Abstract
Community-based health insurance target those employed in the rural and informal sector in urban by pooling risks and protect households from out-of-pocket expenditures when receiving health facility services. However, Ethiopian community-based health insurance is schemes characterized by low enrollment. The aim of this study is to analyze the determinants of community-based health insurance enrollment in Addis Ababa from behavioral economics and discrete choice experiment insights. A total of 222 households from ten pilot woredas were selected for the study using a simple random sampling technique. A simple social experiment is used to examine the significance of behavioral biases. A discrete choice experiment conducted across three attributes and conditional logit model used to determine the relative importance of the selected attributes and willingness-to-pay for those attributes. In addition, the binary logit regression model is used to estimate the probability of households enrollment in community-based health insurance. The study result indicates that households have the highest willingness to pay for only private health service providers (Birr 1849.6/year) compared to status-quo level. Non-member households’ willingness to pay for comprehensive health service package Birr 2271.7/year. Moreover, this study revealed loss-aversion bias, over-optimistic bias, and herding bias had significantly affected the household decision on community-based health insurance enrollment. The study suggests that behavioral biases affect Community-based health insurance enrollment. The study finding also reveals that respondent households are willingness to pay more for comprehensive health service package and for health insurance scheme that includes private health service providers. In addition, the study concludes eligible household enrollment decision varied based on their socio-demographic and household characteristic. This study recommends the need to consider mandatory community-based health insurance schemes and apply targeting intervention (coverage) to the particular group.
Highlights
In Ethiopia, based on the 6th edition of national health accounts, about 33% of the total health care expenditure is attributed to the out of pocket payment by the households it is the largest proportion of health spending [19]
This study found private health provider (OR=5.5337) had the largest effect on the probability of Community-Based Health Insurance (CBHI) alternative being chosen by households who are not members in the CBHI scheme (See Table 5)
Member Households The estimation result confirms that the alternatives with public and private health service provider increased the odds (OR=22.1305) of choosing a given alternative by households who are members in the CBHI scheme
Summary
In Ethiopia, based on the 6th edition of national health accounts, about 33% of the total health care expenditure is attributed to the out of pocket payment by the households it is the largest proportion of health spending [19]. To expand financial protection to reduce the financial burden that health spending imposes on households and to upsurge health service utilization, the Ethiopian government implements demandside initiatives called Community-Based Health Insurance (CBHI). Abel Eshetu and Abrham Seyoum: Community-Based Health Insurance Enrollment and Determinants In Addis Ababa: Insights from Behavioral Economics and Discrete Choice Experiments households from out-of-pocket expenditures when receiving health facility services. Health sector transformation plan 2015/16 - 2019/20 [19] sets a target to establish community-based health insurance (CBHI) schemes in 80% of woredas and enroll at least 80% of households, and plan to collect 375.6 million USD from members. Ethiopian CBHI schemes characterized by low enrollment It is similar in many Sub-Saharan Africa countries enrolment not often reaches more than 10% of the eligible population with some exceptions in countries such as Rwanda and Ghana [9]
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More From: International Journal of Health Economics and Policy
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