Community Based Health Insurance Scheme (Mutuelles) in Rwanda: an evaluative note using household surveys

Abebe ShimelesDevelopment Research DepartmentAfrican Development BankOctober 2009

1. Introduction According to WHO (2005), every year 100 million

people are driven into poverty due to catastrophic expenditure on health related needs.

Certainly the problem is more pervasive in Africa where there are little risk-mitigating mechanisms against health-related negative shocks.

Out of pocket household health expenditure is generally high and non-monotonic across the income divide: poor income countries spend as much as middle income economies as a share of household income (see Figure 1) with considerable variation on health outcomes.

Figure 1: Share of household out of pocket expenditure on health in 47 African countries

Congo, Democratic Republic

Liberia

Zimbabwe

Gambia

Guinea-BissauNiger

Ethiopia

Malawi

MozambiqueRwanda

Guinea

Central African RepublicAngola

Sierra Leone

UgandaMali

Madagascar

Zambia

Chad

Congo

TanzaniaBurkina FasoTogoComoros

Ghana

Benin

Mauritania

DjiboutiCôte d'Ivoire

Kenya

NigeriaSenegal

Sao Tome and Principe

Cameroon

Lesotho

Sudan

Morocco

Cape Verde

GabonNamibia

Equatorial Guinea

Botswana

Swaziland

Egypt

Tunisia

South Africa

Mauritius

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4.0

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.05

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0.0

5.1

.15

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4 5 6 7 8log income

Share of health expenditure Share of health expenditure

2. CBIs in Rwanda In recent years Community-based health

insurance schemes (CBHIs) emerged in Africa in response to failures by the state and the market to provide health insurance (e.g. Ghana, Senegal & Rwanda)

CBHIs in Rwanda however are perhaps the largest (close to 85% coverage in 2008) and linked to national health policy.

2. CBI’s in Rwanda (contd)

Some of the features of CBIs in Rwanda include: Premiums are flat (earlier it used to be

different across CBIs) Members have access to basic health

care services and medication at a discount rate.

2. CBI’s in Rwanda (contd) Why is CBHIs in Rwanda interesting?

. Scale up took place in the midst of controversy. The pros and cons are as follows:

Rwanda being a poor country and basic health services are unaffordable to the majority (despite government subsidy), CBHIs are the only alternative to increase demand for modern health care and reduce illness related consumption risk

2. CBI’s in Rwanda (contd) Others argue that flat rate is inherently

discriminatory. The insurance premium is high for the extreme poor (about 6% of total income) and in fact could reduce health service utilization due to other layers of expenses. Since subscription to the program is not “voluntary”, there is no guarantee that the poor are protected from health related income shocks.

3. Objectives of the paper

Do CBIs increase demand for modern health care services?

Are insured households protected from catastrophic out of pocket health-related expenditure?

Do the poor fare well compared to the non-poor since they contribute proportionately more to the system than the non-poor?

4. Data Nationally representative household survey

conducted in 2005/06 covering 6900 households.

The data is standard Living Standard Measurement Survey complete with information on household demographics, consumption, income, labor market conditions, education and health, etc.

According to the survey 34% of households were members of CPIs (39% rural and 22% urban areas).

4. Data (contd..)

21% of households reported as having fallen sick in the previous two weeks of the survey.

Of these only 30% sought medical care.

Overall, 20% of households reported positive health related expenditure

5. Variable definition

Dependent variables Dummy if a household sought treatment

from health providers after reporting sick Dummy if a household experienced

“catastrophic” expenditure which is defined as top decile of the share of health expenditure to total expenditure.

5. Variable definition (contd..)

Covariates Dummy if a household was enrolled in

community based health insurance scheme (key variable of interest)

Age, size of household, sex of head, level of education, real consumption expenditure in adult equivalent, district dummies, urban dummy, disability status, etc..

6. Estimation issues Membership in CBHIs is very likely not

random so that there is a real possibility of households self-selecting into the system which introduces biases into its effect on the dependent variables.

One example is sick people self-selecting into the insurance system

Or the flat premium provides built-in incentives to well-off households

Well-run districts get far more members than weaker districts, etc..

6. Estimation issues(continued)

Generally membership to CBIs was driven by the following factors: Household consumption quintile (richer

households tend to enroll) Demographic factors are important: Male

headed households, large families and older family heads tend to enroll into CBIs.

