Small area estimation of health insurance coverage for Kenyan counties

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