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Ruprechts-Karls University of Heidelberg

Department of Tropical Hygiene

and Public Health

Postgraduate Master of Science Degree Course

“Community Health and Health

Management in Developing Countries”

The influence of the factor distance on utilisation of a Health

Center in rural Guinea and its implications for the associated

health insurance scheme

Mai – June 2000

Author: Götz Huber

Field Study Tutor: Dr. Michael Marx

Local Tutor : Dr. Franz von Roenne

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1. EXECUTIVE SUMMARY ................................................................................. 5

2. INTRODUCTION ............................................................................................. 6

2.1 Background of the study .............................................................................. 6

2.2 The context of the health insurance scheme “Maliando” .............................. 7

2.3 Guinea: Geography and socio-demograhic data .......................................... 9

2.4 History ........................................................................................................ 10

2.5 Socio-economic situation ........................................................................... 10

2.6 Health sector analysis ............................................................................. 11

2.6.1 The Guinean Health system................................................................ 11

2.6.2 Bamako Initiative (BI) in Guinea ......................................................... 11

2.6.3 Implementation of core BI strategies ................................................... 12

2.7 Background Information of the study area ................................................. 14

2.7.1 Profile of the natural region „Guinee forestière“ ................................. 14

2.7.2 German Agency for Technical Cooperation (GTZ)/ PSR .................... 14

2.7.3 PRIMA / “Maliando” ............................................................................. 15

2.7.4 District Health Sector with the Health Center Yende ........................... 16

2.8 Purpose of the study .................................................................................. 16

2.9 Research question ..................................................................................... 17

2.10 Research objectives ............................................................................... 17

2.10.1 General objective ................................................................................ 17

2.10.2 Specific objectives .............................................................................. 18

3 LITERATURE REVIEW ................................................................................. 19

3.1 Introduction ................................................................................................ 19

3.2 Health service utilisation ............................................................................ 19

3.3 Health care decision process ..................................................................... 21

3.3.1 Illness concepts ........................................................................................ 22

3.3.2 Utilisation of traditional medicine .............................................................. 22

3.4. Utilisation in Guinea ................................................................................... 23

3.4.1 Curative care ........................................................................................ 23

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3.4.2 Antenatal care and EPI ....................................................................... 24

3.5. Utilization related to distance (geographical accessibility) ......................... 24

3.6. Utilization related to costs (financial accessibility) ...................................... 25

3.6.1. Household expenditure for health Guinea ........................................... 25

3.6.2 Costs incurred of overcoming distance ............................................... 26

3.7 Utilization related to Quality of care ............................................................ 28

3.7.1 Perceived quality and reliability ........................................................... 28

3.7.2 Donabedian’s classification of quality of care ....................................... 29

3.8. Mutual health insurance schemes ............................................................. 30

3.8.1 Data on utilisation rates "Maliando" previous Studies ......................... 33

3.9 Methodological aspects .............................................................................. 33

3.9.1 Quantitative techniques used in the study .......................................... 33

5.9.1.3 Concept of distance decay ................................................................ 35

5.9.1.4 Population Census ............................................................................. 35

5.9.1.5 Global Positioning System (GPS) ...................................................... 36

5.9.1.6 Catchment area population ................................................................ 36

5.9.1.7 Coverage/ utilisation data .................................................................. 37

5.9.1.8 Calculating accessibility ..................................................................... 38

5.9.1.9 Calculating Utilisation ........................................................................ 38

3.9.2 Qualitative methods .................................................................................. 39

3.9.2.1 Combination of qualitative and quantitative approaches .................... 39

4 METHODOLOGY .......................................................................................... 41

4.1 Study area description and study population ............................................ 41

4.2 Time frame ................................................................................................. 41

4.3 Study type and design ................................................................................ 41

4.4 Finance and logistics. ................................................................................. 41

4.5 Data collection............................................................................................ 42

4.5.1 Data collecting tools .......................................................................... 42

4.5.2 Selection, staff training and Pretest ...................................................... 43

4.5.3 Data collection procedure/ Computer Data entry .................................. 44

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4.6. Data analysis .............................................................................................. 46

4.7 Validation .................................................................................................... 46

4.8 Limitations .................................................................................................. 47

4.9 Ethical considerations ............................................................................... 47

5. RESULTS ...................................................................................................... 48

5.1 Introduction ................................................................................................ 48

5.2 Population census ...................................................................................... 48

5.2.1 Global figures ...................................................................................... 48

5.2.3 Relating distance to the population figures ......................................... 49

5.3.1 Assessment of Outpatient register (CPC) and of preventive Services .... 49

5.3.1.1 Origin of attendees ........................................................................... 49

5.3.1.2 Age groups ........................................................................................ 50

5.3.1.3 General features attendance curative care (CPC) ............................. 50

5.4.1 Summary of the Findings .................................................................... 56

6. Discussion ................................................................................................... 58

6.1 Introduction ................................................................................................ 58

6.2 Institutional versus administrative catchment area population/Health ........ 58

6.3 Results from the “Monitorage” December 1999 .......................................... 59

6.3.1 Accessibility ........................................................................................ 59

6.3.2 Curative Care Utilisation from within the Yende sub-district ............... 60

6.4 Member and Non-member utilisation rate ................................................. 61

6.5 “Maliando”/ PRIMA ..................................................................................... 62

7. CONCLUSIONS AND RECOMMENDATIONS ................................................ 65

8. ANNEXE ........................................................................................................ 66

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1. EXECUTIVE SUMMARY

With the introduction of the Bamako Initiative in 1988 in Guinea, user fees were

introduced in a strategy of community financing mechanisms. A major problem is

the occurrence of permanent or temporary exclusion from health care. Locally

developed and district based health insurance schemes were considered to

provide a solution to this problem. The health insurance scheme “Maliando”

established in rural Guinea which is concerned by this study has only achieved

very low coverage rates. In a retrospective study using quantitative methods the

outpatient register of a rural health center was assessed to examine the influence

of the factor distance on its utilisation and the implications for the “Maliando”

health insurance scheme. To provide the basis for utilisation rate calculations, a

population census comprising 19.961 persons living in 2834 households was

conducted. Precise utilisation rate calculations for concentric distance bands were

done and relationships between distance and utilisation rates established. People

living in the 2 km range consulted 4,3 times as much the health center than people

living 6 km away.

Members of the insurance scheme living in a distance of 11-15 km use the health

center half as much as members living close (1-5 km). This may provide the

organizers of the scheme with arguments to introduce gradients in fees (sliding

scales according to distance) for the premium level. General observations about

the problems of “Maliando” were made during community meetings. The difference

in the perceived quality of care was a major reason for misunderstandings

between the health services and its users. There were unrealistic expectations of

the health services, Injections were considered high quality treatment and

standard drugs like Chloroquine, Cotrimoxazol and Aspirin were not appreciated.

People perceive the variety of drugs offered at the health center as too restrictive.

Considerable efforts by the promoters of the scheme have to be done to overcome

the misconceptions, that providers and users speak a common language.

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2. INTRODUCTION

2.1 Background of the study

In the framework of health system reform in Guinea, a major emphasis in the

attempts to improve health system performance has been to strengthen the health

information system (HIS). HIS capacity has been strengthened on the national and

the district level. Important progress has been achieved. Epidemiological and

service data are regularily compiled into reports and the results published in

regional and national sanitary reports. Clinicians in first line health services spend

a considerable time on data collection and report writing. The reports consist of

morbidity and health facility attendance as well as performance statistics.

Routinely collected service statistics give information on how many people were

served. The translation of the absolute figures into population based rates is done

by relating the figures to the population served, the clinic catchment area. These

data of utilisation and coverage, even if somewhat imprecise, are important for

comparative and monitoring purposes.

The collection of data of health service utilisation and coverage is useful to give

information on the prevalence of health problems and to monitor the effectiveness

and efficiency of health facilities. Low coverage may point to the existence of a

problem and may give rise for interventions to reduce unmet needs.

Doubts have been raised regarding the accuracy of the data, especially on

utilisation and coverage, for the following reasons:

1. The clinic catchment area is usually defined as the administrative area.

The institutional catchment area often does not correlate with the

administrative division (mismatch).

2. The data from large national surveys are often outdated and of dubious quality,

because the population often does not give true figures in a census for fear that

it may be used for tax purposes. In other occasions before elections the

numbers may be inflated.

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3. The numbers are only rarely updated at sub-district level and population

density figures are mostly unavailable at small administrative units. It is

therefore difficult for local health staff to produce reasonable accurate

estimates. It would often require a basic census to provide denominator data to

calculate coverage.

These problems with data from the existing HIS all add up so that the actual

figures presented may far divert from the truth and distort performance statistics.

This study intends to provide reliable data of the population living in the radius of

15 km and tries to investigate through the assessment of the patient register from

which area surrounding the rural health center Yende patients are drawn. The

utilisation of the different services of the HC is investigated and the effect of

distance demonstrated. This health center is the first line health care provider for

the district based health insurance scheme ”Maliando“.

2.2 The context of the health insurance scheme “Maliando”

After the introduction of community financing mechanisms with the Bamako

Initiative (BI), 1988 in Guinea, it was observed that certain groups within the

population were excluded from medical care because they could not afford to pay

the treatment fees at health center level. Another group delays treatment (in

average 2,75 days) when money has to be searched for borrowing (Roque, 1995).

At least 30% face temporary exclusion in the third trimester of the year as rural

household’s during that time face an important drop in income. 4 % of the

population, the indigents, could at no time afford paying for health care.

Excemptions to protect the poor from the full burden of fees were not effective. In

addition it was observed that utilisation rate of the health centers reduced because

of degrading quality of services delivered at health center level (Sylla, 1998).

There was also a growing dissatisfaction amongst the users because of the rigidity

of the system, the “Minimum" Package of Activities, the rigid use of flowcharts and

drugs at the health center’s curative consultation and because of the poor

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perceived quality of the public health care sector services. People increasingly

perceived the existing (public) offer as poor value for their money (Criel, 1999).

To find a solution to the problems, the Ministry of Health Guinea together with the

German Technical Cooperation-Guinea launched the action research project

“PRIMA” (Projet de Recherche sur le Partage des Risques maladies) in

September 1996 in the district (Prefecture) Kissidougou to study the feasibility and

potential of local health insurance schemes in Guinea/ Conakry.

The concept of a Mutual Health Organisation was developed and called MUCAS

(Mutuelle Communautaire d’Aire de Sante). The first MUCAS, called ““Maliando””

(Solidarity), started in 1998 in the sub-district (Sous-Prefecture) Yende. The

scheme targets the catchment area of the Yende health centre and covers

medical treatment at the primary level and at secondary level care for specified

surgical conditions (Cesarean Section, incarcerated hernia, extra-uterine

pregnancy, operable uterine tumor and appendectomy) and pediatric inpatient

care in two contracted hospitals including transport fees. ““Maliando”” takes as well

free of charge the care of a limited number of indigents.

Members of the ““Maliando”” insurance scheme once a year pay a premium

depending on family size, which covers for all illness episodes experienced by

family members for treatment at the health center including drugs. In addition a

small amount (“ticket moderateur”) has to be paid to reduce abuse (Sylla, 1998).

