Stunting remains a significant public health burden in sub-Saharan Africa and has far reaching consequences. Identifying the drivers of stunting and high burden regions is key to developing effective and targeted intervention strategies. The objective of the study was to identify the risk factors and explore spatial patterns of stunting across counties in Kenya. Secondary data from 2022 Kenya Demographic Health Survey (KDHS) was utilized. A total of 13,016 children aged between 0 - 59 months were included in the analysis. A multilevel logistic regression was applied to identify individual, household and community level determinants of stunting, spatial regression models to analyze spatial dependency and geographically weighted regression to explore spatial heterogeneity in the association between childhood stunting and county level determinants. In the multilevel logistic regression, Children from urban residence exhibited a significantly increased odds of stunting compared to those in rural areas (aOR = 1.25, 95% CI: 1.03 - 1.51, p = 0.02). Children from households categorized as poorer, middle, richer, and richest all exhibited significantly reduced odds of stunting compared to those from the poorest households. Children whose mothers had attained secondary education exhibit higher odds of stunting compared to those with no education (aOR = 1.32, 95% CI: 1.01 - 1.72, p = 0.04). Male children show significantly higher odds of stunting compared to females (aOR = 1.50, 95% CI: 1.33 - 1.70, p < 0.001). Children aged 12-23 months exhibit the highest odds of stunting (aOR = 2.65, 95% CI: 2.23 - 3.14, p < 0.001) compared to those aged < 6 months). Spatial analysis indicated that stunting prevalence varies geographically, with some areas exhibiting higher clustering. The geographically weighted regression further revealed that the influence of socioeconomic and climatic factors on stunting prevalence differed across locations highlighting the need for geographically targeted interventions.
| Published in | International Journal of Data Science and Analysis (Volume 10, Issue 3) |
| DOI | 10.11648/j.ijdsa.20241003.12 |
| Page(s) | 49-60 |
| 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 |
Child Malnutrition, Stunting, Prevalence, Multilevel Logistic Regression, Odds Ratio, Geographically Weighted Regression, Spatial Regression, Climate Change
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APA Style
Masit, J., Malenje, B. M., Imboga, H. (2024). Spatial Patterns and Risk Factors of Stunting Among Under-five Children in Kenya: A Multilevel and Spatial Analysis. International Journal of Data Science and Analysis, 10(3), 49-60. https://doi.org/10.11648/j.ijdsa.20241003.12
ACS Style
Masit, J.; Malenje, B. M.; Imboga, H. Spatial Patterns and Risk Factors of Stunting Among Under-five Children in Kenya: A Multilevel and Spatial Analysis. Int. J. Data Sci. Anal. 2024, 10(3), 49-60. doi: 10.11648/j.ijdsa.20241003.12
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Download@article{10.11648/j.ijdsa.20241003.12,
author = {Jackline Masit and Bonface Miya Malenje and Herbert Imboga},
title = {Spatial Patterns and Risk Factors of Stunting Among Under-five Children in Kenya: A Multilevel and Spatial Analysis},
journal = {International Journal of Data Science and Analysis},
volume = {10},
number = {3},
pages = {49-60},
doi = {10.11648/j.ijdsa.20241003.12},
url = {https://doi.org/10.11648/j.ijdsa.20241003.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241003.12},
abstract = {Stunting remains a significant public health burden in sub-Saharan Africa and has far reaching consequences. Identifying the drivers of stunting and high burden regions is key to developing effective and targeted intervention strategies. The objective of the study was to identify the risk factors and explore spatial patterns of stunting across counties in Kenya. Secondary data from 2022 Kenya Demographic Health Survey (KDHS) was utilized. A total of 13,016 children aged between 0 - 59 months were included in the analysis. A multilevel logistic regression was applied to identify individual, household and community level determinants of stunting, spatial regression models to analyze spatial dependency and geographically weighted regression to explore spatial heterogeneity in the association between childhood stunting and county level determinants. In the multilevel logistic regression, Children from urban residence exhibited a significantly increased odds of stunting compared to those in rural areas (aOR = 1.25, 95% CI: 1.03 - 1.51, p = 0.02). Children from households categorized as poorer, middle, richer, and richest all exhibited significantly reduced odds of stunting compared to those from the poorest households. Children whose mothers had attained secondary education exhibit higher odds of stunting compared to those with no education (aOR = 1.32, 95% CI: 1.01 - 1.72, p = 0.04). Male children show significantly higher odds of stunting compared to females (aOR = 1.50, 95% CI: 1.33 - 1.70, p < 0.001). Children aged 12-23 months exhibit the highest odds of stunting (aOR = 2.65, 95% CI: 2.23 - 3.14, p < 0.001) compared to those aged < 6 months). Spatial analysis indicated that stunting prevalence varies geographically, with some areas exhibiting higher clustering. The geographically weighted regression further revealed that the influence of socioeconomic and climatic factors on stunting prevalence differed across locations highlighting the need for geographically targeted interventions.},
year = {2024}
}
TY - JOUR T1 - Spatial Patterns and Risk Factors of Stunting Among Under-five Children in Kenya: A Multilevel and Spatial Analysis AU - Jackline Masit AU - Bonface Miya Malenje AU - Herbert Imboga Y1 - 2024/08/26 PY - 2024 N1 - https://doi.org/10.11648/j.ijdsa.20241003.12 DO - 10.11648/j.ijdsa.20241003.12 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 49 EP - 60 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20241003.12 AB - Stunting remains a significant public health burden in sub-Saharan Africa and has far reaching consequences. Identifying the drivers of stunting and high burden regions is key to developing effective and targeted intervention strategies. The objective of the study was to identify the risk factors and explore spatial patterns of stunting across counties in Kenya. Secondary data from 2022 Kenya Demographic Health Survey (KDHS) was utilized. A total of 13,016 children aged between 0 - 59 months were included in the analysis. A multilevel logistic regression was applied to identify individual, household and community level determinants of stunting, spatial regression models to analyze spatial dependency and geographically weighted regression to explore spatial heterogeneity in the association between childhood stunting and county level determinants. In the multilevel logistic regression, Children from urban residence exhibited a significantly increased odds of stunting compared to those in rural areas (aOR = 1.25, 95% CI: 1.03 - 1.51, p = 0.02). Children from households categorized as poorer, middle, richer, and richest all exhibited significantly reduced odds of stunting compared to those from the poorest households. Children whose mothers had attained secondary education exhibit higher odds of stunting compared to those with no education (aOR = 1.32, 95% CI: 1.01 - 1.72, p = 0.04). Male children show significantly higher odds of stunting compared to females (aOR = 1.50, 95% CI: 1.33 - 1.70, p < 0.001). Children aged 12-23 months exhibit the highest odds of stunting (aOR = 2.65, 95% CI: 2.23 - 3.14, p < 0.001) compared to those aged < 6 months). Spatial analysis indicated that stunting prevalence varies geographically, with some areas exhibiting higher clustering. The geographically weighted regression further revealed that the influence of socioeconomic and climatic factors on stunting prevalence differed across locations highlighting the need for geographically targeted interventions. VL - 10 IS - 3 ER -
Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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