Neonatal health is a critical component of overall public health, providing the groundwork for a healthy life and making a substantial contribution to the social and economic advancement of any nation. Despite the progress that has been made in reducing the global neonatal mortality rate, substantial regional disparities persist, particularly in Sub-Saharan Africa. In Kenya, the NMR stands at 21 deaths per 1,000 live births (as of 2022) which is higher than the global average. The main objective for this study was to perform risk factor and spatial pattern analysis of neonatal mortality in Kenya. A multivariate logistic regression model was fitted that identified urban residence, underweight birth weight status, unimproved water sources, and non-hospital deliveries (especially in non standard locations) as the significant contributors of neonatal mortality in Kenya. Getis-Ord Gi statistics identified Wajir, Garissa, and Lamu counties as major hotspots in Kenya after showing a strong spatial clustering of high neonatal mortality rates. GWLR, utilized in this study, revealed that climatic factors, such as temperature and aridity, impact neonatal mortality differently across regions in Kenya. Generally, higher temperatures are a significant risk factor for neonatal mortality, particularly in arid counties like Mandera, Wajir, Garissa, Tana River, and Lamu.
| Published in | American Journal of Mathematical and Computer Modelling (Volume 10, Issue 2) |
| DOI | 10.11648/j.ajmcm.20251002.12 |
| Page(s) | 54-65 |
| 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), 2025. Published by Science Publishing Group |
KDHS, WHO, GWLR, NMR, ANC, UNICEF
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APA Style
Nyabuto, G. M., Malenje, B., Wanjoya, A. (2025). Risk Factor and Spatial Pattern Analysis of Neonatal Mortality in Kenya. American Journal of Mathematical and Computer Modelling, 10(2), 54-65. https://doi.org/10.11648/j.ajmcm.20251002.12
ACS Style
Nyabuto, G. M.; Malenje, B.; Wanjoya, A. Risk Factor and Spatial Pattern Analysis of Neonatal Mortality in Kenya. Am. J. Math. Comput. Model. 2025, 10(2), 54-65. doi: 10.11648/j.ajmcm.20251002.12
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Download@article{10.11648/j.ajmcm.20251002.12,
author = {Getrude Moraa Nyabuto and Bonface Malenje and Anthony Wanjoya},
title = {Risk Factor and Spatial Pattern Analysis of Neonatal Mortality in Kenya},
journal = {American Journal of Mathematical and Computer Modelling},
volume = {10},
number = {2},
pages = {54-65},
doi = {10.11648/j.ajmcm.20251002.12},
url = {https://doi.org/10.11648/j.ajmcm.20251002.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20251002.12},
abstract = {Neonatal health is a critical component of overall public health, providing the groundwork for a healthy life and making a substantial contribution to the social and economic advancement of any nation. Despite the progress that has been made in reducing the global neonatal mortality rate, substantial regional disparities persist, particularly in Sub-Saharan Africa. In Kenya, the NMR stands at 21 deaths per 1,000 live births (as of 2022) which is higher than the global average. The main objective for this study was to perform risk factor and spatial pattern analysis of neonatal mortality in Kenya. A multivariate logistic regression model was fitted that identified urban residence, underweight birth weight status, unimproved water sources, and non-hospital deliveries (especially in non standard locations) as the significant contributors of neonatal mortality in Kenya. Getis-Ord Gi statistics identified Wajir, Garissa, and Lamu counties as major hotspots in Kenya after showing a strong spatial clustering of high neonatal mortality rates. GWLR, utilized in this study, revealed that climatic factors, such as temperature and aridity, impact neonatal mortality differently across regions in Kenya. Generally, higher temperatures are a significant risk factor for neonatal mortality, particularly in arid counties like Mandera, Wajir, Garissa, Tana River, and Lamu.},
year = {2025}
}
TY - JOUR T1 - Risk Factor and Spatial Pattern Analysis of Neonatal Mortality in Kenya AU - Getrude Moraa Nyabuto AU - Bonface Malenje AU - Anthony Wanjoya Y1 - 2025/06/03 PY - 2025 N1 - https://doi.org/10.11648/j.ajmcm.20251002.12 DO - 10.11648/j.ajmcm.20251002.12 T2 - American Journal of Mathematical and Computer Modelling JF - American Journal of Mathematical and Computer Modelling JO - American Journal of Mathematical and Computer Modelling SP - 54 EP - 65 PB - Science Publishing Group SN - 2578-8280 UR - https://doi.org/10.11648/j.ajmcm.20251002.12 AB - Neonatal health is a critical component of overall public health, providing the groundwork for a healthy life and making a substantial contribution to the social and economic advancement of any nation. Despite the progress that has been made in reducing the global neonatal mortality rate, substantial regional disparities persist, particularly in Sub-Saharan Africa. In Kenya, the NMR stands at 21 deaths per 1,000 live births (as of 2022) which is higher than the global average. The main objective for this study was to perform risk factor and spatial pattern analysis of neonatal mortality in Kenya. A multivariate logistic regression model was fitted that identified urban residence, underweight birth weight status, unimproved water sources, and non-hospital deliveries (especially in non standard locations) as the significant contributors of neonatal mortality in Kenya. Getis-Ord Gi statistics identified Wajir, Garissa, and Lamu counties as major hotspots in Kenya after showing a strong spatial clustering of high neonatal mortality rates. GWLR, utilized in this study, revealed that climatic factors, such as temperature and aridity, impact neonatal mortality differently across regions in Kenya. Generally, higher temperatures are a significant risk factor for neonatal mortality, particularly in arid counties like Mandera, Wajir, Garissa, Tana River, and Lamu. VL - 10 IS - 2 ER -
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
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