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Poverty identification for a pro-poor health insurance scheme in Tanzania: reliability and multi-level stakeholder perceptions.

Kuwawenaruwa A, Baraka J, Ramsey K, Manzi F, Bellows B, Borghi J - Int J Equity Health (2015)

Bottom Line: We compared the distributions of the three wealth measures, and the consistency of household poverty classification using cross-tabulations and the Kappa statistic.However, it failed to pick up half of those living below the "basic needs" poverty line as being poor.It is important to ensure the strategy is efficient and less costly than alternatives in order to effectively reduce health disparities.

View Article: PubMed Central - PubMed

Affiliation: Ifakara Health Institute, Plot 463, Kiko Avenue Mikocheni, P.O. Box 78 373, Dar es Salaam, Tanzania. ajoachim@ihi.or.tz.

ABSTRACT

Background: Many low income countries have policies to exempt the poor from user charges in public facilities. Reliably identifying the poor is a challenge when implementing such policies. In Tanzania, a scorecard system was established in 2011, within a programme providing free national health insurance fund (NHIF) cards, to identify poor pregnant women and their families, based on eight components. Using a series of reliability tests on a 2012 dataset of 2,621 households in two districts, this study compares household poverty levels using the scorecard, a wealth index, and monthly consumption expenditures.

Methods: We compared the distributions of the three wealth measures, and the consistency of household poverty classification using cross-tabulations and the Kappa statistic. We measured errors of inclusion and exclusion of the scorecard relative to the other methods. We also gathered perceptions of the scorecard criteria through qualitative interviews with stakeholders at multiple levels of the health system.

Findings: The distribution of the scorecard was less skewed than other wealth measures and not truncated, but demonstrated clumping. There was a higher level of agreement between the scorecard and the wealth index than consumption expenditure. The scorecard identified a similar number of poor households as the "basic needs" poverty line based on monthly consumption expenditure, with only 45 % errors of inclusion. However, it failed to pick up half of those living below the "basic needs" poverty line as being poor. Stakeholders supported the inclusion of water sources, income, food security and disability measures but had reservations about other items on the scorecard.

Conclusion: In choosing poverty identification strategies for programmes seeking to enhance health equity it's necessary to balance between community acceptability, local relevance and the need for such a strategy. It is important to ensure the strategy is efficient and less costly than alternatives in order to effectively reduce health disparities.

Show MeSH
National Health Insurance Fund (NHIF) Scorecard. Distribution of the household based on the NHIF scorecard within the surveyed households in Mbarali and Kilolo District: Tanzania
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Fig3: National Health Insurance Fund (NHIF) Scorecard. Distribution of the household based on the NHIF scorecard within the surveyed households in Mbarali and Kilolo District: Tanzania

Mentions: The histograms based on the wealth index and consumption expenditure are skewed to the left, indicating that the majority of the sample are of lower wealth levels, with a more limited number of observations from higher wealth groups (Figs. 1 and 2). Both distributions also suffer from leftward truncation. In contrast, the histogram based on the scorecard was relatively normally distributed (Fig. 3). However, clumping was an issue for the scorecard, which may be explained by the smaller number of indicators used in its generation.Fig. 1


Poverty identification for a pro-poor health insurance scheme in Tanzania: reliability and multi-level stakeholder perceptions.

Kuwawenaruwa A, Baraka J, Ramsey K, Manzi F, Bellows B, Borghi J - Int J Equity Health (2015)

National Health Insurance Fund (NHIF) Scorecard. Distribution of the household based on the NHIF scorecard within the surveyed households in Mbarali and Kilolo District: Tanzania
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4666058&req=5

Fig3: National Health Insurance Fund (NHIF) Scorecard. Distribution of the household based on the NHIF scorecard within the surveyed households in Mbarali and Kilolo District: Tanzania
Mentions: The histograms based on the wealth index and consumption expenditure are skewed to the left, indicating that the majority of the sample are of lower wealth levels, with a more limited number of observations from higher wealth groups (Figs. 1 and 2). Both distributions also suffer from leftward truncation. In contrast, the histogram based on the scorecard was relatively normally distributed (Fig. 3). However, clumping was an issue for the scorecard, which may be explained by the smaller number of indicators used in its generation.Fig. 1

Bottom Line: We compared the distributions of the three wealth measures, and the consistency of household poverty classification using cross-tabulations and the Kappa statistic.However, it failed to pick up half of those living below the "basic needs" poverty line as being poor.It is important to ensure the strategy is efficient and less costly than alternatives in order to effectively reduce health disparities.

View Article: PubMed Central - PubMed

Affiliation: Ifakara Health Institute, Plot 463, Kiko Avenue Mikocheni, P.O. Box 78 373, Dar es Salaam, Tanzania. ajoachim@ihi.or.tz.

ABSTRACT

Background: Many low income countries have policies to exempt the poor from user charges in public facilities. Reliably identifying the poor is a challenge when implementing such policies. In Tanzania, a scorecard system was established in 2011, within a programme providing free national health insurance fund (NHIF) cards, to identify poor pregnant women and their families, based on eight components. Using a series of reliability tests on a 2012 dataset of 2,621 households in two districts, this study compares household poverty levels using the scorecard, a wealth index, and monthly consumption expenditures.

Methods: We compared the distributions of the three wealth measures, and the consistency of household poverty classification using cross-tabulations and the Kappa statistic. We measured errors of inclusion and exclusion of the scorecard relative to the other methods. We also gathered perceptions of the scorecard criteria through qualitative interviews with stakeholders at multiple levels of the health system.

Findings: The distribution of the scorecard was less skewed than other wealth measures and not truncated, but demonstrated clumping. There was a higher level of agreement between the scorecard and the wealth index than consumption expenditure. The scorecard identified a similar number of poor households as the "basic needs" poverty line based on monthly consumption expenditure, with only 45 % errors of inclusion. However, it failed to pick up half of those living below the "basic needs" poverty line as being poor. Stakeholders supported the inclusion of water sources, income, food security and disability measures but had reservations about other items on the scorecard.

Conclusion: In choosing poverty identification strategies for programmes seeking to enhance health equity it's necessary to balance between community acceptability, local relevance and the need for such a strategy. It is important to ensure the strategy is efficient and less costly than alternatives in order to effectively reduce health disparities.

Show MeSH