Economic analysis of malaria burden in kenya
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Transcript of Economic analysis of malaria burden in kenya
CONCEPTUAL FRAMEWORK FOR ECONOMIC ANALYSIS OF THE BURDEN
OF MALARIA IN KENYA
DR. NANYINGI MARK
Contact author: Dr. Mark Nanyingi, +254721117845, [email protected]
Epidemiology, Risk and Burden of Malaria in Kenya
Noor et al., 2009, BMC Infectious Diseases 2009, 9:180
P. falciparum parasite rate(PfPR)
Lake Endemic
High
land E
pidem
ic Pr
one
Coast Endemic
Low Risk
Semi Arid Seasonal Risk
MALARIA BURDEN IN KENYA 30-50% of OP and 20% of all
admissions to health facilities. ≃170 million working days are
lost to the disease each year 20% of all deaths in children <5 yr. Vulnerable group: pregnant women
and children <5 yr. (KMIS 2010)
RISK CLASSIFICATION BY REGION Endemic Lake and coastal (≤20%) Epidemic-prone highland (5 ≤20%) Seasonal transmission (<5%) Low-risk (<0.1).
National Malaria Strategy targets to reduce morbidity and mortality associated with malaria by 30%.
Presenter
Presentation Notes
A model-based map produced in 2009 shows the intensity of P. falciparum transmission in Kenya as defined by the proportion of infected children aged 2–10 years in the community. Spatial distribution of P. falciparum malaria in Kenya at 1×1 km spatial resolution INDICATING endemicity classes: HIGHLAND EPIDEMIC: This increase in minimum temperatures during the long rains favours and sustains vector breeding, resulting in increased intensity of malaria transmission. The whole population is vulnerable and case fatality rates during an epidemic can be up to ten times greater than those experienced in regions where malaria occurs regularly ENDEMIC: Endemic areas: Areas of stable malaria have altitudes ranging from 0 to 1,300 metres around Lake Victoria in western Kenya and in the coastal regions. Rainfall, temperature and humidity are the determinants of the perennial transmission of malaria. The vector life cycle is usually short with high survival rate because of the suitable climatic conditions. Transmission is intense throughout the year with high annual entomological inoculation rates. Seasonal malaria transmission areas: This epidemiological zone comprises arid and semi-arid areas of northern and southeastern parts of the country that experience short periods of intense malaria transmission during the rainy seasons. Temperatures are usually high and water pools created during the rainy season provide the malaria vector’s breeding sites. Extreme climatic conditions like the El Niño southern oscillation lead to fl ooding in these areas, resulting in epidemic outbreaks with high morbidity rates because of the low immune status of the population. Low risk malaria areas: This zone covers the central highlands of Kenya including Nairobi. The temperatures are usually too low to allow completion of the sporogonic cycle of the malaria parasite in the vector. However, higher temperatures and changes in the hydrological cycle associated with climate change are likely to increase the areas suitable for malaria vector breeding with the introduction of malaria transmission in areas where it did not previously exist
The Economic Burden of Malaria in Baringo, Kenya Background:
Africa accounts for 60% of the 350–500 million clinical malaria cases globally and a total malaria cost of $12 billion (WHO,2000).
In Africa the monthly average household expenditure on malaria treatment and prevention ranges between ($2 -$25) and ($15- $20)(Onwujekwe, 2013).
Economic impacts: human and economic costs lead to overall poor quality of life with private expenditure towards consultations, treatments, hospitalization. Human health impacts lead to low productivity and lost incomes.
In western Kenya, the cost per malaria episode and cost per Disability-Adjusted Life-Year (DALY) averted have been estimated at $4.62 for AQ3-AS3.
Baringo county is a semi arid seasonal risk zone for malaria epidemics, records show that annually, atleast 50% of the population suffers from at least one episode of malaria while children under 5yrs have an average of 2–4 attacks of malaria
Estimating the economic burden of malaria in Baringo is necessary to provide a
basis or platform for advocacy and resource allocation in addressing public health
***3 days of amodiaquine-artesunate (AQ3-AS3)
Presenter
Presentation Notes
About 12 % incidence has been recorded in the last quarter.
Study Area: Baringo county
The subcounties were selected based on historical occurrence of disease, security and accessibility
Objectives :
To estimate the magnitude and prevalence of malaria in Baringo County
To estimate the microeconomic costs of malaria on households’ income.
To determine the ability and willingness to pay for malaria control
What is the prevalence rate of malaria in Baringo and which are the most
vulnerable regions?
What are the cost implications of the households’ expenditure on malaria
prevention and treatment? (by how much do these expenditures reduce
households’ income?)
What are the implications of lost working days due to malaria? And by
how much does this loss reduce countys’ total output?