After two completed years of functioning the MUCAS ““Maliando”” has reached a

critical point because the membership rate had decreased dramatically. Having

dropped from 1352 to 1014 adherents, less than 600 people enlisted again. The

following observations were made during community meetings for the possible

reasons of the low acceptance of the health insurance scheme. In summary the

main reasons are the following:

The insurance premium is considerd as too high

There were unrealistic expectations of the health services, injections are

considered high quality treatment, “Strong medicine“, common drugs like AAS,

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Chloroquine, and Cotrimoxazole are not appreciated and considered low

quality treatment

People do not value the comprehensive services offered at the health center,

the medical act is seen as dispensing drugs and people compare the quantity

of drugs and the price they pay for it at the health center with the cost of the

same drugs if purchased directly at the street vendor

There was a lack of understanding and comprehension of the basic elements

of health insurance to prevent abuse and reduce moral hazards, the main

advantages of health insurance were insufficiently known

This study hopes as well to contribute to the reasons and possible solutions to the

low acceptance of the insurance scheme.

2.3 Guinea: Geography and socio-demograhic data

The Republic of Guinea is a tropical West African country of approximately 7.52

million inhabitants with a total land area of 246,000 km2. Guinea is located on the

Atlantic coast between Guinea- Bissau and Sierra Leone and borders inland with

Senegal, Mali, Ivory Coast and Liberia. Guinea is divided into four natural regions

(Lower, Middle, Upper and Forest Guinea) and/but administratively into seven

administrative regions with 33 Districts (“Prefectures”).

The capital Conakry has approximately 1.5 million inhabitants and contains over

20 % of the national population.

Important progress has been achieved in the health sector through the introduction

of Bamako initiative (BI) policies since 1988. Demographic and health indicators

improved. From 1985 to 1999 the following indicators have reduced: crude birth

rate from 47 to 41, crude death rate from 23 to 17, total fertility from 6 to 5.5,

infant mortality rate from 162 to 137 (1997), Under Five mortality rate from 259 to

215 (1995). (MSP, 1997). The life expectancy at birth is 47 years and the average

number of children per women is 5.7 (MSP, 1997). 47 % of the population are

under the age of fifteen. 18 percent of Guinean’s are under five years old (CIHI,

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1995). The population consists of various major ethnic groups (last data of from

1965) including the Fulanis, Peuls (40%), Malinke (Mandingos, 35%), Soussou,

Kissiens, and other “forest ethnicity’s" . An estimated 85 % of Guinean’s are

Muslim just 5% are Christians, mainly Roman Catholics.

2.4 History

Guinea gained independence from its colonial power France in 1958 in a

referendum rejecting Charle de Gaulle‘s offer to be member of the French

community . During 26 years Guinea was ruled as a one party socialist state by

the President Sekou Toure. In 1993 after an interim military government the

country held it‘s first multi parti presidential election. Guinea has experienced a

massive influx of refugees from Liberia (in 1996 an estimated 600.000) and from

Sierra Leone (200.000) due to civil wars in these countries (EIU, 1995).

2.5 Socio-economic situation

Although Guinea is generally considered to be one of the world's poorest nations,

the country's level of gross national product (GNP) per-capita was about $460 in

1991, above the median for sub-Saharan nations. The rural sector employs 80%

of the working population but produces only 30% of the GDP and a mere 4% of all

exported goods (Marx, 1995).

Mining is the major source of national income and foreign exchange revenue.

Guinea is the world's second-largest producer of bauxite, which accounted for 60

percent of export value in 1991, and also has significant reserves of diamonds,

gold, iron and uranium. Bauxite and aluminum production have declined drastically

in the 1990s. Traditionally the major source of public finances, mining revenues

dropped from over two-thirds of total government revenues in 1989 to just 30 % in

1994. Since the mid 1980s the government liberalized its highly regulatory

economic policies which resulted in some macroeconomic improvements. The

informal sector is expanding but the formal sector continues to lag behind. (EIU,

1995)

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In the UNDP's Human Development Index, a composite score based on various

indicators of education, health and economic conditions, Guinea finished last for

1992. One quarter of all live born fail to reach the age of five.

Only about thirty percent of adult Guinean’s are literate, including just 19 % of

adult women. The World Bank estimates that in 1991 only 37% of the nation’s

school age population were enrolled in primary school while the rate for secondary

education was just 10% (CIDI, 1995).

2.6 Health sector analysis

2.6.1 The Guinean Health system

The 1997 budget of the health sector was in 51 Billion Guinean Francs (approx.

42.420.000 US$) which made up 8% of the state budget. 57% of the health

budget is provided by donors (MSP, 1997).

The organisation of Public Sector Health Services is decentralized. A medical

officer coordinates the activities at the regional level (Inspection Regionale de la

Santé; IRS). In each of the country's prefectures, a director of health (Directeur

Prefectorale de la Santé, DPS) supports PHC programs in health centers and is

assisted by the head of the district hospital. The officers in charge of health

centers are responsible for supervising personnel at the sub-district level. Public

health facilities in 1997 included two universities, 7 regional hospitals, 26 district,

hospitals 349 health centers, 298 postes de santé (MSP, 1997). The distribution of

health centers follows the administrative division, one Health Center (HC) per Sub-

District. For more distant zones Health Posts (HP) exist.

2.6.2 Bamako Initiative (BI) in Guinea

The government of Guinea was one of the first to adopt the Bamako initiative (BI)

in 1987, with strong support from UNICEF. The model centered around community

co-management, cost sharing and decentralization of the health sector, as means

to the broader objective of universal access to primary health care. Guinea

together with Benin were the core PHC countries in this strategy to ensure

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sustainability of health services, improve their effectiveness in terms of coverage

of essential care, minimize operating costs and develop viable financing schemes.

The other major aspects of health system reform were the creation of district

health teams, the decentralization of health services together with the involvement

of the communities in the health service administration and an essential drug

policy with the establishment of the district pharmacies (“cellule de medicaments

essentielles”).

2.6.3 Implementation of core BI strategies

In a macroeconomic evaluation of Guinea, Camen (1992) stated that Guinea's

national program could claim significant success in creating a PHC "umbrella"

through local participation and financing. Knippenberg (1997), in a review of BI

activities in various African countries, found, that the introduction and

implementation of policies following the recommendations of the Bamako Initiative

has improved accessibility, utilisation and coverage of basic health. He states that

the major remaining problem is the equity issue. The exclusion of certain elements

of the population from curative care for financial reasons poses as well for Criel

(1998) a significant problem. However exclusion from curative care is not

associated with exclusion from preventive care (Knippenberg, 1997).

2.6.3.1 Rationalization of Service delivery

The minimum care packages (MCP) focusses on most cost-effective health

interventions (Primary curative consultation; CPC, Expanded Program of

vaccination; EPI, Pre- and post-natal consultation and Family Planning at health

center level). This includes outreach activities for EPI and Antenatal care to ensure

better access to preventive components of MCP. Continuity of care is ensured

through defaulter tracking.

Essential drugs (generics) are available at affordable prices at PHC level through

the establishment of district pharmacies.

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To rationalize service delivery standardized diagnostic and treatment flow charts

were developed. Flow charts help to reduce costs by limiting over-prescription and

facilitate the management of drugs and equipment and facilitates supervisory

control of drug consumption.

Funds generated by community financing are locally retained and managed by

health professionals in collaboration with health management committees.

Community financing is able to recover a substantial part (up to 50%) of recurrent

expenditures of primary health facilities (Knippenberg, 1997).

The MoH introduced very advanced Health Information and Management

Systems. The National HIMS was completely revised and standardized at the

national level. The health facilities prepare their own budget and regular control

mechanisms are built in the system to check for efficiency and good use of

financial and other resources (MSP, 1994). With monthly reporting forms Monthly

indicators are checked and performance evaluated. Guidelines hame been

elaborated for the use of the flow charts and reporting form. Hardcopy patient

cards for preventive services (EPI, ANC,FP) for health center use and for

distribution to clients (“carnet de sante“) exist.

A core element of the HIS is the six monthly monitoring (“monitorage”) for all the

health structures. The team is made up of the DPS, senior doctors of the district

which meets with the officer in charge of the center and the department heads and

members of the management committees. The objective is to make a critical

review of the activities. For each service percentages or figures for availability,

accessibility, utilisation, adequate and effective coverage are established. Figures

for target population are commonly available.

The monitoring provides local staff and members of the health committee with the

opportunity to assess the community coverage of the minimum care package’s

components, determine scores of performance, to identify problem areas and

potential solutions, to micro-plan, to define budgets, to enhance effective

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management and to monitor current trends and results. Goals for the next

monitoring are set and previously set goals verified.

Once local monitoring is completed and budgets are established in all regional

health centers the results are collated, presented and discussed at higher levels.

2.7 Background Information of the study area

2.7.1 Profile of the natural region „Guinee forestière“

Guinee Guinee forestière covers 22% of the total surface of Guinea and is a fores

area in the South-Eastern extreme of the country. Part of it is mountainous area

(massif Fountain). With a rainy season covering eight months of the year it is the

country‘s source of agro-industrial plantation crops (coffee, tea, cacao, palm oil)

and forest products.

2.7.2 German Agency for Technical Cooperation (GTZ)/ Projet Sante Rurale

The Rural health project (RPH) is a project in the framework of German Guinean

cooperation between the Ministry of Health (MoH) and GTZ.

Since 1983 the GTZ supports the health services in the Prefectures of

Kissidougou, Gueckedou and Faranah covering 600 000 inhabitants and

280 000 refugees from neighbouring Sierra Leone and Liberia. In 1997 two other

prefectures in the subregion, Dinguiraye and Dabola were included in the project

zone. The project supported the district Management team (DHMT) in

implementing the Bamako Initiative for preventive and curative services and the

introduction of “monitorage“ activities. Rural health centers, health posts and 2

district hospitals were constructed or renovated.

A maintenance system and prefectoral Pharmacies (PP) were established in the

districts. More recently technical assistance was more directed to the regional

level to transfer capacity in health financing and (global budgeting, monitoring of

expenses). The activities involved more assistance to refugees prioritizing

reproductive health services. Activities to improve quality were introduced.

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2.7.3 PRIMA (Projet de Recherche sur le Partage des Risques maladies) and

the MUCAS “Maliando”

The technical follow up of PRIMA was entrusted with the NGO Medicus Mundi

Belgium (MMB). Initially GTZ was the main funding agency, additional funding

came later on from the Belgium Agency for Development Co-operation. The

objectives of the PRIMA project were to design and test a model of a community-

based organisation risk sharing in order:

To reduce the phenomenon of financial exclusion through strenghtening of

solidarity mechanisms (permanent and temporary)

To develop more genuine forms of community participation in the management

and supply of district based health services (to have better quality of care)

To create a stable source of revenue for the health services

To create a counter-power (contre pouvoir) to the health services and thereby

a leverage for more quality of health care

To create the basis for a sustainable (local and national) capacity in terms of

promotion of community based health insurance schemes (Criel, 1999)

Several studies about the problems of financial exclusion had been conducted

(Rocque, 1995). A rapid rural appraisal (MARP) study investigated the

populations perception of the existing public health care delivery system, the

people's demands and expectations in terms of health care delivery explored pre-

existing (endogenous) mechanisms of mutual aid (Sylla, 1998). In a retrospective

analysis of existing data from the existing routine Health Information System,

health services utilisation patterns of primary and secondary care institutions were

determined (Criel, 1999).

The MUCAS model is in essence a partnership between the health services and

the population which reconciles two different models important in health financing:

The self-governed “syndicate” model, with the general tendency to subordinate

technical imperatives to the social demands of the users and the “technocratic”

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model (Type HMO) with the tendency to subordinate social needs of the users to

technical considerations expressed by health staff (Sylla, 1998).

2.7.4 District Health Sector with the Health Center Yende

The health center Yende with its two associated health posts Walto and Faindou in

the sub-district Yende is part of a two-tier system of the district Kissidougou

(administrative region Faranah), with 15 health centers, 9 health posts and one

district hospital. The HC Yende is located in the very south of the sub-district.