Some districts have significantly higher enrollment than others

But, there is also substantial pressure from local administrators that may be correlated with the above variables (the higher the stake, the higher the rate of compliance-richer and educated households tend to comply more than poor ones, etc. )

7. Empirical method

Broadly speaking, the empirical literature uses two approaches to deal with the above research questions: econometric models (regression approach) and the matching estimator commonly used in the evaluation literature though conceptually the two are related

7. Empirical approach (contd..)

The general specification of the econometric model follows the latent variable approach with endogenous dummy regressor (Heckman, 1974 and others) (see equation below)

7.1 regression approach (a bi-variate discrete choice model)

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7.1 regression approach (contd..)

It is safe to assume that membership to the CBIs is endogenous in the econometric model for a number of reasons (σ12 #0):

7.2. Matching estimator This is a popular method used extensively

in the evaluation literature. Some focus on “before” and “after” a program and often most focus on “with” and “without” a program

The idea is to create a “treated” versus “control” group that would be matched on the basis of specified household and community characteristics.

Works well when the bias introduced by unobserved factors are minimum.

8. Discussion of results Finding instruments that affect health

utilization and health related risk only through membership to insurance is not easy.

We identified two potential instruments. One is cluster level enrollment rate (to isolate some of the confounding factors in individual decision)

and the other is a dummy whether or not a household owns title deeds for land ownership (proxy for well-run districts)

Table 1: marginal effects of membership to Mutuelles on selected variables: simple probit (with Blundell-Smith, 1986 test for Weak-exogenity)

Coefficient p-value

Weak-exogenity test (p-values)

Utilization of modern health care (households that reported sick) .1599*** [0.000] 0.3828 Utilization of modern health care among the insured poor .1714*** [0.001] 0.7052 Utilization of modern health care among the insured non-poor .16756** [0.006] 0.458 Out of pocket catastrophic health expenditure (all households) -0.028*** [0.000] 0.993 Out of pocket catastrophic health expenditure (all households with positive health expenditure)

-.2923*** [0.000] 0.9127

Out of pocket catastrophic health expenditure (poor households with positive health expenditure)

-.3226*** [0.000] 0.795

Out of pocket catastrophic health expenditure (non-poor households with positive health expenditure)

-.2632*** [0.000] 0.3358

Table 2: Average treatment effect of being insured on selected variables using Matching Estimator

Coefficient p-value

Number of observations

Utilization of modern health care (households that reported sick)

0.146** 0.000 786

Utilization of modern health care (households that were poor and reported sick)

.085** .046 397

Utilization of modern health care (households that were non-poor and reported sick)

0.269*** 0.000 390

Out of pocket catastrophic health expenditure (all households with positive health expenditure)

-0.164*** .001 273

Out of pocket catastrophic health expenditure (poor households with positive health expenditure)

-.228** .010 101

Out of pocket catastrophic health expenditure (non-poor households with positive health expenditure)

-.239** 0.001 101

6. Discussion of results (contd..)

Conditional on other household and community level covariates (age, sex of head of the household, educational attainment, dummies for district, dummies for serious illness, occupation, etc) we find that membership to CBHIs have significant and positive impact on: Utilization of modern health care

and protection of households from catastrophic health related expenditure, which is indeed reassuring.

6. Discussion of results (contd..)

Generally the poor do not seem to have come out badly, though the non-poor seem to have better access.

Catastrophic expenditure is not any different between insured and uninsured among households that reported sick.

Generally the results with the Matching Estimator are very comparable and consistent (see Table 2)

6. Discussion of results (contd..)

Despite the weak result on the effect of CBIs on health related expenditure, it is possible to see that generally insured households have less health related expenditure risk than uninsured households (see Figure 2 and Figure 3 below)

Figure 2: Health expenditure profile for the uninsured

-1

1

3

5

7

9

11

13

15

1 120 239 358 477 596 715 834 953 1072 1191 1310 1429 1548 1667 1786 1905 2024 2143 2262 2381

healthexpenditurespercapita totalexpenditurespercapita povertyline

Figure 3: Health expenditure profile for the insured

0

5

10

15

20

25

30

1 125 249 373 497 621 745 869 993 1117 1241 1365 1489 1613 1737 1861 1985 2109 2233 2357

Households ranked by real per capita consumption expenditure

No

rmali

zed

exp

en

dit

ure

Normalized total consumption expenditure minus expenditure on health

Normalized consumption expenditure per adult equivalent

Nomalized poverty line

Summary and conclusions CBIs in Rwanda play an important role

in increasing demand for modern health care controlling for other factors. In general, membership increases health service utilization by about 17% more among the non-poor than the poor.

When illness strikes, the CBIs seem to protect member households from catastrophic expenditure.

Summary and conclusions The potential of CBIS seem to be very

high among the non-poor than the poor in both cases that may reinforce the inequity inherent in the system.