chronic
diseases. Belonging to one of the five exempted groups itself means a lower propensity
of paying but a higher propensity of paying in 2007. For disposable materials that were
brought by patients, the only significant predictor is year 2007.
To study the size of reported payments for inpatient care, we carry out linear regression.
We apply linear regression analysis only for respondents who reported that they have
paid for different types of inpatient hospital services (see Table 4.5). We use the specific
amount paid as a dependent variable.
The linear models are significant for disposable materials that were paid officially. For
disposable materials that are paid officially, significant predictors are year 2007, being
married and type of settlement. For pharmaceuticals that were paid officially, significant
predictors were an indicator for the year 2003, as well as belonging to one of the five
exempted groups. For transport costs, a significant predictor is year 2007.
96
Chapter 4T
able
4.4
: Res
ults
of l
ogis
tic
regr
essi
on; d
epen
dent
var
iabl
e –
repo
rted
pay
men
ts (y
es/n
o) fo
r in
pati
ent
hosp
ital
ser
vice
s du
ring
the
last
12
mon
ths
(for
tho
se
who
wer
e ho
spit
alis
ed)
Exp
lan
ator
y va
riab
les
incl
ud
ed in
th
e an
alys
is
Hos
pit
alis
atio
n
(for
mal
fe
es)
Ph
arm
a-ce
uti
cals
(f
orm
al f
ees)
Dis
pos
able
m
ater
ials
(f
orm
al f
ees)
Lab
orat
ory
serv
ices
(f
orm
al f
ees)
Tra
nsp
ort
(for
mal
fe
es)
Ph
arm
a-ce
uti
cals
b
rou
ght
by
the
pat
ien
t
Dis
pos
able
m
ater
ials
b
rou
ght
by
the
pat
ien
t
Ort
hop
aed
ic
mat
eria
ls
bro
ugh
t b
y th
e p
atie
nt
Mon
ey
req
ues
ted
by
med
ical
sta
ff
Gif
ts t
o m
edic
al
staf
f
BSE
BSE
BSE
BSE
BSE
BSE
BSE
BSE
BSE
BSE
Con
stan
t 0
.78*
0.34
-0.4
60.
34-2
.16*
0.52
-1.5
8*0.
49-0
.19
0.30
-0.1
80.
35-2
.64*
0.67
-2.4
8*0.
85-2
.45*
1.28
-3.5
8*0.
45
2003
-1.1
3**
0.22
-0.1
70.
10-0
.12
0.32
0.1
50.
31-0
.34
0.24
-0.3
60.
23-0
.50
0.44
-0.1
40.
66-1
.43
1.04
-0.3
70.
29
2007
-0.8
9**
0.19
-0.1
80.
18-0
.78*
*0.
28 0
.60*
0.40
-0.3
00.
16-0
.71*
*0.
19-1
.25*
0.38
-0.1
30.
47-1
.37
0.74
-0.4
60.
54
sele
cted
exe
mpt
ed
grou
ps-1
.19*
*0.
19-0
.36*
*0.
11-0
.12
0.22
-0.6
8*0.
26-0
.17
0.14
-0.2
10.
16 0
.16
0.25
0.1
40.
42-0
.90
0.51
0.0
70.
58
2003
x s
elec
ted
exem
pted
gro
ups
0.0
30.
30 0
.28
0.29
-0.5
50.
44-0
.42
0.46
0.1
50.
26-0
.08
0.30
-0.1
70.
54-0
.64
0.77
1.2
11.
26-0
.25
1.26
2007
x s
elec
ted
exem
pted
gro
ups
1.3
7**
0.23
0.0
90.
22 0
.04
0.34
0.1
60.
32-0
.12
0.19
0.4
4**
0.22
-0.9
70.
52-0
.49
0.56
-0.6
31.
26-0
.93
0.28
gend
er-0
.96
0.09
-0.1
80.
10-0
.05
0.15
-0.0
90.
14-0
.07
0.08
-0.0
10.
10-0
.02
0.20
-0.1
80.
25-0
.60
0.40
0.6
3**
0.14
mar
riag
e 0
.47*
*1.
05-0
.08
0.11
0.5
8*0.
18 0
.08
0.16
0.1
80.
09 0
.16
0.11
0.3
00.
22-0
.08
0.26
-0.2
00.
43 0
.30*
0.15
wor
k st
atus
0.9
80.
61 0
.09
0.06
0.0
90.
09 0
.00
0.09
-0.0
90.
05 0
.08
0.06
-0.0
20.
13-0
.01
0.15
0.0
10.
26-0
.01
0.08
urba
n 0
.11
0.09
0.1
50.
10-0
.21
0.15
0.3
1*0.
14 0
.05
0.08
0.1
90.
10-0
.18
0.20
-0.3
00.
25 0
.22
0.40
0.1
60.
13
Edu
cati
on le
vel
0.0
00.
00 0
.00
0.00
0.0
00.
00 0
.00
0.00
0.0
00.
00 0
.00
0.00
0.0
00.
00 0
.00
0.00
0.0
00.
00 0
.00
0.00
Hou
seho
ld s
ize
0.0
20.
03 0
.05*
0.02
0.0
60.
04-0
.05
0.04
0.0
7**
0.02
-0.0
10.
03 0
.02
0.06
-0.0
80.
07-0
.02
0.11
0.0
50.
04
Chr
onic
le d
isea
se-0
.43*
*0.
01-0
.40*
*0.
12-0
.09
0.18
-0.5
4*0.
17-0
.21*
*0.
01-0
.72*
*0.
13 0
.21
0.22
-0.0
40.
30-0
.14
0.44
0.2
70.
14
-2LL
(Nag
el. R
²)24
51.3
1 (0
.14)
2515
.26
(0.0
3)13
16.0
5 (0
.04)
1442
.95
(0.0
4)30
46.2
0 (0
.02)
2435
.43
(0.0
6)84
6.06
(0.0
7)60
7.99
(0.0
2)27
7.44
(0.0
9)16
45.1
7 (0
.07)
N18
5719
3812
7116
6916
5012
3296
751
338
623
52
p< 0
.05
** p
< 0
.01
4
Exemption mechanism
97
Tabl
e 4.
5: R
esul
ts o
f lin
ear
regr
essi
on; d
epen
dent
var
iabl
e –
amou
nt p
aid
for
inpa
tien
t ho
spit
al s
ervi
ces
duri
ng t
he la
st 1
2 m
onth
s, in
din
ars;
1 U
SD ≈
88.
3 di
nars
(for
tho
se w
ho p
aid
for
hosp
ital
isat
ion)
Exp
lan
ator
y
vari
able
s in
clu
ded
in t
he
anal
ysis
Hos
pit
alis
atio
n
(for
mal
fee
s)P
har
mac
euti
cals
(f
orm
al f
ees)
Dis
pos
able
m
ater
ials
(f
orm
al f
ees)
Lab
orat
ory
serv
ices
(for
mal
fe
es)
Tra
nsp
ort
(for
mal
fee
s)P
har
mac
euti
cals
b
rou
ght
by
the
pat
ien
t
Dis
pos
able
m
ater
als
bro
ugh
t b
y th
e p
atie
nt
Ort
hop
aed
ic
mat
eria
ls
bro
ugh
t b
y th
e p
atie
nt
Mon
ey r
equ
este
d b
y m
edic
al s
taff
Gif
ts t
o m
edic
al
staf
f
BSE
BSE
BSE
BSE
BSE
BSE
BSE
BSE
BSE
BSE
Con
stan
t 30
11.9
1255
.838
44.8
450.
632
35.7
934.
518
48.9
371.
511
02.5
97.9
3552
.295
8.3
3019
.411
96.8
1000
0.1
4100
7280
.077
70.0
2578
.955
8.6
2003
-137
8.60
2816
.20
-214
6.97
*11
02.8
931
34.6
426
67.7
286
3.53
1747
.42
-100
.71
244.
05-2
119.
9823
72.0
520
41.4
531
82.6
5-7
695.
0710
967.
67-1
5270
.21
3053
1.74
-616
.39
1313
.53
2007
-147
4.92
2261
.16
-160
8.92
685.
4277
60.8
1*23
74.7
815
48.7
188
5.50
-96.
65*
210.
68-3
658.
3720
20.0
435
1.59
2323
.63
-552
2.98
7806
.69
-241
66.5
921
905.
5656
9.35
809.
49
Sele
cted
exe
mpt
ed
grou
ps40
11.7
423
96.7
8-1
801.
62*
782.
1917
50.0
018
71.0
2-1
007.
.40
958.
88-1
88.3
216
4.49
-189
3.85
1572
.98
840.
5516
49.9
5-4
004.
7668
60. 7
523
895.
3921
597.
80-7
34.4
480
0.73
2003
x s
elec
ted
exem
pted
gro
ups
-150
2.60
4336
.68
2526
.95
1426
.61
-431
5.37
3837
. 05
-782
2717
27.0
5-1
06.1
031
2.92
6087
.28*
3112
..38
-341
1.98
3749
.05
3190
.19
1241
7.06
-461
91.7
241
022.
9630
9.99
1682
.13
2007
x s
elec
ted
exem
pted
gro
ups
-375
8.00
2955
.96
1243
.05
1096
.80
-953
.83
2882
.48
-107
.52
1205
.03
-38.
3022
7..8
416
47.5
323
63.6
6-2
678.
5036
24.1
638
42.3
693
87.4
445
595.
3842
582.
19-7
45.7
412
85.2
3
Gen
der
-118
5.32
1253
.47
-698
.34
498.
88-5
74.1
113
37.3
741
0.80
516.
18-8
4.87
106.
3654
5. 3
110
94.9
326
0.71
1640
.52
424.
5143
20.5
4-9
496.
1013
755.
1746
.76
883.
97
Mar
riag
e22
45.7
814
03.4
032
6.05
529.
7739
52.4
5*15
86.7
151
4.39
572.
0425
.34
114.
01-2
504.
37*
1189
.19
-847
.67
1646
.12
3025
.83
3956
.85
-907
4.11
1607
8.25
426.
6092
4.63
Wor
k st
atus
-147
0.80
812.
42-1
46.5
631
3.26
686.
8881
9.03
-355
. 47
333.
46-8
0.58
64.0
7-1
443.
21*
651.
9618
00.8
710
64.5
112
47.3
623
64.9
7-1
8933
.796
51.6
6-1
104.
60*
529.
77
Urb
an-1
304.
8612
22.1
5-4
56.8
249
1.92
2599
.21*
1316
.43
278.
3651
6.88
-88.
4110
8.59
-156
6.22
1043
.51
-134
3.24
1608
.83
972.
7742
41.3
3-1
5930
.12
1616
9.57
-118
7.78
822.
26
Edu
cati
onal
leve
l64
3.97
*29
4.34
-2.3
60.
000.
000.
0015
0.25
112.
270.
000.
000.
000.
000.
000.
0011
15.7
510
20.3
326
43.5
827
73.1
70.
000.
00
Hou
seho
ld s
ize
10.0
336
8.41
87.5
013
8.73
-131
.60
390.
6810
8.40
163.
0938
.12
28.5
685
6.63
*30
7.14
906.
7946
4.64
468.
5513
32.0
1-1
5930
.12
1616
9.57
477.
02*
227.
69
Chr
onic
le d
isea
se-2
484.
4114
92.0
7-1
052.
6859
7.28
581.
7215
00.6
0-1
062.
5065
5.43
-85.
8012
1.32
-303
8.27
*14
12.4
0-3
030.
7716
63.9
1-4
494.
1445
90.6
2-2
532.
4915
200.
37-1
582.
3789
2.58
R²
00.0
300
.03
00.0
600
.09
00.0
100
.05
00.1
000
.09
00.5
100
.08
N88
858
820
523
398
157
611
172
2930
4
p< 0
.05
98
Chapter 4
4.5 Discussions and conclusions
Our results confirm that the selected exempted groups included in our study, pay for both
outpatient services as well as for inpatient care. However, payments are less frequently
reported for outpatient care than for inpatient services, except for the year 2007. This
difference between the services is expected since the official co-payments for inpatient
health care in Serbia are much higher than those for outpatient care. For example, one
day in hospital is two times more expensive than an examination by a GP (Sl. glasnik
RS”, br. 1/2007, 52/2007 i 99/2007). Moreover, the absence of statistically significant
regression models for outpatient payments implies a low propensity of paying by the five
exempted groups. Therefore, these exempted groups can still benefit from outpatient
care. However, some exempted users (even though it is a small percentage) reported
payments for outpatient care. Thus, we cannot perceive the exemption mechanism in
outpatient care as effective.
Regarding inpatient hospital care during 2002, 2003 and 2007, the five exempted
groups reported formal, “bought & brought goods”, informal and indirect payments. The
highest amounts for hospitalization were paid in 2003 by the group older than 65 and
the lowest amounts were reported by the group younger than 15 also in 2003. The five
exempted groups also report a high frequency of paying for pharmaceuticals that they
brought to the hospital. Respondents classified as older than 65 as well as respondents
classified as unemployed and with low family income, emerged as the most vulnerable
groups (since they cannot pay for health care services). It needs to be pointed out that
respondents, who met two exemption criteria, and also reported that they paid formally
and informally. They also reported that they bought & brought medical goods (e.g.
pharmaceuticals, disposable material) to the hospital. Thus, the exemption mechanism
in inpatient hospital care is also not effective.
We recognize that our study has certain limitations since we only explore the out-
of-pocket payments by some of the exempted groups, and only those types of payments
that are included in the LSMS data. Also, the information on informal patient payments
is sensitive and to some extent, informal patient payments might be underreported.
Nevertheless, our analysis shows that the implementation of the exemption mechanism
in Serbia, has failed, in particular for the elderly people (older than 65 years) and the poor
(low family income and unemployed).
Although some elderly are capable to pay the official co-payments, they are among
the most frequent users of both outpatient and inpatient health care (as shown by our
results as well). The evidence for other countries also indicates that the elderly have
more hospital admissions and a longer length of stay than other groups (Heinrich et
al., 2008). The accumulated patient co-payments might present a significant financial
burden for this group if the exemption mechanism is not effective. Moreover, the
4
Exemption mechanism
99
increased utilization of hospital care by elderly people can affect the implementation of
the exemption mechanism. The accumulated costs of hospital services used by the elderly
(when exempted from official co-payments) can increase the need for public funds for
health care used by this group. If the required funds are not transferred to the providers of
hospital care, or are transferred but with a delay, health care providers might be reluctant
to grant exemptions to elderly patients (as well as to other exempted groups).
As we have mentioned earlier, patients with low income have to make additional
efforts to achieve their exempted status. This includes additional documents, time and
money. Our results also show that this group pays more for pharmaceuticals, disposable
materials and orthopedic devices brought by patients to the hospital than other population
group. This complies with the evidence from other low- and middle-income countries
(McIntyrea et al., 2006; Tatar et al., 2007). People, who are perceived as poor, are not
asked directly for money, but they are asked to bring pharmaceuticals to the hospital.
This indirectly saves money for providers. At the same time, the shadow nature of these
“bought & brought goods” payments makes them an even more significant burden on
the patients’ household budget (Chapter 3). Raising awareness among patients regarding
goods that they are entitled to receive free-of-charge during their hospitalization, could
empower the patient and decrease this type of patient payments.
With regard to informal payments, all five exempted groups as well as other population
groups report such payments. Therefore, we cannot claim that informal payments are a
substitute for official co-payments. The inability of official co-payments to substitute
the informal ones is discussed by Ensor (2004) for Eastern European countries. The
experience from developing countries also confirms that official fees for health services,
do not necessarily replace the informal payments (Belli et al., 2004; Ensor, 2004). This
indicates that the attention of Serbian policy-makers should focus on finding suitable
strategies for dealing with informal payments in the health care sector and with the
corruption in general.
When we consider the time-perspective, we observe that in 2003, immediately after
the reforms started, the situation became more favorable for the five exempted groups
regarding the inpatient care. In 2007, the situation was more similar to 2002 when
the reforms started. A possible explanation is the political situation in Serbia in 2004
when a new conservative government took power (Bilic & Georgaca, 2007). The policy
of this government affected the health care reforms in terms that they stopped all reforms
during 2004-2008.
Although previous studies have addressed the catastrophic and impoverishing effects
of out-of-pocket patient in Serbia (Chapter 2; Bredenkamp et al., 2010), our results
suggest that further research should also explore the economic implications of out-
of-pocket patient payments for specific exempted groups. Future research should also
100
Chapter 4
explore the issue of leakage and under coverage, which we were not able to identify using
the LSMS data.
This chapter has analyzed the out-of-pocket payments for outpatient and inpatient
hospital care by five exempted groups in Serbia during the period of the post-war health
care reforms. The empirical results suggest that individuals eligible for an exemption
still report payments for hospital services. These payments include official co-payments
but also “bought & brought goods”, indirect and informal payments.
Thus, despite the wish of the Ministry of Health to promote equity as a leading
goal of the health care reforms, the implementation of the exemption mechanism, both
in outpatient and inpatient hospital care, is failing. The failures are visible in terms
of content (as described in the background section) as well as in terms of application
process (as shown by our results). Policy-makers should pay attention to the transparency
of legalization and supporting regulations, the effective targeting of exempted groups
and providing better access to public health services. The existence of clear guidelines
regarding the exemption mechanism and their availability to patients can decrease
confusion and resistance among providers, but may also increase the awareness of
potential recipients (Bitran & Giedion, 2003). Research from low and middle income
countries shows that low awareness, lack of information and fear that health care will not
be provided adequately are leading causes of failure of the exemption mechanism (Jacobs
et al., 2007; Nyonator & Katzin, 1999).
Moreover, instead of asking eligible people to apply for exemption, a targeting
mechanism through the insurance system itself may be more effective and less costly
for users. In particular, our results are based on experiences in a health care system that
requires direct formal payments from the users of health care services to the providers of
these services. The existence of a formal payment channel between users and providers
may well contribute to the existence of informal payments. Giving the national insurance
company the possibility to collect the official co-payments instead of the health care
providers could eliminate the formal monetary aspect in the patient-provider relation
and could contribute to the elimination of informal payments. This could be a policy
consideration also in other countries where user co-payments are paid directly to providers
and where informal payments are widely spread. Such strategy could also help to avoid
the payment of official co-payments by exempted groups.
The Ministry of Health in Serbia should improve on the design and the adequate
implementation of the exemptions from patient co-payments. The exemption policy in
Serbia should be pro-poor oriented exempting primarily those who cannot pay or use
health care frequently. Besides, the exemption policy should be based on the health status
of the users to avoid catastrophic effects of accumulated patient payments by frequent
users, such as chronic patients (Chapter 2; Bredenkamp et al., 2010). The exemption
mechanism should include clearly defined criteria on who is eligible for an exception
4
Exemption mechanism
101
and disseminate information about this patients’ right among exempted groups (Bitran
& Giedion, 2003). Awareness among the exempted groups that they have this right
(with no consequences for the quality of care received) could diminish the problem of
stigmatization and may encourage exempted groups to use this right.
There should be also an adequate financial plan to assure that public funds are
transferred directly from insurance company to health care providers for health care
provided to exempted groups. The monitoring of the implementation process should
be more transparent (Balabanova, 2007). The possibility of stigmatization, a negative
providers’ attitude and resistance towards the exemption mechanism can be solved by
changing attitudes of both providers and health care consumers towards exemptions.
Our results can be useful for other countries that are in the process of ongoing health
care reforms and are introducing or have introduced formal user co-payments for health
care services. As suggested by our results and discussion, policy-makers in these countries
should not limit themselves to having an exemption mechanism that accompanies the
system of user co-payments. They also need to pay attention to both the design and the
implementation of this exemption mechanism, in order to be able to effectively protect
all vulnerable population groups.
CHAPTER 5
The Effects of Chronic Diseases on Poverty
Submitted as:
Arsenijevic, J., Pavlova, M., & Groot, W. (2014). The effects of chronic diseases on poverty
104
Chapter 5
Abstract
Background: Chronic diseases are more likely to occur among poor individuals, and at
the same time, patients with chronic diseases have a higher probability of becoming poor.
This implies a double-sided relation between chronic diseases and poverty. The existence
of a joint causality can lead to biased estimates of the poverty effects provoked by chronic
diseases. The aim of this chapter is to model the casual effects of chronic diseases on
poverty when other common factors are controlled for.
Methods: To explore this joint causality, we use LSMS data for Serbia for 2007. We
apply an instrumental variable approach. We also present results of OLS regressions. As
outcome variables, we use indicators of pre-payment poverty and catastrophic effects
of out-of-pocket patient payments for different types of chronic diseases. Instrumented
variables are indicators of chronic diseases: cardiovascular diseases, diabetes mellitus
and cancer within the household. We use two groups of instruments: The first group is
related to health-related lifestyle behavior (e.g. smoking behavior and eating habits). The
second group of instrument variables consists of environmental variables like living in
an area affected by uranium during the NATO bombing and being a refugee during the
period 1999-2007.
Results: Our results show that all three chronic diseases can impose an economic burden
on households when other relevant factors are controlled for. Different risk factors are
related to different chronic diseases. Also, some chronic diseases (diabetes mellitus) can
cause both pre-payment poverty and catastrophic effects.
Conclusions: In order to decrease the poverty effects caused by chronic diseases, policy
makers in Serbia should provide more effective exemption mechanisms to patient
payments. Improvements in the organization of care for patients with a chronic disease
can also enhance the efficiency of service utilization and thus, decrease the level of out-of-
pocket patient payments.
5
The effects of chronic diseases on poverty
105
5.1 Introduction
Chronic non-communicable diseases are a major cause of financial hardship for patients
and their households (Abegunde & Stanciole, 2008; Adeyi, Smith, & Robles, 2007;
Bloom & Finlay, 2009; Russell, 2004). Once diagnosed, chronic diseases frequently
require continuous use of health care. When the health care system heavily relies on
out-of-pocket patient payments, increased utilization of health care due to a chronic
disease imposes a high financial burden on the patient and the household. In this way,
a chronic disease can push even non-poor households into poverty and can drive already
poor households deeper into poverty (Sachs, 2001; Xu et al., 2010) 2001; Xu et al., 2010.
Therefore, chronic diseases present not only a health problem but also a social problem
(Sachs, 2001) 2001.
At the same time, poverty is a cause of getting a chronic disease (Beaglehole et al.,
2011; Tunstall-Pedoe, 2006). Poverty is associated with several factors that contribute
to developing a chronic disease, like material deprivation, unhealthy living conditions
and bad nutrition (Tunstall-Pedoe, 2006). Besides poverty, other risks factors for
developing a chronic disease include aging, heredity, an unhealthy life style (smoking
habits, alcohol consumption, unhealthy diet and physical inactivity) and environmental
factors (pollution, stress)(Alleyne et al., 2013). While some of these risk factors are non-
modifiable like ageing and heredity, others like an unhealthy life style factors can be
changed. However, some of those risk factors, particularly health-related life style risk
factors, are associated with income.
As outlined above, the relation between chronic diseases and poverty is double-sided,
and while chronic diseases can provoke poverty, poverty can also be a trigger for developing
a chronic disease. Taking in account this joint causality between chronic diseases and
poverty, the increased prevalence of chronic diseases and their poverty effects can result
in prolonged social inequalities when patients are required to pay out-of-pocket for the
services they use (Tagoe, 2012).
Studies that examine the poverty effects of out-of-pocket patient payments for chronic
diseases, have not taken in account this complex joint relation between chronic diseases
and poverty (Bonu, Rani, Peters, Jha, & Nguyen, 2005; Engelgau, Karan, & Mahal,
2012; Xu et al., 2003). Those studies implicitly assume that the effect of chronic diseases
on poverty is one-sided, and indicators of chronic diseases are treated as exogenous
predictors (for example in regression equations). The findings of these studies suggest
that when chronic diseases are present, the poverty effects of out-of-pocket patient
payments are statistically significant. The existence of a joint causality implies that poor
people are more likely to develop a chronic disease(Alleyne, et al., 2013) while patients
with a chronic disease have a higher probability to become poor(Wagstaff, 2002; Xu, et
al., 2003). This can lead to biased estimates of the poverty effects provoked by chronic
106
Chapter 5
diseases if the joint causality is not taken into account. Only few studies have examined the
joint relation between poverty and general health status, but without paying particular
attention to chronic diseases(Buddelmeyer & Cai, 2009). Addressing the joint relation
between chronic diseases and their poverty effects can provide better insight into the role
of other risk factors for developing a chronic disease (e.g. eating habits, environmental
factors, smoking behavior) (Tunstall-Pedoe, 2006).
The aim of this chapter is to model the causal effects of chronic diseases on poverty
effects of out-of-pocket patient payments when other common factors are controlled for.
To explore this joint causality, we use LSMS data for Serbia for 2007(Chapter 1). We apply
an instrumental variable (IV) approach (Khandker, Koolwal, & Samad, 2010; Wooldridge,
2012). We also present the results obtained from ordinary least squares (OLS) regressions.
We include in the analysis the three most common chronic conditions classified as leading
chronic diseases (WHO, 2005), namely diabetes mellitus, cardiovascular diseases and
progressive diseases. These diseases are also identified as an economic burden for patients
and their households in various countries (Adeyi, et al., 2007; Alleyne, et al., 2013;
Gordon, Scuffham, Hayes, & Newman, 2007; Rasekaba, Lim, & Hutchinson, 2012).
In Serbia, estimated total costs of diabetes mellitus are 6% of total health expenditure
(Bjegovic et al., 2007). Diabetes mellitus, cardiovascular diseases and cancer are three
diseases with highest prevalence in Serbia since 2000. They are also leading causes for
mortality (Janković, Simić, & Marinković, 2010). Since the total out-of-pocket patient
payments in Serbia are estimated as 35% of total health expenditure, which is a rather
high rate compared to other European countries(Vuković & Perišić, 2011), although
people with diagnosed chronic diseases in Serbia, are partially exempted from official
co-payments, we expect that they also have a high probability to experience poverty.
5.2 Methods: data and statistical analysis
In this study we use the Serbian LSMS data for 2007. As we outlined in Chapter 1,
the data contain variables that can be used as indicators of household wealth, like total
household consumption. Also, information on out-of-pocket patient payments for both
inpatient and outpatient care are available. The data also contain information about the
presence of chronic diseases among household members.
We first describe the variables included in the estimation of models. Then, we outline
the statistical methods that we apply.