2.7.5 Health Center Yende

The HC has 15 staff from which 5 have health qualifications. It offers fixed services

(CPC, CPN, PEV, deliveries, FP and TB treatment) outreach activities for CPN,

PEV and laboratory services (Roenne, 2000). In average 680 new patients consult

for CPC monthly. Yende HC is the only first level care institution contracted by the

MUCAS ““Maliando””. The total budget for the health center is around 20.600 US$,

from which 12.800 US$ are state subsidies (salaries, equipment etc) and 7.800

US$ are generated by the HC. 30% of it’s revenue is generated by the contract

with ““Maliando””.

2.8 Purpose of the study

This study will contribute to give answers to the following questions:

From where do people come to obtain medical care at the health center

Yende?

Until which distance from the health center do people still may see benefits

from the insurance scheme?

How does the aspect distance influence the peoples decision to attend or not

to attend the health center or to join or not to join the health insurance

scheme?

Which distance distance represents a real barrier for the household to obtain

medical care?

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As the health center is the first line health care provider for the district based

health insurance scheme „”Maliando”“ the results may be useful for the managers

and organizers of the scheme to have realistic figures about the target population

for the health insurance scheme in order to better target promotional and other

activities. It may as well serve as basis for thoughts weather the attractiveness of

the insurance scheme can be increased for the population living further away from

the health center by offering reduced cheaper insurance packages (e.g. only for

certain services or for catastrophic events).

Possible further uses of the study could be to provide decision makers and health

planners on district and national level with realistic figures about the geographical

accessibility of Yende HC, which could be used for strategic planning.

2.9 Research question

How does the factor distance influence utilization of the health center Yende?

What can be the implications for the finding for the health insurance scheme

“Maliando”?

2.10 Research objectives

2.10.1 General objective

1. To obtain exact figures for the population living in villages surrounding the

health center Yende, as denominator for utilisation rate and coverage

calculations

2. To document the general utilisation pattern from existing service data in

different subgroups of the population (sex, age groups, insured/uninsured)

3. To quantify the utilisation of the HC Yende in relation to distance by analysing

the patient register for the period of one year (June 1999 – Mai 2000)

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4. To determine the group of villages surrounding the HC Yende patients which

are sufficiently using its services (institutional catchment area of the health

center Yende)

5. To compare the figures obtained in the study with existing figures from the

existing health information system (“Monitorage”, Coverage/utilization figures)

2.10.2 Specific objectives

To conduct a population census of villages surrounding HC Yende until 15 km

distance

1. To establish per capita utilisation rates of the villages living within 15 km

distance of the HC Yende

2. To extract from the existing patient register of the health facility information

about utilisation pattern of different subgroups of the population (vulnerable

groups, insured/ uninsured)

3. To establish whether the utilisation pattern is different in age groups and

between the sexes and how the utilisation pattern is different in the insured and

noninsured group

4. To determine how distance affects the utilisation for different illness categories

and services (EPI, antenatal care (ANC), Family Planning)

5. To calculate curative care, immunization, antenatal care and Family Planning

coverage with the newly developed institutional catchment area population

6. To quantify the difference of utilisation and coverage data using administrative

(sub-district Yende) or catchment area population

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3 LITERATURE REVIEW

3.1 Introduction

It seems to be paradox that in developing countries like in Guinea in many

instances the coverage with public health facilities is not sufficient and a large

unmet demand for health exists, but at the same time the existing health facilities

are under-utilized. This review of literature introduces to the basic concepts of

access to health care, tries to show how the decision making process takes place

in a household in a given illness episode and presents the possible options for

health care which are available. It introduces and describes the factors that

influence health care utilisation and especially looks at what hinders patients to

use government health facilities. Concrete average figures for utilisation and

coverage of different services in rural health centers in developing countries are

given to relate the concrete data obtained in the study. Experiences with health

insurance schemes will be mentioned.

3.2 Health service utilisation

Health service utilisation can be expressed as the proportion of people in need of

a service who actually receive it in a given period (Parks, 1995). It can be

measured as the Health service utilisation rate which is a rough indicator and

reflects the availability, accessibility, affordability and acceptability of the service

and the perception of the quality of the activities offered.

Before a person can or will use health services he must perceive a need for them.

He must be aware of his condition, and feel it warrants medical intervention; the

appropriate services must be available (within a reasonable time/ distance); the

services must be acceptable (he must have confidence in the technical

competence and humanness of the facility and its provider); and he must have the

ability to obtain the services (the necessary income and/or insurance and time).

Each of these prerequisites may comprise a barrier for utilisation. The use of

services is therefore not equated with the access to services( Matomora, 1997).

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Access to health services

The technical definition by WHO defines access to health services as the estimate

of the proportion of the population that can reach appropriate local health services

by local means of transport in no more than one hour. Recently WHO has revised

its definition to the proportion of the population having treatment for common

diseases and injuries and a regular supply of the essential drugs on the national

list within one hour’s walk or travel (CIHI, 1995). Access is the “actual use of

personal health services and everything that facilitates or impedes this use“

(Matomora, 1997).

A relationship may exists between utilisation of health services and health needs

and status. Utilisation rates give some indicators of the care needed by the

population and therefore the health status of a population, but it does not fully

explain health care behavior. Even if demand exists and is expressed by a

population there can be important barriers to utilisation by factors such as

availability and accessibility of health services (Parks, 1995).

Accessibility to health services is in practical terms the extend to which the

population in need can use the services, the patients ability to get to the services.

According to the operationalisation model of Canales accessibility to health care

can be divided into three dimensions, geographical, financial and cultural

accessibility (Matomoro, 1997).

Geographical accessibility

Geographical accessibility is defined as road distance and the cost of overcoming

that distance by public transport opposed to institutional accessibility which is

concerned with the question whether a facility is fully available or if social ,

economic and cultural barriers constrain its use (Ayeni, 1987).

21

3.3 Health care decision process

The decision what to do in a particular illness episode is an outcome of the

patient’s health care decision making process which is influenced by the attitude of

an individual but often more important by the household’s attitude towards health

and the health care systems (Parks, 1995). Health care behavior does depend to

a large extend on the individuals and the household’s own concepts of health,

disease and healing (Bichmann, 1991). The household production of health

concept looks at the household’s options and choices and their criteria for

decision-making (Bermann, 1994). When symptoms appear the patient may adopt

an active or passive attitude towards the disease. This usually depends upon the

gravity of the illness, socio-cultural variables, the quality of existing services and

by previous experiences with the different structures/forms of care (Matomora,

1997).

If an active attitude is chosen it can be directed towards Family Care, self-

treatment, towards traditional structures or official medical structures. In general

patients during an illness will start by using home remedies and self-medication

relying on the advice of the extended family. Self-care is the first step in the search

for treatment of mild and well known symptoms. Only if this fails the patient turns

for professional advise (Mwabu, 1986).

If the illness is perceived as more serious and if sufficient funds are available there

is a very high likelihood of a patient consulting more than one provider for advice

or treatment. This sequential decision-making process can be repeated several

times. The visit pattern will vary greatly according to type and stage of illness, the

household’s perception about the illness and on its ability to afford a visit. This so

called “healer shopping“ is a characteristic feature of health care choice. It is

based on trial and error. The utilisation of health care facilities depends on the

actual success of the treatment (Hielscher and Sommerfeld, 1985).

22

3.3.1 Illness concepts

Most usage is sequential although some is simultaneous. In the latter cases the

patient assumes several differential causational possibilities. Indigenous illness

concepts are unstable and are adopted or discarded in the course of the search

for treatment. If no relief is obtained, then it is considered that the illness has been

wrongly classified. Consequently follows a new classification and new treatment

follows. (Hielscher and Sommerfeld,1985)

3.3.2 Utilisation of traditional medicine

Modern medical practitioners are few in number compared to the total population

of the developing world. Moreover, they are unevenly distributed, concentrated

primarily in the urban areas. Often they are physically inaccessible to many rural

people. Whereas traditional healers are widely available in rural areas. But

widespread utilisation of traditional healers or other indigenous health resources

can not be simply explained by this. Often they are culturally, socially, and

environmentally closer to the people and so more accessible. They are more

sympathetic and often less expensive. They use local resources (herbs, etc), local

technology and local labor.

Even where modern services are well available, traditional healers will continue to

be used. It is the result of the belief that some categories of illness like mental

disorders or bone fractures are believed to be associated with witchcraft and for

which Western medicine will have no effective answer (Paul, 1985).

This belief cuts across educational lines, although it is less predominant in more

“educated” households. In the study by Okafor (1983) between 65 % and 70% of

the less literate and the illiterate households have a positive attitude to “native

medicine”. High prices prevent people from utilizing modern resources. Folk

medicine is usually available to everybody at a low prize. Payment in barter is also

accepted (Hielscher and Sommerfeld, 1985).

23

Access to good quality care and medicine free of charge does not ensure that

people seek proper treatment. Only having enough money for hospital or clinic

treatment is no sufficient reason for choosing it. However much the likelyhood of a

patient choosing/getting/paying for effective treatment is affected by the cost and

acceptability of the available options. It depends at least as importantly on other

things happening at the same time and on whether, in the event the patient has

ready access to the combination of resources appropriate to it (Wallmann, 1996).

Shortage of non money resources, time, confidence, someone to mind the

children may be more decisive than the presence of cash. The viability of any

system is limited by the scarcest element. Studies on household costs in Burkina

Faso showed that time costs are often substantial and make up one third of the

costs (Sauerborn and Nougtara, 1996).

3.4. Utilisation in Guinea

3.4.1 Curative care

Household surveys conducted in Guinea and Benin in 1990/91 after the

introduction of an essential drug policy and fees for services with community

financing showed that a large part of the population does not use health centers.

In Guinea the poorer population has few alternative sources of care and often opts

not to seek care. The average number of illness episodes was 1,0 episodes per

person/ year. Between ¼ to 1/3 of the episodes lead to seeking care at public

health centers. The other 1/3 to ½ illness episodes were treated at home, the rest

looked for other sources of care (Soucat and Gandaho, 1997).

Following the revitalization of health centers and the re-establishment of drug

availability, utilisation of curative care initially in Guinea increased significantly and

stabilized at around 0,3 visits per capita (1997: 0,26).

24

3.4.2 Antenatal care and EPI

After the introduction of BI policies utilisation of prenatal care went from less than

5% in 1987 to almost 55% in 1993 (1997; 59%). 60% of pregnant women

benefited from at least one antenatal visit. In 1997 the average number of contacts

per antenatal visit was 2.52.

Tetanus Toxoid treatment of pregnant women increased progressively from 6% in

1986 to 66% in 1995. In 1997 the average number of contacts was 2.52. 18% of

deliveries are done at health center level. Half of all deliveries take place in the

home, unattended by a professional midwife. Traditional birth attendants deliver

43% of the children (Soucat and Gandaho, 1997) , (MSP, 1997).

1995 after seven years of program implementation, EPI coverage (the proportion

of children having received DPT3 before the age of one), reached 73% (from less

than 5% in 1986).

3.5. Utilization related to distance (geographical accessibility)

Health care facilities in third world countries serve a relatively small area

surrounding the facility. As most patients travel on foot and few have access to

motorized transport, distance significantly affects the utilisation of health services.

Patients living some distance away from a health care facility tend to delay using

its services, usually preferring alternate forms of treatment, including self-

treatment with traditional or commercially available medicines (Airey, 1989). The

major users of the services are the population immediately living around health

facilities. The proportion of people from larger distances declines exponentially.