Research questions :
Justification of study:
The study shall contribute to existing literature by quantifying
the prevalence rate and magnitude of the malaria burden at a
local level.
It will also identify the most vulnerable groups so as to
undertake appropriate policy intervention.
The cost implication of household expenditures on malaria
treatment and prevention methods both at macro and
household level shall also be quantified.
The implications of lost working days on county’s output
due to malaria shall be quantified.
Presenter
Presentation Notes
A model-based map produced in 2009 shows the intensity of P. falciparum transmission in Kenya as defined by the proportion of infected children aged 2–10 years in the community. Spatial distribution of P. falciparum malaria in Kenya at 1×1 km spatial resolution INDICATING endemicity classes: HIGHLAND EPIDEMIC: This increase in minimum temperatures during the long rains favours and sustains vector breeding, resulting in increased intensity of malaria transmission. The whole population is vulnerable and case fatality rates during an epidemic can be up to ten times greater than those experienced in regions where malaria occurs regularly ENDEMIC: Endemic areas: Areas of stable malaria have altitudes ranging from 0 to 1,300 metres around Lake Victoria in western Kenya and in the coastal regions. Rainfall, temperature and humidity are the determinants of the perennial transmission of malaria. The vector life cycle is usually short with high survival rate because of the suitable climatic conditions. Transmission is intense throughout the year with high annual entomological inoculation rates. Seasonal malaria transmission areas: This epidemiological zone comprises arid and semi-arid areas of northern and southeastern parts of the country that experience short periods of intense malaria transmission during the rainy seasons. Temperatures are usually high and water pools created during the rainy season provide the malaria vector’s breeding sites. Extreme climatic conditions like the El Niño southern oscillation lead to fl ooding in these areas, resulting in epidemic outbreaks with high morbidity rates because of the low immune status of the population. Low risk malaria areas: This zone covers the central highlands of Kenya including Nairobi. The temperatures are usually too low to allow completion of the sporogonic cycle of the malaria parasite in the vector. However, higher temperatures and changes in the hydrological cycle associated with climate change are likely to increase the areas suitable for malaria vector breeding with the introduction of malaria transmission in areas where it did not previously exist
Ethical consideration:
The study protocol was approved by both institutional and
national Scientific Ethics Review Board.
Each respondent gave a signed informed consent, the heads of
each facility gave an informed consent before data
abstraction.
Consent was obtained from the hospital authorities to use
anonymized data extracted from the hospital database for the
study.
Some patients that were interviewed during the study also had
information obtained from their medical records and gave
their consent both for the interviews and the data abstraction.
Methodology: Study Design
The study will use cost of illness(COI) approach to evaluate the burden of
malaria. The evaluation will be based on private direct costs (PDC) and
private indirect cost (PIC) of malaria attack per episode. COI is estimated
in an accounting sense using direct cost of malaria, indirect cost of
malaria, and institutional cost of malaria care.
Production function Approach (PFA)??? Suitable for country level.
The data required has 2 components: micro data involving cost of illness
to individuals or households and macro data involving cost pertaining to
disease control programmes.
Willingness to Pay (WTP)for malaria care is estimated using contingent
valuation method by administering a household survey. The odds that a
household or individual will be WTP to avoid malaria care at a given cost
is estimated by multinominal probit function.
Presenter
Presentation Notes
Production function Approach (PF) is used to capture the total loss or reduction in output (GDP) caused by malaria morbidity and mortality. The approach postulates that the quantity of a given output that is produced is a function of several factors including the capital stock, labour force and the quality of labour employed The ability to pay for malaria care is assessed through the income and expenditure structure of households that were obtained through a household survey
Analytical Framework
Cost of illness (COI) method, one of the standard frameworks for analyzing
and quantifying the economic burden of malaria (Asenso-Okyere, et. al, 2009).
For each episode of malaria:
COI= PD+PIC+IC Private Direct Cost(PDC), Private Indirect Cost(PIC) and Institutional Cost(IC)
COI= PD+PIC Data requirements and Sources
Primary Data: At the micro level a cross-sectional survey using household
questionnaire at sub-county level (Jimoh, et. al 2007 ). The population will be
made up of households with malaria episodes during the last 3 months of
the survey in the selected areas. Malaria index- parasitemia(PfPR)
Secondary Data: (IC)?? information on cost of malaria surveillance,
detection, treatment, control and prevention from health facilities
extraction, morbidity/ mortality figures collected. Labour index
Presenter
Presentation Notes
The questionnaire sought to gather the following data: demographic and socio-economic characteristics of households, direct cost of a malaria episode to the household (out-of-pocket expenses), indirect cost in the form of productivity lost by malaria patients, caretakers and substitute labour, protection strategies of households against malaria attack and the cost involved as well as households’ standard of living. In addition, households’ willingness to pay for malaria prevention and control was solicited through contingent valuation. Available questionnaire to be discussed?? More information the malaria status of children under the age of five years, cost of treating malaria episode, household assets and monthly food expenditure. Data on transportation costs to and from the facility where treatment was sought and any other costs incurred in the course of seeking treatment for malaria was also Collected. DISCLAIMER not able to collect IC data : Information on costs to the health care system-drugs, investigations, facility running costs (recurrent and non-recurrent) was abstracted from the accounts records and these were validated by both the head of accounts department and the facility head. The capital cost of the buildings were estimated by an estate valuer and a health economist. Depreciation rates were applied to both buildings and vehicles. Estimation of vehicles and laboratory equipment were not applicable to the health centre facilities as they run only outpatient services.