5
The effects of chronic diseases on poverty
107
5.2.1 Outcome variablesWe use two outcome variables: pre-payment poverty and the catastrophic effects of out-
of-pocket payments. We first create a binary indicator of pre-payment poverty. As in
previous studies (Wagstaff, 2008), households with total household consumption lower
than the absolute poverty line are classified as poor. As we mentioned in Chapter 2, the
absolute poverty line is calculated by the Republic Statistics Office of Serbia. It is defined
as minimum food basket plus other goods that households with minimum basket food
consumption are supposed to spend, and it is estimated as 8,883 CSD (≈ 100 Euro) per
person per month. Based on this, we have calculated the poverty line at the household
level by multiplying the absolute poverty line per person by the number of household
members. Thus, if the total household consumption is less than the absolute poverty
line for that household, the household is classified as poor and the indicator is coded as 1
(poor), otherwise it is coded as 0 (non-poor). We use this indicator as an outcome variable.
To assess the financial burden of out-of-pocket patient payments for different types of
chronic diseases, we apply catastrophic health care expenditure approaches.
Catastrophic health care expenditures occur when the total out-of-pocket patient
payments exceed a certain threshold (Xu, et al., 2010). The threshold is defined as a
proportion of the total household consumption and can vary from 5 to 40% (Chapter 2).
We use the threshold of 40% because it is the most conservative measure of catastrophic
effects (Xu et al., 2006). To identify the households that experience catastrophic health
care expenditure, we create a binary indicator of catastrophic health care expenditure
(including all households). If the total out-of-pocket payments of a household exceed
40% of the total household consumption, this binary indicator is coded as 1 (catastrophic
expenditure), otherwise it is coded as 0 (no catastrophic expenditure). This is also an
outcome variable in our analysis.
5.2.2 Indicators of chronic diseases Data regarding chronic diseases of household members are collected using questions
about the diagnosis of diseases. For example: “Are you diagnosed with diabetes mellitus
by a medical doctor?” Similar questions are asked for other chronicle diseases including
cardiovascular diseases (hypertension, myocardial infarction, etc.) and progressive diseases
(cancer). For the purpose of our analysis, we create three binary indicators for diabetes
mellitus, cardiovascular diseases and progressive diseases respectively.
If at least one household member is diagnosed with a given disease, the respective
diseases-specific indicator is coded as 1 (household members diagnosed with such
disease), otherwise it is coded as 0 (no household member diagnosed with such disease).
All respondents who did not give a yes-answer to the questions on the three chronic
diseases, are considered as non-diagnosed by a medical doctor as having these chronic
diseases.
108
Chapter 5
5.2.3 Covariates We use variables presenting household characteristics as covariates in all our models.
More precisely, we use age of the head of the household, type of settlement, gender of the
head of the household, level of education of the head of the household, nationality of the
head of the household, household size and income percentiles. Since health care insurance
is compulsory and virtually everyone is insured, we do not include this as covariate.
5.2.4 Instrumental variable approachAs we have outlined before, in this study we apply an IV approach (Wooldridge, 2012).
The IV approach is a regression model that is applied when at least one of the predictors
is endogenous (Khandker, et al., 2010). If endogenous predictors are used in a linear
(OLS) or non-linear (probit, logistic) regression, they can give inconsistent parameter
estimates. One way to overcome the problems with an endogenous predictor is to find
instrumental variable(s), i.e. exogenous variables that are related to the endogenous
predictor but not related to the outcome variable. The most known form of IV approach
is the two stage least square method (2SLS) (Wooldridge, 2012). In the first stage
regression, the instrumental variable is used as a regressor for the endogenous predictor.
Predicted values of the endogenous predictor are used in a second stage regression, with
the outcome variable as a dependent variable. In order to apply 2SLS approach, both
endogenous predictor/predictors and outcome variables should be continues. However, in
our study both endogenous predictor and outcome variables are of a binary nature. Since
the previous literature shows that conventional 2SLS models are appropriate when both
the outcome and endogenous variable are discrete once valid instruments are found, we
apply the most commonly used 2SLS model in our analyses (Angrist & Krueger, 2001).
The 2SLS model for binary predictors is based on a two-stage regression. In the first stage
regression of our analysis, we regress the binary disease-specific indicators described above
(diabetes mellitus, cardiovascular diseases and cancer) on the instrumental variables and
covariates. In the second stage regression, we regress the binary indicators of poverty,
and catastrophic health care expenditure on the predicted values of the disease-specific
indicators (based on the first stage regression) and the same set of covariates. The
regression model is defined below by equations 1 and 2:
where yi is one of the outcome variables (i.e. the indicator of poverty or catastrophic
health care expenditure); z is one of the instrumented variables (i.e. one of the disease-
specific indicators that define whether the household has a member diagnosed with
5
The effects of chronic diseases on poverty
109
diabetes mellitus, cardiovascular chronic diseases or progressive diseases), and is the
predicted value of z obtained in the first stage regression. Covariates common for equation
1 and equation 2, are denoted as x1, x
2, etc., while instrumental variables are denoted as
instrument1, instrument
2, etc. and they are included in equation 2 only. The error terms
are denoted with δ and ε, for equation 1 and equation 2, respectively.
First equation 2 is estimated (first stage regression) and then equation 1 (second stage
regression). We use the software package Stata 9 (command ivregress). Since we have two
outcome variables and three instrumented variables, we have six different models.
5.2.5 Instrumental variables One of the challenges in performing the IV approach is to identify relevant and valid
instruments. Overall, good instruments should satisfy two main criteria (Cawley &
Meyerhoefer, 2012; Wooldridge, 2012). First, they should be correlated with the
instrumented variable (in our study having a household member diagnosed with diabetes,
cardiovascular diseases or progressive diseases), which is known as the relevance criterion.
Second, they should not be correlated with the error term in the model of the outcome
variable, which is known as the validity criterion.
In order to identify valid instruments, we have first searched the existing literature
(Abegunde & Stanciole, 2008; Adeyi, et al., 2007; Alleyne, et al., 2013; Basu, Stuckler,
McKee, & Galea, 2013; Beaglehole, et al., 2011; Geneau et al., 2010; Ludwig et al., 2013;
Mayer-Foulkes & Pescetto-Villouta, 2012; Murphy, Mahal, Richardson, & Moran, 2013;
Russell, 2004). After we identified possible instruments, we have applied statistical tests
to test for the relevance and validity of the instruments.
According to the literature, good instruments cover a wide range of characteristics
from genetic characteristics, number of relatives to environmental factors. We have
identified potential instruments and classified them in two groups of variables.
The first group is related to health-related life style behavior (e.g. smoking behavior
and eating habits such as sweet food consumption or alcohol consumption). In the previous
literature, health related life style behavior such as smoking and alcohol consumption
have been considered as endogenous variables (Eisenberg & Quinn, 2006; Fletcher,
2011;Mullahy, 1997). It is also known that an unhealthy life style is more often observed
among poor population groups (Cutler et al., 2011), while recent studies show that rich
people consume alcohol more moderately than the poor (van Kiippersluis & Galama,
2013). The relation between poverty status and engagement in health related life style
behavior can question the suitability of smoking behavior and alcohol consumption as
suitable instruments. However, recent studies for Serbia show that there is no difference
in wealth status among smokers (Djikanovic et al., 2011). Similar findings are observed
for alcohol consumption (Jankovic et al., 2010). Therefore, we have used alcohol
consumption and smoking as potential instruments. Moreover, these variables show no
110
Chapter 5
strong correlation with our indicators of poverty and catastrophic expenditure, which is
in line with previous studies in Serbia mentioned above.
Smoking behavior is measured in the LSMS data as the number of cigarettes consumed
per month per household. We use this to create an indicator of smoking behavior (1 for
households with smokers and 0 otherwise). We also create an indicator of the eating habits
of the household. For this purpose, we calculate the total food consumption, fat food
consumption, sweet food consumption and alcohol consumption from data available in
LSMS. Based on Basu et al. (2013), we have grouped different food items in different food
categories (fat, sweet etc.). Also, we have summed up the quantity of different food items
for each food category and expressed them as kilojoules per household per month(Basu,
et al., 2013). We use the share of fat food as a percentage of total food consumption as our
first indicator related to eating habits, the share of sweet food as a percentage of total food
consumption as a second indicator and the share of alcohol on total food consumption as
a third indicator of eating habits.
The second group of instrumental variables consists of environmental variables like
living in an area affected by uranium during the North Atlantic Treaty Organization
(NATO) bombing and being a refugee during the period 1999-2007 (both coded as 1 in
case of an yes-answer and coded as 0 in case of a no-answer). We expect that people living
in areas affected by uranium bombs will develop more often cancer (Egawa et al., 2012)
and cardio-vascular diseases (Douple et al., 2011). Exposure to the war situation and
the necessity to live the home, can lead to the development of many diseases, including
cardiovascular diseases and diabetes, among refugees (Spiegel & Salama , 2000). Among
the refugees in ex-Yugoslavia, cardiovascular diseases and diabetes mellitus occurred as
long-term consequences of prolonged stress and the use of inadequate coping mechanisms
(such as denying) (Vlajkovic, 2000). Although being a refugee can be associated with
lower income, this was for the majority of refugees in Serbia only a short-term effect.
Recent studies show that in the period 2005-2008, the majority of refugees in Serbia
did not differ in their wealth status compared with the general population (Bajec, 2008;
UNDP, 2006).
To check for the relevance of the instruments, we run the first stage regression as a
OLS model using potential instrument variables as predictors of each of the indicators
of chronic diseases (Wooldridge, 2012). Data are presented in Appendix 1 (Table A.1).
When diabetes mellitus is used as a dependent variable, non-significant predictors are the
share of fat food in the total food consumption and living in an area affected by uranium
during the NATO bombing. For cardiovascular diseases, the non-significant predictor
are the share of fat food in total households’ consumption and being a refugee during
the period 1999-2007, while for the progressive diseases, the only significant predictors
are alcohol and smoking consumption. Therefore, based on this relevance test, non-
significant predictors for a chronic disease are excluded as potential instrument variables.
5
The effects of chronic diseases on poverty
111
With regard to the validity of the instruments, we should analyze the correlation between
the instruments and the error term in the model of the outcome variable. This correlation
is not possible to check upon directly since we cannot observe the error term before
the model is estimated (Wooldridge, 2012). Therefore, we first test if the potential
instruments are correlated with one of three outcome variables. If a potential instrument
is correlated with an outcome variable, it should not be used in the final model (Hausman
& Taylor, 1981). Our results of this initial validity test are presented in Appendix of this
dissertation (Table A.2). We have considered that instruments are correlated with an
outcome variable if the correlation coefficient is significant and higher than ±0.2.
Based on the relevance test and the initial validity tests described above, for the pre-
payment poverty model, we include the presence of a refugee in the household and alcohol
consumptions as instruments. We use same instruments for all three chronic diseases.
Regarding catastrophic health care expenditures, for diabetes mellitus we choose the
share of sweet food consumption in total household consumption, alcohol consumption,
number of cigarettes consumed per households, and presence of the refugees in the
households as instrument variables. The same set of variables is chosen for cardiovascular
diseases with the only difference that the presence of refugees in the household is replaced
with municipalities affected by uranium rich-bombs. The only instrument for progressive
diseases is the number of cigarettes per household. We have also checked whether the
instruments are correlated with each other or with other covariates. We did not find
significant correlations with the chosen instruments.
With regard to the validity of the final models, we use the endogeneity test known
as the Hausman test. The Hausman test examines whether the endogenous predictor is
truly endogenous. In other words, the Hausman test checks the hypothesis if there is any
correlation between the error term in the first stage regression and the error term in the
second stage regression (H0: cov(εi, δi)=0). If the H
0 is true both OLS and instrumental
variable estimators are consistent and therefore it is not necessary to use 2SLS. If the null
hypothesis is rejected, 2SLS is required (Hausman, 1978).The Hausman test is provided
in STATA as a post estimation test (command: estat endogenous). We report this test
for each of our 6 models. Another test is performed to examine whether the instruments
are correlated with the error term from the second stage regression. The test is known
as the over identification test or Sargan test. The Sargan test is also provided in STATA
(command estat overid). The results of the two tests are presented for each of the 6
models separately in Table 5.2 and Table 5.3 respectively. Also, we independently run
the second stage regression using a ordinary least square regression (OLS) model as a
“naïve” model that does not capture the endogeneity of the instrumented variable. We
compare the estimators obtained from the OLS regression with those obtained from the
IV model in order to establish the validity of obtained estimators. The details regarding
this comparison are presented in the result section.
112
Chapter 5
5.3 Results
In table 5.1, we present the results of the descriptive statistics for each of the variables
used in our analyses. Our results show that the average consumption of fat food is higher
than the average consumption of sweet food. The consumption of cigarettes is quite
common in Serbia: 53.8% of households have a smoker residing. Of the three chronic
diseases, cardiovascular diseases are most prevalent within the household (39.8%). The
prevalence of diabetes mellitus is 10.1% and prevalence of progressive diseases is 16.3%.
Regarding the outcome variables, the incidence of catastrophic effects is distributed
among all income quintiles, while pre-payment poverty based on consumption is present
only among the lower income quintiles (cross tabulation data that are not presented).
Table 5.2 presents the results of the first three IV models where we use the binary
indicator of pre-payment poverty as a second stage dependent variable and each of
the three chronic conditions as first stage dependent variables. In these models, we
use only two instrumental variables (namely alcohol consumption and the presence of
refugees in the household), since the other instrumental variables are correlated with the
binary indicator of pre-payment poverty. We estimate these 2SLS models including all
households in the sample (N=5,557).
The results of the first stage regressions in Table 5.2 suggest that being diagnosed
with diabetes mellitus is more probable to happen in households with refugees. A
higher consumption of alcohol is negatively related with having a household member
with diabetes or cardiovascular diseases. Larger households and households with a lower
income have a higher chance of a progressive disease.
The results of the second stage regressions in Table 5.2 related to diabetes mellitus
show that households where the head of the household is a man, households in rural areas,
households that have children younger than 18, and households with a lower level of
income, have a higher chance of being poor. Similar results are observed for cardiovascular
diseases: households with a head with lower level of income and households with children
older than 7 have a lower chance to be poor.
5
The effects of chronic diseases on poverty
113
Tab
le 5
.1: D
escr
ipti
ve s
tati
stic
s fo
r th
e to
tal s
ampl
e of
hou
seho
lds
(N=
5557
)
Fre
qu
enci
esM
edia
nM
inM
ax
Ou
tcom
e va
riab
les
Pre
-pay
men
t po
or (b
ased
on
hous
ehol
d co
nsum
ptio
n)1
= p
oor
(7.3
%);
0 =
non
-poo
r (9
2.7%
)0.
000
1
Impo
veri
shin
g ef
fect
s of
hea
lth
care
exp
endi
ture
1 =
impo
veri
shin
g ef
fect
- ye
s (3
.3%
) 0 =
impo
veri
shin
g ef
fect
-no
(96.
7%)
0.00
01
Cat
astr
ophi
c ef
fect
s of
hea
lth
care
exp
endi
ture
1 =
cat
astr
ophi
c ef
fect
-ye
s (2
.4%
) 0 =
cat
astr
ophi
c ef
fect
-no
(97.
6%)
0.00
01
Inst
rum
ente
d va
riab
les
Dia
gnos
ed d
iabe
tes
wit
hin
the
hous
ehol
ds1
= y
es (1
0.1%
) 0 =
no
(89.
9%)
0.00
01
Dia
gnos
ed c
ardi
o-va
scul
ar d
isea
se w
ithi
n th
e ho
useh
olds
1 =
yes
(39.
8%) 0
= n
o (6
0.2%
)0.
000
1
Dia
gnos
ed p
rogr
essi
ve d
isea
se in
abd
omen
wit
hin
the
hous
ehol
d1
= y
es (1
6.3%
) 0 =
no
(83.
7%)
0.00
01
Inst
rum
enta
l var
iab
les
Shar
e of
fat
food
in t
otal
food
con
sum
ptio
nP
erce
ntag
e of
fat
food
in t
otal
food
con
sum
ptio
n12
.00
0.1
87.8
8
Shar
e of
sw
eet
food
in t
otal
food
con
sum
ptio
nP
erce
ntag
e of
sw
eet
food
in t
otal
food
con
sum
ptio
n33
.00
0.1
98.7
0
Num
ber
of c
igar
ette
s co
nsum
ed p
er h
ouse
hold
Con
tinu
ous
from
0 t
o m
axim
um28
0.00
044
80.0
0
Shar
e of
alc
ohol
in fo
od c
onsu
mpt
ion
Per
cent
age
of a
lcoh
ol in
tot
al fo
od c
onsu
mpt
ion
1.21
080
.73
Mun
icip
alit
ies
affe
cted
by
uran
ium
–ric
h bo
mbs
1
= y
es (4
8.3%
); 0
= n
o (5
1.7%
)0.
480
1
Pre
senc
e of
ref
ugee
s in
the
hou
seho
ld
1 =
yes
(4.1
%);
0 =
no
(95.
9%)
0.00
01
Cov
aria
tes
Hou
seho
ld t
he s
ize
From
1 u
p to
12
3.00
112
Gen
der
of t
he h
ead
of t
he h
ouse
hold
1 =
mal
e (7
2.6%
); 0
= fe
mal
e (2
7.4%
)1.
000
1
Nat
iona
lity
of t
he h
ead
of t
he h
ouse
hold
1 =
Ser
bian
(85.
9%);
0 =
oth
ers
(14.
1%)
1.00
01
Age
of t
he h
ead
of t
he h
ouse
hold
From
15
up t
o 98
57.0
015
98
Num
ber
of k
ids
youn
ger
than
7 y
ears
wit
hin
the
hous
ehol
dFr
om 0
up
to 4
0.00
04
Num
ber
of k
ids
olde
r th
an 7
and
you
nger
tha
n 18
yea
rs
From
0 u
p to
60.
000
6
Inco
me
perc
enti
les
Per
cent
iles
(1-5
)3.
000
5
Edu
cati
on o
f the
hea
d of
the
hou
seho
ld0
= u
p to
pri
mar
y sc
hool
; 2 =
up
to s
econ
dary
sch
ool;
3
= u
p to
hig
h sc
hool
3.79
13
114
Chapter 5T
able
5.2
: Res
ults
of t
he 2
SLS
mod
els;
dep
ende
nt v
aria
ble:
pre
-pay
men
t po
or a (
N=
5557
)
Exp
lan
ator
y va
riab
les
n
clu
ded
in t
he
anal
ysis
Poo
r (1
= y
es; 0
= n
o)In
stru
men
ted
= d
iab
etes
mel
litu
s (1
= y
es; 0
= n
o)
Poo
r(1
= y
es; n
o =
0)
Inst
rum
ente
d c
ard
iova
scu
lar
dis
ease
s (1
= y
es; 0
= n
o)
Poo
r(1
= y
es; n
o =
0)
Inst
rum
ente
d p
rogr
essi
ve d
isea
ses
(1 =
yes
; 0 =
no)
Coe
ffici
ent
SEC
oeffi
cien
tSE
Coe
ffici
ent
SE
Seco
nd
sta
ge r
egre
ssio
n r
esu
lts
Hou
seho
ld s
ize
-0.0
110.
317
-0.0
24*
0.01
2-0
.017
0.01
1
Type
of s
ettl
emen
t-0
.026
*0.
008
-0.0
230.
013
-0.0
070.
014
Gen
der
of t
he h
ouse
hold
hea
d *0
.017
0.00
9 0
.031
0.01
5 0
.037
*0.
017
Nat
iona
lity
of t
he h
ouse
hold
hea
d-0
.009
0.01
1-0
.016
0.01
7-0
.012
0.01
8
Age
of t
he h
ouse
hold
hea
d-0
.008
0.00
03-0
.005
*0.
002
-0.0
010.
001
Edu
cati
onal
leve
l of t
he h
ouse
hold
hea
d-0
.009
0.00
3-0
.006
0.00
4
Num
ber
of k
ids
youn
ger
than
7 y
ears
0.0
54*
0.01
0 0
.089
*0.
002
0.0
530.
019
Num
ber
of k
ids
olde
r th
an 7
yea
rs 0
.040
*0.
007
0.0
73*
0.02
0 0
.056
0.01
7
Inco
me
perc
enti
les
-0.0
356*
0.03
3-0
.031
*0.
005
-0.0
29*
0.00
5
Dia
bete
s m
elli
tus
0.9
52*
0.31
7
Car
dio-
vasc
ular
dis
ease
s 0
.074
*0.
246
Pro
gres
sive
dis
ease
s 1
.04
0.38
5
Con
stan
t 0
.187
*0.
045
0.2
11*
0.05
4 0
.079
*0.
041
a Con
sum
ptio
n be
low
the p
over
ty li
ne o
f 888
3 C
SD p
er m
onth
;*
p< 0
.01
** p
<0.
05
***
p< 0
.10
5
The effects of chronic diseases on poverty
115
Tab
le 5
.2: R
esul
ts o
f the
2SL
S m
odel
s; d
epen
dent
var
iabl
e: p
re-p
aym
ent
poor
a (N
=55
57) (
cont
inue
d)
Exp
lan
ator
y va
riab
les
in
clu
ded
in t
he
anal
ysis
Poo
r (1
= y
es; 0
= n
o)In
stru
men
ted
= d
iab
etes
mel
litu
s (1
= y
es; 0
= n
o)
Poo
r(1
= y
es; n
o =
0)
Inst
rum
ente
d c
ard
iova
scu
lar
dis
ease
s (1
= y
es; 0
= n
o)
Poo
r(1
= y
es; n
o =
0)
Inst
rum
ente
d p
rogr
essi
ve d
isea
ses
(1 =
yes
; 0 =
no)
Coe
ffici
ent
SEC
oeffi
cien
tSE
Coe
ffici
ent
SE
Firs
t st
age
regr
essi
on r
esul
ts
Shar
e of
alc
ohol
in t
otal
food
con
sum
ptio
n-0
.009
*0.
003
-0.0
02*
0.00
1-0
.005
0.02
4
Pre
senc
e of
ref
ugee
s in
hou
seho
ld 0
.057
*0.
020
0.0
320.
031
-0.0
010.
001
Hou
seho
ld s
ize
0.0
20*
0.00
3 0
.043
*0.
006
0.0
25*
0.00
5
Type
of s
ettl
emen
t 0
.012
0.00
8 0
.009
0.01
4-0
.009
0.01
0
Gen
der
of t
he h
ouse
hold
hea
d 0
.002
0.00
8-0
.015
0.01
5-0
.017
0.01
2
Nat
iona
lity
of t
he h
ouse
hold
hea
d-0
.009
0.01
1-0
.001
0.01
8-0
.004
0.01
4
Age
of t
he h
ouse
hold
hea
d 0
.003
*0.
0003
0.0
09*
0.00
1 0
.002
0.00
0
Edu
cati
onal
leve
l of t
he h
ouse
hold
hea
d-0
.002
20.
002
-0.0
08*
0.00
4-0
.003
0.00
2
Num
ber
of k
ids
youn
ger
than
7 y
ears
-0.0
35*
0.01
0-0
.091
*0.
015
-0.0
31*
0.01
2
Num
ber
of k
ids
olde
r th
an 7
yea
rs-0
.023
*0.
0069
-0.0
750.
010
-0.0
36*
0.00
8
Inco
me
perc
enti
les
-0.0
010.
0034
-0.0
070.
005
-0.0
29*
0.00
5
Con
stan
t 0
.090
*0.
026
0.1
40*
0.04
1 0
.027
*0.
032
Hau
sman
tes
tF(
1,55
45)=
22.5
386
(p =
0.0
000)
F(1,
5545
) = 2
9.63
79 (p
= 0
.000
0)F(
1,55
45) =
25.
2162
(p =
0.0
0)
Sarg
an t
est
chi2
(1) =
2.6
9289
(p =
0.1
008)
chi2
(1) =
.000
013
(p =
0.9
971)
chi2
(1) =
.867
216
(p =
0.3
517)
a Con
sum
ptio
n be
low
the p
over
ty li
ne o
f 888
3 C
SD p
er m
onth
;*
p< 0
.01
**
p<
0.0
5
***
p< 0
.10
116
Chapter 5T
able
5.3
: Res
ults
of t
he 2
SLS
mod
els;
dep
ende
nt v
aria
ble:
cat
astr
ophi
c ef
fect
s of
hea
lth
care
exp
endi
ture
(N=
5557
)
Exp
lan
ator
y va
riab
les
incl
ud
ed in
th
e an
alys
isC
atas
trop
hic
eff
ects
(1 =
yes
; 0 =
no)
Inst
rum
ente
d =
dia
bet
es m
elli
tus
(1 =
yes
; 0 =
no)
Cat
astr
oph
ic e
ffec
ts(1
= y
es; n
o =
0)
Inst
rum
ente
d c
ard
iova
scu
lar
dis
ease
s(1
= y
es; 0
= n
o)
Cat
astr
oph
ic e
ffec
ts(1
= y
es; n
o =
0)
Inst
rum
ente
d p
rogr
essi
ve d
isea
ses
(1 =
yes
; 0 =
no)
Coe
ffici
ent
SEC
oeffi
cien
tSE
Coe
ffici
ent
SE
Seco
nd
sta
ge r
egre
ssio
n r
esu
lts
Hou
seho
ld s
ize
0.0
08*
0.00
2 0
.045
0.00
3 0
.017
*0.
004
Type
of s
ettl
emen
t 0
.003
0.00
4 0
.004
0.00
5 0
.005
0.00
5
Gen
der
of t
he h
ouse
hold
hea
d 0
.001
0.00
5 0
.003
0.00
5-0
.005
0.00
7
Nat
iona
lity
of t
he h
ouse
hold
hea
d 0
.002
0.00
6 0
.001
0.00
6 0
.001
0.00
7
Age
of t
he h
ouse
hold
hea
d 0
.000
0.00
0-0
.000
70.
0000
5 0
.001
**0.
000
Edu
cati
onal
leve
l of t
he h
ouse
hold
hea
d 0
.001
0.00
1 0
.002
0.00
1-0
.003
0.00
1
Num
ber
of k
ids
youn
ger
than
7 y
ears
-0.0
050.
006
0.0
030.
007
-0.0
17*
0.00
5
Num
ber
of k
ids
olde
r th
an 7
yea
rs-0
.001
0.00
4 0
.006
0.00
5-0
.013
0.00
4
Inco
me
perc
enti
les
-0.0
03**
0.00
1-0
.003
0.00
2-0
.006
**0.
002
Dia
bete
s m
elli
tus
0.1
66*
0.07
5
Car
dio-
vasc
ular
dis
ease
s 0
.158
*0.