According to the principle of economic rationality consumers will look for care in

the nearest health care facility in order to minimise route distances and so reduce

costs (Egunjobi, 1983).

The distance separating potential patients from the nearest health facility is an

important barrier to its use, particularly in the rural areas. The greater the distance

the less services are used. Long distances can be actual obstacles to reaching

25

health facilities and they can be an disincentive even to trying to seek care. In

developing countries, the effect of distance of service use becomes stronger when

combined with the lack of transportation and with poor roads, which contribute

towards indirect costs of visits (Noorali and Luby, 1999).

In a study by Bichmann (1985) in Benin it was found that the majority of users

(60 %) come from the vicinity (less than 5 km) although only (28%) of the total

district population lived within the distance. On the other hand 20% take the

trouble to walk long distances to attend outpatient clinics. The study by Egunjobi,

examining factors influencing the choice of hospitals in Nigeria showed that

although proximity was the leading factor in consumers choice (32%), other factors

like quality of service (25%), relative living in hospital town (15%), Finance (12%),

Ease of transport (11%), and others (5%) play a major role. The rate of distance

decay in utilisation levels varies according to the type of facility, socio-

demographic variables and illness. Travel by younger children depends on the

availability of an adult or elder sibling to escort or carry the child. Elderly patients

are liable to find long distances a significant if not an absolute barrier to obtaining

treatment ( Stock, 1983)

In the case of underutilisation of a clinic when the quality of the services provided

is perceived as low, it is not the inability to travel that prevents the use of health

clinics but the expectation of receiving unsatisfactory service. By increasing the

quality of the services people will “go the extra mile” and travel more for the

services and utilisation rate will increase.

3.6. Utilization related to costs (financial accessibility)

3.6.1. Household expenditure for health Guinea

With an average fee level of, on average 1,1 US$ (Other BI projects Africa;

average 1,5) or US$ 0,19 – 0,56 p/y the household expenditure was on average 4

US$ per year. This would permit 3,6 full treatments per year. The out of pocket

26

expenditure on health care is probably much higher because of travel costs and

under the table payments (Knippenberg, 1997).

Hotchkiss (1998) found in Nepal that in rural areas 74% of modern treatment

expenditures is spent on pharmaceuticals, (86%) It is higher in urban areas. The

vast majority of household expenditures are used for drug purchase on the private

market. Health care accounted for 5,5% of total household expenditure. The share

of total expenditure devoted to health increases with the level of household

income. Rural households spend more on health care than urban households after

controlling for income status.

3.6.2 Costs incurred of overcoming distance

Another variable which has been extensively examined in the literature is financial

cost for receiving care. Cost and distance often go hand in hand as longer

distances entail higher transportation costs. The demand for a health institution

should decline as costs of reaching it increases. The cost to obtain medical care

can be divided into two categories and make up the overall costs

(Airey, 1989):

Direct costs which include facility fees, the cost of medications and other

supplies.

Indirect costs which include transport , other costs for food and lodging and

the cost of time otherwise spent productively (opportunity costs).

Additional costs incur when drugs are unavailable at the health facility and these

have to be purchased at private pharmacies “Under-the-table- payments” are

frequent and add to the costs. In the case of inpatient treatment, indirect costs

such as transport expenses, including the return ticket, daily expenses for food

and to lodging of family members in the town of the hospital may be significantly

higher than the direct costs. Prospective patients, especially women, do not travel

alone to a health facility. They are accompagnied by other adults and by children

who cannot be left at home alone, because caretakers are not available. All the

additional people swell the cost for transport (Thaddeus and Maine, 1994).

27

The opportunity costs of the time used to seek health services, the time spent

getting to, waiting for and receiving health services is lost from other, more

productive activities such as farming, fetching water or wood for fuel, herding,

trading, cooking and so on. Especially women carry out a large majority of these

tasks, the value of their time and the competing demands on it are major

determinants (Thaddeus and Maine, 1994). The cost of time otherwise spent

productively, e.g. the loss of agricultural working time, is particularily critical during

planting and harvest time. Especially people of lower socioeconomic classes can

often not afford any loss in income.

The effect of cost on service utilisation is commonly assessed through interviews

and surveys of users and non-users in which respondents are asked to give

reasons for their choice of actions when they are ill. A proportion of these

respondents give financial constraints as a major reason for not seeking care, or

for seeking one form of care rather than another.

But many studies indicate that the financial costs for receiving care is not a major

determinant of the decision to seek care (Thaddeus and Maine, 1994).

A survey conducted in Nigeria revealed five factors that influenced people‘s

decision to seek traditional or western medical care: Respondents ranked cost and

distance fourth and fifth (Thaddeus and Maine, 1994). Kloos (1990) reported that

in Ethiopia, cost of services was often a less important consideration in utilisation

than were quality of services and perceived efficacy of treatment. The study by

Egunjobi (1983) examining factors influencing the choice of hospitals in Nigeria

showed that although proximity was the leading factor in consumers choice (32%),

finance was at fourth place with 15%, among other factors like quality of service

(25%), relative living in hospital town (15%) and ease of transport (11%). Cost in

contrast is most likely to affect compliance with prescribed treatment as the cost of

medications is often very high (Thaddeus and Maine, 1994).

The cost/ benefit ration of using medical services may be viewed differently in

emergency cases.

28

3.7 Utilization related to Quality of care

Examining the literature of quality of care in the primary health care setting often

poor quality of consultation is observed with a high proportion of prescription

problems. Consultations largely center around the prescription of a drug and

ignore the wider requirements of good medical practice. The process of diagnosis

is often too poor to ensure correct diagnosis ( limited training and skills of most

staff). Often although some aspects of the history of the complaint are reviewed,

little examination is undertaken. A high proportion of the diagnoses are identified

as symptoms or other diagnoses.

A study conducted in rural Burkina Faso by Krause et al (1998) showed that in

only 20% of the diagnoses the nurses took a sufficient history and in only 40%

they conducted a sufficient clinical examination. Only 12 % of the diagnosis was

based on sufficient history taking and adequate clinical examination. The results

were low diagnostic quality, dissatisfaction of the population with the health care

services offered and low utilisation rates.

3.7.1 Perceived quality and reliability

Quality and reliability are critical for the attractiveness of services. If patients have

serious doubts that they will obtain the services they desire or they do not trust

that a hospital provides an adequate backup to their local primary care provider

(Stock, 1983), it is unlikely they see advantages for themselves and their family.

People hesitate to travel several kilometres to a dispensary for treatment when

they know that it is often closed and the supply of injectable drugs or other drugs

are often exhausted (Stock, 1983). The experience with the introduction of user

fees showed that higher costs of the provision of health services associated with

quality improvements did effectifely increase utilisation of the services.

Haddad and Fournier (1998) studied the criteria for quality of health services as

determinants for utilisation , perceived by the population in Guinea, using focus

group discussions. Open discussions of what the participants considered as

29

important aspects of good medical care were conducted and the identified criterias

listed hierarcically under five categories, based on how frequently they were

mentioned.

1.Technical competence of the health care personnel, 2. Interpersonal relations

between the patients and health care provider, 3. Availability and adequacy of

resources and services, 4. Accessibility, 5. Effectivness of care.

3.7.2 Donabedian’s classification of quality of care

The most widely used classification of quality of care was developed by

Donabedian (1980, 1988) who developed three major categories:

1. Structure denotes the attributes of the setting in which care occurs; Material

resources (facilities, equipment, money), human resources (number and

qualification of personnel), organisational structure (medical staff organisation,

peer review, methods of reimbursement).

2. Process denotes what is actually done in giving and receiving care. It includes

the patient activities in seeking care and carrying it out as well as the

practitioner’s activities in making a diagnosis and recommending or

implementing treatment.

3. Outcome denotes the effects of care on the health status of patients and

populations, health status (Improvements in the patient’s knowledge and

salutary changes in the patient’s behaviour, degree of patient’s satisfaction with

care)

Donabedians category of process was devided into the components technical

competence and interpersonal relations. Eight criteria were greately valued. Most

of them relate to the structure (availability of drugs and accessibility of the health

facility) or the process of care (reception of patients, overall care, good clinical

examination, dispensing drugs and/ or good drugs). Recovery of health is the most

30

frequently encountered, suggesting that quality of health services is first and

foremost judged in terms of outcomes. Little emphasis is placed on preventive

services.

A previous study by Haddad and Fournier (1995) regarding quality, cost and

utilisation of health services conducted in Zaire had simular results and showed

that the top qualitiy mentioned in semi-structured intervieuws of villagers were the

nurse’s interpersonal qualities (respect, patience, courtesy, attentiveness,

friendlyness and straightforwardness). Technical qualities (good treatment, good

work, good diagnosis and punctuality) came only second to interpersonal qualities.

The availability of drugs was as well given high priority. Technical competence is

often overestimated in relation to good behaviour.

Increasingly it has been acknowledged that the technocratic perspective of what is

good quality of care defined by health professionals does not necessarily

correspond with the perceived quality of care by the population. Any study on

quality of care has to investigate user satisfaction, which is the patient’s judgement

on the quality and goodness of care (Donabedian, 1980). This is associated with

improved compliance and as well health improvement

Users appear very sensitive to aspects of interpersonal relations they have with

professionals as well as the technical quality of the care provided (Winefield H,

1995).

3.8. Mutual health insurance schemes

As stated in the introduction health insurance schemes are considered to be a one

of the solutions for the problem of financial exclusion. Formal health insurance

systems as concluded in an overview of 23 sub-saharan countries by Vogel, have

not promoted greater equity in access to health services by the poor. Locally

developed and district based schemes targeting poor rural self-employed

31

populations are considered to be more successful. The problem is that there is

still very little analytical information available about such schemes (Criel, 1997).

Bennet (1997) who examined 80 insurance schemes, concluded that although

some risk sharing schemes are clearly successful, a number of common failings is

evident. Schemes in low-income countries have generally only achieved limited

population coverage. With a few exceptions, cost recovery rates are low, the

schemes examined by her had very little ability to protect the poorest parts of the

society. She observed that many schemes suffer of a poor design. She noted that

very few schemes appear to have used graduated premiums according to income

or household location or exemption mechanisms. She recommended to make

greater use of these mechanisms.

It was mentioned in the study by Yi (1998), that villagers of remote rural

communities in Ghana perceived the difficulty of transport as the greatest

obstacle of establishing health insurance schemes in those areas.

Noterman (1995) observed in Zaire that the payment of the same subscription fee

for households living far away from the hospital penalized them. In fact these

households actually subsidize the hospital care of households living close to the

hospital, who use it more frequently.

The further the people live from the hospital the higher are the indirect costs and

the higher the opportunity costs of an admission. Noterman proposed the

implementation of gradients in fees (sliding scales according to distance) . This

was tried out in Bwamanda in Zaire by setting different co-payment level according

to distance from the health center to the hospital. It was stopped, however, after

one year because there was no positive impact on the hospital admission rates of

the more remote insured population and because of the more complex

managemet and control procedures required (Criel and Kegels, 1997).

Atim, (1998) in a synthesis of research in nine west African countries gave the

following recommendations concerning design features that enhance the schemes

success: A mandatory reference or gatekeeper system, a requirement for

compulsary participation or at least mandatory family membership, a waiting

32

period for new members, the use of efficient provider mechanisms, the inclusion of

essential and generic drug policies in their agreements with providers as well as

preventive and promotive services in the benefits packages.

For promoters the major recommendation were to reinforce the institutional,

managerial, and administrative capacities of the health insurance schemes in such

areas as setting up adequate HIS systems, setting premiums, managing funds,

pricing and assessing the quality of care. The organizers of the scheme need

information on health spending and utilization and risk patterns to be able to set

premiums at levels that would be self financing (Atim, 1997).

