Identification of poor households for premium exemptions in Ghana's National Health Insurance Scheme: empirical analysis of three strategies.
until further notice
SourceTropical Medicine & International Health, 15, 12, (2010), pp. 1544-1552
1 december 2010
Article / Letter to editor
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Primary and Community Care
Tropical Medicine & International Health
SubjectN4i 3: Poverty-related infectious diseases; NCEBP 7: Effective primary care and public health
OBJECTIVES: To evaluate the effectiveness of three alternative strategies to identify poor households: means testing (MT), proxy means testing (PMT) and participatory wealth ranking (PWR) in urban, rural and semi-urban settings in Ghana. The primary motivation was to inform implementation of the National Health Insurance policy of premium exemptions for the poorest households. METHODS: Survey of 145-147 households per setting to collect data on consumption expenditure to estimate MT measures and of household assets to estimate PMT measures. We organized focus group discussions to derive PWR measures. We compared errors of inclusion and exclusion of PMT and PWR relative to MT, the latter being considered the gold standard measure to identify poor households. RESULTS: Compared to MT, the errors of exclusion and inclusion of PMT ranged between 0.46-0.63 and 0.21-0.36, respectively, and of PWR between 0.03-0.73 and 0.17-0.60, respectively, depending on the setting. CONCLUSION: Proxy means testing and PWR have considerable errors of exclusion and inclusion in comparison with MT. PWR is a subjective measure of poverty and has appeal because it reflects community's perceptions on poverty. However, as its definition of the poor varies across settings, its acceptability as a uniform strategy to identify the poor in Ghana may be questionable. PMT and MT are potential strategies to identify the poor, and their relative societal attractiveness should be judged in a broader economic analysis. This study also holds relevance to other programmes that require identification of the poor in low-income countries.
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