059
Pro
gres
sive
dis
ease
s 0
.035
0.01
6
Con
stan
t-0
.023
0.01
5-0
.093
0.44
-0.0
150.
017
* p<
0.01
**
p<
0.05
**
* p<
0.1
0
5
The effects of chronic diseases on poverty
117
Tab
le 5
.3: R
esul
ts o
f the
2SL
S m
odel
s; d
epen
dent
var
iabl
e: c
atas
trop
hic
effe
cts
of h
ealt
h ca
re e
xpen
ditu
re (N
=55
57) (
cont
inue
d)
Exp
lan
ator
y va
riab
les
incl
ud
ed in
th
e an
alys
isC
atas
trop
hic
eff
ects
(1=
yes;
0=
no)
Inst
rum
ente
d=
dia
bet
es m
elli
tus
(1=
yes;
0=
no)
Cat
astr
oph
ic e
ffec
ts(1
=ye
s; n
o=0)
Inst
rum
ente
d c
ard
iova
scu
lar
dis
ease
s(1=
yes;
0=
no)
Cat
astr
oph
ic e
ffec
ts(1
=ye
s; n
o=0)
Inst
rum
ente
d p
rogr
essi
ve d
isea
ses
(1=
yes;
0=
no)
Coe
ffici
ent
SEC
oeffi
cien
tSE
Coe
ffici
ent
SE
Fir
st s
tage
reg
ress
ion
res
ult
s
Shar
e of
sw
eet
food
in t
otal
food
con
sum
ptio
n-0
.001
3*0.
0002
-0.0
012*
0.00
3-
-
Shar
e of
alc
ohol
con
sum
ptio
n in
tot
al fo
od c
onsu
mpt
ion
-0.0
01*
0.00
03-0
.002
*0.
005
--
Num
ber
of c
igar
ette
s co
nsum
ed p
er h
ouse
hold
-0.0
000.
000
-0.0
001
0.00
0 0
.001
*0.
000
Mun
icip
alit
ies
affe
cted
by
uran
ium
-ri
ch b
ombs
0.0
44*
0.01
2-
-
Pre
senc
e of
ref
ugee
s in
hou
seho
ld 0
.055
*0.
020
--
--
Hou
seho
ld s
ize
0.0
23*
0.00
4 0
.473
**0.
006
0.0
20*
0.00
5
Type
of s
ettl
emen
t 0
.014
**0.
008
0.0
020.
014
-0.0
060.
010
Gen
der
of t
he h
ouse
hold
hea
d 0
.000
20.
0009
-0.0
140.
015
-0.0
21*
0.01
1
Nat
iona
lity
of t
he h
ouse
hold
hea
d -0
.009
60.
012
-0.0
050.
018
-0.0
030.
014
Age
of t
he h
ouse
hold
hea
d 0
.003
*0.
002
0.0
09*
0.00
1-0
.027
*0.
002
Edu
cati
onal
leve
l of t
he h
ouse
hold
hea
d -0
.003
0.00
2-0
.010
*0.
003
-0.0
030.
002
Num
ber
of k
ids
youn
ger
than
7 y
ears
-0.3
69*
0.01
0-0
.094
*0.
015
-0.0
25*
0.01
2
Num
ber
of k
ids
olde
r th
an 7
yea
rs-0
.234
*0.
007
-0.0
76*
0.01
0-0
.031
*0.
009
Inco
me
perc
enti
les
-0.0
002
0.00
3-0
.007
0.00
5-0
.007
**0.
004
Con
stan
t-0
.323
0.02
8 0
.093
*0.
044
0.0
040.
032
Hau
sman
tes
tF(
1,55
45) =
3.3
5723
(p =
0.0
670)
F(1,
5545
) = 6
.226
37 (p
= 0
.012
6)F(
1,55
45) =
3.2
0389
(p =
0.0
735)
Sarg
an t
est
chi2
(3) =
4.6
7877
(p =
0.1
969)
chi2
(3) =
1.8
9878
(p =
0.5
937)
-
* p<
0.0
1 **
p<
0.0
5 **
* p<
0.1
0
118
Chapter 5
Regarding progressive diseases, households where the head of the household is a man
and with a lower level of income, have a higher chance to be poor. With regard to the
instrumented variables in the second stage regression, having a household member with
diabetes mellitus, cardiovascular diseases or progressive diseases significantly predict
being poor. However, as indicated in Appendix 1, Table A.3, when we apply ordinary least
square regression without instrumental variables, diabetes mellitus and cardiovascular
diseases are significantly related to pre-payment poverty, while progressive disease is not
significant predictor.
Table 5.3 presents the results of the tree 2SLS models using catastrophic health care
expenditures as the second stage dependent variable and the three indicators of chronic
conditions as first stage dependent variables. Results refer to the whole sample of
households (N = 5,557).
The first stage regressions in table 5.3 indicate that level of sweet food intake as a
share of total food consumption, alcohol consumption and the presence of a refugee in
the household are significant predictors of having a member with diabetes mellitus. Also,
households with a member diagnosed with a cardiovascular disease are more likely to live
in municipalities affected by uranium bombs, with a lower number of children and with
a head of the household with a lower education.
Based on the second stage regression results in table 5.3, instrumented diabetes
mellitus and cardio-vascular diseases are the significant predictors of catastrophic health
care expenditure. The same holds for a lower household income. Contrary to diabetes
mellitus and cardiovascular diseases, instrumented progressive disease is not a significant
predictor of catastrophic health care expenditures. The results regarding catastrophic
health care expenditures and progressive diseases show that households without children,
households from low income groups and households that consume cigarettes have a
higher probability of having one member diagnosed with a progressive disease. However,
when we apply OLS regression without using instrumental variables (see Appendix Table
A.3), all three indicators of chronic diseases are significant predictors of catastrophic
health care expenditure.
5.4 Discussion and conclusions
In this study, we have examined the pre-payment poverty and catastrophic effects of
out-of-pocket patient payments on households with at least one member diagnosed with
a chronic disease. For that purpose, we have applied a 2SLS model using instrumental
variables. The results from the OLS regression without instruments (comparable with the
second stage regression) show lower absolute coefficients for all three chronic diseases,
while results obtained from the IV models show higher values for all coefficients related to
5
The effects of chronic diseases on poverty
119
three chronic diseases. Also, when the IV approach is used, results regarding pre-payment
poverty show that having a household member with diabetes mellitus or cardiovascular
diseases is a significant predictor of poverty in Serbia. On the other side, being diagnosed
with progressive diseases is not a significant predictor of poverty. However, the results
from the OLS regression (without instrumented variables) show that the presence of
any of the three chronic diseases is a significant predictor of pre-payment poverty. For
catastrophic expenditure models, both IV regression and OLS regression consistently
indicate that that having a household member with diabetes mellitus or cardiovascular
diseases is a significant predictor of catastrophic payments but not progressive diseases.
Another diversity that should be mentioned is the negative coefficients of the diseases
indicators in OLS when the outcome variable is pre-payment poverty. This questions
whether the instruments used are the most adequate indicators. But a possible reason
can be also found in the fact that the estimations of the coefficients provided by the IV
models are more accurate (since we captured the endogeneity) than estimation of the
coefficients provided by OLS.
Although, validity tests for all models show that instruments are strong, we found
some differences when comparing the coefficients of corresponding covariates in the IV
models and OLS models. In the models where the pre-payment poverty is used as an
outcome variable, the coefficients of the corresponding covariates in the OLS models and
IV models are more diverse (considerably changing the absolute value and direction)
when compared with models where the outcome variable is catastrophic health care
expenditure. The possible reasons can be found in the nature of the instrumental variables
used, namely being a refugee or alcohol consumption. For example, there might be a
relation between these instrumental variables and poverty. But as we mentioned before,
in our sample we did not find significant correlation between those two variables and pre-
payment poverty. Furthermore, results from the literature for Serbia also show that there
is no relation between a healthy life style and poverty (Djikanovic et al., 2011). Similar
findings are observed regarding refugee status and poverty (UNDP, 2006). Another
reason can be that there is a strong joint relation between the catastrophic payments and
chronic diseases, and a less strong joint relation between poverty and chronic diseases.
5.4.1 Chronic diseases, out-of-pocket patient payments and economic burden The results from the second stage regression when instruments are used, show that all
three chronic diseases can impose an economic burden on households, but some of the
predictors can be overestimated if we do not account for the joint relation between a
chronic disease and poverty (as discussed above). Our results also show that there are some
differences between the three chronic diseases. While diabetes mellitus and cardiovascular
diseases are significant predictors of the catastrophic effects, progressive diseases are not.
120
Chapter 5
This means that households with diabetes and cardiovascular diseases are spending a
significant part of their annual budget on health care. Reasons for the high economic
burden provoked by diabetes mellitus and cardiovascular diseases can be found in the
nature of the disease but also in the organization of the health care system in Serbia.
Diabetes mellitus is known as an expensive disease (Bjegovic, et al., 2007). The treatment
of diabetes mellitus includes polymorph pharmacotherapy and several types of disposable
materials. Although pharmacotherapy and disposable materials (needle, glucose meter,
test strips etc.) should be on “the positive list” and covered by the health insurance in
Serbia, very often physicians in public health institution prescribe brand names that are
not covered by compulsory health insurance (Biorac, Jakovljević, Stefanović, Perović, &
Janković, 2009). The organization of the health services related to treatment of diabetes
mellitus is another obstacle. The lack of counselling services within primary health
care also increases the probability of an economic burden related to diabetes mellitus.
Without receiving proper counselling regarding the behavioral risks factors, patients
are forced to rely on medical treatment applied in primary and secondary health care.
Health services related to diabetes mellitus are organized through primary secondary and
tertiary care. In all of these three levels, official co-payments are charged, sometimes for
the same services at two different levels (results of laboratory analyses from primary care
are usually not used in hospitals and therefore a new laboratory analysis is performed by
the hospital). Additional and not always necessary, out-of-pocket patient payments can
increase the economic burden not only to patients but also to their families. Moreover,
patient diagnosed with diabetes mellitus are partially exempted from some types of
out-of-pocket patient payments (Official Gazette Republic of Serbia, 2009). However,
the types of services where the exemption mechanism should be applied are not clearly
defined (Chapter 4). This means that very often exemption mechanism towards patients
diagnosed with diabetes mellitus is not applied because of complicated administration
regarding the partial exemption.
Previous studies that have estimated the direct and indirect costs for 99 persons
diagnosed with diabetes mellitus in a small city in Central Serbia have shown that
real patient’s costs are 2.28 times higher than the national estimates (Biorac, et al.,
2009). Although those studies do not examine the economic burden from a households’
perspective, they show the same trend as the results in our study. Moreover, previous
epidemiological studies (Bjegovic, et al., 2007; Matejić, Kesić, Marković, & Topić,
2008; Tepavcevic, Matejic, Gazibara, & Pekmezovic, 2011; Dejana Vuković, Bjegović,
& Vuković, 2008) have shown that the majority of the cases diagnosed with diabetes
mellitus are properly registered by the national statistical office, since the diagnosis
includes few variations: Diabetes Mellitus Type I and Diabetes Mellitus type II and
Gestational Diabetes. Since in our analyses we include only officially diagnosed cases,
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accurate registration can also be an explanation for the high catastrophic effects of out-of-
pocket patient payments related to diabetes mellitus.
However, instrumented cardiovascular disease is also a significant predictor
of catastrophic health care expenditure. The reasons can be found in the complex
organization of health care services (both at the primary, secondary and tertiary level).
Also, the network of private health care providers that are related to cardiovascular
diseases is quite spread in Serbia (which is not the case with endocrinologists) (Institute of
Public Health of Serbia, 2013). In this chapter we only address the out-of-pocket patient
payments in public health care services. This implies that the real burden provoked by
both diabetes mellitus and cardiovascular diseases can be even higher for households and
their members.
The model regarding the catastrophic effects of out-of-pocket patient payments for
progressive diseases is not significant. The OLS estimators show that the presence of
a progressive disease is not a significant predictor of catastrophic effects. In Serbia, a
progressive disease can be diagnosed and treated only in public hospitals. The exemption
mechanism is usually applied there. However, previous studies have shown that there
are a high number of premature deaths related to progressive diseases. This implies that
many of patients that are diagnosed with one of progressive diseases never receive proper
treatments (Matejic, Vukovic, Pekmezovic, Kesic, & Markovic, 2011).
5.4.2 Factors associated with the presence of chronic diseasesAlthough it is not the main goal of our study, using an IV approach allows us to assess the
impact of some life-style and environmental factors on the incidence of chronic diseases.
The results from the first stage regressions show that different risk factors are associated
with different chronic diseases. Significant instrumental variables for having diabetes
mellitus are having a refugee in the household. The share of alcohol consumption and
of sweet food in total household consumption is negatively associated with diagnosed
diabetes mellitus. Other eating habits like smoking habits are not significant risk factors
for households that already have at least one member diagnosed with diabetes mellitus.
This may be because households with a member diagnosed with diabetes mellitus have
already changed some of their eating habits in order to adjust to the needs of the ill
member (Basu, et al., 2013). A significant instrumental variable for a cardiovascular
disease is living in municipalities that have been exposed to uranium NATO bombs.
These results can be biased by the fact that the NATO bombing is related to stress
which is one of the risk factors specific for cardiovascular diseases. However, living in
municipalities affected by uranium bombs, is not a significant predictor for a progressive
chronic disease like cancer. One of the reasons is that the effects of the NATO bombing
cannot yet be observed (data were collected seven years after the NATO bombing). On the
other side, a significant predictor of cancer is the consumption of cigarettes. In a nutshell,
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our results have shown that environmental factors play an important role in developing
diabetes mellitus and cardiovascular diseases, while life style variables are more related to
progressive diseases. Those results are consistent with previous epidemiological studies
in Serbia (Bjegovic, et al., 2007; Janković, et al., 2010).
5.4.3 LimitationsThe limitations of this study are related to the data. We use cross-sectional data. The
LSMS data for Serbia are collected for 2002, 2003 and 2007, but they are not panel data.
Therefore, we only use data for 2007 as the most recent data. The complex nature of the
economic burden should include a depletion of assets and coping mechanisms (using
savings, borrowing the money) that households apply during a longer period of time.
Moreover, patients and their households change their consumption and spending over
time. Also, the treatment of chronic diseases is characterized by fluctuation over time
(Russell, 2004). This fluctuation also affects medical spending. Longitudinal data may
provide a better insight into those problems. One possibility to overcome the limitations
of cross-sectional data and the fact that chronic diseases have long-term effects is to
use a catastrophic medical expenditure risk approach (Flores & O’ Donnell, 2013). This
approach is based on an ex-ante perspective and uses the measures of downside risks
rising from unexpected health shocks. Furthermore, contrary to catastrophic health care
expenditure that is based on actual medical expenditure (ex-post measures), this risk
approach is based on the estimation of the risk. In this way, it would also be possible to
estimate the risks of catastrophic health expenditures in cases of new chronic diseases
but also in case of comorbidities (Flores & O’ Donnell, 2013) Another limitation is
related to the use of consumption as an indicator of wealth. Although, we are aware that
other indicators of wealth like income and expenditure can be also used, we perform our
analyses using only consumption. Since Serbia is a middle-income country with a widely
spread informal economy, we consider consumption as the most appropriate indicator
of wealth. The advantages and disadvantages of using consumption as the measurement
of wealth have been discussed in Chapter 2. We also have limitations related to the
instruments. However, our choice of instruments is limited by the data available in
the LSMS data we used. Furthermore, all estimations for instrumental variables are
calculated on a household level. This means that we do not know if the person diagnosed
with certain chronic diseases is engaged in unhealthy life style behavior. However, recent
studies show that life style behavior is similar among the family members living in the
same households (Khader & Alsadi, 2008).
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5.4.4 Policy implications and conclusionsIn 2008, the Serbian government signed the Stabilization and Association agreement
with EU. As a part of this agreement, the first national poverty reduction strategy has
been published. The document is addressing the possible policy implications for poverty
reduction in Serbia. The policy implications are related to health care and poverty
reduction mostly addressing problems with long-term care, preventive programs and
exemption mechanisms for vulnerable groups (unemployed, minorities etc.) (Vukovic &
Perisic, 2011). However, our results show that people diagnosed with one of the three
leading chronic diseases are also a vulnerable group. As we described above, out-of-
pocket patient payments related to chronic diseases can provoke a high economic burden
for households. Although some patient with chronic diseases should be exempted from
official co-payments, exemption mechanisms usually does not work in practice. As we
outlined in Chapter 4, there is a complicated administration procedure to obtain the
exempted status. Since people diagnosed with chronic diseases usually use health care
more often, policy makers should facilitate administrative procedures regarding the
exemption mechanism.
As part of the poverty reduction strategy, the Serbian Ministry of Health has started
two prevention programs: one is related to alcohol consumption and other is designed to
decrease the level of smoking among chronically sick. Although preventive programs can
decrease the burden of disease in the future, they do not protect the households with a
sick member from the economic burden of out-of-pocket patient payments. For a proper
strategy of poverty reduction, policy makers in Serbia should pay attention not only to
the prevention of chronic diseases but also to the protection of those who are diagnosed
with a chronic disease.
The possible reason for the economic burden provoked by chronic diseases can also be
the organization of the health care system. There is no clear structure in the treatment
of chronic diseases. Patients diagnosed with a chronic disease are treated in primary,
secondary and tertiary care. Usually there is no direct communication between the
different levels, and same diagnostic procedures are repeated several times on different
levels, as mentioned earlier. Each procedure has to be paid for separately, which means
that repeating procedures exposes the patient and their household to even higher burden
(Gavrilovic & Trmcic, 2013). The existence of informal patient payments makes their
burden even higher.
Another reason for the high economic burden is the long waiting lists in public
health care facilities. Patients diagnosed with a chronic disease require more frequent
diagnostic procedure and more frequent physician’s check-ups. Since waiting lists are
long, patients are usually referred to an outpatient private clinic (Chapter 6). However,
the costs of those private clinics are not covered by the official insurance system and the
private clinics do not apply the exemption mechanism. In our study, we estimate the
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economic burden provoked by public health services only. However, the total economic
burden that households with a member diagnosed with a chronic disease experience can
be much higher. The inclusion of private health institution in the compulsory insurance
system may therefore be considered.
CHAPTER 6
Shortcomings of maternity care in Serbia
Published as:
Arsenijevic, J., Pavlova, M., & Groot, W. (2014). Shortcomings of Maternity Care in Serbia. Birth, 41(1), 14-25.
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Abstract:
Background: This chapter examines process-related quality and access indicators and
patient payments for maternity care in Serbia.
Methods: We apply a method of triangulation using data collected through three sources:
online questionnaires filled in by mothers who delivered in one of the maternity wards in
Serbia in the period 2000-2008, research publications and official guidelines.
Results: Our results show that many women who delivered in a maternity wards in Serbia
indicate problems with the treatment they receive. The existence of informal patient
payments as well as so called “special connections” make the position of Serbian women
in maternity wards vulnerable, especially when they have neither connections nor the
ability to pay. Problems in the communication with medical staff (obstetricians, other
physicians, midwifes and nurses) during the process of birth are also frequently reported.
Conclusions: Actions should be taken to improve bedside manners of medical staff. In
addition, the government should consider the involvement of private practitioners paid
by the national insurance fund to create competition and decrease the need for informal
payments and “connections.”
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6.1 Introduction
Previous research in the field of maternity care in CEE countries has focused on macro-
level access indicators (i.e. indicators at a system level). These macro indicators do not
capture the quality of the services provided but only measure clinical outcomes (e.g.
maternal mortality ratio, rate of cesarean sections) and service provision in general (e.g.
presence of a skilled attendant at birth, availability of emergency obstetric care). Another
problem with the system-level indicators in CEE is the reliability of the data, such as gaps
in the registration and incorrect or false registrations (UNFPA, 2009). Underreporting
of maternal deaths, unattended home births and induced abortions are suspected to bias
statistics in some of these countries. For example, many cases of maternal mortality are
counted as death caused by other reasons (Stamenkovic, 2011). Aside from the clinical
outcomes, the process indicators of maternity care (quality of care, patient payments for
maternity care, accessibility and policy regulation) are rarely examined, and especially not
from the perspective of the users of these services (Stepurko et al., 2013). For example,
few studies that describe the system of maternity care in CEE countries have emphasize
problems with patient payments in maternity wards (Danilovich, 2010; Stepurko et
al., 2013; Szende & Culyer, 2006; Vian et al., 2006). During the communist period,
health care services in these countries, including maternity care, were provided for free.
During the transition period however, in some CEE countries, official co-payments and
other formal patient charges, regulated by official policy arrangements were introduced.
Quasi-formal payments (not strictly regulated by the government) also appeared. At the
same time, informal patient payments (cash and in-kind gifts) become widely spread
(Danilovich, 2010; Stepurko et al., 2013). Informal payments are payments that are
either initiated by the patient/patient’s relatives to obtain better quality of care or quicker
access, or requested by medical staff (physicians, nurses) to provide needed care. In some
CEE countries, informal payments exist both on top of and independently from formal
patient charges.
The objective of this chapter is to examine process-related indicators regarding the
accessibility of maternity care, the quality of care received, patient payments and policy
regulations for maternity care in Serbia (one of CEE countries) (Ronsmans, 2001). We
examine those indicators using data from different sources: data collected by an online
questionnaire among women who used maternity care, as well as data from published
studies and official guidelines. We analyze the data combining qualitative and quantitative
techniques. Using three different sources of information with possible counteracting
biases to examine the same phenomenon, we apply triangulation as a research strategy
(Denzin, 1978, Tashakkori & Teddlie, 2003). This helps us to overcome biases in our
analysis and to provide a comprehensive picture of maternity care in Serbia.
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6.2 Background
As we outlined in Chapter 1, health care reforms in Serbia started in 2002, with the main
focus on the improvement of the technical infrastructure and financial sustainability. As
in some other CEE countries, official co-payments were introduced in order to strengthen
the efficiency and sustainability of the health care system. Since the country had passed
through a difficult civil war and the rates of new born babies had been decreasing
since 1991, the government wanted to increase the fertility rate by exempting child
delivery from the official co-payments. This exemption policy was combined with a set
of regulations that allow fully paid parental leave of up to 12 months for mothers. The
regulations include some other benefits like free public transport for pregnant women
and new-born children (Chapet 4).
Maternity care in Serbia is an integrated part of the Serbian health care system
inherited from the former Yugoslavia. Similar to the other CEE countries, the system
is centralized and administratively regulated by the Ministry of Health. Primary and
secondary health care units are involved in the provision of maternity care. Prenatal
care is organized through primary health care centers so called “domovi zdravlja”. The
services provided at these centers include regular follow-ups like laboratory analyses,
ultrasounds examination but also administrative measures, like confirming pregnancy,
determining pregnancy leave and the referral for the maternity ward. Some anecdotal
evidence (Blic, 2011) shows that prenatal care is mostly provided in the private sector
because of increased demand and the lack of supply in the public facilities. When
delivery is approaching, however, pregnant women are obliged to go to the public sector
maternity ward. According to the Serbian law, it is obligatory to deliver in one of the
state owned maternity wards. Currently, 76 maternity wards are operating in different
cities in Serbia (Institute of Mother and Child Health Care of Serbia, 2009). The network
of maternity wards is equally spread in the different geographical areas, which is an
important advantage. Every pregnant woman who has compulsory health care insurance
and a referral from a primary health care physician should have access to a maternity
ward.
During the civil war (1991-2000), maternity wards like all others hospitals in Serbia
were lacking basic supplies (oxygen, pharmaceuticals, blood for transfusion) and medical
staff received their salaries with delay (Chapter 1). However, in 2000, after the political
changes, the WHO and the World Bank provided humanitarian aid to Serbian hospitals
with special attention to the maternity wards (Jeffery, 2003). It was expected that the
government and humanitarian aid would improve the situation in the maternity wards.
Previous literature regarding the organization of maternity care in Serbia is limited.
However, the few existing studies (Andrejic, 2010, Shiffmana et al., 2002) show some
patterns of behavior within the current system of maternity care. The care process is still
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operating within a hierarchical model with the obstetrician at the top of the pyramid
while the interests of users are mostly neglected. Obstetricians usually have obtained a
master degree in medicine accompanied with five years of practical education so called
“specijalizacija”. Since the midwives are educated in vocational school, their position
within the hierarchical model is lower compared to obstetricians and other physicians
(Stambolovic, 1996). During childbirth, the role of the midwife is undermined
(Stambolovic, 1996). The technocratic model of childbirth is followed. The delivery is
guided by the obstetrician using an active approach, which assumes that all women,
even those identified as “low-risk”, need intensive monitoring in the hospital, often need
medication and often or always need episiotomy and artificial delivery (vacuum, forceps
etc.) (Kloosterman &Thiery, 1977). In this way, the Serbian system resembles systems in
some high-income countries where the delivery is also not perceived as a physiological
process, but rather as a medical event (Chalmers, 2012).
Regulations within the Serbian system are not transparent and change very often. The
care process is “closed”, i.e. the presence of the family is not allowed and the presence of
the father is “discouraged”. Consequently, future mothers become even more dependent
on the health care providers. The obligation to deliver in a hospital (it is not allowed to
deliver at home) in an isolated environment is defended by obstetricians using arguments
like it is not hygienic to deliver at home and there are no facilities to allow the father to be
present in the hospital. Health care users keep silent about this - there is an “unwritten”
rule that experiences from maternity wards are not spoken about in public (Andrejic,
2010).
But, in 2008, in a very short period of time (2-3 weeks), several babies and/or their
mothers died in maternity wards in Belgrade. Their families were complaining about
inadequate care and corruption in maternity wards. Inspired by this event, Branka
Stamenkovic, an ordinary women who occasionally wrote a blog on a popular website,
described in one of her blogs the experiences of her own delivery (Stamenkovic, 2011).
Many women commented on her blog sharing similar experiences. Women, involved in
the discussion in the blog, decided to raise this in public and the unwritten rule of silence
was broken. The Ministry of Health put out a very strong announcement that they wanted
to explore the information published on the blog. However, Branka Stamenkovic founded
a civil initiative “Mother Courage” and invited the Minister of Health and a few highly
regarded obstetricians to take part in a public discussion. During the public debate on
the national TV channel, Branka Stamenkovic invited all women who delivered in the
last ten years to visit the website of “Mother Courage” and to fill in the questionnaire
about their own experiences during delivery.
