Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment

Romuald Daniel Boy-ngbogbele; Oscar Ngesa; Thomas Mageto; Celestin Kokonendji

doi:doi:10.11648/j.acm.20261501.14

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Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment

Romuald Daniel Boy-ngbogbele^*, Oscar Ngesa, Thomas Mageto, Celestin Kokonendji

Published in Applied and Computational Mathematics (Volume 15, Issue 1)

Received: 21 December 2025 Accepted: 4 January 2026 Published: 20 January 2026

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Abstract

Problem considered: Reliable estimation of childhood malnutrition remains a major public health challenge in low-and middle-income countries, where large-scale surveys such as the Demographic and Health Surveys (DHS) often suffer from measurement error and data heterogeneity. Ignoring these issues can bias prevalence estimates and distort the identification of socioeconomic determinants. Methods: This study develops a hierarchical Bayesian logistic regression model that accounts for both measurement error and clustering effects by region and survey year. The model incorporates known sensitivity and specificity to adjust for outcome misclassification and includes random effects to capture between-region and temporal variability. Using simulated DHS-like data, the corrected model was compared to an uncorrected counterpart in terms of key performance metrics-prevalence, area under the Receiver Operating Characteristic(ROC) curve (AUC), and accuracy-across survey years (2004, 2011, 2018, and 2022). Results: The Bayesian correction improved predictive accuracy and reduced bias in prevalence estimates. The corrected model achieved consistently higher AUC values (0.882-0.930) compared to the uncorrected model (0.878-0.928), and exhibited lower mean squared error (0.121 vs. 0.137). The inclusion of regional and temporal random effects effectively captured unobserved heterogeneity. Posterior parameter estimates revealed several significant socioeconomic predictors influencing child malnutrition. Conclusion: The proposed Bayesian hierarchical framework demonstrates improved accuracy and robustness in estimating malnutrition prevalence when accounting for measurement error. These findings highlight the importance of error correction and multilevel modeling for more reliable health policy decision-making based on survey data.

Published in	Applied and Computational Mathematics (Volume 15, Issue 1)
DOI	10.11648/j.acm.20261501.14
Page(s)	35-48
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), 2026. Published by Science Publishing Group

Keywords

Childhood Malnutrition, Hierarchical Bayesian Modeling, Measurement Error Correction, Multilevel Logistic Regression, Socioeconomic Determinants

References

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APA Style

Boy-ngbogbele, R. D., Ngesa, O., Mageto, T., Kokonendji, C. (2026). Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment. Applied and Computational Mathematics, 15(1), 35-48. https://doi.org/10.11648/j.acm.20261501.14

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ACS Style

Boy-ngbogbele, R. D.; Ngesa, O.; Mageto, T.; Kokonendji, C. Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment. Appl. Comput. Math. 2026, 15(1), 35-48. doi: 10.11648/j.acm.20261501.14

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AMA Style

Boy-ngbogbele RD, Ngesa O, Mageto T, Kokonendji C. Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment. Appl Comput Math. 2026;15(1):35-48. doi: 10.11648/j.acm.20261501.14

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@article{10.11648/j.acm.20261501.14,
  author = {Romuald Daniel Boy-ngbogbele and Oscar Ngesa and Thomas Mageto and Celestin Kokonendji},
  title = {Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment},
  journal = {Applied and Computational Mathematics},
  volume = {15},
  number = {1},
  pages = {35-48},
  doi = {10.11648/j.acm.20261501.14},
  url = {https://doi.org/10.11648/j.acm.20261501.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20261501.14},
  abstract = {Problem considered: Reliable estimation of childhood malnutrition remains a major public health challenge in low-and middle-income countries, where large-scale surveys such as the Demographic and Health Surveys (DHS) often suffer from measurement error and data heterogeneity. Ignoring these issues can bias prevalence estimates and distort the identification of socioeconomic determinants. Methods: This study develops a hierarchical Bayesian logistic regression model that accounts for both measurement error and clustering effects by region and survey year. The model incorporates known sensitivity and specificity to adjust for outcome misclassification and includes random effects to capture between-region and temporal variability. Using simulated DHS-like data, the corrected model was compared to an uncorrected counterpart in terms of key performance metrics-prevalence, area under the Receiver Operating Characteristic(ROC) curve (AUC), and accuracy-across survey years (2004, 2011, 2018, and 2022). Results: The Bayesian correction improved predictive accuracy and reduced bias in prevalence estimates. The corrected model achieved consistently higher AUC values (0.882-0.930) compared to the uncorrected model (0.878-0.928), and exhibited lower mean squared error (0.121 vs. 0.137). The inclusion of regional and temporal random effects effectively captured unobserved heterogeneity. Posterior parameter estimates revealed several significant socioeconomic predictors influencing child malnutrition. Conclusion: The proposed Bayesian hierarchical framework demonstrates improved accuracy and robustness in estimating malnutrition prevalence when accounting for measurement error. These findings highlight the importance of error correction and multilevel modeling for more reliable health policy decision-making based on survey data.},
 year = {2026}
}

