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Small area estimation of health insurance coverage for Kenyan counties

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  • Noah Cheruiyot Mutai

    (Freie Universität Berlin
    Taita Taveta University)

Abstract

Health insurance is important in disease management, access to quality health care and attaining Universal Health Care. National and regional data on health insurance coverage needed for policy making is mostly obtained from household surveys; however, estimates at lower administrative units like at the county level in Kenya are highly variable due to small sample sizes. Small area estimation combines survey and census data using a model to increases the effective sample size and therefore provides more precise estimates. In this study we estimate the health insurance coverage for Kenyan counties using a binary M‑quantile small area model for women ( n = 14,730 ) and men ( n = 12,007 ) aged 15 to 49 years old. This has the advantage that we avoid specifying the distribution of the random effects and distributional robustness is automatically achieved. The response variable is derived from the Kenya Demographic and Health Survey 2014 and auxiliary data from the Kenya Population and Housing Census 2009. We estimate the mean squared error using an analytical approach based on Taylor series linearization. The national direct health insurance coverage estimates are 18 % and 21 % for women and men respectively. With the current health insurance schemes, coverage remains low across the 47 counties. These county-level estimates are helpful in formulating decentralized policies and funding models.

Suggested Citation

  • Noah Cheruiyot Mutai, 2022. "Small area estimation of health insurance coverage for Kenyan counties," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(3), pages 231-254, December.
    • Handle: RePEc:spr:astaws:v:16:y:2022:i:3:d:10.1007_s11943-022-00312-8
    DOI: 10.1007/s11943-022-00312-8
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    References listed on IDEAS

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    1. Nikos Tzavidis & Li‐Chun Zhang & Angela Luna & Timo Schmid & Natalia Rojas‐Perilla, 2018. "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 927-979, October.
    2. Farrell, Patrick J & MacGibbon, Brenda & Tomberlin, Thomas J, 1997. "Empirical Bayes Small-Area Estimation Using Logistic Regression Models and Summary Statistics," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 101-108, January.
    3. Ray Chambers & Hukum Chandra & Nicola Salvati & Nikos Tzavidis, 2014. "Outlier robust small area estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 47-69, January.
    4. Ray Chambers & Nicola Salvati & Nikos Tzavidis, 2016. "Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 453-479, February.
    5. Jiming Jiang & P. Lahiri, 2001. "Empirical Best Prediction for Small Area Inference with Binary Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 217-243, June.
    6. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    7. Hukum Chandra & Kaustav Aditya & U C Sud, 2018. "Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India—An application of small area estimation techniques," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    8. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    9. Cantoni E. & Ronchetti E., 2001. "Robust Inference for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1022-1030, September.
    10. Moses Aikins & Philip Teg-Nefaah Tabong & Paola Salari & Fabrizio Tediosi & Francis M Asenso-Boadi & Patricia Akweongo, 2021. "Positioning the National Health Insurance for financial sustainability and Universal Health Coverage in Ghana: A qualitative study among key stakeholders," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
    11. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
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    Cited by:

    1. Timo Schmid & Markus Zwick, 2022. "Editorial," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(3), pages 167-170, December.

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