Kutzin (1997) stated that a scheme should be designed in a way that keeps

premiums low. He thinks that it is important that the services covered by the

insurance scheme initially focus on relatively high cost low frequency events. Atim

(1997) stressed also the fact that the schemes have more success if they are set

up around a health provider with a good reputation for good quality in terms of

waiting time, staff attitude towards patients and drug availability. This is confirmed

by other authors. Clinic management, staff quality and morale, drug supply and

relations with the community as a whole are probably more important influences

on utilisation than a payment scheme (Soucat et 1997).

Criel (1999) stated that the major determinants for high subscription rates are the

general performance of a health facility as well as the quality of its interaction with

the community.

Gilson (1997) summed up her finding after examining the literature about

experiences after the introduction of user fees. Risk sharing schemes should not

be seen as a source of finance but rather as ways of organizing health service

financing and delivery. Establishing a health insurance scheme may enable to

introduce organizational changes such as tighter referral control, contracting

arrangements between purchasers, accreditation and service quality improvement,

and performance related pay. Insurance should be seen as a supporting strategy,

not an exclusive ”financing alternative” .

33

3.8.1 Data on utilisation rates of “Maliando” insurance members in previous

studies

In an assessment by Diallo (2000) of records of attendances of “Maliando” health

insurance members a total number of 1796 patient contacts within the period June

1999 to Mai 2000 by 1014 “Maliando” members were reviewed. The patients came

from 21 villages from the UNEM Mata (subunit of the health insurance scheme)

and from 11 and 5 villages of the UNEM Touffoudou and Yende. The calculated

total Utilisation rate for “Maliando” members was 2,08. In the document villages in

the 5 km radius were differentiated from villages beyond the 5 km range.

Utilisation rates were 2,33 and 1,54.

Hohmann mentioned in the evaluation of PRIMA (1999) that on the basis of an

asessment of records from August – November 1998 the average utilisation rate

for “Maliando” members were 1,68 contacts per year. Range 0-5 km: 2,0

5-15 km: 1,24. In the study by von Roenne (2000) the global utilisation rate for the

total population is 0.56 with large differences between the insured group (1.9) and

the uninsured group (0.47). Members of the insurance scheme use the HC 4 times

as much as non-members but represent only 7% of all patient contacts.

3.9 Methodological aspects

3.9.1 Quantitative techniques used in the study

Spatial Analysis

The impact of spatial factors on health care behavior has been major themes in

medical geographical research. It has been general practice to explain consumer

behaviour patterns in terms of spatial accessibility (Stock, 1983)

34

For utilization studies a variety of distance measures can be employed. Distance

can be measured as a straight-line joining the point of origin and the destination,

or the actual measured length of journey. Travel costs or travel time may be used

as frictional effects of distance (Stock, 1983).

The functional distance to a facility may be greater than the physical distance to

the closest facility. The simple Euclidean distance (physical distance between the

patients homes and the health center) is a sub-optimal measure of distance as it

ignores physical barriers such as rivers, swamps, hills, the road and traffic system

and socio-cultural factors (Mueller and Smith, 1998). Differences in physical

terrain, availability of public or private transport, and patients access to alternative

forms of transport (motor vehicles, bicycle and foot) all affect the functional

distance. Moreover such functional distances may change frequently particularly

seasonally like in the rainy season (Ayeni, 1987).

3.9.1.2 Per capita utilisation rate

To measure utilisation by stating or comparing total numbers of attending patients

is not adequate as it does not take the population distribution into account. Per

capita utilisation rates are a much more sensitive indicator of the level of use of a

health facility. It relates the number of patients from each sublocation of a health

facility to its general population (the denominator).

Stock related the per capita utilisation rate to the factor distance in a study of rural

health facilities in Nigeria by calculating the per capita utilization rates for

concentric distance bands around each health facility. These bands had a width of

2 km up to 10 km from the center and 5 km thereafter (Stock, 1983).

In a further step the decline of the per capita utilisation rate (% decline/ km) can be

calculated. The same method was used in a study by Osibogun (1998), who

investigated the outpatient register of a rural health center in Nigeria.

35

He compared the percentage of users coming from the vicinity (<5km) with the

percentage of the population which lives within the 5 km range (Utilisation

differentials).

In a study by Criel (1999) the impact of the Bwamanda hospital insurance scheme

on the hospital utilization pattern was studied. Health center areas in relation to the

referral hospital were chosen. In the study the total admission rates per health

center area were determined, stratified by insurance status. Utilisation differentials

between the insured and non-insured population were established.

5.9.1.3 Concept of distance decay

A dominant theme in studies of health service utilization is the concept of distance

decay. The rate of interaction (utilization) with the health services tends to vary

inversely with distance. Distance decay describes the relationship between

distance and the rate of utilisation as a negative exponential function.

The rate of distance decay in utilisation levels will vary according to the type of

facility, socio-demographic variables and illness and can be established for these

variableS (age, sex, diagnosis, symptoms,diagnosis, services used be established

(Stock, 1983)

5.9.1.4 Population Census

To calculate the per capita utilisation rate it is necessary to have a good estimate

or better exact figures about the population (denominator data).

For this purpose an estimated population is usually derived from updating older

census data by extrapolation, adding the annual population growth rate (Airey,

1989).

In practice this procedure poses problems as the data from large national surveys

are often outdated and of dubious quality. They are only rarely updated at sub-

district level and population density figures are mostly unavailable at small

administrative units. It is therefore difficult to produce reasonable accurate

36

estimates. It would often require a basic census to provide denominator data to

calculate coverage (Satia, 1994)

The planning of a household census is similar to the planning and implementation

of household survey. In the training modules for Household Surveys by WHO

(1988) the aspects good selection of surveyors, adequate training time and pre-

testing of the forms is considered essential. For high quality data good field

supervision is a crucial aspect. Quality control measures have to be implemented

at all stages of the survey.

5.9.1.5 Global Positioning System (GPS)

During the study a GPS system was used to help to establish physical distances.

The GPS system uses earth orbiting satellites, which regularly transmit precisely

timed signals. These are received by a special electronic device which provides

direct measurement of the position on the earth’s surface. When the GPS device

is activated the site is located and defined in longitudinal and latitudinal

coordinates. The GPS system can then calculate distances between recorded

locations (UNFPA, 1996).

5.9.1.6 Catchment area population

A catchment area should ideally comprise a population of 10.000 per primary

health care unit. It is the geographical area from which the majority of its patients

of a health unit is drawn (Kloos, 1990).

This can be the basis for the planning for the establishment of health centers.

Pangu, (1988) documented this when examined the planification strategy in Zaire.

The limits of the planned health centers were traced starting from the peripherie of

the reference hospital, aiming basically to allocate a population of 10 000 to a

health center area taking into account infrastructure conditions.

The “Monitorage” guidelines for health centers of the Ministry of Health Guinea

states that curative care and preventive services are only accessible for the

37

population living within a radius of 10 km. For health posts the same distance

applies for curative care applies but for preventive services like EPI, ANC and

Family Planning it is only the population living in the 5km zone that have access to

care. The catchment area as defined by the Ministry of Health concerning

preventive services as well includes the population of villages where effective

outreach activities (strategie efficace) had been conducted . Effective outreach is

that at least 3 ANC and EPI sessions were organized in the 6 month monitoring

period (MSP, 1994). This is reflected in the denominator, the population served,

for accessibility calculations.

Other indicators used in the monitorage are availability, effective coverage

(examining quality of the services rendered) and management indicators (finance ,

utilisation of drugs ).

To determine the catchment area of a health facility with a rapid assessment

method, Nordberg (1993) asked health staff to fill out questionnaires, to identify

on a map the areas from where the majority of its patients are drawn and to

estimate the total number of people living in the area.

Criel (1996) developed a data collection tool which aimed to define the catchment

areas. The exercise consisted of monitoring the utilization of the curative services

of all existing health facilities during a period long enough to control for seasonal

variations. Curative care was considered a good proxy for the overall utilisation

pattern of a health facility. Detailed maps were drawn and excact population

figures established. After compiling and mapping, each of the villages was

assigned to a given facility. In this methodology the catchment area is the actual

used area, determined by the patients preferences

5.9.1.7 Coverage/ utilisation data

In the following the methodology is explained how coverage and utilisation rates

are calculated during the monitoring (“monitorage”) sessions (MSP, 1994), (MSP,

1999) The figures for the monitoring of December 1999 for the six month period

38

from June to November are given in order to be later compared in the results

section with the data from the current study (Diallo, 1999).

The monitoring team based its calculation on the following population figures: The

target population of the whole sub-district Yende is 17.319. The sub-district

population figures are yearly updated from older census data by multiplying the

previous year population with the factor 1,027. For the 6 monthly monitoring the

total target population was divided by the factor 2.

5.9.1.8 Calculating accessibility

The accessible population for the health center Yende combined with two

associated heath posts was considered to be 12751. This figure is the population

which is located in the proximity of 10 km of the HC and the two HP. Relating the

figure 12.751 to the total sub-district population of 17.319 gave the figure of 74 %

for accessibily. Taking this into consideration the monitoring team based its

calculation on the figure of 15.660 (accessibility: 90%).

5.9.1.9 Calculating Utilisation

Curative Care (CPC)

The health center during that period had 4.135 attendances and the health posts

combined 892 which gave the total figure of 5.027. Related to the whole

population of the sub-district Yende( 17.319 / 2 = 8.6595) gave the figure of 58%

(= 0,58 utilisation rate)

Antenatal care (ANC)

The target population of pregnant women (expected deliveries) is calculated

through the formula target population/ 2 (sub-district Yende) x the factor 0,045

which resulted into the figure of 390. 381 pregnant women had received antenatal

services during the reporting period (Coverage of 99%).

39

Adequate Coverage is the percentage of women with at least 3 ANC contacts and

2 Tetanus Toxoid vaccinations. The figure indicated in the report is 96%.

The effective coverage was 83 %.

Expanded program of immunisation (EPI)

The target population is the same as in antenatal care. Utilisation calculations are

based on the formula Target population / 2 x 0,040 (346). The total number of

children vaccinated in the reporting period were 344 (utilisation 99%). Adequate

coverage is the percentage of children who have received all the required

vaccinations (4 sessions) before reaching the age of one (96%). The effective

Coverage was 92 %.

Family Planning (FP)

The target population for Family Planning services are women in reproductive age

derived from the formula Target popultion x 0,060 which resulted in the figure of

1039. The ANC department had 120 clients (Utilisation rate 12 %).

Adequate and effective coverage were both 6 %.

3.9.2 Qualitative methods

3.9.2.1 Combination of qualitative and quantitative approaches

Quantitative methods like the ones applied in the current study can be

complemented with qualitative research (Focus Group Discussions, In depth -

Interviews, case studies, observational fieldwork) in order to find explanations for

unusual unexpected results. The most commonly advanced reasons for combining

the 2 methods are for different stages of a project, to compensate for each other’s

shortcomings and for the purposes of triangulation. Qualitative methods may be

used to develop sophisticated quantitative research tools. Qualitative work might

also follow a large scale survey to explore further the mechanisms by which

variables are connected.