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TY  - JOUR
T1  - Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment
AU  - Romuald Daniel Boy-ngbogbele
AU  - Oscar Ngesa
AU  - Thomas Mageto
AU  - Celestin Kokonendji
Y1  - 2026/01/20
PY  - 2026
N1  - https://doi.org/10.11648/j.acm.20261501.14
DO  - 10.11648/j.acm.20261501.14
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 35
EP  - 48
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20261501.14
AB  - Problem considered: Reliable estimation of childhood malnutrition remains a major public health challenge in low-and middle-income countries, where large-scale surveys such as the Demographic and Health Surveys (DHS) often suffer from measurement error and data heterogeneity. Ignoring these issues can bias prevalence estimates and distort the identification of socioeconomic determinants. Methods: This study develops a hierarchical Bayesian logistic regression model that accounts for both measurement error and clustering effects by region and survey year. The model incorporates known sensitivity and specificity to adjust for outcome misclassification and includes random effects to capture between-region and temporal variability. Using simulated DHS-like data, the corrected model was compared to an uncorrected counterpart in terms of key performance metrics-prevalence, area under the Receiver Operating Characteristic(ROC) curve (AUC), and accuracy-across survey years (2004, 2011, 2018, and 2022). Results: The Bayesian correction improved predictive accuracy and reduced bias in prevalence estimates. The corrected model achieved consistently higher AUC values (0.882-0.930) compared to the uncorrected model (0.878-0.928), and exhibited lower mean squared error (0.121 vs. 0.137). The inclusion of regional and temporal random effects effectively captured unobserved heterogeneity. Posterior parameter estimates revealed several significant socioeconomic predictors influencing child malnutrition. Conclusion: The proposed Bayesian hierarchical framework demonstrates improved accuracy and robustness in estimating malnutrition prevalence when accounting for measurement error. These findings highlight the importance of error correction and multilevel modeling for more reliable health policy decision-making based on survey data.
VL  - 15
IS  - 1
ER  -

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Author Information

Romuald Daniel Boy-ngbogbele

Department of Mathematics, Institute for Science, Technology and Innovation, Pan African University, Nairobi, Kenya

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Oscar Ngesa

Department of Mathematics, Taita Taveta University, Nairobi, Kenya
Thomas Mageto

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

Department of Mathematics, Marie and Louis Pasteur University - UFR Sciences and Techniques, Besanc’on Cedex, France

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Boy-ngbogbele, R. D., Ngesa, O., Mageto, T., Kokonendji, C. (2026). Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment. Applied and Computational Mathematics, 15(1), 35-48. https://doi.org/10.11648/j.acm.20261501.14

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ACS Style

Boy-ngbogbele, R. D.; Ngesa, O.; Mageto, T.; Kokonendji, C. Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment. Appl. Comput. Math. 2026, 15(1), 35-48. doi: 10.11648/j.acm.20261501.14

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AMA Style

Boy-ngbogbele RD, Ngesa O, Mageto T, Kokonendji C. Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment. Appl Comput Math. 2026;15(1):35-48. doi: 10.11648/j.acm.20261501.14

