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

Otieno Otieno; Mathew Kosgei; Nelson Onyango Owuor

doi:doi:10.11648/j.bsi.20230801.13

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

Otieno Otieno, Mathew Kosgei, Nelson Onyango Owuor

Published in Biomedical Statistics and Informatics (Volume 8, Issue 1)

Received: 3 February 2023 Accepted: 23 February 2023 Published: 20 March 2023

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Abstract

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.

Published in	Biomedical Statistics and Informatics (Volume 8, Issue 1)
DOI	10.11648/j.bsi.20230801.13
Page(s)	14-21
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

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

References

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[2]	Black, R. E., Levin, C., Walker, N., Chou, D., Liu, L., Temmerman, M., Group, D. R. A. (2016). Reproductive, maternal, newborn, and child health: key messages from disease control priorities 3rd edition. The Lancet, 388 (10061), 2811-2824.
[3]	McGuire, J. W. (2006). Basic health care provision and under-5 mortality: a cross-national study of developing countries. World Development, 34 (3), 405-425.
[4]	Child Mortality. (2022, January 20). UNICEF DATA. https://data. unicef.org/topic/child-survival/under-five-mortality/
[5]	Nations, U. (2016). The Sustainable Development Goals 2016. eS-ocialSciences. http://www.un.org/sustainabledevelopment/health/.AccessedMar2016.
[6]	Wanjiru, W. H. (2021). Improved balanced random survival forest for the analysis of right censored data: application in determining under five child mortality (Doctoral dissertation, Moi University).
[7]	Bereka, S. G., Habtewold, F. G., Nebi, T. D. (2017). Under-five mor-tality of children and its determinants in Ethiopian Somali regional state, Eastern Ethiopia. Health Science Journal, 11 (3), 1.
[8]	Khan, J. R., Awan, N. (2017). A comprehensive analysis on child mor-tality and its determinants in Bangladesh using frailty models. Archives of Public Health, 75 (1), 1-10.
[9]	Sharma, A. K., Dutta, R. (2020). Determinants of child survival at the household level: An insight of the method of factor analysis. In Contem-porary Issues in Sustainable Development (pp. 253-271). Routledge India.
[10]	Corsi, D. J., Neuman, M., Finlay, J. E., Subramanian, S. V. (2012). Demographic and health surveys: a profile. International journal of epi-demiology, 41 (6), 1602-1613.
[11]	Burnham, K. P. (2000). Model selection and multimodel inference. A practical information-theoretic approach.
[12]	Nelson Owuor Onyango (2009). On the Linear Mixed Effects Regression (lmer) R Function for Nested Animal Breeding Data. CS-BIGS 4 (1): 44-58. http://erepository.uonbi.ac.ke:8080/xmlui/ handle/123456789/38574
[13]	Searle, S. R., Casella, G., McCulloch, C. E. (2009). Variance compo-nents. John Wiley Sons.
[14]	Patterson, H. D. (1971). Thompson R. Recovery of inter-block informa-tion when block sizes are unequal. Biometrika, 58, 545-554.
[15]	Duchateau, L., Janssen, P., Rowlands, J. (1998). Linear mixed models. An introduction with applications in veterinary research. ILRI (aka ILCA and ILRAD).
[16]	Ayiko, R., Antai, D., Kulane, A. (2009). Trends and determinants of under-five mortality in Uganda. East African journal of public health, 6 (2), 136-140.
[17]	Nasejje, J. B., Mwambi, H. G., Achia, T. N. (2015). Understanding the determinants of under-five child mortality in Uganda including the estimation of unobserved household and community effects using both fre-quentist and Bayesian survival analysis approaches. BMC public health, 15 (1), 1003.
[18]	Sreeramareddy, C. T., Kumar, H. N., Sathian, B. (2013). Time Trends and Inequalities of Under-Five Mortality in Nepal: A Secondary Data Analysis of Four Demographic and Health Surveys between 1996 and 2011. PLoS ONE, 8 (11): e79818. doi: 10.1371/journal.pone.0079818.

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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. https://doi.org/10.11648/j.bsi.20230801.13

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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

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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

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@article{10.11648/j.bsi.20230801.13,
  author = {Otieno Otieno and Mathew Kosgei and Nelson Onyango Owuor},
  title = {On Multi-Level Modeling of Child Mortality with Application to KDHS Data 2014},
  journal = {Biomedical Statistics and Informatics},
  volume = {8},
  number = {1},
  pages = {14-21},
  doi = {10.11648/j.bsi.20230801.13},
  url = {https://doi.org/10.11648/j.bsi.20230801.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20230801.13},
  abstract = {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.},
 year = {2023}
}

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TY - JOUR
T1 - On Multi-Level Modeling of Child Mortality with Application to KDHS Data 2014
AU - Otieno Otieno
AU - Mathew Kosgei
AU - Nelson Onyango Owuor
Y1 - 2023/03/20
PY - 2023
N1 - https://doi.org/10.11648/j.bsi.20230801.13
DO - 10.11648/j.bsi.20230801.13
T2 - Biomedical Statistics and Informatics
JF - Biomedical Statistics and Informatics
JO - Biomedical Statistics and Informatics
SP - 14
EP - 21
PB - Science Publishing Group
SN - 2578-8728
UR - https://doi.org/10.11648/j.bsi.20230801.13
AB - 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.
VL - 8
IS - 1
ER -

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Author Information

Otieno Otieno

School of Sciences and Aerospace Studies, Moi University, Eldoret, Kenya
Mathew Kosgei

School of Sciences and Aerospace Studies, Moi University, Eldoret, Kenya
Nelson Onyango Owuor

School of Mathematics, University of Nairobi, Nairobi, Kenya

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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. https://doi.org/10.11648/j.bsi.20230801.13

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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

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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

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@article{10.11648/j.bsi.20230801.13,
  author = {Otieno Otieno and Mathew Kosgei and Nelson Onyango Owuor},
  title = {On Multi-Level Modeling of Child Mortality with Application to KDHS Data 2014},
  journal = {Biomedical Statistics and Informatics},
  volume = {8},
  number = {1},
  pages = {14-21},
  doi = {10.11648/j.bsi.20230801.13},
  url = {https://doi.org/10.11648/j.bsi.20230801.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20230801.13},
  abstract = {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.},
 year = {2023}
}

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