Abstract
Child mortality is high in Sub-Saharan Africa compared to other regions in the world. In Kenya, the risk of mortality is assumed to vary from county to county due to diversity in socio-economic and even climatic factors. Recently, the country was split into 47 different administrative regions called counties, and health care was delegated to those county governments, further aggravating the spatial differences in health care from county to county. The goal of this study is to evaluate the effects of spatial variation in under-five mortality in Kenya. Data from the Kenya Demographic Health Survey (KDHS-2014) consisting the newly introduced counties was used to analyze this risk. Using a spatial Cox Proportional Hazard model, an Intrinsic Conditional Autoregressive Model (ICAR) was fitted to account for the spatial variation among the counties in the country while the Cox model was used to model the risk factors associated with the time to death of a child. Inference regarding the risk factors and the spatial variation was made in a Bayesian setup based on the Markov Chain Monte Carlo (MCMC) technique to provide posterior estimates. The paper indicate the spatial disparities that exist in the country regarding child mortality in Kenya. The specific counties have mortality rates that are county-specific, although neighboring counties have similar hazards for death of a child. Counties in the central Kenya region were shown to have the highest hazard of death, while those from the western region had the lowest hazard of death. Demographic factors such as the sex of the child and sex of the household head, as well as social economic factors, such as the level of education, accounted for the most variation when spatial differences were factored in. The spatial Cox proportional hazard frailty model performed better compared to the non-spatial non-frailty model. These findings can help the country to plan health care interventions at a subnational level and guide social and health policies by ensuring that counties with a higher risk of Under Five Child Mortality (U5CM) are considered differently from counties experiencing a lower risk of death.
Highlights
The burden of mortality in children has remained a key area of concern for nations and organizations in the world
The statistically significant determinants of child mortality included the sex of the child, age of the respondent at first birth, gender of the household head, and whether a family had multiple sets of twins
These encompass the set of demographic risk factors associated with child survival, while the socio-economic factors associated with child survival included the highest level of education
Summary
The burden of mortality in children has remained a key area of concern for nations and organizations in the world. The year 2018 recorded approximately 5.3 million children and infant deaths worldwide. The risk of under-five mortality in the WHO Africa region was 76 deaths per 1000 live births, which was eigth-times higher than the WHO European region [1]. This is far from ideal and is a worrying situation. The rate of mortality in Kenya in the same period was 46.7 deaths per 1000 live births.
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