Copy | Download

@article{10.11648/j.acm.20261501.14,
  author = {Romuald Daniel Boy-ngbogbele and Oscar Ngesa and Thomas Mageto and Celestin Kokonendji},
  title = {Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment},
  journal = {Applied and Computational Mathematics},
  volume = {15},
  number = {1},
  pages = {35-48},
  doi = {10.11648/j.acm.20261501.14},
  url = {https://doi.org/10.11648/j.acm.20261501.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20261501.14},
  abstract = {Problem considered: Reliable estimation of childhood malnutrition remains a major public health challenge in low-and middle-income countries, where large-scale surveys such as the Demographic and Health Surveys (DHS) often suffer from measurement error and data heterogeneity. Ignoring these issues can bias prevalence estimates and distort the identification of socioeconomic determinants. Methods: This study develops a hierarchical Bayesian logistic regression model that accounts for both measurement error and clustering effects by region and survey year. The model incorporates known sensitivity and specificity to adjust for outcome misclassification and includes random effects to capture between-region and temporal variability. Using simulated DHS-like data, the corrected model was compared to an uncorrected counterpart in terms of key performance metrics-prevalence, area under the Receiver Operating Characteristic(ROC) curve (AUC), and accuracy-across survey years (2004, 2011, 2018, and 2022). Results: The Bayesian correction improved predictive accuracy and reduced bias in prevalence estimates. The corrected model achieved consistently higher AUC values (0.882-0.930) compared to the uncorrected model (0.878-0.928), and exhibited lower mean squared error (0.121 vs. 0.137). The inclusion of regional and temporal random effects effectively captured unobserved heterogeneity. Posterior parameter estimates revealed several significant socioeconomic predictors influencing child malnutrition. Conclusion: The proposed Bayesian hierarchical framework demonstrates improved accuracy and robustness in estimating malnutrition prevalence when accounting for measurement error. These findings highlight the importance of error correction and multilevel modeling for more reliable health policy decision-making based on survey data.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Modeling the Impact of Socioeconomic Determinants on Childhood Malnutrition: A Hierarchical Bayesian Approach with Measurement Error Adjustment
AU  - Romuald Daniel Boy-ngbogbele
AU  - Oscar Ngesa
AU  - Thomas Mageto
AU  - Celestin Kokonendji
Y1  - 2026/01/20
PY  - 2026
N1  - https://doi.org/10.11648/j.acm.20261501.14
DO  - 10.11648/j.acm.20261501.14
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 35
EP  - 48
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20261501.14
AB  - Problem considered: Reliable estimation of childhood malnutrition remains a major public health challenge in low-and middle-income countries, where large-scale surveys such as the Demographic and Health Surveys (DHS) often suffer from measurement error and data heterogeneity. Ignoring these issues can bias prevalence estimates and distort the identification of socioeconomic determinants. Methods: This study develops a hierarchical Bayesian logistic regression model that accounts for both measurement error and clustering effects by region and survey year. The model incorporates known sensitivity and specificity to adjust for outcome misclassification and includes random effects to capture between-region and temporal variability. Using simulated DHS-like data, the corrected model was compared to an uncorrected counterpart in terms of key performance metrics-prevalence, area under the Receiver Operating Characteristic(ROC) curve (AUC), and accuracy-across survey years (2004, 2011, 2018, and 2022). Results: The Bayesian correction improved predictive accuracy and reduced bias in prevalence estimates. The corrected model achieved consistently higher AUC values (0.882-0.930) compared to the uncorrected model (0.878-0.928), and exhibited lower mean squared error (0.121 vs. 0.137). The inclusion of regional and temporal random effects effectively captured unobserved heterogeneity. Posterior parameter estimates revealed several significant socioeconomic predictors influencing child malnutrition. Conclusion: The proposed Bayesian hierarchical framework demonstrates improved accuracy and robustness in estimating malnutrition prevalence when accounting for measurement error. These findings highlight the importance of error correction and multilevel modeling for more reliable health policy decision-making based on survey data.
VL  - 15
IS  - 1
ER  -

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