40

The logic of this combination of different methods (Triangulation) is based on the

premise “that no single method adequately solves the problem of rival causal

factors. Because each method reveals different aspects of empirical reality,

multiple methods for observations must be employed”. (Patton, 1989) “The

findings from alternative sources enable researchers to make more subtle and

sophisticated analysis: any marked differences can be highlighted, investigated

and explained “ (Dowell, 1995)

It has been recognized that qualitative approaches can enhance quantitative

studies in four ways; by providing insight into the process of data construction, by

helping to identify the relevant variables for a study, by furnishing explanations for

unexpected and anomalous findings and by generating hypotheses or research

questions for further investigation (Barbour, 1999)

41

4 METHODOLOGY

4.1 Study area description and study population

The catchment area population (15 km radius) was estimated to comprise 3000

households living in 53 sectors/ villages with the total of 17.250 inhabitants

(Roenne, 2000). A private health center is situated at 2 km distance from Yende in

the district Gueckedou, other health centers are in more than 20 km distance.

4.2 Time frame

The study was carried out from the May 3rd to July 1st 2000. The first 4 weeks

were used for preparation and further adaptation of the research tools. Data

collection was carried out in the whole of June. Part of the data analysis was

done in the last week of June, but the major part after returning to the home

country.

4.3 Study type and design

This is a retrospective study using quantitative methods. Information from personal

communication and quantitative data collected by other researchers was used to

complement and enrich the study. It was organised in different steps using 3

different methods of data collection.

1. Census of the population living in 15 km proximity of the HC Yende

2 Assessment of the outpatient register for first contact Curative Care

Consultation

3. Assessment of patient cards for preventive services (ANC, EPI, and Family

Planning) kept at health center level

4.4 Finance and logistics.

For the population census one 4 wheel drive vehicle was provided by PRIMA, 2

motorcycles by PLAN Guinee, a NGO operating in Yende subdistrict as well as

from the EPI department of the health center Yende. The budget was provided by

42

GTZ/ PSR. Two computers of PRIMA were used for data entry for the review of

the outpatient register.

4.5 Data collection

4.5.1 Data collecting tools

4.5.1.1 Population Census

The total number 20.002 persons were counted in 2844 households in the area of

15 km surrounding Yende. For data collection 3 census forms were based on

prepared material and adapted in Excel format. The first form was printed on

green A4 hardcopy, the color of the health insurance scheme with the heading

“Maliando” Household Card”. After completion of the card by the censors it was

left with the household. The censors transcribed the information on a separate

form, the “household summary” form which did not contain names but a summary

of the information. It contained the number of household members in different age

groups, the sexes and resident status. In addition it contained information about

the number of wives of the household head, his profession and a listing of the

main economic activities in hierarchical order. A separate form in the census

condensed general information about the village, like distances to health

structures, road conditions, information about sanitation, schools and other.

A GPS (Global Positioning System) handhold electronic device, was available to

determine physical distances of the villages to the HC Yende. During supervision

visits and supply and collection of material to the censor groups with the vehicle,

the GPS system was activated in different villages by the main supervisor. In

addition previously collected data about distances existed in Excel Files.

4.5.1.2 Assessment of outpatient register

The outpatient records of the Yende health center was asessed and information of

the total number of visits to the center between the 12-month period covering

43

June 1st 1999 and Mai 31st 2000 extracted. The following variables were noted:

the village or sector of town of origin, the age, the sex, the profession, the

insurance status, the documented complaints, diagnoses and the drugs prescribed

as treatment for the presenting complaint.

Data entry sheet were developed in the statistical software package “File Maker”

converting the column headings of the patient register into categories.

To reduce typing time, the entry sheet was prepared in such a way that during the

time of data entry most data could be selected from preexisting lists and were

made available in form of rolling bands on the screen. The correct values were

entered by choosing from a menu moving with tabulation and the keyboard curser.

Only numbers had to be typed or in exceptional cases new expressions.

4.5.1.3 Asessment of patient cards (ANC, EPI, FP)

Records on these activities existed in form of hardcopies for ANC, EPI and FP,

which were kept in the offices of the respective programs at the HC. The forms

had annotations distinguishing between fixed strategy contacts (the patients came

by themselves to the health center) and contacts which resulted as part of an

outreach strategy. A form was designed, in which a complete list of approximately

180 villages where listed in the order of administrative units.

It contained columns for the three programs which each had the following 3

subdivisions; patients/clients registered, number of contacts, Outreach strategy

Yes or No. The collected data was then entered into a prepared FM file.

4.5.2 Selection, staff training and Pretest

4.5.2.1 Population census

Three census teams were formed, each with one supervisor, 3 censors and a

guide. The supervisors were three PRIMA staff. The three censors of each team

were made up of a regional head of the health insurance “Maliando”, a health

worker of the district and an independent censor who was involved previously in

44

qualitative research activities with PRIMA. A one day training session of the

censors was conducted. The responsibilities of censors and supervisors were

defined, the different forms explained and pre-tested, which resulted into

corrections of the forms.

4.5.2.2 Assessment of outpatient register

Two teams were formed, each consisting of a trained secretary experienced in

entering data in computer software, with notions of medical expressions from

previous typing work. They were assisted by a secondary school student, whose

task was to dictate from the register. The training of the teams, explaining data

entry and about technical terms took 2 days. The list of medical expressions for

the different categories was printed out and relationships between complaints,

diagnosis and treatment explained in order to facilitate understanding and

recognition of written data.

4.5.3 Data collection procedure/ Computer Data entry

4.5.3.1 Population Census

A one day mapping session was conducted, which gathered members of

“Maliando” and health workers involved in outreach activities. Three large

handdrawn maps which had been copied beforehand from existing maps and the

existing villages and roads were discussed, verified or corrected by the

participants. Additional small villages or separate groupings of houses (“hameau”)

belonging to villages were added. The distances between the villages were noted

and combining all information a detailed movement of the three census teams

were planned.

Local government and health authorities of the two Prefectures concerned had to

be informed and a written permission with the signature and stamps of all

administrative levels obtained (“ordre de mission”). Two meetings of community

leaders of the area were used to inform the population about the census activities.

45

As well the information was given through churches and mosques. The local

authorities actively supported the program.

The census lasted 14 days. The censors moved door to door and usually asked

the household heads for the information about household members. Movements of

the censors were mainly done on foot but one pickup and 2 motorbikes were as

well in the field. For the town of Yende, as well as other larger villages detailed

maps with the streets and numbered buildings were prepared beforehand by

“Maliando” members. This method was especially useful to identify households,

which did not provide the information at the first visit and where the censors had to

return.

Data of distances to Yende as operational distance and walking time, were

collected by questioning a group of villagers of the different towns and noted on a

separate form. The selected villagers were asked to give values for perceived or

known distances and walking time to Yende. In addition we obtained data for 20

villages on physical distances using the GPS system.

4.5.3.2 Assessment of Outpatient register

The data from 4996 patient contacts from June 1999 to January 2000 were

extracted from five separate patient register hard copy books and entered into the

computer. Each team did approximately 150 data entries each day.

4.5.3.3 Assessment of patient cards (ANC, EPI,FP)

For ANC and FP the midwife in charge of the program sorted out all forms which

included contacts of the period between June 1999 to Mai 2000.

One by one the forms were read and each patient for whom a card existed was

noted once as registered client of the program with her village of origin. In addition

the number of ANC or FP contacts during the one year time period were noted

down as well as whether the contact was part of the fixed or outreach strategy.

For EPI the health agent responsible sorted out all hardcopy forms and the

coordinator of the study noted down the information. In a second stage all

46

information was entered in a prepared „File Maker“ entry sheet and later exported

as an Excel file for further data analysis.

4.6. Data analysis

4.6.1 Population Census

A team of 4 persons with a supervisor transferred the data from the household

forms on another sector/village synthesis sheet. Each line represented the

information of one household. At completion of one sector/village sheet in the

bottom line all figures were added up, giving the total figures. The information on

profession and economic activities was synthesized on a separate sheet.

The data was transferred in a “File Maker” file, and after completion exported into

Exel format for further analysis. The patient entries of contacts with no location

stated (237) and of not concerned Sub-districts 31 were eliminated from the

sample, which made a total of 7122 contacts used for data analysis.

4.6.2 Assessment of Outpatient register

To obtain the final results several steps were necessary. The data from the

population census provided the denominator data. The review of the patient

register provided the exact number of patients having attended the health center

from the different towns (Nominator).

As a result it was possible to calculate per capita utilisation rates for the different

services offered at the HC for each village. The villages surrounding the HC (15

km) were grouped into concentric distance bands every two km (1-2, 3-4, 5-6, 7-8,

9-10, 11-12, 13-14, 15-16 km). As a result the per capita utilisation rate for each

of these concentric distance bands were calculated.

4.7 Validation

To ensure a correct census the supervisors were constantly in the field with the

censors and checked the data sheets after completion of a working day.

47

During data entry of data from the outpatient register on most days a health

professional (medical doctor or nurse) was in proximity to answer comprehension

questions. The teams were instructed to mark any expression they did not

understand for easy recognition. The data on provenience, age, sex, profession,

insurance status and diagnosis should be considered as reliable, as no problems

were observed during the time of data entry. In contrast the data on complaints

and treatment need again to be examined thoroughly if these data should be used

to evaluate the quality of establishing diagnosis and the corresponding treatment..

During data compilation of the census data each synthesis form was double

checked by a supervisor to avoid calculation or transcription errors.

4.8 Limitations

A document review can only be as accurate and complete as the primary data

source is. 269 patient entries (3,6 %) of the data entered into the computer had to

be eliminated during analysis because the data on the origin of the patient was not

stated or could not be read.

4.9 Ethical considerations

Consent for the study was obtained from the district administrative and health

authorities. Participation in the study was voluntary. Permission was sought from

the chief of the clinic Yende and the head of the different departments

To examine the data of their services.

48

5. RESULTS

5.1 Introduction

This study was aimed to describe the attendance pattern of the health center

Yende related to the factor distance. To determine utilisation rates a precise

denominator for the calculation was needed. For this reason it was deemed

necessary to conduct a population census. In the following the results of the

census and the assessment of the outpatient register and documentation of the

preventive services (ANC, EPI, FP)

5.2 Population census

5.2.1 Global figures

During the census 86 town sectors of Yende villages and separate units of villages

(“hameau”) up to 19 km distance from Yende were visited. The great majority of

the villages were in the 15 km range (93%). The total counted population was

19.961 living in 2834 households (19.224 in the 15 km range). Three additional

villages should as well be within the 15 km range where the census did not reach.

These were not included in the study. The proportion of Yende’s share of the

population living within the 15 km radius is 57,4 % (11.032 people). The rest

42,6% (8192 persons ) live in the neighboring sub-districts Boloudou,

Guendembou and Temessadou.

5.2.2 General population description

48 % of the counted population were male, 52 % female. The percentages of the

age groups were the following: 0-11 months 3.6%, 1-4 years 13.3%, 5-14 years

30.2%, 15-49 years 42%, 50+ years 11%. The percentage of women in the

reproductive age (15-49 years) was 23.5%.

49

5.2.3 Relating distance to the population figures

For the purpose of analysis the area surrounding Yende was divided into

concentric distance bands of 2 and 5 km. The following figures represent the

number of persons living within the concentric distance bands. Half of the persons

counted was living in a radius from 5 km; 9870 (51,3 %). In the area from 6 - 10

km 5781 lived (30 %) and 3573 (18,6 %) people were living in the remaining 11 –

15 km. In summary 81,3 % of the population were living in the 10 km cycle.

5.3.1 Assessment of Outpatient register (CPC) and of preventive Services

(ANC, EPI, FP)

5.3.1.1 Origin of attendees

There were a total of 7.390 reported visits to the during the 12 months of the

period under study which were entered into the computer. From these 269

entries were eliminated as the village of origin was not stated and entries from

towns and villages which were definitely from outside the region (7221). In the

further data process to allocate the villages into concentric distance bands 47

patient contacts were lost and 7074 were used as basis for the calculations.