The voluntaries of “Mother Courage” checked the identity of the women (e-mail
address, personal data, day of delivery etc.) that were responding to the questionnaire.
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The existence of an anonymous on-line questionnaire has been the first public civil
action to improve the conditions in the Serbian maternity wards. The civil initiative
was supported by many public figures (actors, politicians, etc.) but also by some
well-known obstetricians. After 657 questionnaires were collected on the website
of “Mother Courage”, they named three main problems in Serbian maternity care:
poor communication, corruption and outdated medical protocols that are still in use
(Stamenkovic, 2011). With the aim to solve the shortcomings, the Ministry of Health
translated in Serbian the WHO guidelines for communication: “General principles of
communication with pregnant women and their families” and “General principles of
treatment and care in the maternity ward” (Institute of Mother and Child Health Care
of Serbia, 2009). The aim was to improve the communication between medical staff
and pregnant women, and to change the dominant active approach to childbirth and
replace it with a natural physiological delivery approach (Ministry of Health, 2010).
Although, the Ministry of Health has promised to implement the WHO communication
guidelines, these guidelines are not fully implemented in practice but are only adjusted
to fit the current organization of maternity care in Serbia (Ministry of Health, 2010). The
maternity ward in Pancevo was an exceptional case in that it actually implemented some
of these guidelines (Andrejic, 2009). In addition to the unsuccessful implementation of
the guidelines, problems caused by corruption exist (Andrejic, 2009). Among others,
this refers to informal patient payments. Also, the absence of patient-centered care still
continues in the maternity wards in Serbia.
The “Mother Courage” initiative ended in 2011 as it was not possible to come to an
agreement among the main stakeholders about the problems in the maternity wards.
The Ministry of Health has published the results of their own research (Banjac et al.,
2010) that paint an idealistic picture of maternity wards in Serbia. However, anecdotal
evidence confirms the continued existence of corruption, outdated protocols and poor
communication in maternity wards in Serbia. In this chapter, we use the data collected
by the Mother Courage initiative to describe and analyze the process related indicators
in maternity wards in Serbia. We compare the results to data from other sources, namely
published studies and guidelines.
6.3 Methods
In order to study the process-related indicators of accessibility, quality, payments and
regulations in maternity care in Serbia, we apply a mixed-method approach, more
precisely a fully mixed sequential approach (Leech & Onwuegbuzie, 2009) using both
qualitative and quantitative data. We use different sources of information on indicators in
Serbian maternity care: the data collected through the online portal of Mother Courage,
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a literature review of published studies as well as official documents and guidelines.
Thus, by combining data from different sources, we apply the method of triangulation
as a research strategy. Triangulation combines different data and methods to counteract
possible biases in the data sources. Also, triangulation allows us to observe variations
within the same data sources but also among different data sources (Happ et al., 2006;
Jick, 1979), which helps us to verify our findings. The different sources of data and the
analytical techniques applied to these data (i.e. our research phases), are subsequently
described.
6.3.1 Online questionnaireThe questionnaire is designed by the public civil initiative “Mother Courage”. The
questionnaire is posted on an online portal (website) and respondents can register through
that portal to fill in the questionnaire but remain anonymous to the wider public. The user
verification is done by “Mother Courage” based on personal data provided by respondents
and matching them with hospital records.
The questionnaire consists of 22 open-ended questions. Since the questions are open-
ended, the data are qualitative. The sample of respondents is biased: only women who
are able to use internet and are motivated to answer, are included in the sample (see also
background section). In total, 657 women participated till 2011 (when our analysis was
carried out). Some of these respondents (415 in total) did not follow the format of the
questionnaire and instead reported their experience as a story. Data in this format are
difficult to compare to data collected by the questionnaires and we have excluded those
cases from the analyses. Also, some of the respondents (144 in total) were reporting
experiences from the period 1991-2000 (the war period) and we have also excluded them.
We use data collected between 2000 (the year when main political changes have occurred
in Serbia) and 2008 (the year when the last interview that followed the format of the
questionnaire was provided). Only few questionnaires (3 in total) were filled in by fathers,
and moreover, two of them were not present at the moment of childbirth. Therefore, we
present data of only one father who was present during the delivery. Finally, 95 semi-
structured questionnaires filled in by mothers and one questionnaire filled in by father,
are included in the analyses.
We use framework analysis (Srivastava, 2009) to analyze the qualitative data collected
through the questionnaire. Framework analysis is a technique used to analyze qualitative
data for social policy research. A framework analysis does not require collection of data
during certain time-scale, but allow researchers to quantify qualitative data (Richie &
Spencer, 2002). In framework analyses, five steps are applied: 1) familiarization with
data; 2) identification of a thematic framework, 3) indexing the data following the
framework; 4) charting the data in accordance with the identified theme; and 5) mapping
and interpretation (Ritchie & Spencer, 1994).
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Since our study is focused on process-related indicators in maternity care, we identify
several themes: accessibility to maternity care, quality of care, patient payments, and
policy regulations. We also add the themes: innovations in maternity care as well as health
and system indicators at a macro level applied by the UN and the WHO (Hulton et al.,
2000; Ronsmans, 2001). For further analysis, we operationalize accessibility through
three dimensions: spatial, temporal and psychological using the approach of Berchi &
Achart (1980). The spatial dimension refers to geographical access to health care facilities.
The temporal dimension refers to waiting time, while the psychological dimension
considers social distance between providers and health care users (communication skills
of the staff). Quality of care is operationalized as clinical quality (obstetric care and
procedures, quality of equipment and level of physician skills) and social quality of care
(e.g. facility maintenance) (Berchi & Achart, 1980). We operationalize patient payments
through several dimensions: type of payment, receiver of payment, frequency of payment,
magnitude of payment, purpose of payment and attitude towards payments (Stepurko
et al., 2013). Policy regulations are operationalized as regulations regarding payments,
equity, and governmental/hospital protocols (Hulton et al., 2000). Innovations in
maternity care in Serbia are operationalized by the presence of the partner during delivery
and innovations regarding immediate breastfeeding and rooming-in (the practice of
keeping a newborn infant in a crib near the mother’s bed instead of in a nursery during the
hospital stay ) (Stepurko et al., 2013). Health system indicators are represented through
maternal and infant mortality, rate of caesarean sections and rate of emergency delivery,
as well as the presence of a skilled care provider (accredited health care professional). We
present the results along these themes.
Furthermore, we quantify the responses to the questions to analyze the information
collected by the open-ended questions quantitatively. A detailed description of the
questions and quantified variables are presented in Table 6 1. The quantitative data are
analyzed using descriptive statistics (statistic package SPSS 17.00) and are presented
through the same set of themes identified in the qualitative analysis. Data on social
demographic characteristics are not collected in the questionnaires, which precludes
further analysis on variations across population groups.
6.3.2 Literature reviewIn our literature search, we systematically identify publications on Serbian maternity
care (both in English and Serbian) and screen them for their relevance. We use the
following key words: maternity care, child delivery, child birth and obstetric care in
combination with the words Serbia, empirical study, access, equity, quality, reforms. All
possible combinations of keywords are used. The following bases are searched: PubMed,
Picarta, EBSCO (which also includes Psychinfo, Psychoarticles, Socindex and Medline),
Informahealth, Cochrane and Google Scholar. We consider a publication to be relevant
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if it examines the characteristics of the Serbian maternity care system particularly when
it focuses on the process-related indicators that we have mentioned above. We divide
the publications selected for the review in two groups. The first group includes articles
that are written by independent researchers and we label them as “non-government”
publications. The second group of articles includes articles that are written by researchers
appointed to the Serbian public sector as a part of their regular working tasks. We label
them as “pro-government” publications. The data from the literature review are analyzed
using the framework analysis described above, i.e. the same sets of themes and indicators
are applied.
6.3.3 Official and hospitals guidelines and institutions’ websitesWe also review data from official guidelines that report on process-related indicators
(rate of delivery with complications like hemorrhaging, rate of caesarean section, but also
maternal mortality ratio). Also, we search the websites of international organizations (e.g.
UN, WHO, WB), as well as those of Serbian hospitals and Ministry of Health to identify
and compare the values of health indicators as well as other indicators to those identified
in research publications. Besides the official guidelines, we include hospital guidelines
as well. Each maternity ward in Serbia has its own official guideline and few of them are
available online. We analyze the data using the framework analysis and the set of themes
and indicators described above.
The results from the three research phases are presented descriptively as well as in the
form of tables. The focus is on the comparison across of the different sources of data as
well as on the identification of gaps in research and policy.
6.4 Results
We present our results per theme (described in the methods section) referring to all
source of data: questionnaires, literature reviews and review of guidelines. Qualitative
data obtained from the questionnaires, are summarized and supported by quotations (see
Box 6.1). Quantitative data are presented in Table 6.1 and Table 6.2. The majority of
the respondents in our sample (68.5%) describe childbirths that occurred in one of the
maternity wards in Belgrade. Only two questionnaires are from the south of Serbia, while
the questionnaires from Vojvodina (15.8%) and Central Serbia (14.0%) are almost equally
represented. For a comparison across different sources of data that we use, qualitative
findings from all sources are presented in Table 6.3.
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6.4.1 Accessibility Results from all three sources (qualitative and quantitative data from online questionnaire,
both groups of publications -“non-government” and “pro-government” and the official
guidelines) show that spatial access to maternity care in Serbia is adequate. In particular,
official guidelines in Serbia indicate that the networks of maternity wards are equally
spread over different geographical areas (Institute of Mother and Child Health Care
of Serbia, 2009). The analyses of the qualitative data collected through the on-line
questionnaire show that even small cities (like Gornji Milanovac, Vrsac, Pancevo, Cuprija)
have their own maternity wards.
The main problem reported in the qualitative data regarding spatial access to hospital
care is related to “outdated referral”. As the women explain, according to Serbian
regulations, every woman has to have a referral from the state provider that is not older
than 3 weeks. Since the date of a childbirth cannot be exactly predicted, 12 (12.63%) of
women from our sample report that they faced problems because of an “outdated referral”.
Before these women could be officially admitted, they had to wait at the reception for a
“new referral” from the primary care physician to be brought to them by their relatives.
Hospital guidelines also confirm that the requirement for the pregnant women to have a
referral for hospital admission complicates access to care in some cases.
Regarding temporal access, the qualitative data from the questionnaires show
problems with the waiting time for certain procedures. This includes procedure such as
epidural analgesia, additional ultrasound etc. The main obstacle regarding psychological
accessibility relates to communication problems between users and providers. As many
as 65 (68.4%) of the women in our sample describe medical staff as very distant and
non-approachable (see Box 6.1, Quotation 1).
6
Shortcomings of maternity care in Serbia
135
Box 6.1: Selected quotations from the on-line interviews
Quotation 1:
“I had very good connections, so everything went well with my delivery. But
anyway, the way they communicated with me made me to feel like I am in prison.”
Quotation 2:
“They did not bring my baby for regular breastfeeding. I’ve asked them-what
is wrong? They replied –he is sick, ask the pediatrician, on the floor above us. I
went upstairs with a urinary catheter and an infusion bottle in my hands. I saw
a woman in a white coat and I asked her where I can find the pediatrician, she
replied: you have just found one, and are you lucky. Do you like how I look?
Probably you have never seen a pediatrician in your village that is why you all
come and look for me!”
Quotation 3:
“I did not have any influence on the course of the delivery neither doctor has ever
informed me what is going to happen.”
Quotation 4:
“This hospital was terribly dirty-they had not painted the walls since the hospital
was opened in 1800. Everywhere you could see a lot of rats-they were the only
ones that could eat the hospital food. I’ve seen much better conditions in the
stables in our village-at least stables have heating and warm water.”
Quotation 5:
“The worst thing after delivery is that you cannot have any visitors. In the state a
woman is after delivery it is most important to have the support from your family.
I really had the impression that they do not allow visits just because they want to
punish us – I could not find any rational reason.”
Quotation 6:
“Yeah, visits are allowed neither during nor after delivery because of hygienic
reasons, of course if you are the cousin of the nurse on night shift-then anyone
without infection can come and visit you.”
136
Chapter 6
Quotation 7:
“I was a witness- I was sharing a room with her. She was very young, 18. She could
not deliver naturally, they said to her you need a Caesarian but you have to pay.
Her husband is a farmer, they do not have money. So he had to sell the cow (one of
two) and to give the money to the doctor. After that they put her on the program
for a Caesarian section.”
Quotation 8:
“I did not give money to anyone that was the main problem.”
Quotation 9:
“There is no necessary Caesarian, there is only paid one!”
Quotation 10:
“I was placed in a so-called baby friendly ward-there is nothing friendly
about it! The nurse would come only during the night at the same
time to wake up us and our sleeping babies, yelling on us what kind of
mothers you are-you are sleeping and I am supposed to feed your babies.”
Several patterns of communication failure can be identified. For example, the
quantification of the data shows that 51 (53.7%) of the women in the sample report
derogative communication. These include addressing pregnant women by inappropriate
cynical or derogative nicknames (e.g. that the women is lazy or fat), shouting at patients,
and showing no respect towards patients (see Table 6.1). Women also describe the
incapability of staff to interpret women’s behavior and to address their needs (see Box 6.1,
Quotation 2). For example, very often, future mothers describe that they did not receive
any answer to the questions they asked (see Box 6.1, Quotation 3). In one interview
provided by a father, he describes that the communication with health professionals was
adequate, but that they have communicated mostly with him, not with his wife. He was
not involved in the decision making process, but he was informed by the health care
providers what is going to happen.
Also, he stated that he was the main support to his wife and that this was making him
to feel that he is an active part of the process.
Data from “non-government” publications are consistent with data obtained from
the questionnaires regarding the problems with temporal and psychological access to
maternity care (Andrejic, 2010; Janevic et al., 2011; Shiffmana et al., 2002). In contrast
to this, “pro-government “publications (Banjac et al., 2010) do not report problems with
temporal and psychological access (see Table 6.3).
6
Shortcomings of maternity care in Serbia
137
6.4.2 Quality of careRegarding clinical quality, the data from the on-line questionnaires suggest problems
with the obligatory procedures. For example, admission procedures, which also involve
obligatory shaving and enema, are described by the majority of interviewed women (70.4%
of the sample) as very bad and humiliating (see Box 6.1, Quotation 6). Many women in
our sample (72.6%, see Table 6.1) experienced inconveniences, felt humiliated and were
treated with low respect during the medical procedures. As many as 19 (20.0%) of the
women in our sample reported that they had manual vaginal examination in a situation
that they perceived as inconvenient. For example, when a group of students visited the
maternity ward, manual vaginal examinations were performed for instructional purposes.
Half of these women were not asked for permission (10 out of 20). Some of them were not
informed about what was going to happen (16 of 20).
Medical equipment is described as not working or not properly used by 11 (11.57%)
women. For example, beds that can be adjusted to sitting position were not connected
with the electrical supplier so they did not work, or every woman was connected to a
ctg (machine that measure the heart rates of the baby) during the birthing process but
the needle of the ctg did not work. However, problems with equipment are more often
reported during the period 2000-2005, compared with the period 2005-2008.
Problems with the accommodation (social quality) are also often reported (62 women,
i.e. 65.7% of the sample). For example, women report problems due to a lack of hot
water, rooms with insects, lack of clean linen in the room, non-cleaned toilets and the
very poor quality of the food served (see Box 6.1, Quotation 4). Problems with room are
most often described (55 women, 57.89%) than problems with bathroom (41 women,
43.15%) and problems with food (10 women, 10.5%). Contrary, in one interview filled
in by father, he emphasizes that because of his presence, his wife was in a single patient
room that was very well equipped. She did not need to share the room or bathroom with
other women.
Despite these quality problems, the level of physician skills is generally rated as good
by the mothers interviewed. This finding is confirmed by the quantitative results where
39 women (41.1% of the sample) perceive the physicians’ skills as adequate (see Table
6.1). However, the physicians’ motivation is frequently questioned by the women in our
sample.
Qualitative data from the questionnaires also show that quality improvements in
maternity services leave much to be desired. Among others, this relates to the visits
of family members that are not officially allowed after delivery. Women emphases that
without family members, they felt alone, sad and isolated after the delivery (see Box 6.1,
Quotation 5). Some hospitals allow visits - usually only one hour during the afternoon. In
our sample, women report that informal connections play an important role to be allowed
visitors (see Box 6.1, Quotation 6).
138
Chapter 6
The other sources of data that we used confirm the above findings. Regarding the
clinical quality of maternity care, qualitative data from “non-government “publications
and official guidelines (Andrejic, 2010; Janevic et al.,2011; Shiffmana et al., 2002)
are consistent with results obtained from the questionnaire. The “pro-government”
publications do not examine the quality of care.
6.4.3 Patient paymentsWomen in our sample frequently report that they have to make quasi-formal payments
and informal payments for maternity services. Quasi-formal payments are charged for so
called standard services (such as the presence of skilled persons during the delivery, for
the delivery itself, for any type of analgesia etc.) that should be provided free of charge.
Overall, quasi-formal hospital payments are most often reported for epidural analgesia.
The reported amount for an epidural varies from hospital to hospital and is about
100-200 euro. This amount has to be paid in advance by the pregnant women. In case
the epidural is not used (e.g. in case of a caesarean section), the patient does not get any
money back, as stated by some respondents in our sample.
In addition to the official charges for standard services, women also report informal
patient payments. The existence of informal patient payments is reported by all three
sources. However, their magnitude varies from 2% in the “pro-government” publications
to 22.1% in the online questionnaires (Banjac et al., 2010; Janevic et al., 2011). Data
reported in “non-government” publications show frequency similar to the data reported
in the online questionnaires (10-14% of the maternity care users). Data reported in “pro-
government” publications are related to 2009, while data related to “non-government”
publications are for the period 2000-2010, and data from questionnaires are related to
the period 2000-2008. Only women, who have reported informal patient payments are
asked whom they bribed. As reported by our respondents, receivers of informal patient
payments are obstetrician, anesthesiologists, and midwives-nurses.
6
Shortcomings of maternity care in Serbia
139
Tab
le 6
.1: Q
uest
ions
and
res
pons
e re
late
d to
pro
cess
-rel
ated
indi
cato
rs (N
=95
)
Yes
No
Mis
sing
val
ues
Did
you
hav
e an
y in
conv
enie
nce
duri
ng y
our
stay
in h
ospi
tal?
69(7
2.6%
)26
(27.
4%)
none
Nam
eN
ickn
ame
Mis
sing
val
ues
How
did
the
y ca
ll y
ou in
the
hos
pita
l? B
y yo
ur n
ame
and
surn
ame
or b
y de
roga
tive
nic
knam
es?
44(4
6.3%
)51
(53.
7%)
none
Yes
No
Mis
sing
val
ues
Did
you
nee
d to
ask
for
perm
issi
on t
o se
e yo
ur c
hild
or
coul
d yo
u do
tha
t at
any
tim
e?65
(68.
4%)
28 (2
9.5%
)2(
2.1%
)
Yes
No
Mis
sing
val
ues
Did
you
hav
e an
y pr
oble
ms
wit
h br
east
feed
ing?
56(5
8.9)
%34
(35.
8%)
5(5.
3%)
good
bad
Mis
sing
val
ues
How
wou
ld y
ou r
ate
the
prof
essi
onal
kno
wle
dge
of o
bste
tric
ians
?39
(41.
0%)
48(5
0.5%
)8(
8.4%
)
Yes
No
Mis
sing
val
ues
Hav
e yo
u ev
er d
oubt
the
val
idit
y of
cur
rent
med
ical
pro
toco
ls a
nd r
ules
in d
eliv
ery
unit
s?
76(8
0.0%
)18
(18.
9%)
1(1.
1%)
Yes
No
Mis
sing
val
ues
Did
you
nee
d to
bri
be s
omeo
ne?
21 (2
2.1)
%69
(72.
7%)
5(5.
3%)
Yes
N
o M
issi
ng v
alue
s
Did
you
use
con
nect
ions
? 26
(27.
4%)
64(4
5.2%
)5(
5.3%
)
Vag
inal
C
esar
ean
Mis
sing
val
ues
Wha
t w
as t
he t
ype
of c
hild
birt
h-va
gina
l or
Ces
area
n?56
(58.
9%)
36(3
7.9%
)3(
3.2%
)
Fem
ale
Mal
eM
issi
ng v
alue
s
Wha
t w
as t
he g
ende
r of
the
obs
tetr
icia
n w
ho w
as a
ssis
ting
the
bir
th?
27(2
8.4%
)35
(36.
8%)
33(3
4.7%
)
Yes
No
Mis
sing
val
ues
Did
you
hav
e an
y pr
oble
ms
wit
h eq
uipm
ent
62(6
5.7%
)33
(34.
3)%
none
140
Chapter 6T
able
6.2
: Inf
orm
al p
atie
nt p
aym
ents
and
som
e qu
alit
y of
car
e in
dica
tors
a
Rep
orte
d b
rib
es
Rep
orte
d c
onn
ecti
ons
Tot
al s
amp
le
No
Yes
Mis
sin
gN
oY
esM
issi
ng
Per
ceiv
ed le
vel o
f ob
stet
rici
an’s
sk
ills
:
Bad
M
oder
ate
G
ood
M
issi
ng v
alue
s
21(2
2.1%
)
14(1
4.7%
)
33(3
4.1%
)
1(1.
1%)
9(9.
5%)
7(7.
4%)
5(5.
3%)
none
1(1.
1%)
3(3.
2%)
1(1.
1%)
none
26(2
7.4%
)
15(1
5.8%
)
22(2
3.2%
)
1(1.
1%)
4(4.
2%)
6(6.
3%)
16(1
6.8%
)
none
1(1.
1%)
3(3.
2%)
1(1.
1%)
none
31(3
2.6%
)
24(2
5.3%
)
39(4
1.1%
)
1(1.
1%)
Ob
stet
rici
an’s
gen
der
:
Mal
e
Fe
mal
e
M
issi
ng v
alue
s
27(2
8.4%
)
17(1
8.9%
)
25(2
6.3%
)
7(7.
4%)
9(9.
5%)
5(5.
3%)
1(1.
1%)
1(1.
1%)
3(3.
2%)
19(2
0.0%
)
23(2
4.2%
)
22(2
3.2%
)
15(1
5.8%
)
3(3.
2%)
8(8.
4%)
1(1.
1%)
1(1.
1%)
3(3.
2%)
35(3
6.8%
)
27(2
8.4%
)
33(3
4.7%
)
Typ
e of
ch
ild
bir
th:
V
agin
al
C
esar
ean
M
issi
ng v
alue
s
38(4
0.0%
)
30(3
1.6%
)
1(1.
1%)
14(1
4.7%
)
6(6.
3%)
1(1.
1%)
4(4.
2%)
none
1(1
.1%
)
43(4
5.3%
)
19(2
0.0%
)
2(2.
2%)
9(9.
5%)
17(1
7.9%
)
none
4(4.
2%)
none
1(1.
1%)
56(5
8.9%
)
36(3
7.9%
)
3(3.
2%)
Per
cep
tion
of
hos
pit
al p
roto
cols
:
Bad
G
ood
M
issi
ng v
alue
s
51(5
3.7%
)
17(1
7.9%
)
1(1.
1%)
21(2
2.1%
)
none
none
4(4.
2%)
1(1.
1%)
none
52(5
4.7%
)
11(1
1.6%
)
1(1.
1%)
20(2
1.1%
)
6(6.
3%)
none
4(4.
2%)
1(1.
1%)
none
76(8
0.0%
)
18(1
8.9%
)
1(1.
1%)
Exp
erie
nce
of
inco
nve
nie
nce
:
No
Y
es
33(3
4.7%
)
46(4
8.4%
)
2(2.
1%)
19(2
0.0%
)
1(1.
1%)
4(4.
2%)
13(1
3.7%
)
51(5
3.7%
)
12(1
2.6%
)
14(1
4.7%
)
1(1.
1%)
4(4.
2%)
26(2
7.4%
)
69(7
2.6%
)
Tot
al s
amp
le
69(7
2.6%
)21
(22.
1%)
5(5.
3%)
64(6
7.4%
)26
(27.
4%)
5(5.
3%)
95(1
00%
)a P
erce
ntag
es in
dica
te %
of t
he to
tal s
ampl
e (N
= 9
5).
6
Shortcomings of maternity care in Serbia
141
The most frequently bribed health providers according to our sample, are obstetricians
(12 women report that they have bribed them, while only 4 women report that they have
bribed a midwife/nurse). Women, who report informal payments, describe that they gave
money to the obstetrician to secure his/her presence during the delivery and to secure
better quality of care. Additionally, to secure the application of epidural analgesia in
time, money is also given to the anesthesiologist (thus, in addition to the quasi-formal
payments described above). The highest amount of informal payment that is reported in
our sample is 500 euro. Women who have reported informal patient payments still report
problems with quality of care. For example, 21 women (22.1% of those who bribed
someone) report bad hospital protocols, and 19 women (20.0% of the sample) report that
they have experienced inconveniences during their stay in the hospital. Furthermore,
they also perceive hospital protocols as bad (22.1%) and only 5.3% of them perceive
obstetrician skills as good.
Asked if they have bribed someone, women that have filled in the online questionnaire
answered with yes or no, but some of them answered with “I had connections”. In our
sample, 26 women (27.4%) reported that they had connections. They state that they
could avoid informal payments by using “personal connections”, e.g. friends, colleagues
or relatives, who worked in hospitals. Thus, “personal connections” helped them to assure
special treatment and adequate care. They consider those “connections” as a means to
secure help. Having “connections” means the presence of someone whom they can trust.
The number of respondents who reported “personal connections” is increasing during
the period 2005-2008, while at the same time, number of respondents who reported
informal patient payments is decreasing. Moreover, women with “connections” report
fewer inconvenience (14 women or 14.7%) than those who have paid informally (19
women or 20.0%) (See Box 6. 1, Quotation 7-9). Attitudes regarding informal patient
payments are also examined through the qualitative data collected in the questionnaire.
Women, who bribed physicians, generally do not approve of informal patient payments,
but they state that they paid informally for the safety of their child. Among women,
who did not bribe anyone and did not have “connections”, 14 (14.73%) state that they
regretted this fact and they would do so next time.
