Evidence map›Paper›PMID 38625443›Full record

ArticleHealth economics review2024

A Hierarchical Bayesian approach to small area estimation of health insurance coverage in Ethiopian administrative zones for better policies and programs.

Yegnanew A Shiferaw, Seyifemickael Amare Yilema, Yikeber Abebaw Moyehodie

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Article in Health economics review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yegnanew A ShiferawDepartment of Statistics, University of Johannesburg, PO Box 524, Johannesburg, 2006, South Africa.
Seyifemickael Amare YilemaDepartment of Statistics, College of Natural and Computational Science, Debre Tabor University, PO Box 272, Debre Tabor, Ethiopia. samarey1981@gmail.com.ORCID http://orcid.org/0000-0002-9445-6038
Yikeber Abebaw MoyehodieDepartment of Statistics, College of Natural and Computational Science, Debre Tabor University, PO Box 272, Debre Tabor, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sample surveys are extensively used to provide reliable direct estimates for large areas or domains with enough sample sizes at national and regional levels. However, zones are unplanned domains by the Demographic and Health Survey (DHS) program and need more sample sizes to produce direct survey estimates with adequate precision. Conducting surveys in small areas (like zones) is too expensive and time-consuming, making it unfeasible for developing countries like Ethiopia. Therefore, this study aims to use the Hierarchical Bayes (HB) Small Area Estimation (SAE) model to estimate the Community-Based Health Insurance (CBHI) coverage at the zone levels in Ethiopia. To achieve this, we combined the 2019 Ethiopia Mini-Demographic and Health Survey (EMDHS) data with the 2007 population census data. SAE has addressed the challenge of producing reliable parameter estimates for small or even zero sample sizes across Ethiopian zones by utilizing auxiliary information from the population census. The results show that model-based estimates generated by the SAE approach are more accurate than direct survey estimates of CBHI. A map of CBHI scheme coverage was also used to visualize the spatial variation in the distribution of CBHI scheme coverage. From the CBHI scheme coverage map, we noticed notable variations in CBHI scheme coverage across Ethiopian zones. Additionally, this research identified areas with high and low CBHI scheme coverage to improve decision-making and increase coverage in Ethiopia. One of the novelties of this paper is estimating the non-sampled zones; therefore, the policymakers will give equal attention similar to the sampled zones.

Indexed as

Disaggregated level CBHI schemeEthiopiaHierarchical Bayes modelSmall area estimation

Identifiers

PMID38625443
PMCPMC11020459

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