The curative care users which were included in the analysis (7121) came from

six sub-districts. From Kissidougou district: Yende 6140 (86,3 %) and from

Gueckedou district, Bolodou 607 (8,5 %), Guendembou 233 (3,3 %),

Temessadou 109 (1,5 %) and Kondiadou 23 (0,32 %). In total 13,7 % of the

users came from the neighboring districts.

From the total population living in the 15 km range 11254 live in Yende sub-

district. 5.987 patients from Yende sub-district consulted the HC from within the

15 km range and 153 from beyond. 5363 came from the areas 1-5 km 5363 (89

%), 6-10 km 413 ( 6,9 %), 11-15 km 211 (3,5 %).

50

Related to the total figure of patients which consulted at the HC Yende from the

whole of the Yende sub-district (7121) the figures were the following:

1-5 km 5368 (75,3 %), 6-10 km 413 (6,9 %), 11-15 km 211 (3 %), 16-20 km 17

(0,2 %), 21-25 km 56 (0,7 %), 26 – 30 km 40 (0,6), 31 – 35 km 24 (0,3 %), 36-

40 km 16 (0,2).

5.3.1.2 Age groups

The percentages of the age groups were the following: 0-11 months 3.6%, 1-4

years 13.3%, 5-14 years 30.2%, 15-49 years 42%, 50+ years 11%. The

percentage of women at reproductive age (15-49 years) was 23.5%.

5.3.1.3 General features attendance curative care (CPC)

More females than males attend the health center. 58 % of all patients were

female, 41,4 % male which makes a female / male ratio of 1,4. The Division of

patients between the different age groups was the following;

0 – 11 months 13,7 %, 1-4 years 18,9 %, 5 – 14 years 14 %, 15 – 49 years 44,2

%, more than 50 years 9,2 %.

Attendance varied by age and was highest In Infants (<I years). As well children

from less than five years old attended more than can be expected from their

proportion of the general population (13,3 %). The age group 5-14 years

attended half the number of times expected by their proportion of the population

(30,2). The other age groups attendance rate corresponded with their

proportion.

Table 2: Outpatient attendance compared with census data

0–11 Months 1 – 4 Y 5 – 14 Y 15 – 49 Y 50+ Y

Gen. CPC 13,7 % 18,9 % 14 % 44,2 % 9,2 %

Census 3,6 % 13,3 % 30,2 % 42% 11%

Guinea 97 4,0 % 14 % 26 % 56 %

51

4.3.1.4 General Distance

The great majority of the patients attending the health center for curative care

came from the area immediately surrounding the health center. From within the

distance of 5 km attended 5629 patients (79 %), from the distance of 6-10 km

859 (12,1 %) , from 11- 15 km 339 (4,8 %) and the rest from more than 15 up

to 40 km distance 276 patients (3,9 %). In summary from within 10 km came 91

% and from within 15 km 95,9 % of all patients.

4.3.1.5 Sexe-Distance Interaction

The female/ male ratio is 1,4 from 1 – 5 km, from 6-10 km 1,3 , between 11 and

15 km 1,6 and for the area going beyond 15 km it stays at the same 1,6.

Comparing with the average female/ male ratio of 1,4 for CPC the general trend

of females attending significantly more than males is even more pronounced

with distance.

4.3.1.6 Age groups-Distance Interaction

Infants are generally brought more often to the health center 1-5 km 15,8 %,

their attendance declines drastically with distance. 6-10 km 9,6 %,

11-15 km 0 %. Practically no infants are brought to the health center the

parents living more than 10 km away.

The inverse situation is the case with the age group of less than 5 years. Their

attendance rate is proportionate at 1-5km 14 % , but then increases steeply to

33 % 6-10 km and increases even further at 11-15 km to 41 %. The age group

5-14 years attends only half as much compared with their proportion in the

population. In the other age groups differences are less marked.

52

Table 3: Attendance Outpatient care Age groups in relation to distance

Distance 0–11 Months 1 – 4 Y 5 – 14 Y 15 – 49 Y 50+ Y

1 – 5 km 890 (15,8 %) 790 (14 ) 795(14,2 ) 2603 (46,2 ) 551(9,8 )

6 – 10 km 83 (9,6) 291(33,9) 103 (12 ) 334 (38,9) 48 (5,6 )

11–15 km 0 140 (41,3) 60 (17,7 ) 103 (30,4 ) 36(10,6 )

15 + km 0 120 (43 ) 40 (14,5 ) 97 (35,1 ) 19 (6,9 )

4.3.1.7 Distance Decay

There is a steady drop in utilisation rate of patients coming from locations

further away from the health center (distance decay). The area surrounding the

health center was divided into concentric distance band of 2 km. The following

values represent the number of CPC contacts with attendance rates; 1-2 km:

5183 (0,69), 3-4 km: 309 (0,20), 5-6 km: 277: (0,16), 7-8 km: 438 (0,16), 9-10

km: 233 (0,11), 11-12 km: 163: (0,12), 12-14 km: 52 (0,04), 15 km: 120 (0,12).

The medium utilisation rate in the 15 km radius is 0,35.

raph 1: Utilisation rate of Outpatient care related to distance

53

Utilisation rate for Yende Sub-district

The utilisation rate from within the 15 km zone is 5987 / 11254 = 0,53

(Utilisation 53 %). The remaining population of the Yende sub-district outside

the 15 km radius should be 17.319 minus 11.254 which equals 6.065. The

utilisation rate of this population is 0,025.

35 of the population of the Yende sub-district population lives beyond the 15

km radius. The utilisation rate for the population coming from within the sub-

district Yende is 5.987 / 17.319 which equals 0,35.

4.3.1.8 Utilisation of Preventive Services

The results for preventive services can not be compared as for CPC contacts.

The health center Yende does not usually provide ANV, EPI and Family

Planning services to people coming from other sub-districts than the sub-district

Yende. For coverage calculations only the area of the 15 km radius surrounding

Yende belonging to Yende district containing 11.032 people (57,4 %) can be

taken into consideration. Therefore Coverage for concentric distance bands can

not be calculated as usual for preventive services.

Antenatal care and Family Planning

The ANC section of the health center Yende had during the year under study,

according to the Antenatal forms reviewed, (“fiche consultation prenatale”) kept

at the office 885 clients with the total number of 2609 contacts ( average 2,8

ANC contacts per patient). Of these 208 clients were part of the outreach

strategy (24 %). As described before the coverage calculation has to be

modified by multiplying by the factor 0,574. Considering this factor the overall

coverage rate for the share of the sub-district Yende of the 15 km range was

0,41. From the total of 885 ANC clients 622 were from within the 15 km range

(70,3 %).

In addition the ANC department saw 158 Family Planning clients with an

average of 2,16 contacts per client.

54

Expanded Program of Immunization

The EPI section vaccinated 764 children with in average 2,9 contacts per child.

389 (51 %) children were vaccinated as part of an outreach strategy. The

coverage rate for the children within the 15 km range in the sub-district Yende

was 0,39. If the health center would not have engaged into EPI outreach

activities the coverage rate would have been 0,26 (only fixed strategy). 471

children (61,6 %) of the children vaccinated were living in the 15 km range.

4.3.1.9 Distance and coverage rates

The coverage rate for ANC and EPI compared with utilisation for CPC is as

described in the following table;

Table 4: Comparison of utilisation rates Outpatient-, Antenatal- care, Expanded Program

of Immunization and Family Planning (5 km Distance Bands)

Dist.Bands Total

Pop.

CPC Util.Rate ANC Couv. EPI Couv. FP

1-5 km 9870 5592 0,57 464 1,04 317 0,98 134

5-10 km 5781 848 0,15 78 0,30 79 0,30 8

11-15 km 3573 335 0,09 80 0,50 75 0,66 5

Total 19224 6775 0,35 622 0,72 471 0,67 147

55

Table 5: Comparison of utilisation rates Outpatient-, Antenatal- care, Expanded Program

of Immunization and Family Planning (2 km Distance Bands)

Dist.Bands Total

Pop.

CPC Util.Rate ANC Couv. EPI Couv. FP

1-2 km 7486 5183 0,69 397 1,18 270 1,02 131

2-4 km 1578 309 0,20 61 0,86 39 0,98 3

5-6 km 1710 277 0,16 21 0,27 19 0,31 2

7-8 km 2812 438 0,16 37 0,29 35 0,37 3

8-10 km 2065 233 0,11 26 0,28 33 0,26 3

11-12 km 1374 163 0,12 55 0,89 45 1,07 1

12-14 km 1187 52 0,04 10 0,19 12 0,36 2

15 km 1012 120 0,12 15 0,33 18 0,46 2

Total 19224 6775 0,35 622 0,72 471 0,67 147

There is a marked decrease in utilisation for all services with greater distance.

There is the exception of the PEV utilisation rate at 9-10 km with 0,62 which

may be due to active outreach activities.

4.3.1.10 Attendance “Maliando” Members

The number of patient contacts of the 1114 “Maliando” insurance members was

1999 which represents 27,8 % of all attendance’s. Considering the max 9 %

coverage rate of the insurance scheme this is a high number. This observation

corresponds with the fact that “Maliando” insurance members use about 4 times

as much the health facility compared with non-members. In this study

“Maliando” members use the HC 5 times as much.

695 members (69 %) live within the 5 km , 260 (26 %) in the 6-10 km and

5, 8 % in the11- 15 km range. The health insurance scheme attracts very few

members from distant locations.

56

As a further step utilisation rates of insurance and Non-Insurance members for

concentric distance bands of 5 km width were established with increasing

distance for both groups. The following table represents the decrease in

utilisation rate of Members and Non-members of the health insurance

Table 6: Utilisation rates of Non-Members and Members

Non-Members Members

1 – 5 km 0,52 1,96

6 – 10 km 0,12 1,44

11 – 15 km 0,09 1,02

Compared with the group living within the 5 km radius the insured group in the

15 km radius used half as much the services of the health center for curative

care. The decrease in utilisation according to distance is much more marked in

the Non-Member group. In the 10 – 15 km distance band their utilisation rate

drops by the factor 5,8 compared with the insured group (2). The relationship

Member to non-Member is 3,8 within the 5 km range, 12 within the 5-10 km

range and stays about equal with 11,3 in the 11-15 km range. Beyond 10 km

distance affects both groups equally. The utilisation rate of Non-Members

declines in the range 5-10 km by the factor 4,3 whereas it drops in the Member

group only by the factor 1,4. The average utilisation rate of members is 1,77 in

the current study (contacts; 1793, members; 1014).

Graph 2: Utilisation Rates according to insurance status

5.4.1 Summary of the Findings

The major users are the population immediately living around the health center.

79 % of the patients came from within the distance of 5 km and 91 % live up to

10 km. The proportion of people coming from larger distances declines

exponentially.

57

There is an important difference between sexes in attendance rates, with

woman attending 41 % more often than the males. The study shows age as a

factor in the utilisation of health services. Those under the age of 5 years made

more use of the health center services than any other group, the highest

attendance has the infant group which shows 3,8 times more attendance than

expected by their proportion of the population.

There were marked differences in the distribution of age groups of the

population attending from different concentric distance bands.

Infants attendance rate decrease significantly with distance, Under five

attendance rate increases with distance.

Surprisingly much less people from the neighboring sub-districts attend the

health center compared with their proportion of the population. Yende

comprises only 57 % of the population in the 15 km radius, but provides 86,3 %

of all the patients of the health center.

“Maliando” members living in a distance of 11 –15 km use the services half as

much as members living within the 5 km range.