“Non-government” publications also confirm the existence of quasi-formal payments
(Andrejic, 2010). The presence of quasi-formal payments is caused by the discrepancy
between official and hospital guidelines. Although the former claim that maternity care
is free-of-charge, the latter show the actual charges by the hospitals.
6.4.4 Policy regulations and innovation in maternity careWe observe a mismatch between official guidelines and hospital regulations. In
particular, hospital protocols define obligatory procedures that are inconsistent with
official government protocols such as obligatory shaving and enema. Also, visits of family
142
Chapter 6
members are usually not allowed by hospital regulations, while the official protocols do
not forbid these visits.
Regarding innovations in maternity care, according to the official guidelines (see
Table 6.3), the presence of the father is allowed. However, fathers are obliged to bring the
results of laboratory tests to prove that they do not have an infectious disease. Test should
not be older than seven days. They also have to pay an official entrance fee in advance.
The amount of this fee varies from hospital to hospital (Narodni Front, 2012). Data from
the questionnaire show that the fee ranges from 50 euro to 150 euro. Since the time of a
childbirth cannot be exactly predicted, it is clear that not many fathers are able to meet
these requirements.
Problems with other innovations such as immediate breastfeeding are usually
explained by a lack of skills of nurses to help and encourage mothers to start breastfeeding
immediately after birth (Becker &, Zisovska, 2008). Special cases are the “baby-friendly”
wards that are designed to allow mothers and their babies to be constantly together, i.e.
rooming-in (Becker &, Zisovska, 2008).
The quantitative data from the on-line questionnaires show that, 61.5% of women
in our sample report problems with breastfeeding. For example, the maternity wards are
usually characterized as poorly organized since patients are often left without any help
from medical staff regarding breastfeeding (see Box 6. 1, Quotation 10).
Data from “non-government” publications confirm the problems related to
immediate breastfeeding described above (Banjac et al., 2010). However, some of the
official guidelines report the opposite and describe “baby-friendly” maternity wards or a
rooming-in approach as highly regarded by mothers (Janevic et al., 2011).
6.4.5 Health system indicatorsQualitative data from the questionnaires show that a skilled person is present during
the delivery but only some of the time. Usually a midwife and/or a physician are present
during the admission procedure and when the birth is approaching. During the course of
the childbirth, women are usually left alone. In exceptional cases, for those that have paid
informally and/or had “connections”, medical staff was present during the whole course of
delivery. Data from other sources namely from the websites of international organizations
(e.g. UN, WHO, WB) report that 98% of all deliveries in Serbia are attended by skilled
persons (Jeffery, 2003; UNFPA, 2009). To a certain extent, this macro indicator does not
correspond to the data from the questionnaire since it does not refer to the presence of a
skilled person during the entire birth process.
Qualitative data obtained from the sample show that a cesarean section is perceived
by medical staff as a desirable and safe way of delivery. In our sample, 36 women
(37.9%) report a Caesarian section, mostly women who had “connections”. Women with
“connections” report that by opting for a caesarean section planned in advance, they could
6
Shortcomings of maternity care in Serbia
143
secure the presence and involvement of the obstetrician of their own choice. Data obtained
from the WHO suggest that official rate for Caesarian sections in Serbia for 2008 was
19.3%, which is higher than in some EU countries but lower than in some other EU
countries (see Table 6.3). Also, some women in our sample express a preference for having
a normal physiological delivery 6 (6.31%), instead of induced labor or a Cesarean section.
Data regarding maternal mortality obtained from the UN and the WHO show a
higher value of maternal mortality for 2010 compared with 2007. This is explained by
poor reporting procedures (Jeffery, 2003; UNFPA, 2009).
6.5 Discussion and conclusions
In our study, we examine several different micro-indicators of the accessibility, service
quality and patient payments in maternity wards in Serbian hospitals, as well as policy
regulations and innovations in maternity care (see Table 6.3) to present a comprehensive
picture of the care delivery process in Serbia and its shortcomings. Our focus is on the
users’ perspective. We apply a mix-methods approach, which provides more insight into
the problems of maternity care in Serbia.
We recognize that the online data that we use, represents a non-representative
purposive sample (i.e. only women who have delivered in a certain period of time are
included and also, the questionnaire is available only to women who are capable of using
the internet, mostly from urban areas, and women motivated to participate). This is a
limitation of our study. To overcome this limitation, we use other sources of information
(research publications and official guidelines), applying triangulation as a research
strategy. The method of triangulation indicates similarities across the different sources
of data that we used, but also some discrepancies. We also use our qualitative data to
examine the possible trend changes during different periods of time.
Results on the spatial accessibility are consistent through all types of resources.
Results regarding the psychological accessibility (communications) are similar within
two sources: online questionnaires and non-government publications. Communication
characterized by a derogative style accompanied with disrespect for the pregnant women
is reported by 53.7% of women. However, the “pro-government group” of publication
does not report those problems. Previous literature has reported that Ministry of Health
has recognized this problem (Ministry of Health, 2010). In order to improve the
communication skills of physicians with management positions, the Ministry of Health
has provided training for them (Supic et al., 2010). Evaluation of the training has shown
that problems with communication skills still exist. Results regarding the hospital
protocols also show similarities between online questionnaires and non-government
publication. Both sources describe the existence of procedures like obligatory shaving and
144
Chapter 6
enema during every delivery, which is reported in other CEE countries as well (Chalmers,
1997; Danishevski et al., 2006; Szende &, Culyer, 2010). However, there is no evidence
that this procedure has any positive effects on the childbirth. Results from all sources are
consistent regarding the obstetrician’s skills that are perceived as very good. However,
the magnitude of reported problems with different types of equipment is decreasing in
the period 2005-2008, in comparison with the period 2000-2005.
Informal patient payments are reported in the online questionnaires but also in the
publications found in our literature review. However, their magnitude is varying through
different sources (from 2% in “pro-government group” of publications to 22.1% in
online collected questionnaires). According to the online questionnaires, obstetricians
are most often bribed, while midwives are rarely bribed. This might be related to their
lower position in the hierarchical system. A similar situation is observed in other CEE
countries (Danishevski et al., 2006; Stepurko et al., 2013).
Data collected through the online questionnaire allow us to combine results
regarding informal patient payments with some aspects of quality of care. Women who
have reported informal patient payments, still experience inconveniences and only 5.3%
of them rate their obstetrician skills as good. On the other side, women who did not
bribe anyone (including women with “connections”) more frequently (17.9%) perceive
obstetrician skills as good.
Our results show that women with “connections” are most likely to be satisfied with
obstetrician skills (see Table 6.2) and experience fewer inconveniences. It is not clear
whether the “personal connections” represent an exchange of favors or a more secure
way of informal payments – bribing with trust. According to our results, the position of
women with “connections” is most favorable compared with those who paid informally
or those who did not bribe anyone. On the other side, they also experience inconveniences
in the communication with medical staff and problems in the application of medical
procedures (see Box 6. 1, Quotation 1).
Our data do not show, whether women who did not bribe anyone, did not have money
to do so or were satisfied with their obstetricians. However, 14 (14.73%) of them reported
that they would use informal patient payments next time in order to get better care.
We have also observed in the online questionnaires and “non-government” publications
that magnitude of informal patient payments is decreasing for the period 2005-2008.
Since the data from “pro-government” publications are collected during 2009, this is the
possible explanation for the low rate of informal patient payments reported there.
The high magnitude of informal patient payments in maternity wards has been
described in other European countries (Danishevski et al., 2006; Stepurko et al., 2013;
Szende &Culyer, 2006; Vian et al., 2006) as well. Moreover, in Ukraine, Hungary,
Greece and Albania, informal patient payments are most often reported in maternity care
(Kaitelidou et al., 2013; Stepurko et al., 2013; Szende & Culyer, 2006; Vian et al., 2006).
6
Shortcomings of maternity care in Serbia
145
The patterns are different in different countries. While in Ukraine, informal patient
payments are a part of the open negotiations with the obstetricians about the childbirth,
in Greece informal patient payments are usually paid for elective Cesarean sections, as
a most secure way of delivery (Kaitelidou et al., 2013; Stepurko et al., 2013). While
in Ukraine informal patient payments are given to obstetrician as an act of solidarity
since their income is low, in Albania, informal payments are usually given as an act of
gratitude (Stepurko et al., 2013; Szende & Culyer, 2006). In Greece, the main reason
for informal payments is an extra service, e.g. elective Caesarian section (Kaitelidou et
al., 2013). Also, legal regulations regarding informal patient payments are different
in different countries. Informal patient payments were legalized in Hungary, while in
Albania and Greece although wide spread, they are considered illegal. In Ukraine, quasi-
formal payments in the form of “donations” by the patient to medical institutions exist
and are also expected by the providers (Stepurko et al., 2013). Also, similar to our study,
the importance of “personal connections” to receive adequate maternity care is reported
in other countries as well (Stepurko et al., 2013).
Although informal patient payments are spread in public maternity wards in Serbia
and a large group of women report problems with the care they receive, there is no
alternative channel to receive maternity care during childbirth. Childbirth in private
maternity hospitals are still impossible due to the strict laws and regulations that forbid
giving birth outside the official institutions.
Also, we observe a slow diffusion of innovations in Serbian maternity hospitals. One of
the reasons is the education of medical staff that is still based on the “active approach“(see
Background section) during childbirth. Medical staffs are usually reluctant to accept
innovations. Especially obstetricians consider their role as most important during the
process of childbirth and they perceive any innovation that increases the role of the
patients as a decrease of their own power (Stambolovic, 1996).
Another problem is related to the high rate of Caesarian sections, reported also in
different sources.
An elective Caesarian section is sometimes considered as the safest way of delivery
(Kaitelidou et al., 2013). Women with “connections” most often reported Caesarian
section in the online questionnaires.
Despite the problems in Serbian maternity wards, users of maternity care are silent
about this in public. This silence among the health care users can be explained by two
paradigms that prevail in Serbia. One is the “patriarchal paradigm” (Bracewell, 1996)
and the other one is described as the “doctor-centered paradigm” (Chalmers, 1997).
The first paradigm defines the role of women as a mother and housewife. The
experience of motherhood is highly important for every woman.
The suffering during the childbirth will be overcome by the happiness of motherhood.
The childbirth is performed outside or at the backside of the house in the presence of a
146
Chapter 6
midwife. The presence of the father is considered as a shame. This paradigm was renewed
in Serbia as a part of the nationalistic ideology during the civil war in ex-Yugoslavia
(the early 1990s). Even, the Serbian Academy of Art and Science published an official
manifest in 1990, appealing on Serbian women to leave their jobs and fight against the
“white plague” – a new disease for the Serbian people. The term “white plague” refers to
the fact that the number of new born babies in Serbia is lower than during the previous
communistic period.
The second paradigm describes the childbirth as a technological intervention
with a high use of medical techniques (the technocratic approach). The childbirth is
not considered as a natural physiological process but as an actively conducted medical
event. The woman’s body is a machine that produces a child as a final product and
individual differences and preferences are not considered important. The obstetrician’s
role is perceived as the most important and the most responsible during the birth. This
paradigm prevailed during the period of the Socialistic Federative Republic of Yugoslavia
(1945 – 1990). During the period of the economic crises and the civil war in the 1990s,
the “doctor-centered” paradigm was combined with the pro-nationalistic “patriarchal
paradigm” shaping the current system of maternity wards. The emergence of informal
patient payments, the increased lack of resources and the halting health care reforms
further framed the system of maternity care in Serbia, while hiding and neglecting major
problems within the system.
The civil initiative “Mother Courage”, who collected the on-line data for our study,
raised the issue of the bad conditions in maternity wards in Serbia for the first time in
public. However, from raising awareness of this issue to actually changing the system
requires some additional steps. Further research should focus among other issues, on
the preferences of women regarding the childbirth. Increasing the knowledge of health
care users about their rights is important but has to be accompanied with a government
initiative to implement reforms within the current system. Also, future research should
examine the attitude and perceptions of different health care professionals, i.e. midwives
and obstetricians. The role of the midwife should be enhanced as well as the consideration
of a child delivery as a normal physiological event. The objective should be that equal
access to medical procedures and equal treatment are provided for every pregnant woman.
We have examined process related indicators of maternity care in Serbia. However,
most of the data that we found are qualitative. The validity of qualitative data is enhanced
by an analysis of their credibility, transferability and dependability (Baji et al., 2011).
In order to ensure the validity of our data and to combine them with quantitative
findings (Baji et al., 2011), we have applied a mixed-method approach with triangulation
as a research strategy. Triangulation allows us to compare different sources of data
(credibility) and to build comprehensive picture of maternity care in Serbia. Also,
triangulation allows us to compare our findings with those form other CEE countries.
6
Shortcomings of maternity care in Serbia
147
In order to present comprehensive picture of maternity wards in Serbia, we present our
findings using both triangulation and a qualitative time trends perspective. All three
sources of our data indicate that spatial accessibility and obstetricians skills are the most
positive indicators of maternity care in Serbia. Combining different aspects of maternity
care (quality, accessibility, patient payments we could draw some conclusions based on
our findings. Physicians’ skills are mostly observed as good, but their communication
with patients and the way they treat pregnant women are described as poor. Although
some efforts are made by the Ministry of Health to improve the level of communication
among the health care professionals, problem still exists. Besides the problems regarding
the communication, our data also show problems with equipment, different types of
patient payments and slow diffusion of innovations. Maternity wards in Serbian hospitals
usually have the necessary equipment but very often this equipment is not used or it is
overused. Informal patient payments and “connections” are used by the pregnant women
as a way to secure better care.
However, some improvements are observed during the period 2005-2008, mostly
regarding the affordability of equipment but also regarding hygienic improvements.
At the same time, the intensity (but not the magnitude) of informal patient payments
is decreasing, while so called “connections” are more often reported. Women with
“connections” are more likely to replace the “active way of delivery” with the more
technocratic model, i.e. elective Caesarian section, increasing the cost of delivery (Varjacic,
2005). Our results do not provide the data about women’s preferences regarding the child
birth. Future research should focus on the examination of preferences of health care users
towards maternity care, but also on examination of attitude of health care professionals.
In order to provide conditions for childbirth in accordance with women’s preferences
the government should regulate policies regarding the childbirth process. Positive
aspects like the broad network of maternity wards and good obstetrician skills are good
starting points. Enhancing the position of midwifes can also improve the communication
between the patient and providers and secure more respect for different preferences. The
government may include private providers, paid by the Republic Institute of Health
Insurance (HIF). Including private providers can decrease the need of informal patient
payments and “connections”.
148
Chapter 6T
able
6.3
: Ind
icat
ors
of m
ater
nity
car
e in
Ser
bia
base
d on
dif
fere
nt d
ata
sour
ces
(que
stio
nnai
res
coll
ecte
d on
line
, gui
deli
nes,
lite
ratu
re r
evie
w)a
Cha
ract
eris
tics
of m
ater
nity
ca
re d
urin
g th
e ch
ildb
irth
Lite
ratu
re r
evie
wG
uide
line
sQ
uest
ionn
aire
s co
llec
ted
onli
ne
“Non
-gov
ernm
ent
grou
p”“P
ro-g
over
nmen
t gr
oup”
Offi
cial
gui
deli
nes
Hos
pita
l gui
deli
nes
Acc
essi
bili
ty
Spat
ial
Sati
sfied
Sati
sfied
Acc
ess
to m
ater
nity
war
d is
a r
ight
of e
very
pre
gnan
t w
omen
Onl
y w
ith
prop
er r
efer
ral
Pro
blem
s w
ith
refe
rral
Tem
pora
lLo
ng w
aiti
ng t
ime
for
cert
ain
proc
edur
es li
ke u
ltra
soun
dsSa
tisfi
edN
o w
aiti
ng li
sts
Long
wai
ting
tim
e fo
r ce
rtai
n pr
oced
ures
like
ult
raso
unds
etc
.
Psy
chol
ogic
alLo
w le
vel o
f com
mun
icat
ion
Goo
d le
vel o
f com
mun
icat
ion
Pat
ient
-pro
vide
r co
mm
unic
atio
n pr
oble
ms
Qua
lity
of c
are
Cli
nica
l qua
lity
of c
are
Old
pro
toco
lsH
igh
leve
l of p
hysi
cian
ski
llsO
ld p
roto
cols
Old
pro
toco
lsO
ld p
roto
cols
Hig
h le
vel o
f phy
sici
ans
skil
ls, b
ut
low
leve
l of m
otiv
atio
n
Soci
al q
uali
ty o
f car
eN
o fa
mil
y vi
sits
Sati
sfied
No
visi
tors
No
visi
tors
Pat
ient
pay
men
tsla
ck o
f mot
ivat
ion
Type
of p
aym
ents
Info
rmal
pay
men
tsQ
uasi
-for
mal
Low
leve
l of i
nfor
mal
pa
ymen
tsFr
ee o
f cha
rge
Qua
si-o
ffici
alIn
form
al p
aym
ents
Qua
si-f
orm
al“s
peci
al c
onne
ctio
ns”
Rec
eive
r of
pay
men
ts-
--
-P
hysi
cian
s an
d nu
rses
Freq
uenc
y of
pay
men
ts14
.7 u
p to
39.
2% in
form
al
paym
ents
1.9%
info
rmal
pay
men
tsM
ostl
y in
form
al p
aym
ents
(22.
1%)
Mag
nitu
de o
f pay
men
ts-
-50
0 eu
ro
Pur
pose
of p
aym
ents
Bet
ter
qual
ity
of c
are
Gra
titu
deB
ette
r qu
alit
y of
car
eSa
fety
Att
itud
e to
war
ds p
aym
ents
Neg
ativ
e-
Neg
ativ
ea T
rian
gula
tion
ref
ers t
o re
sear
ch st
rate
gy th
at a
llow
s us t
o co
mbi
ne b
oth
qual
itat
ive a
nd q
uant
itat
ive d
ata
from
dif
fere
nt so
urce
s in
orde
r to
ove
rcom
e pos
sibl
e bia
ses
6
Shortcomings of maternity care in Serbia
149
Tab
le 6
.3: I
ndic
ator
s of
mat
erni
ty c
are
in S
erbi
a ba
sed
on d
iffe
rent
dat
a so
urce
s (q
uest
ionn
aire
s co
llec
ted
onli
ne, g
uide
line
s, li
tera
ture
rev
iew
) (co
ntin
ued)
a
Ch
arac
teri
stic
s of
mat
ern
ity
care
du
rin
g th
e ch
ild
bir
th
Lit
erat
ure
rev
iew
Gu
idel
ines
Qu
esti
onn
aire
s co
llec
ted
on
lin
e
“Non
-gov
ern
men
t gr
oup
”“P
ro-g
over
nm
ent
grou
pO
ffici
al g
uid
elin
esH
osp
ital
gu
idel
ines
Pol
icy
regu
lati
on
Reg
ulat
ion
rega
rdin
g th
e pa
ymen
tsFr
ee o
f cha
rge
Free
of c
harg
eFr
ee o
f cha
rge
Qua
si-o
ffici
al p
aym
ents
Reg
ulat
ion
rega
rdin
g th
e ho
spit
al p
roto
cols
Old
pro
toco
lsSa
tisfi
edO
ld a
nd u
nnec
essa
ry p
roto
cols
Inno
vati
on in
mat
erni
ty c
are
Pre
senc
e of
fam
ily
mem
bers
Not
all
owed
-N
ot a
llow
edN
ot a
llow
ed, e
xcep
t fo
r sp
ecia
l co
nnec
tion
s
“roo
min
g in
”W
itho
ut h
elp
Sati
sfied
Wit
hout
any
hel
p
Hea
lth
ind
icat
ors
Mat
erna
l mor
tali
ty14
.48(
2007
); 19
.91(
2009
);17.
57 (2
010)
Infa
nt m
orta
lity
dur
ing
birt
hbin
fant
dea
th p
er 1
000l
ive
birt
hs=
7(20
09)
Rat
e of
Cae
sare
an s
ecti
on19
.3%
(cal
cula
ted
by W
HO
(201
1) fo
r a
peri
od 2
000-
2010
, nom
inat
or is
the
num
ber
of c
aesa
rean
sec
tion
and
de
nom
inat
or is
the
num
ber
of t
otal
bir
ths
in d
efine
d po
pula
tion
169.
3 pe
r 10
00 b
irth
s fo
r 20
07 a
nd 2
38.5
4 pe
r 10
00
birt
hs fo
r 20
10 (
calc
ulat
ed b
y In
stit
ute
of P
ubli
c H
ealt
h Se
rbia
)
36(3
7.9%
) of 9
5 ca
ses
Rat
e of
em
erge
ncy
ch
ild
bir
th
Pre
senc
e of
ski
lled
per
son
Occ
asio
nall
yO
blig
ator
yO
blig
ator
yO
blig
ator
yO
ccas
iona
lly
a Tri
angu
lati
on r
efer
s to
rese
arch
stra
tegy
that
all
ows u
s to
com
bine
bot
h qu
alit
ativ
e and
qua
ntit
ativ
e dat
a fr
om d
iffe
rent
sour
ces i
n or
der
to o
verc
ome p
ossi
ble b
iase
sb R
efer
s to
peri
nata
l mor
tali
ty r
ates
-num
ber
of d
eath
s occ
urri
ng in
the p
erio
d fr
om a
bout
thre
e mon
ths b
efor
e to
one m
onth
aft
er b
irth
CHAPTER 7
General Discussion
152
Chapter 7
7.1 Introduction
The main goal of this dissertation was to examine the effects of out-of-pocket patient
payments on vulnerable population groups in the public health care system in Serbia.
The social protection policy in the health care system in Serbia emphasizes the financial
protection of vulnerable population groups, which includes mechanisms like compulsory
health care insurance and protection from high out-of-pocket patient payments (Chapter1).
In this dissertation, we specifically focus on the effects of out-of-pocket payments
for public health care services and goods on vulnerable population groups. We first
examined the effects of out-of-pocket patient payments on household budgets (Chapter
2). Furthermore, we examined the financial burden provoked by different types of out-of-
pocket patient payments, namely official co-payments, informal payments and payments
for “bought & brought goods” (Chapter 3). The exemption from official co-payments is
used by policy makers in Serbia as a mechanism to protect vulnerable population groups.
In Chapter 4, we outlined the design and implementation of the current exemption
mechanism in Serbia, and we examined the effectiveness of this mechanism. In addition
to that, we also examined to which extent certain population groups that are considered
as vulnerable, namely chronically sick and pregnant women, experience the effects of the
social protection policy (Chapter 5 & 6). In Chapter 5, we examined the financial burden
of three leading chronic diseases in Serbia (diabetes mellitus, cardiovascular diseases and
cancer) on Serbian households. In Chapter 6, we explored the financial protection of
pregnant women but also the accessibility to maternity care, non-medical quality of care
and policy regulations.
For the analyses presented in this dissertation, we combined two methods. Quantitative
data analyses, based on a representative survey data collected by the World Bank in 2002,
2003 and 2007, are used in Chapter 2 to 5. Mixed-methods, combining qualitative and
quantitative data analyses are used in Chapter 6 (data collected by literature review, on-
line semi-structured questionnaires and data collected from official guidelines). The main
findings of this dissertation are presented in this chapter by six statements. Each statement
is discussed and finalized suggestions for further research and policy recommendations.
7.2 Discussion of main findings
Although out-of-pocket patient payments are a higher burden for the poor, their catastrophic
effects are experienced by all socio-demographic groups.
Out-of-pocket patient payments can impose a high financial burden on the poor and
more specifically on the lowest consumption-based quintile (Habicht et al., 2006;
7
General discussion
153
Limwattananon et al., 2007; Xu et al., 2006). Even if the introduction of out-of-pocket
patient payments in the public health care system is accompanied with social protection
mechanisms, like exemption mechanisms or compulsory health insurance, as it is in
Serbia, the financial burden for poor population groups remains high. However, non-poor
population groups may also experience the financial burden if they are exposed to health
care costs over a longer period of time like in case of a chronic disease.
In this dissertation, we use two different approaches - namely the impoverishing effects
and catastrophic health care expenditure approach - to estimate the burden created by
out-of-pocket patient payments in the public health care system. Within each approach,
we use different indicators of wealth (income and consumption) and different thresholds
as cut-off points. Irrespective of the approach we apply, the results indicate that out-of-
pocket patient payments for public health care services and goods create a burden for
poor population groups (Chapter 2). When we calculate the impoverishing effects using
income as an indicator of wealth, the share of poor people (the lowest consumption-based
quintile) who go below the absolute poverty line is 20.5%, while among the rich, it is
1.2%. Since the impoverishment approach uses a poverty line as a cut-off point, this
result is to be expected. People who are close to the poverty line are more likely to go
below poverty line after the subtraction of health care costs. However, when we estimate
the effects of out-of-pocket patient payments using catastrophic health care expenditure
(based on both indicators of wealth), the burden is nearly equally experienced by all
income groups in Serbia.
Not only the chosen approach plays a role in estimating the burden of out-of-pocket
patient payments, but also the choice of an indicator of wealth and the cut-off point
play a role. When we calculate the catastrophic health care expenditures, the cut-off
point is a predefined threshold presenting a given proportion of income/consumption
(the indicator of wealth), while when we calculate the impoverishing effects the cut-off
point is an absolute number (a certain amount of money).
This means that based on catastrophic health expenditures, even rich people can
spend a high proportion of their income on health care services, but they do not necessary
become poor because of it. Furthermore, when the threshold is set lower, a high burden
is also experienced by the richest population groups in Serbia. For example, the incidence
of individuals who experience catastrophic health expenditure when the 10% threshold
is used, amounts to 42.9% among the poor (the poorest consumption percentile) and
48.3% among the rich (the highest consumption percentiles) (Chapter 2).
The application of different indicators of wealth within the same approach also
leads to different estimates of the burden created by out-of-pocket patient payments.
Catastrophic effects estimated using income as an indicator of wealth, are highest among
the low-income quintiles, while when consumption is used as an indicator of wealth,
the burden is heaviest among the middle-income quintiles. This means that although
154
Chapter 7
people from middle-income quintiles do not spend a significant proportion of their
income on health care, they might reduce the consumption of some other goods and/or
services (education) in order to be able to pay for health care (van Doorslaer et al., 2007).
Moreover, this result shows that in countries like Serbia, with a large informal economy,
consumption is perceived as a more accurate indicator of wealth than income (Haughton
& Khandker, 2009). For example, in order to avoid taxes, many companies in Serbia do
not register their employees (Krstic, 2008).
