Predictors of community-based health insurance enrollment among reproductive-age women in Ethiopia based on the EDHS 2019 dataset

A study using SHAP analysis technique, 2024

Bibliographic Data

ID22066870
AuthorsSisay Yitayih Kassie (Hawassa University, corresponding author), Solomon Abuhay Abebe (Hawassa University), Mekdes Wondirad (Hawassa University), Samrawit Fantaw Muket (Hawassa University), Ayantu Melke (Hawassa University), Alex Ayenew Chereka (0000-0001-7181-0412, Mattu University), Adamu Ambachew Shibabaw (0000-0002-8881-5735, Mattu University), Abiy Tasew Dubale (0009-0005-5921-4753, Mattu University), Yitayish Damtie (0000-0001-6783-6097, Jigjiga University), Habtamu Setegn Ngusie (0000-0003-4666-1114, Woldia University), Agmasie Damtew Walle (0000-0001-9583-5876, Debre Berhan University)
Year2025
Volume13
Pages1448055-1448055
Publication date2025-03-20
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1448055
PMID40182527
OpenAlexW4408676235
LanguageEN
References cited35

Background: Out-of-pocket payments for health services can lead to health catastrophes and decreased service utilization. To address this issue, community-based health insurance has emerged as a strategy to provide financial protection against the costs of poor health. Despite the efforts made by the government of Ethiopia, enrollment rates have not reached the potential beneficiaries. Therefore, this study aimed to predict and identify the factors influencing community-based health insurance enrollment among reproductive-age women using SHapley Additive exPlanations (SHAP) analysis techniques. Method: The study was conducted using the recent Demographic Health Survey 2019 dataset. Eight machine learning algorithm classifiers were applied to a total weighted sample of 9,013 reproductive-age women and evaluated using performance metrics to predict community-based health insurance enrollment with Python 3.12.2 software, utilizing the Anaconda extension. Additionally, SHAP analysis was used to identify the key predictors of community-based health insurance enrollment and the disproportionate impact of certain variables on others. Result: The random forest was the most effective predictive model, achieving an accuracy of 91.64% and an area under the curve of 0.885. The SHAP analysis, based on this superior random forest model, indicated that residence, wealth, the age of the household head, the husband's education level, media exposure, family size, and the number of children under five were the most influential factors affecting enrollment in community-based health insurance. Conclusion: This study highlights the significance of machine learning in predicting community-based health insurance enrollment and identifying the factors that hinder it. Residence, wealth status, and the age of the household head were identified as the primary predictors. The findings of this research indicate that sociodemographic, sociocultural, and economic factors should be considered when developing and implementing health policies aimed at increasing enrollment among reproductive-age women in Ethiopia, particularly in rural areas, as these factors significantly impact low enrollment levels

Business · Community health · Economic growth · Economics · Environmental health · Health care · Health insurance · Machine learning · Payment · Population · Public health · Random forest · Reproductive health · Residence · Agricultural risk and resilience · Computer Science · Demography · Global Maternal and Child Health · Healthcare Systems and Reforms · Medicine · Nursing · Finance · Gerontology

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