Surveys are mainly used to obtain reliable estimates for planned domains at national and regional levels. However, the unplanned domains (lower administrative layers) with small sample sizes must be estimated. The direct survey estimates of the non-planned domains with small sample sizes lead to large sampling variability. Thus, small area estimations dealt with managing this variability by borrowing the strength of neighboring areas. The target variables of the study were obtained from the 2016 Ethiopian demographic and health survey (EDHS) and the auxiliary variables taken from the 2007 population and housing census data. Multivariate Fay Herriot (MFH) model was used by incorporating the correlations among the target variables. The model diagnostic measures assured the normality assumption, and the consistency of multivariate small area estimates are valid. Multivariate EBLUPs of the target variables produced the lowest percent coefficient of variation (CV) and root mean square error (MSE). Therefore, multivariate EBLUP has improved the direct survey estimates of undernutrition (stunting, wasting, and underweight) for small sample sizes (even zero sample sizes). It also provided better estimates compared to the univariate EBLUPs. Generally, multivariate EBLUPs of undernutrition produced the best reliable, efficient, and precise estimates for small sample sizes in all zones. Zones are essential domains for planning and monitoring purposes in the country, and therefore these results provide valuable estimates for policymakers, planners, and legislative organs of the government. One of the novelties of this paper is estimating the non-sampled zones, and therefore the policymakers will give equal attention similar to the sampled zones.

Introduction Community-based health insurance (CBHI) is a type of volunteer health insurance that has been adopted all over the world in which people of the community pool funds to protect themselves from the high costs of seeking medical care and treatment for the disease. In Ethiopia, healthcare services are underutilized due to a lack of resources in the healthcare system. The study aims to identify the individual and community level factors associated with community-based health insurance enrollment of households in Ethiopia. Methods Data from the Ethiopian mini demographic and health survey 2019 were used to identify factors associated with community-based health insurance enrollment of households in Ethiopia. Multilevel logistic regression analysis was used on a nationally representative sample of 8,663 households nested within 305 communities, considering the data’s layered structure. We used a p-value<0.05 with a 95% confidence interval for the results. Result The prevalence of community-based health insurance enrollment in Ethiopia was 20.2%. The enrollment rate of households in the scheme was high in both Amhara (57.9), and Tigray (57.9%) regions and low (3.0%) in the Afar region. At the individual level; the age of household heads, number of children 5 and under, number of household members, has land for agriculture, has a mobile telephone, receiving cash of food from the safety Net Program, Owning livestock, and herds of farm animals, wealth index, and at the community level; the region had a significant association with community-based health insurance enrollment. Conclusion Both individual and community-level characteristics were significant predictors of community-based health insurance enrollment in households. Furthermore, the ministry of health, health bureaus, and other concerning bodies prioritize clusters with low health insurance coverage to strengthen health system financing and intervene in factors that negatively affect the CBHI enrollment of households.

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