As outlined in Chapter 1, the LSMS data have been used in Serbia since 2002 to
identify the poverty rate (Bajec et al., 2008). The poverty rate is calculated based on the
absolute poverty line as a threshold and consumption as an indicator of wealth (World
Bank, 2011). People who go below this poverty line are considered poor. The official
poverty rate in Serbia for 2002 was 14.5%, and poverty was more prevalent among people
with a low level of education, among larger households and in rural environments (Bajec
et al., 2008). In 2007, the official poverty rate had decreased to 7.5%, while poverty was
still more prevalent in rural areas, larger households and among people with a low level of
education. Thus, although the absolute poverty rate decreased between 2002 and 2007,
the same groups were at risk of being poor.
In this dissertation, we have examined the association between poverty (being poor or
not) and different socio-demographic groups. We find that the same socio-demographic
characteristics as those mentioned above are associated with poverty in Serbia in 2007
(Chapter 5). Furthermore, we also examined the association between different socio-
demographic groups and impoverishment or catastrophic health care expenditure.
Our results show that catastrophic health expenditure is mostly associated with socio-
demographic characteristics like being single, having a poor perceived health and having
a chronic disease (Chapter 2).
The impoverishment effects are associated with being single, having a poor perceived
health and different types of chronic diseases. Socio-demographic characteristics like living
in rural areas or in larger households are not significantly associated with impoverishing
or catastrophic effects. This suggests that poor people also more often forgo the use of
health care when confronted with high payments (Banerjee & Duflo, 2011). Even though
some of the care foregone might have been unnecessary, postponing the use of health care
that is necessary can be detrimental for one’s health status and can lead to higher health
care costs later on.
Our results show that each of the approaches applied to examine the burden provoked
by out-of pocket patient payments addresses only a specific aspect of this problem.
Therefore, for the purpose of a broader overview, the effects on out-of-pocket patient
payments should be examined using all available approaches.
7
General discussion
155
Further research: Health care users and their families use different coping mechanisms to
prevent the financial burden of health care expenditure. They can borrow money or sell
assets. Although those coping mechanisms can reduce the financial burden for a short-
term period, very often, they have a negative impact on household wealth in the long
run (van Doorslaer, 2007). In this dissertation, we do not have data to examine the use of
coping mechanisms in Serbia. Future studies should pay attention to those mechanisms
since they give a better insight in the extent of the financial burden of out-of-pocket
payments. Since out-of-pocket patient payments are usually non-discretionary shocks,
panel data can clarify the distribution of catastrophic health care expenditure among
different income groups. Also, further studies on the impoverishing and catastrophic
effects of out-of-pocket payments in Serbia can include household expenditures at the
private health care sector, which was outside the scope of this dissertation.
Policy implications: The social protection policy in the Serbian public health care sector
is designed to address a broad range of population groups. However, current policy
makers in Serbia use an absolute poverty line to identify poor population groups. Our
results show that out-of-pocket patient payments are a financial burden not only for
poor population groups but also for non-poor groups. Policy makers should use different
approaches, different thresholds and different indicators of wealth in order to identify
vulnerable groups.
Even if non-poor population groups do not go below the absolute poverty line after
health care spending, they still experience a financial burden. In case of chronic diseases,
when this financial burden is prolonged, non-poor population groups can be pushed into
poverty. Policy makers should not neglect the impact that informal payments (as well as
other forms of patient payments) have on the financial burden.
Payments for “bought & brought goods” are perceived as positive among health care
users, but their catastrophic effects are higher than those provoked by informal patient
payments.
In Chapter 3, we outline different types of out-of-pocket patient payments that exist
in the Serbian public health care sector, namely official co-payments, informal patient
payments and payments for “bought & brought goods”. We also analyze the financial
burden that each type of payments imposes on the household budget.
As described in Chapter 3, the distinction between the official co-payments and
informal patient payments is well-described in the literature (Ensor, 2004; Lewis, 2002;
Stepurko et al., 2010). However, previous studies do not emphasize the difference
between the pure informal patient payments and payments for “bought & brought
goods” (Gaal et al., 2006; Lewis, 2002). In Chapter 3, we make a distinction between
the pure informal payments (such as cash and/or in-kind gifts given on staff’s request
156
Chapter 7
or in a form of gratitude), and payments for “bought & brought goods” (i.e. payments
for goods brought by the patients or their families to the health care facility, such as
disposable materials and pharmaceuticals). We found that this distinction is important
since those two types of out-of-pocket patient payments can affect different population
groups, including those exempted from official co-payments. Moreover, since those two
types of payments are not planned by policy makers, often their scope and effects are not
monitored and they remain unknown.
Payments for “bought & brought goods” are not unique for Serbia. They are also
described in other CEE countries (Stepurko et al., 2013). Previous studies called them
quasi-informal payments (Stepurko et al., 2013). While pure informal payments are
unregistered, payments for “bought & brought goods” are registered at the moment
of purchasing but not visible in the financial flows of the institution that provides the
services. Informal patient payments can be requested or given out of gratitude, payments
for “bought & brought goods” are always requested by health care providers and these
goods are necessary for the patient in treatment.
In Serbia, policy makers neglect informal patient payments, but they do recognize the
existence of payments for “bought & brought goods” (Adzic & Adzic, 2011; Matejic et al.,
2010; Stosic & Karanovic, 2014; Sorensen, 2007; Stanic, 2002). Payments for “bought
& brought goods” are usually described as payments for goods (such as pharmaceuticals)
necessary for the curative process but not available in hospitals (Palairet, 2001). Although
recognized by policy makers, those payments are also rarely examined in Serbia (Adzic &
Adzic, 2011; Stanic, 2002).
Informal patient payments and payments for “bought & brought goods” differ not
only by their nature. As shown by previous research, the attitudes of health care users
towards informal patient payments can be both negative and positive (Stepurko et al,
2010). In Bulgaria, the public attitudes towards informal patient payments are more
negative in case of cash informal payments but more positive in case of in-kind gifts
(Atanasova et al., 2013). Previous evidence from Serbia shows that informal patient
payments are perceived as a negative phenomenon (CESID, 2011). They were usually
related to the time of economic crisis when the salaries of physicians were low (CESID,
2011). Nowadays, when physicians request the informal patient payments, the health
care users’ perception is negative (Agencija za Borbu Protiv Korupcije, 2013). Although
perceived as a negative phenomenon, informal patient payments still exist in Serbia.
Health care users still feel the obligation to give informal patient payments to obtain
good care, have better access to medical services and also as a form of gratitude (Agencija
za Borbu Protiv Korupcije, 2013).
Payments for “bought & brought goods” are always requested by medical staff,
but the perception of Serbian health care users towards them is almost always positive
(Gavrilovic & Trmcic, 2013; TNS Media Gallup, 2011). As we mentioned in Chapter 3,
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General discussion
157
payments for “bought & brought goods” in Serbia have their roots in the time of the civil
war, when hospitals lacked medical materials, supplies, and pharmaceuticals (Palairet,
2001). In such situation, physicians would ask the patient’s family to bring the necessary
goods. Families have perceived this as an act of cooperation from the side of health care
providers (Garfield, 2001; Palairet, 2001). Moreover, from the perspective of health care
users, payments for “bought & brought goods” do not bring any benefit to health care
providers but they are very necessary for the curative process (TNS Media Gallup, 2011).
However, today, although hospitals do not lack basic goods, payments for “bought &
brought goods” continue to exist (Chapter 3). One of the reasons is that when health care
providers ask for “bought & brought goods”, patients and their families do not perceive
this as an act of corruption (CESID, 2011).
At the first glance such requests are not interlinked with benefits for health care
providers. However, health care providers can still benefit from those payments. Hospital
can still declare the use of goods brought by patients as goods provided by the hospital.
Also, it is possible to use the “saved” goods in providers’ private practices, or simply
divide the money claimed for these goods in the form of an extra bonus (TNS Media
Gallup, 2011). The other reason for the existence of payments for “bought & brought
goods” is that those payments are not subject to the Serbian law (Chapter 3) while for
example, informal patient payments are described in the Serbian law as an act of “la
petite” corruption (Krivicni Zakonik, clan, 368). This means, that both patients and
providers do not breach any law in case of payments for “bought & brought goods”
(Gavrilovic & Trmcic, 2013).
Considering the differences between informal patient payments and payments
for “bought & brought goods” mentioned above, we examine the catastrophic and
impoverishing effects provoked by each type of payments. For this purpose, we use
consumption as an indicator of wealth. We consider that payments have catastrophic
effects on the household’s budget if they exceed 10% of the annual household consumption.
As shown in Chapter 3, both informal payments and payments for “bought & brought
goods” provoke catastrophic effects on household budgets. Catastrophic effects provoked
by payments for “bought & brought goods” are highest among health care users from the
lower income quintiles, while informal patient payments provoke the highest burden
among the health care users from the highest income quintiles. Moreover, payments
for “bought & brought goods” are more frequent than pure informal patient payments.
In total, 61.7% of health care users (N = 4976) reported payments for “bought &
brought goods”. At the same time, only 5.7% of health care users reported informal
patient payments. However, the magnitude of informal patient payments is higher
than for payments for “bought & brought goods”. The maximum nominal amount paid
informally (80 000 CSD ≈ 911 USD) for inpatient care is almost two times higher than
the maximum nominal amount reported for payments for “bought & brought goods”
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(50 000 CSD ≈ 570 USD). This suggests that payments for “bought & brought goods”
provoke the heaviest burden among low social economic groups. Besides that, their
catastrophic effects are higher, than the catastrophic effects provoked by informal patient
payments. While 3.7% of health care users in our sample spend more than 10% of their
annual consumption on payments for “bought & brought goods”, only 0.7% exceeds the
same threshold because of informal patient payments. Possible reasons can be found in
the nature of payments for “bought & brought goods”.
Those payments occur in a short period of time, the paid amount is high and the share
of payments for “bought & brought goods“ are higher among health care users from the
lower income groups.
Although payments for “bought & brought goods” do not breach any laws in Serbia
and although they are positively perceived by health care users (TNS Media Gallup,
2013), as suggested by our results, their shadow nature and catastrophic effects impose
an economic burden for health care users (Chapter 3). Payments for “bought & brought
goods” are more frequent and have the more intensive catastrophic effects, compared
to pure informal payments, but are not recognized by both health care users and policy
makers in Serbia as a negative practice. Furthermore, the catastrophic effects provoked by
payments for “bought & brought goods” are experienced by vulnerable groups like the
low-income population groups and population groups diagnosed with chronic diseases
(Chapter 3). According to current social policy in Serbia, poor population groups in
Serbia are exempted from official co-payments (Official Gazette of Republic of Serbia,
2007). The aim is to protect the poor population groups from a possible economic burden
(Gajic-Stevanovic, 2010). However, the catastrophic effects provoked by payments for
“bought & brought goods”, obstruct this intention of policy makers. In this way, poor
population groups are not adequately protected. It seems that payments for “bought &
brought goods” represent a new more sophisticated type of corruption than pure informal
patient payments. While pure informal patient payments more often provoke a higher
burden on the high-income population groups, among those who experience catastrophic
health care expenditure provoked by informal patient payments. 5.4% is from richest
quintile, while 1.3% is from poorest quintile, the burden of payments for “bought &
brought goods” is experienced by 84.8% respondents from the low-income groups which
is slightly lower in comparison with burden experienced by respondents from richest
quintile (Chapter 3, Table 3.2). This implies that even if the poor do not pay informally,
they find a way to bring the pharmaceuticals and/or goods that are necessary for curative
process. As long as social policy in Serbia do not adequately address the payments for
“bought & brought goods” (as mentioned above, payments for “bought & brought goods”
are not illegal), the poor population groups are at risk of economic shock when frequent
health care services are needed.
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General discussion
159
Further research: Our results show that payments for “bought & brought goods” can
provoke an economic burden for health care users. However, our data provide information
about the payments for “bought & brought goods” only for inpatient services.
Also, the data provides no information about the type of treatment or type of
pharmaceuticals that patients pay for. Future research that will include such information
can provide a better ground for developing a new policy tool to detect and decrease
not only informal, but also these quasi-informal patient payments. In particular, studies
should focus on identifying the scope and magnitude of those payments at all levels of
the health care system. Also, identifying the way how those payments are used from
the perspective of health care system will contribute to the development of new more
efficient policy towards their elimination.
Policy implications: Informal patient payments in Serbia, although reported in anecdotal
evidence, are still not reported in empirical studies (Matejic et al., 2011). As we mentioned
above, informal patient payments are overall neglected by policy makers in Serbia (Stosic
& Krstic, 2014). Moreover, our results show that informal patient payments are also
accompanied with payments for “bought & brought goods” (quasi-informal payments).
Payments for “bought & brought goods” are even more frequent than informal patient
payments (Chapter 3). Since the payments for “bought & brought goods” are not illegal,
they are more accepted from patients and their families than informal patient payments.
Nevertheless, they can still provide additional benefits for health care providers. For
example, health care providers can use brought goods in their private practice (Stanic,
2002). Policy makers should design effective strategies to decrease the level of those
payments and to eliminate the reasons for their existence. One way is to increase the
awareness of health care providers about the negative effects that can be provoked by
payments for “bought & brought goods”. Also, the Serbian legislation should recognize
the informal nature of payments for “bought & brought goods”. This will be essential
to protect the vulnerable population groups including the poor. For example, the
government can officially reimburse the costs of goods brought by the patients and their
families that are not covered by the health care providers. This will also help to shed more
light on the exact scope and level of these payments.
The large number of population groups that are exempted from official co-payments in Serbia
is seen as an indicator of a strong social policy, but they are also a tool (means) to buy social
peace.
Official co-payments were introduced in Serbia in 2002, as part of the financial health
care reforms (Bajec et al., 2008). They are accompanied by an exemption mechanism
(Gajic-Stevanovic, 2010). As outlined in Chapter 4, the aim of the exemption mechanism
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is to protect vulnerable population groups from the economic burden provoked by official
co-payments.
Various population groups are exempted: children younger than 15 years, pregnant
women, persons older than 65 years, disabled persons, HIV-infected persons, monks,
people with low family income, unemployed, chronically ill people, military service
servants, people registered as refugees and the Roma population (Chapter 4). The
government explained that the high number of exempted population groups is a reflection
of a long tradition of solidarity and equity in Serbia (Stosic & Krstic, 2014). Despite the
fact the Serbian government promotes equity in the provision of health care, in Chapter
4, we show that the exemption mechanism in Serbia is not effective since individuals
belonging to exempted groups report paying official co-payments. We discuss two main
reasons for the failure of the exemption mechanism in Serbia: the design of the exemption
mechanism and the implementation of the exemption mechanism.
As described in Chapter 4, the design of the exemption mechanism is described in
detail in several legal documents known as guidelines (Official Gazette of RS, n. 1/2007,
52/2007, 99/2007, 14/2008, 20/2008, 7/2009, 82/2009 and 23/2010). Although
the amounts for official co-payments in Serbia are low (Vukovic & Perisic, 2011), the
exemption mechanism is designed to include broad population groups like children
younger than 15 or people older than 65, military servants and monks. Not all members
of those population groups are unable to pay for health care services. For example, people
older than 65 and younger than 15 are the most frequent users of both outpatient and
inpatient health care services during the period 2002-2007 (Chapter 4). However, the
payments for health care services for children are paid by their parents. Not all parents are
unable to pay for health care services. A better policy approach would be to use parents’
income as an indicator for exemption, instead of exempting all children younger than 15.
Regarding people older than 65, they also report official patient payments. However, the
amounts that they report are lower than the amounts reported by other exempted groups.
For example, the lowest official amount for 2007 for pharmaceuticals is paid by persons
older than 65. However, people older than 65 report high amounts for gifts given to
medical staff.
On the other side, people with low income, although exempted, often pay high
amounts. For example, the highest amount in 2002 is paid for hospitalization by
unemployed persons while the highest official amount for 2007 is paid for pharmaceuticals
by disabled persons (Chapter 4).Some population groups with chronic conditions are not
exempted or just partially exempted and they experience the burden provoked by patient
payments.
According to the current law, people diagnosed with diabetes mellitus in Serbia are
partially exempted. In Chapter 5 of this dissertation, we show that people diagnosed
7
General discussion
161
with diabetes mellitus experience an economic burden provoked by out-of-pocket patient
payments.
The implementation of the exemption mechanism also has weaknesses. As outlined in
Chapter 4, since the guidelines are not clear and written in law-centered language, it is very
confusing for patients and health care providers to understand for which services, (partial)
exemption mechanisms should be applied (Biorac et al., 2009). Even when the exemption
mechanism should be applied for all health care services (including pharmaceuticals and
disposable materials), in practice, some population groups face the difficulty to use their
right to free-of-charge health care use. For example, according to the guidelines, people
with a family income lower than the minimum net income in Serbia should be exempted
from official co-payments (Official Gazette of RS, 11/2010). As we explained in Chapter
4, the minimum net income is calculated by the Serbian statistic office and it changes
every 6 months. This means that the right to an exemption can also change every 6
months. In this way, patients might be unaware of the adjustments in official guidelines
that take place at the beginning of every year. Another obstacle in the implementation of
the exemption mechanism is related to the procedure to obtain the exempted status. The
procedure is administratively difficult and time consuming (Vukovic & Perisic, 2011).
Even, when the exempted status is obtained, for some exempted groups, health care
providers have to confirm the status. However, there is no document that defines the
responsibility of health care providers towards the implementation of the exemption
mechanism (World Bank, 2005). In Chapter 6, we show that although pregnant women
are exempted from official co-payments during childbirth, in order to obtain the presence
of obstetrician some of them pay informally. This suggests that the quality of care that
is provided for exempted groups is not always adequate. Moreover, because of difficult
administrative procedures (necessary documents), pregnant women are often pushed to
pay quasi-official fees as well.
Two main stakeholders, the Ministry of Health and HIF, are involved in the
implementation of the exemption mechanism. The Ministry of Health is responsible
to cover the costs of exempted groups for health care services. The ministry provides
the money for exempted groups to HIF, and HIF allocates the resources to health care
providers. However, the percentage of total revenue that HIF allocates to health care
providers for exempted groups has decreased since 2007 (Bajec et al., 2008).
Following the practice established during the civil war, the Ministry of Health sends
the money to HIF irregularly (Chapter 1 & 4). One of the reasons is the lack of resources;
the other reason is the selective allocation of resources (Chapter 4). Moreover, there is not
a particular policy document that defines the responsibility of each stakeholder.
The Ministry of Health emphasizes that despite the economic difficulties, health
care in Serbia is still provided for free for most vulnerable groups. This attitude of the
Serbian government is preserved from the time of civil war. During that period many
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companies did not pay the contributions to the national insurance fund since they were
lacking financial resources (Bajec et al., 2008). On the other side, the government at that
time used the special policy decision to exempt certain population groups from paying
the contributions for complementary health insurance (Stanic, 2002). For example,
military civil servants and monks were not exempted from paying the contributions for
complementary health insurance during the period of the SFRJ (Stanic, 2002). However,
in 1992 those two groups were exempted from contributions. Moreover, in 1997, farmers
were allowed by the special policy act to contribute to compulsory health insurance
with 12 CSD ≈ 0.14 USD per year (Stanic, 2002). During this period, the health care
system was centralized and all managers in hospitals and national insurance fund were
directly appointed by the government (Bajec et al., 2008; Saric & Rodwin, 1993). In
this way, by controlling the management and exempting the “special” population groups
from compulsory health insurance, the political establishment of Slobodan Milosevic
managed to use the health care system as a political tool to buy social peace (Stankovic,
2002). Although the political establishment changed in 2000, the tradition of special
exemptions from compulsory health insurance was transformed into special exemptions
from official co-payments.
However, it was clear that the high number of exempted groups will influence the
financial sustainability of health care system in Serbia. In the period 2003-2008, there
were some attempts to reduce the number of health services that were included in the
insurance package, but there was no attempt to decrease the number of exempted groups
(Adzic & Adzic, 2011). In 2003, the World Bank requested the Serbian government to
decrease the number of exempted groups (World Bank, 2005). The government decided
that retired people will not be exempted from official co-payments (World Bank, 2005).
However, members of this population group use the right to be exempted as people older
than 65 or disabled (Adzic & Adzic, 2011). In this way, the number of exempted persons
did not decrease in total. Since 2003, no government has questioned this type of social
protection and it has never been a topic in the health care reforms (Stosic & Krstic 2014).
During the parliamentary elections in 2004, 2008 and 2012, the main political
parties emphasizes that health care should remain free of charge for vulnerable population
groups (Adzic & Adzic, 2014; B92, 2014). Recent attempts of the previous Minister of
Economy to simplify the exemption procedure and to decrease the number of exempted
individuals led to his resignation (Radulovic, 2013).
Overall, our results show that the most vulnerable groups, namely those who cannot
afford to pay, are not protected. Moreover, the propensity to pay officially for exempted
groups was high in 2002 (the beginning of health care reforms) and in 2007 (5 years
after the health care reforms) (Chapter 4). During this period of 5 years, the exemption
mechanism was never fully implemented. Even when the exemption mechanism is
implemented, like in maternity care, the quality of provided services is low (Chapter
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General discussion
163
6). Although the current exemption mechanism is promoted as a strong tool to achieve
social protection in health care, it is still just a political tool to buy social peace.
Further research: As we mentioned above, some exempted groups experience the economic
burden provoked by out-of-pocket patient payments. However, we do not examine how
each of the exempted population groups is affected by different types of out-of-pocket
patient payments. This can be a relevant topic for future study. Future research should
also explore the issue of leakage and under-coverage for different exempted groups.
Policy implications: Policy makers in Serbia should assure a less complicated implementation
of the exemption mechanism. The guidelines that describe the exemption mechanism
should be made more clear and available for patients. Instead of including a large number
of population groups, future policy should pay attention to the adequate targeting of
eligible groups (for example using the already existing insurance system) and adequate
provision of health care services for those who are exempted. The exemption policy in
Serbia should be pro-poor oriented and based on health care status.
While poverty can be a trigger to develop a chronic disease, chronic diseases can also provoke
poverty.
Poverty is one of the main risk factors for developing chronic diseases (Bonu et al; 2005;
Engelagau et al., 2012; WHO, 2005). Also, poverty is associated with poor access to
health care services. Poor people often forgo the use of health care services in order to
prevent high financial burden (Banerjee & Duflo, 2012).
In case of chronic diseases, forgoing medical treatment usually leads to more serious
conditions that require even more expensive medical care (Banerjee & Duflo, 2012).
Although poverty is a main trigger to develop a chronic disease, chronic diseases keep
the poor population group in a vicious cycle of poverty (Chapter 5).
Additionally, the prevalence of chronic diseases is growing all over the world (Adeyi,
Smith & Roberts, 2007; Abegunde & Stanciole, 2008; Bloom & Canning, 2008; Russel,
2004). Once diagnosed, chronic diseases lead to a frequent use of health care services.
Frequent use of health care services and the life-long duration of chronic diseases can push
even wealthy households into poverty (Alleyne et al., 2013). In this way, chronic diseases
are not only provoked by poverty, but they can also generate poverty. This implies a joint
relation between the poverty and chronic diseases.
In Serbia, poverty is systematically assessed since 2002/3 (Chapter 1). The first policy
document that assesses the effects of poverty on population health and health care system
was published in 2003 (World Bank, 2003). The document reports the increased burden
of chronic diseases, more precisely cancer, cardiovascular diseases, diabetes mellitus
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Chapter 7
and mental diseases. Socio-economic factors related to poverty (unemployment) and
environmental factors (NATO bombing and stress) are seen as the main reasons for the
increased burden of chronic diseases (World Bank, 2003). Individual characteristics like
alcohol consumption, smoking and inactive behavior also contribute to the burden of
chronic diseases (Bjegovic et al., 2007). However, the document also state that patients
with diagnosed chronic diseases and disabled individuals report higher out-of-pocket
patient payments than other population groups (World Bank, 2003). This means that
people with diagnosed chronic diseases are more likely to be pushed into poverty because
of the increased health expenditure (Xu et al., 2007). Although the document reveals
the double relation between the chronic diseases and poverty, this relation has not been
explored. However, the Serbian government used the results from this report to design
the current social protection policy. People diagnosed with certain chronic diseases
are exempted from official co-payments (Chapter 4). The full exemption mechanism
is applied for people diagnosed with cancer, while a partial exemption mechanism is
applied for people with diagnosed diabetes mellitus and cardiovascular diseases (Official
Gazette of Republic of Serbia, 2010).
As mentioned above, the aim of the social protection policy is to prevent a potential
financial burden among those groups (Holzman & Jorgsen, 2001). However, even a well-
designed social policy does not always achieve its goal in practice.
Therefore, policy effects should be evaluated after certain period of time. Nevertheless,
5 years (2007) after this social protection measure (the exemption from official co-
payments) was introduced in Serbia, the effects of three main chronic diseases on poverty
were not explored (Chapter 5). Such analysis is however needed to show to which extent
those population groups are protected in reality. Beside the effectiveness of the current
social policy, this analysis can also show which aspects of current policy need to be
improved.
Therefore, in this dissertation, we explore the relation of the three leading chronic
diseases in Serbia with poverty and the financial burden provoked by out-of-pocket
payments when these diseases are present. For this purpose, we use two-stage least square
instrumental variable (IV) approach known as 2SLS (Chapter 5). To assess the financial
burden caused by out-of-pocket patient payments for different types of chronic diseases,
we use one of the approaches mentioned before in Chapter 2, namely the catastrophic
health care expenditure approach (Xu et al., 2007; Wagstaff, 2008). We first identify
households with at least one member with a diagnosed chronic disease, specifically diabetes
mellitus, cardiovascular diseases and progressive diseases. To control for endogenity in
the disease indicators, we use two groups of instruments. The first group is related to
health-related life style behavior (e.g. smoking behavior and eating habits). The second
group of instrumental variables consists of environmental variables like living in an area
7
General discussion
165
affected by uranium during the NATO bombing and being a refugee during the period
1999-2007.
Our results show that all three diseases are significant predictors for pre-payment
poverty when other factors are controlled for. People diagnosed with one of the three
chronic diseases are more likely to be poor. Out-of-pocket patient payments can push
those households even deeper into poverty, which is described as a vicious cycle of poverty
(Banerjee & Duflo, 2012). A family member who is ill cannot work. In this way, health
expenditure is increasing while the available family income is decreasing (Banerjee &
Duflo, 2012). Households are pushed to use coping mechanisms, including borrowing
money, selling assets, and decreasing other expenditures like education (Flores et al.,
2008). However, those mechanisms have only short-term effects, and in case of prolonged
chronic diseases, households are not able to cope with long-term effects of poverty.
