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On Multi-Level Modeling of Child Mortality with Application to KDHS Data 2014

One of the dominant challenges affecting low and middle countries is the regard of child mortality. It had been a millennium development goal to reduce infant and child mortality by two-thirds in 1990 mortality levels by the year 2015. Therefore, the aspiration to recognize the causal factors of under five child mortality poses a crucial aspect of research. In principal, remarkable progress has been made in bringing down mortality in children under 5 years of age. The global under five mortality rate declined by 59 per cent, from 93 deaths per 1,000 live births in 1990 to 38 in 2019. In Kenya, the infant mortality rate in 2021 is 32.913 deaths per 1000 live births, a 3.36 per cent decline from the year 2020. It was 34.056 deaths per 1,000 live births in 2020, a decline of 3.24 per cent from the year 2019. In Nyanza Province, Kenya, has the highest infant mortality rate (133 deaths per 1,000 live births) while the lowest in Central Province (44 deaths per 1,000 live births). Despite all that improvement, the world is still doubtful to achieve that millennium development goal target number four, of diminishing child mortality. Our study aims to scrutinize on vital covariates affecting child mortality in Nyanza, Kenya. The principal purpose of this paper is to scrutinize the effect of demographic and socioeconomic variables on child mortality. We carried out a series of model evaluations to ascertain the best model under various scenarios bearing in mind the presence of dependencies due to Clusters and households. Then, performed a linear mixed effects model with the best fit based on data from Kenya Demographic and Health Survey (KDHS 2014) which was collected by use of questionnaires. Child mortality from the, KDHS 2014 data, was analyzed in an age period: mortality from the age of 12 months to the age of 60 months. The study reveals that, number of children under 5 in household, number of births in last 5 years, modern family planning and contraceptive use had an exceptional impact on child mortality.

Mixed Models, Correlated Data, Lmer, Glmer, Best Linear Unbiased Predictors (BLUP)

APA Style

Otieno Otieno, Mathew Kosgei, Nelson Onyango Owuor. (2023). On Multi-Level Modeling of Child Mortality with Application to KDHS Data 2014. Biomedical Statistics and Informatics, 8(1), 14-21.

ACS Style

Otieno Otieno; Mathew Kosgei; Nelson Onyango Owuor. On Multi-Level Modeling of Child Mortality with Application to KDHS Data 2014. Biomed. Stat. Inform. 2023, 8(1), 14-21. doi: 10.11648/j.bsi.20230801.13

AMA Style

Otieno Otieno, Mathew Kosgei, Nelson Onyango Owuor. On Multi-Level Modeling of Child Mortality with Application to KDHS Data 2014. Biomed Stat Inform. 2023;8(1):14-21. doi: 10.11648/j.bsi.20230801.13

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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