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, mnanyingi@kemricdc.org

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
HighlandEpidemicProne
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%.

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)

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.

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.

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

Conceptual framework for Cost of Illness of Malaria
MALARIA
MORBIDITYMALARIA
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

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

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, mnanyingi@kemricdc.org

Economic analysis of malaria burden in kenya

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