Furthermore, our results show significant catastrophic effects of out-of-pocket
payments for diabetes mellitus and cardiovascular diseases (Chapter 5). Diabetes mellitus
and cardiovascular diseases are described as expensive diseases in Serbia (Biorac et al.,
2009; Ivanova et al., 2009).
The medical treatment of diabetes mellitus requires a special food regime, use of
many disposable materials and frequent visits to the doctor. The medical treatment of
cardiovascular diseases, in general, also requires frequent use of medical services (Chapter
5). In Serbia, the organization of health care system contributes to the increased financial
burden. People diagnosed with diabetes and cardiovascular diseases have to use health
services from primary to tertiary level (Official Gazette, 2010). Moreover, they are only
partially exempted from official co-payments (Official Gazette, 2010). According to
our results, diagnosed progressive diseases are not associated with catastrophic health
expenditure. The main therapy protocols for chemotherapy and radiotherapy are free
of charge for all population groups (Official Gazette of RS, 2010). This can prevent a
financial burden for people diagnosed with cancer. Also, people diagnosed with cancer
are usually retired based on their disability to work (Official Gazette of RS, 2010).
In this way, they receive a regular monthly income, irrespectively of their previous
working status. This can also be a protective mechanism for financial burden. As we have
mentioned above, poverty is one of the main reasons to develop chronic diseases (WHO,
2005). However, our results have shown that environmental factors can contribute to
certain chronic diseases (Chapter 5). People who are refugees are more likely to experience
diabetes mellitus, while people who were exposed to uranium bombs are more likely to
experience cardio-vascular diseases (Chapter 5). Although previous literature has reported
on the relation between the exposition to uranium bombs and cancer (Bjegovic et al.,
2007) our data do not confirm this.
However, our data show that different chronic diseases are also a reason for poverty.
The organization of health care system can contribute to the financial burden caused by
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chronic diseases (Biorac et al., 2009). As discussed in Chapter 5, the provision of health
care services for chronically sick is a complex process and requires the involvement of
different levels of the health care system. In case when those levels are not coordinated,
patients face problems like the provision of redundant services and additional costs for
them. Additionally, although, social protection measures exist, they do not adequately
target the most vulnerable groups (Chapter 4). As outlined in Chapter 4 disabled
population groups and people diagnosed with certain chronic diseases, such as cancer,
and people with hearing and speaking problems report official co-payments for health
care services even though they should be exempted.
Further research: In our data, we lack data on the way how chronic diseases are diagnosed.
For example, cardiovascular diseases include a broad range of medical diagnosis from
high blood pressure to stroke. Another limitation is related the treatment of chronic
diseases.
The treatment is characterised by fluctuation over time (Russel, 2008) and the real
financial burden can be observed only with longitudinal data. Further research needs to
focus on these issues.
Policy implications: The current health policy in Serbia is dominantly focused on the
prevention of chronic diseases. However, future policy should pay attention to people
who are already diagnosed with a chronic disease. As we outlined in Chapter 5, better
financial protection of vulnerable groups requires effective exemption mechanisms. In
Serbia, policy makers should design less complicated exemption mechanisms. Moreover,
the organization of the health care system should be changed in order to facilitate both
the availability and affordability of health care services for chronically sick. Specifically, in
Serbia, the care for the chronically sick should be provided as much as possible within the
same location. Since health care management of chronically sick individuals requires the
involvement of both outpatient and inpatient services, they need to actively cooperate.
This can be achieved by using electronic medical records for which technical support was
provided in 2008 by the European Agency for Reconstruction (EAR, 2008). In this way,
it is possible to decrease the provision of redundant services at primary and secondary
level. Regarding affordability, the current exemption mechanism should be extended
for all services related to diabetes mellitus and cardiovascular diseases. In case when all
services are included in the exemption mechanism, the implementation of exemption
policy would be easier for both patients’ and providers.
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General discussion
167
The main problem in maternity care in Serbia is not the low level of obstetricians and
midwifes skills, but the absence of compassion on the side of medical staff.
Maternity care is often used as one of the main indicators in evaluating health care systems
(Bonu et al., 2005; Hulton et al., 2000). If maternity care is well organized to provide
affordable services with good quality, this is seen as a sign that the health care system as
a whole is functioning well (Kanya et al.,; 2014Ronsmans, 2001). The specific position
of maternity care within the health care system is related to the outcome of maternity
care in comparison with other health care units (Ronsmans, 2001). While all other of
the units within the health care system work with ill people, maternity care is oriented
towards patients (i.e. pregnant women) of whom the majority is healthy. Only a small
percentage (on average 5%) of all pregnancies in Serbia are diagnosed as pathological
and they are treated within specialized units (Guyton & Hall, 2000; Stambolovic 1996).
Although, giving birth is a natural physiological process, during the process of
delivery many women are perceived as vulnerable due to the value that a new born child
has for the parents and the society (Chalmers, 2012; Moris, 2007). A new born child is
often perceived as an investment in the future and the society acts in a manner to protect
future investments (the new born child) but also to satisfy the needs of the pregnant
women (Moris, 2007). In some countries, where women do not earn their own income
or where the decisions regarding household expenditure are made by other household
members, pregnant women are also perceived as financially vulnerable (Borghi et al.,
2003). This means that maternity care should be organized not only to provide good
physical care but also to provide affordable services with good quality and adequate access
that are also compatible with women’s needs.
In Serbia, there is an additional reason for the specific position of maternity care
(Chapter 6). In particular, the value that the new born child has for the society increased
in the post conflict society (Andrejic, 2010). During the civil war, the total number
of new born children decreased in comparison with the period of the SFRJ (Andrejic,
2010). The government encouraged women to have more children (Andrejic, 2010). At
the same, the difficult economic situation left many women outside their work (Krstic,
2008). The first social protection measure related to this was a full exemption of pregnant
women in maternity wards from official co-payments (Official Gazette of Republic of
Serbia, 2010). Moreover, both prenatal and postnatal care were provided for free (Janevic
et al., 2011). Other social protection measures included one year of paid maternity leave
and free use of public transport for new mothers (Bajec et al., 2008). The government
explained those measures as a consequence of long-term tradition of social protection
within the health care system in Serbia (Adzic & Adzic, 2011). In this way, the Serbian
government wanted to emphasizes the importance of financial protection for expecting
mothers. In terms of maternity care, social protection is also related to quality of care
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(Cameron et al., 2013). Specifically, women should receive care that is in accordance with
evidence-based data but also in accordance with their preferences.
The first evaluative study about maternity wards in Serbia was conducted in 2005
by UNICEF (UNICEF, 2005). The study was focused on indicators at the system level
like maternal mortality or the presence of skilled person during the childbirth. However,
service-related indicators like access to maternity care, patient payments, quality of
care received and policy regulation were not evaluated. In this dissertation, we examine
service related indicators in maternity wards in Serbia. For this purpose, we combine
data collected among women through the online portal of Mother Courage, results of
a literature review of published studies as well as a review of official documents and
guidelines.
We apply mixed methods using both qualitative and quantitative data (Chapter
6). Our results from all three sources show that the main problems in maternity wards
in Serbia are related to the low psychological accessibility (poor bedside manners and
derogative communication) as well as to the various forms of patient payments.
The three reports published by the Serbian government and the World Bank in 2008,
2010 and 2011 that have described the social protection measures in the health care sector
in Serbia, do not report evidence on the implementation of the exemption mechanism
in the maternity wards (Bajec et al., 2008; Government of the Republic of Serbia, 2010;
Vukovic &Perisic, 2011). However, as shown by the results in this dissertation (Chapter
6), although exempted from official co-payments, many women report informal patient
payments and quasi-formal patient payments (official charges set by the facility but
not regulated by the government). Quasi-formal payments are charged by hospitals for
services that should be provided for free (e.g. epidural analgesia). The main reason for
paying informally is to obtain better quality of care and safety for the new born child
(Stepurko et al., 2013). However, our results show that informal patient payments do not
guarantee better quality of care (Chapter 6). Even though some women report informal
patient payments they still experience some inconveniences related to quality of care,
namely problems with equipment and obligatory but non necessary procedures during
the admission. They also report poor bedside manners and derogative communications.
Despite the problems stated above, the level of physician skills is rated well. This
result is consistent in all three sources that we have used. Moreover, data obtained from
the online collected questionnaires show that physician skills are rated as good also
by women who did not pay informally (Chapter 6). However, the majority of women
from this sample (including those who have reported informal patient payments) report
poor bedside manners, derogative communication and lack of compassion on the side
of health care providers (Chapter 6). They often state that they have not been informed
about the medical procedures or that health care providers address them by protocol
number not by name (Chapter 6). Our results also identify a group of pregnant women
7
General discussion
169
who have reported “special connections” instead of informal patient payments. “Special
connections” are described as friends or relatives who work in the hospital and who can
ensure a special treatment and adequate care. “Special connections” represent someone
whom the pregnant women can trust. Our results also show that women with “special
connections” report fewer inconvenience than those who pay informally (Chapter 6).
In this way, special connections represent a type of informal social protection. It means
that pregnant women in Serbia are aware that formal social protection will not ensure
adequate care in maternity wards. The existence of special connections also emphasizes
that formal financial protection in Serbia is a necessary but not sufficient way to ensure
adequate care.
Further research: In Chapter 6, we use data obtained from online collected questionnaire.
The data that we use represent a non-representative purposive sample. This means that we
only include women who have delivered in a certain period of time. Also, we only include
women who are capable of using the Internet, mostly women from urban areas, and
women motivated to participate. In order to overcome this limitation, we use data from
two more sources, namely the literature review as well as a review of official documents
and guidelines. However, a future study that uses a more representative sample can give
a more comprehensive picture about the service indicators in maternity wards in Serbia.
Policy implications: In Serbia, state maternity wards still have a monopoly position. The
medical technocratic approach is dominant during childbirth. The choice for a certain
type of delivery (like normal physiological childbirth, or childbirth with analgesia) is
still limited. The monopoly position is supported by derogative communication of health
care providers (Chapter 6). Although the Serbian government in cooperation with WHO
has organized training for health care providers in order to improve their communication
skills, problems of derogative communication are still present (Chapter 6). Moreover,
training for obstetricians should be accompanied with better management and control in
maternity wards. In order to provide good services in the maternity wards, the Serbian
government should take in account women’s preferences. The government should also
educate physicians to respect those preferences. This means that good physicians’ skills
are a necessary but not a sufficient condition for providing good quality of care. The
current system of medical education in Serbia is focusing on technical skills of future
physicians. The patients’ needs are not recognized as important for the curative process.
The physicians should become aware that the satisfaction of patient needs can lead to
more effective curative outcomes.
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Chapter 7
Social protection should be more than preventing monetary poverty, it should also refer to
social inclusion.
Social protection represents a set of policy measures that help vulnerable population
groups to deal with risk factors (Holzman & Jorgsen, 2001; also in Chapter1). The main
goal of those policy measures is to prevent poverty (Holzman & Jorgsen, 2001; Chapter
1).
Most often, those measures are focusing on financial protection (Holzman & Jorgsen,
2001). However, as outlined in Chapter 1, poverty is a multidimensional concept that
includes both monetary and non-monetary aspects (Atkinson, 2003; Bourgouignon &
Charkavatry, 2003; Kakwani & Silber, 2008). According to the multidimensional nature
of poverty, the new definition of social protection moves towards a broader concept of
social inclusion (WHO, 2005). Social inclusion is the process that prevents people from
being excluded from society because of poverty, lack of life long education, illness or
as a result of discrimination (European Commission, 2004). This definition assumes
that the precursor for social inclusion is compassion (Standing, 2013). Compassion or
compassionate empathy is defined as a recognition of other people needs, feeling with
them and be ready to help when is needed (Goleman, 2007). Following the previous
definition, in terms of health care, financial accessibility of health care services is only
one aspect of social protection (Chapter 1). Social protection also includes good quality of
provided services and patient-oriented care (Cameron et al., 2013).
In this dissertation, the main focus is to examine the effects of out-of-pocket payments
for public health care services and goods on the financial protection of vulnerable
population groups. As discussed above, our results suggest that 1.1% of all respondents
experience impoverishing effects and 0.8% of all respondents experience catastrophic
health care expenditure based on consumption. However, both impoverishing effects and
catastrophic health care expenditure are higher among health care users (Chapter 2 &
3). Furthermore, we have also shown that certain exempted groups still report official
co-payments (Chapter 4). This implies that the current exemption mechanism is not well
designed and not fully implemented (Chapter 4). We also, show that some population
groups perceived as vulnerable like pregnant women pay for their care and some others
like chronically sick experience catastrophic health care expenditure (Chapter 5 & 6).
Those findings suggest that in some cases, financial protection is not fully achieved in the
Serbian public health care sector.
In this dissertation we do not examine other financial aspects of social exclusion related
to health care, like not being able to use health care services because of discrimination,
lack of social insurance coverage or patients’ attitude towards the quality of provided care.
However, in some parts of our study, our results allow us to go further than pure financial
protection. They show that financial aspects are not the only reason for the failure of the
7
General discussion
171
social protection mechanism. We observe the importance of other than financial aspects
examining the financial protection but also quality of care and policy regulations in the
maternity wards in Serbia (Chapter 6).
In Serbia, maternity care is formally free of charge (Chapter 6). Due to the existence
of informal and quasi-official patient payments, this universal financial protection in
maternal care is limited although it still formally exists (Chapter 6). Moreover, some
women in our study stated that the quality of the provided care does not always satisfy
their needs (Chapter 6). Even if women needs are recognized, adequate help is not always
provided (Chapter 6). This is in accordance with previous studies that have examined
the satisfaction with care provided in hospitals in Serbia. This study was based on a
representative sample, and participants report the lowest level of satisfaction with care
provided in maternity wards (Institute of Public Health, 2011). As we mentioned before,
in our study, many women rated the physicians’ skills as good (Chapter 6), but they
report problems in communication with health care providers. In most cases, women in
our study report derogative communication and disrespectful manners (Chapter 6). Good
quality of care does not include only physicians’ skills and good technical knowledge, but
also compassion with patient needs (Cameron et al., 2013).Those results are in accordance
with previous research on health care in Serbia. For example, research conducted in 2005
by the World Bank measured the opinions of Serbian citizens about the health care system
(World Bank, 2005). This study showed that citizens in Serbia perceived the behaviour of
the staff related to patients as the main problem in the health care system (91.7% report
this as an aspect of the highest importance for improving health care) (World Bank,
2005).
Our results regarding the implementation of the exemption mechanism also enlighten
some factors that can lead towards social inclusion. For example, vulnerable population
groups in Serbia often face difficult administrative procedures that are required to obtain
the exemption status (Chapter 4). IHIS and Ministry of Health, as the main stakeholders
responsible for the exemption mechanism, are still organized as purely bureaucratic
systems. Their decisions are usually based on rigid administrative procedures that do
not reflect evidence on the real needs of vulnerable population groups to be exempted
(Chapter 4).
Further research: In this study, we examine the link between out-of-pocket patient payments
in the public health care system and the financial protection of vulnerable groups in
Serbia. As we mentioned above, social protection goes beyond financial protection. It
includes also social inclusion (Chapter 1). In case when social inclusion is not achieved,
some vulnerable population groups might experience discrimination within the health
care system. We do not examine this issue, which can be the focus of further research.
172
Chapter 7
Future studies should also address the possible stigmatization of vulnerable population
groups within health care system and possible financial effects of the stigmatization.
Policy implications: Our results show that the financial aspects of social protection are not
always effectively applied in the Serbian public health care sector. Moreover, the need
for compassion as an additional aspect of social protection is not recognized by health
care providers and policy makers. As we mentioned above, social inclusion is a complex
concept that includes not only financial protection but also acceptance of vulnerable
population groups within society and respect of their needs (WHO, 2005). Policy makers
should do more to increase awareness towards this type of social protection. Recognizing
the need of patients can also facilitate the implementation for financial protection.
7.3 Concluding remarks
The public health care system in Serbia is jointly funded by compulsory health
insurance collected by HIF and the Ministry of Health, as well as by out-of-pocket
patient payments. Health expenditure from state entities (Ministry of Health & HIF)
expressed as a percentage of GDP varied from 6.6%-6.7% in the period 2001-2007 and
it slightly decreased in 2010 to 5.4% (Government of the Republic of Serbia, 2010),
but it is still comparable to some EU countries (Bulgaria, Hungary, Latvia, Lithuania)
(Gavrilovic & Trmcic, 2014). This level of public health care funding is expected to be
a preventive factor for the financial burden (Xu et al., 2007). The nominal amount of
out-of-pocket patient payments is low and very often described as symbolic (Vukovic &
Perisic, 2011). This is also in accordance with the government social protection policy
which aims to prevent the financial burden provoked by the health care expenditure.
However, our results show some weaknesses of this policy, namely impoverishing effects
and catastrophic health expenditure provoked by official co-payments. These effects are
similar as those in other Western-Balkan countries (Bredenkamp et al., 2010). However,
impoverishing effects and catastrophic health care expenditure provoked by payments
for “bought & brought goods” are much higher. This means that the government should
not neglect those types of payments. A future policy should assure protection from those
types of payments. Although the government spends a significant percentage of GDP on
health care, the allocation of this money is not always monitored (Lecic-Tosevski, 2010).
For example, hospitals are still financed through annual fixed budget. This opens the
door for the existence of payments for “bought & brought goods” but also for existence
of other aspect of corruption.
Furthermore, the current level of the financial burden can be decreased by a more
effective exemption mechanism. Better targeting of vulnerable groups is the precursor
7
General discussion
173
for a better implementation of the exemption mechanism (Palmer, 2000). Policy makers
should pay attention to possible obstacles related to the exemption mechanism, like
difficult administrative procedures, stigmatization and discrimination of disadvantage
groups.
A future policy should be more pro-poor oriented but also take in account the health
status. For example, the current policy emphasizes the importance of prevention of
chronic diseases. However, people who are already diagnosed with a chronic disease face
not only a financial burden but also the poorly organized public health care system.
The difficulties in the organization of health care are also observed in maternity wards,
namely old protocols, forbidden visits of family members are some of the indicators of
poor organization. This dissertation provides evidence on the quality of care offered
in maternity wards. Problems with different types of patient payments, social and
psychological accessibility are reported. The inclusion of private health care providers in
the system of compulsory health insurance can decrease the problems related to patient
payments as well as those related to psychological and social accessibility.
Health care reforms in Serbia started in 2002 and they are still ongoing. During
this period some aspects are improved (better technical equipment, better availability
of pharmaceuticals), some others are worsened (waiting lists, existence of different types
of out-of-pocket patient payments). However, many policy drafts have changed since
2002. The main reasons were political. Every time when the government is changed, a
new policy is introduced. This prevents the continuity of health care reforms. In order to
assure positive outcome of the health care reforms, the Serbian government should assure
more transparency within the system and better recognition of patients’ rights.
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Appendix
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Appendix
Table A.1: Ordinary least square regression (OLS) for three outcome variables and possible instruments (N=5557) (Chapter 5)
Diagnosed diabetes within the households
Diagnosed cardio-vascular disease within the
households
Diagnosed progressive disease in abdomen
within the household
B SE B SE B SE
Share of fat food in total food consumption
0.0004 0.0005 0.001 0.0007 0.0001 0.0005
Share of sweet food in total food consumption
-0.0014* 0.0002 -0.001* 0.0004 -0.0002 0.0003
Number of cigarettes consumed per household
0.000 0.000 0.001** 0.000 0.001* 0.000
Share of alcohol in food consumption
-0.013* 0.0003 -0.002* 0.0005 -0.015* 0.0004
Municipalities affected by uranium–rich bombs
-0.011 0.008 0.024* 0.013 -0.012 0.009
Presence of refuges in household
0.0437* 0.201 -0.014 0.033 -0.168 0.0250
R-squared ( R² ) 0.0095 0.0094 0.0041
*p< 0.05;**p 0.1
Table A.2: Correlation coefficient (Spearman’s rho) between potential instruments and outcome variables (N=5557) (Chapter 5)
Catastrophic effects of health care expenditure
Pre-payment poor (based on consumption)
Share of fat food in total food consumption 0.021 -0.034**
Share of sweet food in total food consumption -0.26 0.017
Number of cigarettes consumed per household -0.003 -0.71*
Share of alcohol in food consumption 0.002 -0.11
Municipalities affected by uranium –rich bombs 0.012 -0.33*
Presence of refuges in household -0.015 0.012
*p< 0.05; **p 0.1
Appendix
189
Table A3: Second stage OLS regression (Chapter 5)
Pre-payment poverty
B SE B SE B SE
Diagnosed progressive disease in abdomen within the household
-0.007* 0.009
Diagnosed cardio-vascular disease within the households
-0.023* 0.007
Diagnosed diabetes within the households
-0.024* 0.011
Household size 0.084* 0.003 0.009* 0.003 0.008** 0.003
Type of settlement -0.013** 0.007 -0.012** 0.007 -0.014** 0.008
Gender of head of the household 0.014** 0.008 v0.014** 0.008 0.183 0.069
Nationality of the head of the household
-0.017** 0.009 -0.017** 0.009 -0.017** 0.009
Age of the head of the household 0.002* 0.0002 0.002* 0.001 0.002* 0.001
Number of kids younger than 7 years within the household
0.023* 0.008 0.021* 0.008 0.022* 0.008
Number of kids older than 7 and younger than 18 years
0.019* 0.006 0.017* 0.006 0.018* 0.005
Income percentiles -0.036* 0.003 -0.040* 0.028 -0.037* 0.003
Education of head of the household -0.012 0.002 -0.012* 0.002 -0.012* 0.002
Constant 0.097 0.22 0.094* 0.022 0.095* 0.022
Catastrophic effects of health care costs
B SE B SE B SE
Diagnosed progressive disease in abdomen within the household
0.032* 0.005
Diagnosed cardio-vascular disease within the households
0.022* 0.004
Diagnosed diabetes within the households
0.032 0.006
Household the size 0.010* 0.002 0.010* 0.001 0.010* 0.002
Type of settlement 0.001 0.004 0.006 0.004 0.0054 0.0045
Gender of head of the household 0.001 0.006 0.0003 0.005 -0.0001 0.004
Nationality of the head of the household
0.001 0.000 0.001 0.005 0.0014 0.006
Age of the head of the household 0.001* 0.000 0.0004* 0.0002 0.001* 0.000
Number of kids younger than 7 years within the household
-0.009* 0.005 -0.008** 0.005 -0.009** 0.005
Number of kids older than 7 and younger than 18 years
0.004 0.003 -0.003 0.042 0.004 0.003
Income percentiles -0.004 0.001 -0.004* 0.002 -0.004* 0.002
Education of head of the household
0.001 0.0001 -0.005 0.001 0.0004 0.0011
Constant -0.039* 0.135 -0.036* 0.013 -0.036* 0.013
Summary
192
Summary
Out-of-pocket patient payments and vulnerable population groups in Serbia
Summary Since 2002, the public health care system in Serbia is funded by compulsory health care
insurance contributions, government spending and official co-payments. Similar to other
CEE countries, the official co-payments are accompanied by informal patient payments
and payments for “bought & brought goods” (i.e. payments for goods that should be
provided for free). The co-existence of the three types of out-of-pocket patient payments
can provoke a significant financial burden. To enhance social protection, following the
introduction of official co-payments, the Serbian government introduced exemption
mechanisms. The existence of compulsory health insurance represents another measure of
social protection. Moreover, the Serbian government spent on average 6.7% of GDP in
the period 2001-2007 on direct financing of public health care. This level of government
expenditure is in accordance with EU standards and provides an additional way of social
protection.
Despite the existence of social protection measures, the financial burden produced by
the three types of out-of-pocket patient payments is still largely unknown. The scientific
evidence on this topic is limited. Moreover, the social protection measures address only
the official co-payments, while the informal patient payments and payments for “bought
& brought goods” are unregistered by those measures.
The aim of this dissertation is to examine the financial burden provoked by out-of-
pocket patient payments on vulnerable population groups in Serbia. The dissertation
consists of seven chapters. Here we present the main findings from each chapter.
Chapter 1 presents a broad overview of the development of the public health care
system in Serbia, as well as its current status. The chapter also outlines the concept of
social protection within the health care system in Serbia. We focus mainly on financial
aspects of social protection. We provide this information as background information to
facilitate the understanding of the results from the dissertation. In this chapter, we also
outline the main goal, objectives and methodology that is used in the dissertation. The
main goal of the dissertation is to examine the effects of out-of-pocket patient payments
on vulnerable population groups in Serbia. Following the main goal, we first examine
the effects of out-of-pocket patient payments on the household budget using different
approaches outlined in the literature (Chapter 2). Furthermore, we examine the financial
burden of different types of out-of-pocket patient payments (Chapter 3). We further
examine whether the exemption mechanism effectively protects vulnerable groups
(Chapter 4). In Chapter 5 and 6, we examine to what extent two vulnerable population
groups (namely chronically sick and pregnant women) are protected by the current
social policy. In Chapter 5, we examine the effects of out-of-pocket patient payments on
Summary
193
households with chronically sick members, specifically household members diagnosed
with one of the three leading chronic disease: diabetes mellitus, cardiovascular diseases
and cancer. Chapter 6 addresses financial protection in maternity wards in Serbia, but also
some other indicators like the accessibility of maternity care, non-medical quality of care
and policy regulations. Chapter 7 presents the main findings of the dissertation followed
by suggestions for future research and policy recommendations.
In this dissertation we use two research approaches. In Chapters 2 to 5, we use
quantitative data analyses based on representative data collected by the World Bank
in 2002, 2003 and 2007. In Chapter 6, we apply a mixed–method design combining
qualitative and quantitative data analyses. Data for the analyses in Chapter 6 are
obtained from three different sources, namely literature review, on-line semi-structured
questionnaires and official guidelines.
