Research Article | | Peer-Reviewed

Count Models of Mortality for Children Under Five Years in Kenya

Received: 16 October 2023    Accepted: 6 November 2023    Published: 5 February 2024
Views:       Downloads:
Abstract

Child mortality under the age of five (U5M) is a critical public health concern influenced by several factors. Child mortality rate can be used to gauge a country’s health status and progress towards achieving sustainable development goals. These Goals, were established in 2015 by the United Nations General Assembly, to be accomplished by 2030. The SDG Goal 3 target 2 Strives to achieve a substantial reduction in under-5 mortality rates, with the ambitious goal of lowering them to 25 or fewer deaths per 1,000 live births by the year 2030. Reducing the deaths of toddlers aged five and below is a crucial objective for developing countries, particularly in regions of the continent of Africa that lie south of the Sahara This study delves into the distribution and determinants of U5M in Kenya, employing count models to scrutinize the spatial dynamic distribution and socio-economic variables utilizing information obtained from the 2014 Kenya Demographic Health Survey (KDHS). By uncovering geographical variations in U5M rates, this research aims to inform the design of integrated interventions, thereby reducing the financial burden associated with these interventions and averting redundant resource allocation. The study revealed that region, mothers’ level of education, and socioeconomic factors were significant risk factors associated with under-five mortality. The findings of this study contribute to a more targeted and efficient approach to addressing child mortality, ultimately improving the well-being of Kenya’s youngest population.

Published in International Journal of Statistical Distributions and Applications (Volume 10, Issue 1)
DOI 10.11648/ijsd.20241001.12
Page(s) 10-15
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), 2024. Published by Science Publishing Group

Keywords

Under-Five Mortality, Poisson, Negative Binomial

References
[1] United Nations. "Sustainable Development Goals." United Nations, https://sdgs.un.org/. [Online]. Available: Accessed June, 2022.
[2] World Health Organization, "Noncommunicable Diseases: Key Facts," January 28, 2021. [Online]. Available: http://www.who.int/news-room/factsheets/detail/noncommunicable-diseases.
[3] United Nations Inter-agency Group for Child Mortality Estimation (UN IGME)," 2021.
[4] B. O. Osano, F. Were, and S. Mathews, "Mortality Among 5-17 Year-Old Children in Kenya," Pan Afr Med J, vol. 27, p. 121, Jun. 2017. DOI: 10.11604/pamj.2017.27.121.10727.
[5] M. Burke, S. Heft-Neal, and E. Bendavid, "Sources of Variation in Under-5 Mortality Across Sub-Saharan Africa: A Spatial Analysis," Lancet Glob Health, vol. 4, no. 12, pp. e936-e945, Dec. 2016. DOI: 10.1016/S2214-109X(16)30212-1.
[6] G. T. Shifa, A. A. Ahmed, and A. W. Yalew, "Socioeconomic and environmental determinants of under-five mortality in Gamo Gofa Zone, Southern Ethiopia: A matched case control study," BMC International Health and Human Rights, vol. 18, no. 1, p. 14, 2018. doi: 10.1186/s12914-018-0153-7.
[7] K. Daniel, N. O. Onyango, and R. J. Sarguta, “A Spatial Survival Model for Risk Factors of Under-Five Child Mortality in Kenya,” International Journal of Environmental Research and Public Health, vol. 19, no. 1, p. 399, Dec. 2021, doi: 10.3390/ijerph19010399.
[8] K. C. Sahoo, S. Negi, K. Patel, B. K. Mishra, S. K. Palo, and S. Pati, "Challenges in Maternal and Child Health Services Delivery and Access during Pandemics or Public Health Disasters in Low-and Middle-Income Countries: A Systematic Review," Healthcare (Basel), vol. 9, no. 7, p. 828, Jun. 2021. doi: 10.3390/healthcare9070828.
[9] W. H. Mosley and L. C. Chen, "An analytical framework for the study of child survival in developing countries," Population and Development Review, vol. 10, p. 25, 1984. [Online]. Available: https://doi.org/10.2307/2807954
[10] A. R. Bado and A. Sathiya Susuman, "Women’s Education and Health Inequalities in Under-Five Mortality in Selected Sub-Saharan African Countries, 1990-2015," PLoS One, vol. 11, no. 7, pp. e0159186, 2016. [Online]. Available: https://doi.org/10.1371/journal.pone.0159186.
[11] M. Bango and S. Ghosh, "Reducing infant and child mortality: Assessing the social inclusiveness of child health care policies and programs in three states of India," BMC Public Health, vol. 23, p. 1149, 2023. [Online]. Available: https://doi.org/10.1186/s12889-023-15812-7.
[12] S. Coxe, S. G. West, and L. S. Aiken, "The Analysis of Count Data: A Gentle Introduction to Poisson Regression and Its Alternatives," Journal of Personality Assessment, vol. 91, no. 2, pp. 121-136, 2009. doi: 10.1080/00223890802634175.
[13] A. Fitrianto, "A Study of Count Regression Models for Mortality Rate," CAUCHY, vol. 7, no. 1, pp. 142-151, 2021. [Online]. Available: https://doi.org/10.18860/ca.v7i1.13642.
[14] M. C. Hogan, K. J. Foreman, M. Naghavi, et al., "Maternal mortality for 181 countries, 1980-2008: A systematic analysis of progress towards Millennium Development Goal 5," Lancet, vol. 375, pp. 1609-1623, 2010. DOI: 10.1016/S0140-6736(10)60518-1.
[15] T. Hastie and R. Tibshirani, "Generalized additive models," Statist. Sci., vol. 1, no. 3, pp. 297-310, 1986. [Online]. Available: https://doi.org/10.1214/ss/1177013604.
[16] Kenya National Bureau of Statistics (KNBS), Ministry of Health, National AIDS Control Council, Kenya Medical Research Institute, National Council for Population and Development, "Kenya Demographic and Health Survey 2014; Survey Report," United States Agency for International Development, Rockville, MD, USA, 2015.
Cite This Article
  • APA Style