Conceptual framework for Cost of Illness of Malaria
MALARIA
MORBIDITY MALARIA PROTECTION BEHAVIOURS
MORTALITY
COST OF ILLNESS
DIRECT COST INDIRECT COST INTANGIBLE COST
BORNE BY HOUSEHOLD
BORNE BY INSTITUTION
BORNE BY HOUSEHOLD
BORNE BY INSTITUTION
BORNE BY HOUSEHOLD
BORNE BY INSTITUTION
Presenter
Presentation Notes
Malaria protection efforts, cost of morbidity and cost of mortality have respective frameworks private direct costs, private indirect costs, -travels, waiting,lost time, institutional cost, Intangible cost- willingness to pay has contigent evaluation, willings to accept,
Sampling Frame for primary data collection
Multistage sampling: systematic and purposive due to accessibility and
security then random selection within villages to represent all zones.
Health facilities for data extraction in all the zones.
1000 persons
200 villages
Presenter
Presentation Notes
The sample size for villages, hospitals and respondents will be determined based on a standard formulae
Data Analysis
Data analysis will be undertaken and frequency distributions analyzed.
Summary statistics at both the household and provider levels will be
computed.
For continuous variables, the mean and standard deviation will be
calculated while numbers and percentage Will be determined for
categorical variables.
The direct medical costs = drugs cost, diagnostics, administration fees and
other costs incurred as a result of the treatment of malaria.
The direct non- medical cost consisted of transport fare.
The household’s indirect cost of treatment was the cost attributed to
time lost when taking care of a sick child.
Principal component analysis (PCA) will be conducted to generate a
socioeconomic status (SES) index and wealth quintiles based on per capita
food expenditure and household asset ownership
Expected outputs
Variable Mean SD (Ksh) Mean SD(US$)
Direct Medical costs
Administration fees
Consultancy and Diagnostics
Antimalarial drugs
Others
Direct non medical (Transportation cost)
Indirect cost (Loss of Income
Total-OPD/IPD cost per case
Rift Valley Fever Socio economic Framework
1. Direct effects can be estimated by private and public monetary costs due to RVF in
Baringo. (treatment and control costs)
2. Indirect effects or non-monetary : DALY lost due to RVF that would have been
averted during the 2006 - 2007 outbreak.
Humans
1. Direct costs due to mortality and morbidity of different species
2. Intervention costs : Treatment, control by vaccination
3. Indirect effects or non-monetary : transhumance(migrations)
4. Value chain impacts (VCI)- Producers, tradersm slaughterhouses and butchers
5. Macroeconomic level impacts: Social Accounting Matrix that disaggregates
livestock sector and insights into first round macroeconomic and distribution of
impacts.(suitable for country level?)
6. Climate shocks- weather, deaths
Livestock
Rich and Wanyoike, Am J Trop Med Hyg 2010, 83:52–57
Selected References
1. Kenya Malaria Indicator Survey(KIMS) (2010), Division of Malaria Control,
Ministry of Public Health and Sanitation, Kenya National Bureau of Statistics, pp
112
2. Gallup and Sachs, (2001); The Economic Burden of malaria. Centre for
International Development at Harvard University
3. Onwujekwe et al.(2013). The Economic Burden of Malaria on Households and the
Health System in Enugu State Southeast Nigeria. PLoS ONE 8(11)
4. Salihu and Sanni (2013) Malaria Burden and the effectiveness of Malaria Control
Measures in Nigeria: A Case Study of Asa Local Government Area of Kwara State.
Journal of Economics and Sustainable Development Vol.4, No.3, 2013
5. World Health Report (2002) Reducing Risk, Promoting Healthy Life. Geneva,
WHO
6. World Health Organization (2014). World malaria report 2014.
(www.who.int/malaria)
Contact author: Dr. Mark Nanyingi, +254721117845, [email protected]