In Chapter 2, we examine the effects of out-of-pocket patient payments on the
household’s budget. The literature does not provide consensus how to measure the
financial burden provoked by out-of-pocket patient payments. Therefore, we describe
three different approaches that have been used in previous studies to assess the financial
burden provoked by out-of-pocket patient payments, namely catastrophic health care
expenditure, impoverishing effects and subjective poverty. The objective is to compare
the results across different approaches. Within each approach, different indicators
of household wealth (income, expenditure and consumption) and different poverty
thresholds based on these indicators are applied. Catastrophic health care expenditure
defines out-of-pocket patient spending as catastrophic if it exceeds a certain threshold in
a given period. The threshold represents a pre-defined proportion of household income
or consumption. The threshold is arbitrary and can vary from 5 up to 40% of total
income/consumption. The impoverishing effect of health care spending is measured by
the proportion of households that goes below the poverty line after health care spending
is subtracted from total income or consumption. It is based on a comparison between
the incidence of poverty before and after the subtraction of health care spending by the
household. For the calculation of the impoverishing effects of out-of-pocket payments,
absolute and relative poverty lines are used as thresholds. The two approaches –
catastrophic health care expenditure and impoverishing effects – identify the share of
households/individuals who experience an economic burden due to out-of-pocket patient
payments. Subjective poverty captures the personal perception of being poor. It is usually
measured as the individual evaluation of being poor. Our results indicate that irrespective
of the approach applied, out-of-pocket patient payments have a catastrophic effect on
poor households in Serbia. Moreover, households that are above the absolute, relative and
subjective poverty lines, after the subtraction of out-of-pocket payments, fall below these
poverty lines. The probability of catastrophic out-of-pocket patient payments is higher
among chronically sick household members (namely, people with diabetes and mental
194
Summary
diseases, as well as cardiology diseases in some instances). Perceived health status also
appears to be a significant indicator.
In Chapter 3, we examine the level of different types of out-of-pocket patient
payments in Serbia as well as their effects on household budgets. First we outline the
distinction between the three types of out-of-pocket patient payments in Serbia. We
make a distinction not only between formal and informal patient payments (cash and gifts
in kind given to the physician), but we also analyze the effects of payments for “bought &
brought goods” (i.e. payments for goods brought by the patient to the health care facility
such as disposable materials and pharmaceuticals). The previous literature has considered
payments for “bought & brought goods” as part of informal patient payments. We find
it essential to make a distinction between these two types of patient payments because
they differ in nature. While informal patient payments (such as gifts to the physicians)
remain unregistered, payments for “bought & brought goods” (e.g. for pharmaceuticals
bought in a pharmacy and brought to the hospital) are officially registered at the point
of purchase but not visible in the financial flows of the institution that provides the
services. Thus, in Chapter 3, we estimate the burden of different types of out-of-pocket
patient payments (official co-payments, informal payments and payments for “bought
& brought goods”) using two approaches: catastrophic health care expenditure and the
impoverishing effects of out-of-pocket payments. To assess the catastrophic effects of
different types of payments, we use a threshold of 10%. This means that health care
expenditure has catastrophic effects for individuals/households if it exceeds 10% of their
total consumption. For the assessment of the impoverishing effects, we use the absolute
poverty line as a threshold. Within both approaches - catastrophic health care expenditure
and impoverishing effects - we use consumption as an indicator of wealth. Our results
show that all three types of out-of-pocket patient payments may push Serbian households
into poverty. Thus, out-of-pocket patient payments in Serbia impose a substantial burden
on households. The catastrophic effects of “bought & brought goods” payments are higher
than those of pure informal payments, and comparable to those of official copayments.
In Chapter 4, we focus on the exemption mechanism that accompanies official co-
payments in Serbia. We examine whether selected exempted groups (older than 65 years,
younger than 15 years, disabled, unemployed and people with low household income) pay
official co-payments when they are supposed to be exempted from such fees. We compare
the effects of the exemption mechanism when co-payments were implemented in Serbia,
and 1 and 5 years after their implementation. We compare data on the probability of
paying and the amount of out-of-pocket payments for outpatient and inpatient hospital
services across the five exempted groups that we identified. We also compare the out-of-
pocket payments for outpatient and inpatient hospital services paid by the five exempted
groups with that of other population groups (non-exempted and exempted group that
we could not identify). Our results confirm that the selected exempted groups included
Summary
195
in our study, pay for both outpatient services as well as for inpatient care. However,
payments are overall less frequently reported for outpatient services than for inpatient
services. This difference between the services is expected since the official copayments for
inpatient health care in Serbia are much higher than those for outpatient care. Regarding
inpatient hospital care, the five exempted groups reported formal, “bought & brought
goods”, informal and indirect payments. Our results show that the implementation of
the exemption mechanism, both in outpatient and inpatient hospital care, is failing. The
failures are also visible in terms of the design of the exemption mechanisms.
Chapter 5 of this dissertation examines the relation between the presence of chronic
diseases and poverty. Chronic diseases are a major cause of financial hardship for patients
and their households. Diagnosed chronic diseases usually require a higher utilization of
health care services. When the health care system heavily relies on out-of-pocket patient
payments, increased utilization also increases direct spending by households. In this way,
chronic diseases become a trigger for financial hardship. Moreover, chronic diseases are
more likely to occur among poor individuals, and at the same time, patients with chronic
diseases have a higher probability of becoming poor. This implies a double-sided relation
between the chronic diseases and poverty. The existence of a joint causality can lead to
biased estimates of the poverty effects provoked by chronic diseases. In Chapter 5, we
examine the joint causality between the chronic diseases and poverty using an instrumental
variable approach. As outcome variables, we use indicators of pre-payment poverty and
the catastrophic effects of out-of-pocket patient payments for different types of chronic
diseases. Instrumented variables are indicators of chronic diseases: cardiovascular diseases,
diabetes mellitus and cancer within the household. We use two groups of instruments: The
first group includes indicators of health-related lifestyle behavior (e.g. smoking behavior
and eating habits). The second group of instrumental variables consists of environmental
variables like living in an area affected by uranium during the NATO bombing and
being a refugee during the period 1999-2007. Our results show that all three chronic
diseases can impose an economic burden on households when other relevant factors are
controlled for. However, diabetes mellitus and cardiovascular diseases are significant
predictors of catastrophic health care expenditure, while cancer is not. This result can be
explained by the design of the exemption mechanism in Serbia. Patients diagnosed with
diabetes mellitus and cardiovascular diseases are only partially exempted, while patients
diagnosed with progressive diseases are fully exempted from official co-payments. Our
results also show that different risk factors like life-style behavior and environmental
factors are associated with different chronic diseases. Diabetes mellitus is significantly
associated with having a refugee in the household, while living in municipalities affected
by uranium NATO bombs is significantly associated with cardiovascular diseases.
In Chapter 6 of this dissertation, we provide evidence on the out-of-pocket payments
for maternity care. In Serbia, health care services related to maternity care are officially
196
Summary
free of charge. The social protection policy related to future mothers also includes free
maternity leave up to one year and free public transport. In Chapter 6 of this dissertation,
we examine whether the health care services related to maternity care are provided for
free as defined by the Serbian regulation. Since the modern concept of social protection
does not only include financial protection, we also examine accessibility to maternity
wards in Serbia (geographical, psychological and social accessibility), clinical quality
(obstetric care/procedures, quality of equipment and level of physician skills) and social
quality of care (e.g., facility maintenance), patient payments (type of payment, receiver
of payment, frequency of payment, magnitude of payment, purpose of payment and
attitude toward payments ),policy regulations (regulations with respect to payments,
equity and governmental/hospital protocols), innovations in maternity care (the presence
of the partner during childbirth and innovations about immediate breastfeeding and
rooming-in), and health indicators (health system indicators such as maternal and infant
mortality, cesarean rate, presence of a skilled care practitioner ). For this purpose, we apply
a mixed-method. We use data collected through three sources: online questionnaires
filled in by mothers who delivered in one of the maternity wards in Serbia in the period
2000–2008, research publications, and official guidelines. By combining the data from
different sources we use the method of triangulation. To compare the qualitative data
from all three sources, we apply framework analysis. The results show a good network of
maternity wards in Serbia. On the other hand, many women who gave birth in maternity
wards in Serbia indicate problems with the treatment they received. The existence of
informal patient payments and so-called “special connections” make the position of
Serbian women in maternity wards vulnerable, especially when they have neither
connections nor the ability to pay. Poor communication and lack of sympathetic bedside
manners of medical staff (obstetricians, other physicians, midwives, and nurses) during
the birth process are also frequently reported.
Chapter 7 provides the discussion of the main results, policy implications and
implication for future research. Overall, the results reported in this dissertation show
that out-of-pocket patient payments provoke a financial burden in Serbia. The extent of
the financial burden and the affected population groups are conditioned by the approach
that is used. When we apply impoverishing effects to assess the burden, poor population
groups are mostly affected. When we use the catastrophic health care expenditure
approach, the burden is higher for middle-income groups. Our results also show that
not only the chosen approach, but also indicators of wealth and cut-off points play a role.
Different types of out-of-pocket patient payments also influence the extent of financial
burden. Payments for “bought & brought goods” have most intensive catastrophic
effects. Moreover, catastrophic effects provoked by payments for “bought & brought
goods” are experienced by some vulnerable population groups like low income groups
and population groups diagnosed with chronic diseases. Informal patient payments are
Summary
197
more prevalent among wealthier population groups. The shadow nature of payments for
“bought & brought goods” and their high catastrophic effects require additional attention
of policy makers in Serbia. Although the exemption mechanisms in Serbia include a large
number of population groups, the impoverishing effects of official co-payments are still
high. One of the reasons is that some vulnerable groups like people diagnosed with
diabetes mellitus are only partially exempted. Our results show that the failure of the
exemption mechanism is not only related to the design, but also to its implementation.
Even when the exemption mechanism is fully applied, like in case of maternity care,
vulnerable population groups still report informal patient payments. Those findings
suggest that financial protection is still not achieved. The results from this dissertation
suggest that social protection policy in Serbia related to health care should focus on
population groups that are frequent health care users (chronically sick) and those from
low income groups. Moreover, as our results related to maternity care suggests, social
protection should go beyond financial protection and include also some aspect of social
inclusion like the respect of patients’ rights and transparent communication.
198
Acknowledgements
Acknowledgements
199
Sta sve placaju pacijenti u okviru sistema javnog zdravlja Republike Srbije-osvrt na vulnerabilne grupe
Od 2002 , sistem javnog zdravlja Republike Srbiji se finansira kroz budzetsko davanje
(sredstva koja obezbedjuje vlada Republike Srbije kroz porez gradjana), putem obaveznog
zdravstvenog osiguranja i kroz participaciju pacijenata-korisnika usluga. Slicno kao
i u drugim zemljama Centralne i Istocne Evrope, sistem javnog zdravlja u Srbiji nije
ostao imum na tzv. placanja ispod ruke (mito ili korupcija“, u stranoj literaturi poznato
kao:„informal patient payments“). Pored klasicnog davanja mita u vidu poklona ili
novca zdravstvenom osoblju, u sistemu javnog zdravlja Republike Srbije postoji i tzv.
dodatno davanje u vidu lekova, materijala ili hrane (payments for “bought and brought
goods”). Radi se o sredstvima koja bi trebalo da budu obezbedjena osiguranim licima
u okviru sistema javne zdravstvene zastite ali ih zdravstvene ustanove nemaju ili imaju
u nedovoljnoj i neadekvatnoj kolicini pa porodica pacijenata mora da ih kupi i donese.
Primeri takvog placanja su: donosenje lekova koji su neophodni za lecenje a bolnica ih
nema, donosnje hrane, spavacice itd. Postojanje tri razlicite vrste placanja (zvanicna
participacija, mito /korupcija i davanja u vidu dodatnih sredstava) u okviru sistema javnog
zdravlja Republike Srbije, stavlja korisnike zdravstvenih usluga u nezavidan finansijski
polozaj. Korisnici zdravstenih usuga su na taj nacin izlozeni povecanom finansijskom
riziku. U zelji da zastiti vulnerabilne socijalne grupe, vlada Republike Srbije je donela
zakon po kome su ove grupe izuzete od placanja zvanicne participacije. To je samo jedna
mera socijalne zastite koju je vlada Republike Srbije sprovela u okviru sistema javnog
zdravlja. Samo postojanje zdravstvenog osiguranja takodje predstavlja meru socijalne
zastite. Davanja republicke vlade za zdravstvo koja su tokom perioda 2001-2007 iznosila
u proseku 6.7% republickog BDP, takodje predstavljaju meru socijalne zastite i u skladu
sa standardim EU. Bezobzira na postojanje mera socijalne zastite, korisnici zdravstvenih
usluga u Srbiji se suocavaju sa finansijskim teskocama. Finansijske teskoce su posebno
uocljive medju tzv. vulnerabilnim grupama i prouzrokvane su direktnim i neocekivanim
placanjem iz dzepa.
Glavni cilj ove disertacije je da ispita obim i intezitet finansijskih teskoca prouzrokavnih
placanjem iz dzepa a sa kojima se susrecu vulnerabilne grupe.
Disertacija je organizovana kroz sedam poglavlja. Prvo poglavlje predstavlja uvod u
disertaciju. Ovo poglavlje opisuje danasnji zdravstveni sistem u Srbiji i nacine njegovog
finansiranja. Takodje ovo poglavlje oslikava i aktuelne probleme vezane za finansiranje
zdravstvenog sistema u Srbiji. Poglavlje se takodje odnosi i na probleeme sa kojima se
200
Acknowledgements
suocavaju pacijenti prilikom placanja zdravstvenih usulga. Iako su reforme u javnom
zdravstvu u Srbiji, zapocele jos 2002, do danas nema empirijskih studija koje su se bavile
ovim problemima. Jedan od najupecatljivijih primera sigurno je, postojanje placanja
putem mita. Iako se anegdotski izvori poput dnevnih novina ili televizije redovno
bave ovim problemom, do danas nema empirijskih istrazivanja koja govore o obimu
i intezitetu placanja putem mita u srpskom zdravstvu. Takodje nema ni podataka, ni
koliki je obim i intezitet finansijskog tereta sa kojim se suocavaju korisnici zdravstvenih
usluga a koji moze biti izazvan upravo postojanjem razlicitih vrsta placanja. Cilj ove
disertacije je da odgovori na ova pitanja koristeci empirijske metode istrazivanja. Za
potrebe ove disertacije korisceni su podaci prikljupeni u studiji o zivotnom standardu koja
je sprovedena u periodu 2002-2007 od strane Svetske Banke.Podaci su reprezentativni za
teritoriju Republike Srbije, bez Kosova i Metohije.
Drugo poglavlje ove teze prezentuje ustaljene empirijske metode kojima se ispituje
obim finansijskog tereta prouzrokovanog placanjem zdravstvenih troskova. Koristeci dve
ustaljene metode i podatke o zivotnom standardu, u drugom poglavlju prezentujemo
rezultate koji pokazuju u kom obimu se pojedinci u Srbiji suocavaju sa osiromasenjem ili
katastrofalnim troskovima usled placanja zdravstvenih usluga. Nasi rezultati pokazuju
da procena osiromasenja ili katastrofalnih troskova u mnogome zavisi ne samo od metode
koju koristimo, vec i od izabranih indikatora nivoa zivotnog standarda. Tako, npr. u Srbiji
obim osiromasenja prouzrokovan troskovima za zdravstvene usluge zavisi i od toga da li se
u proceni kao indikator nivoa zivotnog standarda koriste ukupna primanja domacinstva
na mesecnom nivou ili njihova mesecna potrosnja. Poput drugih zemalja sa sivom
ekonomijom i u Srbiji su mesecna primanja domacinstva manja od mesecne potrosnje,
sto govori u prilog postojanja neprijavljenih primanja ili posedovanje dodatnih resursa za
ostvarenje prihoda. Nasi rezultati pokazuju da osobe obolele od hronicnih bolesti, osobe
koje zive u ruralnim podrucjima i oni koji zive sami imaju vecu verovatnocu da se suoce
sa osiromasenjem usled postojanja zdravstvenih troskova.
Trece poglavlje teze ispituje u kojoj meri razlicite vrste placanja-zvanicna participacija
i placanje ispod ruke (mito,korupcija) doprinose osiromasenju gradjana Srbije. Nasi
resultati pokazuju da su efekti placanja u vidu davanja hrane lekova i odstalih sredstva
pogubniji za budzet korisnika zdravstvenih usluga nego placanje ispod ruke. Zvanicna
participacija takodje doprinosi osiromasenju gradjana, posebno tzv.vulnerabilnih grupa
kao sto su stari, deca, oboleli od hronicnih bolesti itd.
Cetvrto poglavlje teze bavi se upravo ovim vulnerabilnim grupama koje su zakonima
Republike Srbije oslobodjene od placanja zvanicne participacije. Iako zakon kao
vulnerabilne grupe navodi 16 kategorija, ukljucujuci i svestenike, u ovom poglavlju
mi identifikujemo cetiri grupe: osobe mladje od 15 godina starosti, osobe starije od 65
godina, osobe sa primanjima ispod republickog proseka i osobe onesposobljene za rad.
Nasi rezultati pokazuju da iako zvanicno oslobodjeni participacije, ove grupe placaju
zdravstvene usluge. Takodje nasi rezultati pokazuju da trenutna polisa izuzimanja od
Acknowledgements
201
participacije ne samo da se ne primenjuje adekvatno, vec nije ni adekvatno dizajnirana.
Tako npr. Nisu sve osobe starije od 65 godina finansijski vulnerabilne. Buduca polisa bi
trebalo da obezbedi bolju identifikaciju osoba koje su zaista finansijski vulnerabilne.
Peto poglavlje ove teze se bavi jos jednom vulnerabilnom grupom-osobama kod
kojih je dijagnostifikovana jedna od tri hronicne bolesti-diabtes mellitus, neki oblik
maligne bolesti ili neko od oboljenja iz grupe kardio-vaskularne bolesti. U ovom
poglavlju ispitujemo efekte placanja iz dzepa na ove subpopulacijske grupe u Srbiji i
na budzete njihovih porodica. Nasi rezultati pokazuju da osobe obolele od dijabetsa ili
nekog od oboljenja iz kardio-vaskularne grupe imaju vecu verovatnocu da budu izlozeni
osiromasenju usled placanja iz dzepa, dok su osobe sa dijagnozom nekog iz grupe od
malignih oboljenja manje izlozene tom riziku. Jedan od razloga je i trenutna organizacija
zdravstvenog sistema u Srbiji-vecina dijagnostickih i terapijskih procedura vezana za
maligna oboljenja se obavlja u tzv.“drzavnim“ustanovama, dok se procedure evzane za
druge dve grupe hronicnih oboljenja vrlo cesto moraju obaviti u nekoj od privatnih
ustanova usled dugackih lista cekanja.
Sest poglavlje ove teze se bavi placanjima i kvalitetom pruzene usluge u porodilistima
u Srbiji. Trudnice se u Srbiji takodje oslobodjene placanja zvanicne participacije i imaju
status vulnerabilne grupe. Cilj ove mere je da se poboljsa stopa radjanja u Srbiji koja je
u stalnom padu od 1991. Nasi rezultati pokazuju da trudnice kao i druge vulnerabilne
grupe placaju tzv. „pre-natalnu“negu a i sam porodjaj. Vrste placanja sa kojima se one
susrecu su razlicita: od mita lekaru do donosenja lekova i spavacica. Takodje nasi rezultati
pokazuju da davanje mita lekaru u porodilistima nije garant kvalitetne usluge. Rezultati
takodje svedoce o postojanju jos jednog nematerijalnog oblika placanja-a to su „veze“.
Trudnice koje su bile zadovoljne kvalitetom pruzene usluge su uglavnom poznavale lekara
ili su do njega dosle preporukom. U takvom slucaju one nisu davale mito u vidu novca,
ali ostaje nejasno da li se ovakva usluga uzvraca nekom drugom uslugom. Istrazivanje
u porodilistima je pokazalo i da finansijska zastita nije jedini vid socijalne zastite koji
nedostaje zdravstvenom sistemu Srbije. Naime vecina trudnica navodi los odnos prema
njima, omalovazavanje i negiranje njihovih potreba kao glavni problem tokom boravka u
porodilistu.
Sedmo poglavlje ove teze prezentuje zakljucke koji su proizisli iz svih prethodnih
analiza.Takodje ovo poglavlje daje i predloge za bolje regulative u sistemu javnog
zdravlja Republike Srbije. Iako Ministarstvo zdravlja Republike Srbije sa ponosom istice
poboljsanja koja su nasrupila od 2002 i pcetka reformi u zdravstvu kao su npr. bolja
dostupnost lekova, ili bolja tehnicka opremljenost bolnica, rezultati naseg istrazivanja
pokazuju i neka pogorsanja-poput veceg obima placanja korisnika zdravstvenih usuga i
nedovoljnog obima zastite tzv. vulnerabilnih grupa. Obzirom da je cilj vlade Republike
Srbije pridruzivanje clanstvu Evropske Unije, pitanja mita i korupcije u zdravstvu ce
predstavljati zasigurno jedan od najvecih izazova u narednom periodu.
Acknowledgements
203
Thank you words
At the end of my PhD, I also felt the personal need to say thank you to some people who
meant a lot to me during the last few years.
To start this PhD would not have been possible without my co-promoter, Milena.
Milena, thank you for believing in me when no one did (including myself). Besides
giving me the chance to pursue my PhD research, you also provided a nice empathic
work-environment. It was nice to come to work and to know that there is always someone
with whom you can share all your doubts and who always has a time for one more coffee!
Thank you for long night readings of all the terrible first drafts. Thank you for sharing
your knowledge with me even when I was resistant to hear you. I will always remember
our discussions about “utility” and “altruism”. You thought me how patience and
consistency are important and that they do not necessary “kill” the creativity. Somehow
you always were there when I needed you, always listening and understanding. For all
these years, thank you for being a great friend and tough criticizer!
To my promoter, Wim - I always admired you capability to find the most rational
solution for most irrational problems. It was a pleasure to work with someone who is not
only a great researcher but also a true “eruditus” with whom I could always discuss literally
everything. Thank you for always being willing to read “just one more draft”, usually in
the train due to my one night deadlines, for helping me to transfer my “dissident ideas”
into nice scientific papers, for finding just one right word that I was always missing, for
putting “the” and “a” in the places where one native speaking Slavic person would never
assume they are needed. I also thank you for endless academic discussions, for inspiring
me to learn how to read Dutch, for being patient with Balkan temperament, and for all
good jokes (usually on my behalf).
Thank you both for “infecting” me with one of the “most addictive contagious disease”
-research. Also, thank you for providing me the opportunity to continue my research here
in Maastricht. Since our previous meetings were full of challenging discussions, good
ideas and laughs, I am looking forward to more of it to come!
I also want to thank some special friends that made this PhD experience unique.
To Katarina, Aca i Relja for being my second family here and for giving me a feeling of
warm Serbian home outside Serbia. Aca, thank you for wonderful party weekends full of
good Serbian food! Relja, hvala ti za sve divne osmehe, divno vreme provedeno u igranju
i za najlepsi kurs holandskog! bedankt voor het spelen en nederlands praten! Draga, tebi
hvala za svu bezrezervnu podrsku svih ovih godina, za sve duge telefonske razgovore,
i za sve neplanirane sastanke u Zondagu. (Dear Katarina, thank you for unconditional
support, long talks and all unplanned parties in Zondag).
204
Acknowledgements
To Fede for wonderful South European jokes so necessary to survive the Dutch weather
and for last minutes arrangements-for going out or for a trip!
Lieve Nora, thank you for all hedonistic Maastricht nights-starting with good dinners
and wine, continue with dancing in Muziekgieterij and finishing with sitting on the
window of Zondag and discussing philosophy, politics and Dutch guys, of course!
To Eva, for making my master year here unforgettable, for all beautiful trips together and
for more others to come! To Marija and Bojan, for great Stockholm evenings.
To Luisa, for being always present, for nice support and for Italian lessons! To Ghislaine,
for sharing an office during last two years, but not only the office-thank you for sharing the
lunches together, experiences and important private moments. I will always remember
our walk on Irish cliffs! To Vera, for wonderful Krakow trips and Maastricht movie
nights! To Eveline for great Monday evenings with jazz music. To Adrienne –for nice
conference in Crete and for tango evenings. To Marla, for early morning coffee walks-so
I can be at work in time ;).
To all my ASSPRO CEE colleagues-being a part of ASSPRO CEE family does not
mean only having nice project meetings in beautiful cities like Budapest or Vilnius, it
also means working with nice people. Special thanks go to my PhD fellows –Petra, Elka,
Marzena and Tania for wonderful ladies nights in Ginger.
To my Serbian friends, who despite all geographical distance and different life
circumstances stay with me all these years. To Misa Matijasevic, my “friend in crime” for
his unconditional support during my application for MTEC scholarship and for the fact
that he was my first visitor here. To Marko for designing the cover page of this thesis. To
all dear people for wonderful summer nights “pod tremom”.
To my parents - who supported me in my choices, although they were different from
convenient Serbian standards. Thank you for raising me to believe in my dreams.
Last, but not least, to my sister Ana, for being my best adviser and my faithful supporter.
For all these years, thank you for being my emotional shelter in every storm and my best
companion in all great moments. Thank you and Moca together, for giving me a nice
felling of home whenever I am in Belgrade. To my nephew Kosta-dragi Kosta, hvala ti
za sve osmehe , za sve zagrljaje, za sve divne razgovore i za sve angry birds igrice koje
sam naucila zahvaljujuci tebi .
Maastricht, 2015
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Curriculum Vitae
Curriculum Vitae
Jelena Arsenijevic was born in 1978 in Kragujevac, Serbia. She obtained a degree in
clinical psychology in Belgrade Serbia, completing the studies as the top student of her
generation. She worked for a number of years in practice (oncology, neuropsychology).
In 2008, she obtained a full scholarship from the Dutch Ministry of Foreign Affairs to
pursue a Master’s degree in Public Health at Maastricht University. Upon graduation,
she worked as a research assistant on the topic of ankyloses spondylitis at the Department
of Health Organization Policy and Economics, Maastricht University.
In 2010, Jelena started her PhD within the FP7 project ASSPRO CEE 2007 focused
on out-of-pocket payments for health care services in Central and Eastern European
countries. In 2012, she obtained a CAPHRI travel grant that allowed her to spend 2
months at the London School of Economics in 2013 when she carried out a study on the
relation between poverty and chronic diseases among elderly in EU countries. Also, in
2013, she was a rapporteur for Slovenia within the Study on Corruption in the Health Care
Sector, commissioned by European Commission. During her PhD she also participated in
exchange programs with University of Krakow and University of Tbilisi, organized by
CAPHRI. At present, she is working as post-doctoral researcher in the CHAFEA project
ProHealth 65+ focused on financing and organization of health promotion activities for
elderly.