Spatial Regression Modeling of Child Survival on the Distribution of Births and Deaths in Kenya Based on the Kenya Demographic and Health Survey (KDHS) 2022

Amos Kipkorir Langat *

Pan African University Institute for Basic Science, Technology and Innovation, JKUAT, Kenya.

Michael Arthur Ofori

Pan African University Institute for Basic Science, Technology and Innovation, JKUAT, Kenya.

John Kamwele Mutinda

University of Science and Technology of China, People’s Republic of China.

Mouhamadou Djima Baranon

Pan African University Institute for Basic Science, Technology and Innovation, JKUAT, Kenya.

Adeladza Kofi Amegah

University of Cape Coast, Ghana.

Lawrence Ndekeleni Kazembe

University of Namibia, Namibia.

*Author to whom correspondence should be addressed.


Abstract

This study used spatial mapping techniques to examine the distribution of births and deaths in Kenya and their relationship with various factors related to child survival, such as maternal age, education, wealth, and access to health services. Data were obtained from the 2022 Kenya Demographic and Health Survey (KDHS). Spatial autocorrelation analyses were conducted to identify clusters of high or low child mortality rates. The results showed significant spatial autocorrelation in child mortality rates, indicating that neighboring areas had similar mortality rates. Factors such as maternal education, wealth, and access to health services were found to be significantly associated with child mortality rates. These findings can inform targeted interventions and policies to reduce child mortality rates in Kenya, particularly in areas with the highest risk of mortality.

Keywords: Child mortality, spatial regression, spatial auto correlation, child survival, KDHS 2022


How to Cite

Langat , A. K., Ofori , M. A., Mutinda, J. K., Baranon , M. D., Amegah , A. K., & Kazembe , L. N. (2024). Spatial Regression Modeling of Child Survival on the Distribution of Births and Deaths in Kenya Based on the Kenya Demographic and Health Survey (KDHS) 2022. Asian Journal of Probability and Statistics, 26(3), 67–79. https://doi.org/10.9734/ajpas/2024/v26i3600

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