Decrease in utilisation according to distance is much more marked in the Non-

Member than the Member group. Between 5 and 10 km the utilisation rate of

Non-Members declines by the factor 4,3 whereas it drops in the Member group

only by the factor 1,4.

The health insurance scheme attracts very few members from distant locations.

58

6. DISCUSSION

6.1 Introduction

As described in the literature review the utilization of a health center is

influenced by several factors one important is the factor distance. In this part

the results of the study are compared with results of a „monitorage“ and a

previous study on utilization of health insurance members by the PRIMA team.

The results are put into context with the current situation of the health insurance

scheme „Maliando“ .

6.2 Institutional versus administrative catchment area

population/Health

The institutional catchment area population is estimated to englobe the

inhabitants of villages and towns living within an 15 km radius of the health

center. The older estimation by von Roenne was that it comprises 17.250

inhabitants (Roenne, 2000) The results of the household survey increased the

estimated figure by 10 % to 19.224. The catchment area does not respect the

administrative repartition and contains areas of 5 sub-districts Yende,

Boloudou, Guendembou and Temessadou. This constitutes a difference with

the administrative catchment area, which is the whole sub-district of Yende.

Previous analysis of the patient register had shown that the population living

more than 15 km up to 40 km away in the North of the health center very little

use its services. Often this population attendance is only during the market days

when they come for other purposes and combine a visit to the health center.

A considerable part of the HC attendees were thought to come from the

neighboring Sub-districts. Even if these populations may without any problem

use the services of the HC belonging to another administrative unit they do not

have access to preventive services (ANC, EPI, FP). The supply of vaccines and

other material corresponds to the administrative population, so their supply is

given to the health center corresponding to their residence. For utilisation or

coverage calculations the administrative catchment area serves as the

denominator, although in reality many of this population realistically do not have

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access. This was one of the assumptions of the study. To approach the subject

the findings of the study are compared with the results of the “monitorage” of

the HC Yende from December 1999.

6.3 Results from the “Monitorage” December 1999

Table: Results “ Monitorage” HC Yende Dec. 1999

Service Denominator

(Target Pop.)

Nominator Util.Rate/

Coverage

Accessibility CPC 17.319 12.751 74%

Prev. Services 17.319 15.011 99%

Utilization/

Coverage

Calculations

CPC 17.319 5027 58%

ANC 390 381 99%

PEV 346 344 99%

FP 1039 120 12%

6.3.1 Accessibility

The figure for the population living in the 10 km range all neighboring sub-

districts is 15.651. From this population only 57,4 % live in Yende sub-district.

According to this calculation 8.984 persons live in the 10 km range of the HC

within the sub-district Yende which represents 52 % of the total sub-district

population. At the time of analysis no figures about the population living within

the 10 km range of the 2 health posts of Walto and Firadou were available.

Combining the findings of this study with the figures as basis for the calculations

for the “monitorage” this population should be 5.676. In this case accessibility

would be 90 % as stated in the “monitorage” report.

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6.3.2 Curative Care Utilisation from within the Yende sub-district

As elaborated in the results chapter the “monitorage” gave a figure of 58% for

the whole sub-district. The average figure in Guinea for CPC utilisation in 1997

was 0,26, with the region of Kissidougou having 0,39 (MSP, 1997).

The figure of 0,35 established by the current study can not be directly

compared as the attendance’s of the health posts are not known and would

raise the utilization rate. Taken the number of attendants of the 2 health posts

from June – November 99 (892) multiplied by 2 an estimation can be made.

Adding the number of attendees of the HC from Yende Sub-district and the

extrapolated attendees of the HP’s the total number is 7924 (Coverage rate 45

%). The difference to the Utilization rate from the “monitorage” in December can

be well explained by the fact that 1250 data entries were not considered. 278

contacts could not be localized and 972 (13,7 %) attendees were not

considered in this calculation as belonging to other sub-districts. Adding this

number would make a coverage rate of 53 %, which is very close to the 58 %

result from the “monitorage”. Normally it is not possible to exactly determine the

origin of patient that’s why there is no other way than to use the mode of

calculation as in the “monitorage”. Patients from the sub-district will as well

attend health centers in other sub-districts which will probably equalize the

situation.

Looking at the small proportion (17 %) of attenders from other sub-districts in

comparison with their population share 42,3 % of the 15 km radius the

difference between institutional and administrative catchment area is less

marked than expected. Yende comprises only 57 % of the population in the 15

km radius, but provides 86,3 % of all the patients of the health center.

It is even surprising how few people use the facility who live relatively close but

are from another sub-district. Several reasons may play a role, one being that a

private health center is established in 2 km distance in southern direction in

Mano, but this does not provide preventive services. An explanation may be

that people would not like to attend where they will not receive comprehensive

services (including ANC, EPI, FP) but from practical observations this seems

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unlikely. Does the percentage of 13 % of attendees from other sub-districts

require special administrative or logistical arrangements? The results of the

study would argue against it.

6.4 Member and Non-member utilisation rate

The utilisation rates obtained in this study for insurance members (1-5 km 1.96,

6-10 km 1,44, 11-15 km 1,02) corresponds with the findings of Hohmann

(1999) and are less than in the documentation by Diallo. The average utilisation

rate of members is 1,77 in the current study (contacts; 1793, members; 1014)

are as well comparable with the other studies. The health insurance scheme

attracts very few members from distant locations. Only 26 % of the members

come from the 6-10 km and 6 % from the11- 15 km range.

Members living more distant to the health center use the health center less and

produce less costs to the insurance scheme. The study shows that insurance

member households living within 5 km of the health center use the services

twice as much than households living beyond 10 km distance.

Households, who live far away and pay the same subscription fee actually

subsidize the households living close to the health facility who use the services

more frequently as stated by Noterman (1995). Remote households can not

see a benefit in joining as they rarely attend the clinic, even if they are sick.

The biggest obstacle to join the health insurance scheme is the premium which

is perceived as being to high for a complete family. Rural households find it

difficult to accept the payment of larger amounts of money in advance for

possible illness. The aspect of being protected against major health expenses

for a whole year and the feeling of security associated with it is not easily

appreciated. In the case of catastrophic illness solidarity amongst community

members usually dictates to help each other. In contrast it would be difficult to

obtain a loan for preventing major expenses for illness.

To improve acceptability of the health insurance in the population graduated

premiums (sliding scales) according to household location could be introduced

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while at the same time the premiums would be still self-financing. This was as

well proposed by Noterman (1995) and Bennet (1997).

Another option would be to suit service packages to the different location of

households for instance by insuring only low frequency high cost events

(catastrophic illness) for lower premiums. This is the basis for the relatively

successful Bwamanda health insurance scheme in Zaire. To put such more

complicated mechanisms into practice organizers of a insurance scheme need

information on utilization and risk patterns to be able to set appropriate

premiums. The current study hopes to provide information on this subject.

6.5 “Maliando”/ PRIMA

Targeting the poor rural mainly self employed populations, the health insurance

scheme “Maliando” faces the same problems as many other district based

schemes. Many schemes have limited population coverage and do not function

cost-efficient. (Bennet, 1997). Despite the low membership figures there are

obvious achievements of the scheme which go beyond the aspect of pure

health financing. One of the major achievements of PRIMA with the MUCA

model (“Maliando”) is that it initiated a dialogue between users and providers of

health services. Despite official proclamations about community participation

and control this is rarely put into reality. Criel stated that a very “utilitarian”

content that is given to community participation still very much prevails among

health care managers. The view of users are too often neglected. The MUCAS

model gives some level of power to the users and defers specific rights and a

forum to formulate claims and complaints. This clearly showed in public

discussions in which “Maliando” members expressed their dissatisfaction with

the quality of services. It is a novum that doctors and nurses are confronted

openly with critiques. It led to a situation that the system that had not changed

much its recommendations and guidelines since the early years of PHC had to

adapt to a new situation. Criel stated (1999) “that there still remains a very

strong grip of the central level on peripheral decision-making and that there

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(still) is a poorly developed culture among district managers to, themselves,

plan and take decisions”.

With the occurrence of the health insurance scheme health managers have to

start to question themselves. The Ministry of Health who is interested in further

research in this issue accepted that in Yende new forms of service delivery

which could be more acceptable to the population may be experimented. One

of the problems are the very strict guidelines which do leave very little room for

negotiations with the clients. If the health agents do not prescribe according to

the guidelines their rating at the “monitorage” will decrease. The perceived

quality of care seems to be a major reason for misunderstanding between the

health services and the users. There an important difference between what the

general population perceives as good quality health care and what the health

services are technically able to provide, following the principle of good standard

practice. It was observed that people do not value the comprehensive services

offered at the health center, equalizing the medical treatment with dispensing

drugs. The amount of drugs received represents the good or bad treatment.

Injections are considered high quality treatment. (“Strong medicine“). People

perceive the drug variety as too restrictive and consider Aspirin, Chloroquine

and Cotrimoxazol as low quality treatment because it is so often prescribed. A

lot of misconceptions exist, technically the treatments offered by the health

services are cost-efficient and usually adequate but not well accepted by the

population. Considerable effort by the promotors of the health insurance

scheme have to be made to overcome these misconceptions in order that

providers and users speak a common language. The problem of questionable

quality of care by the health services shall not be overlooked. Partly through the

influence of “Maliando” the health services are becoming more interested in the

issue and have initiated the participation of Yende HC in the first phase of a

quality improvement project.

Another problem which has come on the open is the frequent „Under- the-

counter- payment“ of health staff in Guinea, which is now discussed openly.

Von Roenne (2000) documented this very detailed for the health center Yende.

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Another problem observed is that although the health center generates enough

money it is not allowed to buy drugs in private pharmacies in case of rupture,

which can always occur. The purchase of drugs have to go through the official

drug procurement channels, which makes a quick solution to a minor problem

difficult. Generally the experience of Maliando proves that establishing a health

insurance scheme may enable to introduce organisational changes and service

quality improvement as mentioned by Gilson (1997).

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7. CONCLUSIONS AND RECOMMENDATIONS

Based on the findings of the study the perceived difference between

administrative and institutional catchment area is much less marked than

expected. Only 13 % of attendees come from neighboring sub-districts.

No major administrative or logistical arrangements are necessary to

accommodate this low percentage of users from other districts. For the

calculation of drugs and vaccines for the health center remote communities are

included which will never attend as the distance is too far. For these populations

the distance constitutes a significant barrier to access and they will not use their

share of the logistics. It is doubtful that in the current situation of budget

restrictions it is feasible to add more health posts to cover isolated populations.

It is more important to improve the quality and therefore the attractiveness of

the existing health facilities.

It is a strategic decision whether health centers should only be allowed to

provide preventive care to the population of their administrative district. The low

quantity of patients does not justify major changes.

The acceptability of the community based health insurance scheme „Maliando“

is low. To increase the attractiveness Maliando members in positions of

responsability have to be better informed about basic medical standarts, to

reduce misconceptions about quality of care, to better appreciate standart

medical treatment. Only if members themselves dispose of sufficient and

convincing arguments about the advantages of the scheme they will have

success in convincing others. Considerable effort by the promotors of the health

insurance scheme have to be made to overcome the mentioned

misconceptions in order that providers and users speak a common language.

The lower co-payment levels for Maliando members living in a distance is not

satisfactory to provide a response to the problem of distance. More powerful

incentives, especially lowering the most important barrier, the annual premium

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may be more adequate. The main recommendation would be to include

information on health spending, utilization and risk pattern in calculating

premiums at levels acceptable to the population. A system of graduated

premiums according to household location (sliding scales) may have a positive

effect. At the same time the health services have to continue to improve the

quality of their services by fully engaging in quality cycle activities

8. ANNEXE