    Mwau, E., Mwalili, S., Mageto, T. (2024). Count Models of Mortality for Children Under Five Years in Kenya. International Journal of Statistical Distributions and Applications, 10(1), 10-15. https://doi.org/10.11648/ijsd.20241001.12

    Copy | Download

    ACS Style

    Mwau, E.; Mwalili, S.; Mageto, T. Count Models of Mortality for Children Under Five Years in Kenya. Int. J. Stat. Distrib. Appl. 2024, 10(1), 10-15. doi: 10.11648/ijsd.20241001.12

    Copy | Download

    AMA Style

    Mwau E, Mwalili S, Mageto T. Count Models of Mortality for Children Under Five Years in Kenya. Int J Stat Distrib Appl. 2024;10(1):10-15. doi: 10.11648/ijsd.20241001.12

    Copy | Download

  • @article{10.11648/ijsd.20241001.12,
      author = {Esther Mwau and Samuel Mwalili and Thomas Mageto},
      title = {Count Models of Mortality for Children Under Five Years in Kenya},
      journal = {International Journal of Statistical Distributions and Applications},
      volume = {10},
      number = {1},
      pages = {10-15},
      doi = {10.11648/ijsd.20241001.12},
      url = {https://doi.org/10.11648/ijsd.20241001.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.ijsd.20241001.12},
      abstract = {Child mortality under the age of five (U5M) is a critical public health concern influenced by several factors. Child mortality rate can be used to gauge a country’s health status and progress towards achieving sustainable development goals. These Goals, were established in 2015 by the United Nations General Assembly, to be accomplished by 2030. The SDG Goal 3 target 2 Strives to achieve a substantial reduction in under-5 mortality rates, with the ambitious goal of lowering them to 25 or fewer deaths per 1,000 live births by the year 2030. Reducing the deaths of toddlers aged five and below is a crucial objective for developing countries, particularly in regions of the continent of Africa that lie south of the Sahara This study delves into the distribution and determinants of U5M in Kenya, employing count models to scrutinize the spatial dynamic distribution and socio-economic variables utilizing information obtained from the 2014 Kenya Demographic Health Survey (KDHS). By uncovering geographical variations in U5M rates, this research aims to inform the design of integrated interventions, thereby reducing the financial burden associated with these interventions and averting redundant resource allocation. The study revealed that region, mothers’ level of education, and socioeconomic factors were significant risk factors associated with under-five mortality. The findings of this study contribute to a more targeted and efficient approach to addressing child mortality, ultimately improving the well-being of Kenya’s youngest population.
    },
     year = {2024}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Count Models of Mortality for Children Under Five Years in Kenya
    AU  - Esther Mwau
    AU  - Samuel Mwalili
    AU  - Thomas Mageto
    Y1  - 2024/02/05
    PY  - 2024
    N1  - https://doi.org/10.11648/ijsd.20241001.12
    DO  - 10.11648/ijsd.20241001.12
    T2  - International Journal of Statistical Distributions and Applications
    JF  - International Journal of Statistical Distributions and Applications
    JO  - International Journal of Statistical Distributions and Applications
    SP  - 10
    EP  - 15
    PB  - Science Publishing Group
    SN  - 2472-3509
    UR  - https://doi.org/10.11648/ijsd.20241001.12
    AB  - Child mortality under the age of five (U5M) is a critical public health concern influenced by several factors. Child mortality rate can be used to gauge a country’s health status and progress towards achieving sustainable development goals. These Goals, were established in 2015 by the United Nations General Assembly, to be accomplished by 2030. The SDG Goal 3 target 2 Strives to achieve a substantial reduction in under-5 mortality rates, with the ambitious goal of lowering them to 25 or fewer deaths per 1,000 live births by the year 2030. Reducing the deaths of toddlers aged five and below is a crucial objective for developing countries, particularly in regions of the continent of Africa that lie south of the Sahara This study delves into the distribution and determinants of U5M in Kenya, employing count models to scrutinize the spatial dynamic distribution and socio-economic variables utilizing information obtained from the 2014 Kenya Demographic Health Survey (KDHS). By uncovering geographical variations in U5M rates, this research aims to inform the design of integrated interventions, thereby reducing the financial burden associated with these interventions and averting redundant resource allocation. The study revealed that region, mothers’ level of education, and socioeconomic factors were significant risk factors associated with under-five mortality. The findings of this study contribute to a more targeted and efficient approach to addressing child mortality, ultimately improving the well-being of Kenya’s youngest population.
    
    VL  - 10
    IS  - 1
    ER  - 

    Copy | Download

Author Information
  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Sections