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
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.
Authors
- Sisay Yitayih Kassie
- Adamu Ambachew Shibabaw (ORCID: https://orcid.org/0000-0002-0207-4746)
- Abiy Tasew Dubale (ORCID: https://orcid.org/0009-0005-5921-4753)
- Alex Ayenew Chereka (ORCID: https://orcid.org/0000-0001-7181-0412)
- Agmasie Damtew Walle (ORCID: https://orcid.org/0000-0001-9583-5876)
- Habtamu Setegn Ngusie (ORCID: https://orcid.org/0000-0003-4666-1114)
- Yitayish Damtie (ORCID: https://orcid.org/0000-0001-6783-6097)
- Ayantu Melke
- Solomon Abuhay Abebe
- Mekdes Wondirad
- Samrawit Fantaw Muket
Institutions
- Debre Berhan University (ET)
- Hawassa University (ET)
- Jigjiga University (ET)
- Woldia University (ET)
- Mattu University
- Injibara University (ET)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2025-03-20
- DOI
- https://doi.org/10.3389/fpubh.2025.1448055
- Citations
- 2
- Primary Topic
- Healthcare Systems and Reforms
- Type
- article
- Field-Weighted Citation Impact
- 4.42