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

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.

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 ROC curve (AUC), and accuracy—across survey years (2004, 2011, 2018, and 2022).

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.

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. Competing Interest Statement The authors have declared no competing interest. Funding Statement The author(s) received no specific funding for this work. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics Approval and Oversight The research described in this manuscript is a secondary analysis of publicly available, de-identified data from the Demographic and Health Surveys (DHS) Program for Cameroon (survey years 2004, 2011, 2018, and 2022). Ethical approval for data collection in each survey was granted by the National Ethics Committee of Cameroon’s Ministry of Public Health and by the ICF Institutional Review Board (IRB), USA, which ensures compliance with the U.S. Department of Health and Human Services regulations for the protection of human subjects (FWA00000845). This secondary analysis was exempt from additional institutional review, as it involved only anonymized data with no identifiable personal information. Access to the datasets was obtained from the DHS Program website (https://dhsprogram.com/data/) after the required registration and authorization procedures. Ethics approval references: Cameroon DHS 2004: National Ethics Committee, Ministry of Public Health (Approval Ref. No. 045/CNE/MPH/04) Cameroon DHS 2011: National Ethics Committee, Ministry of Public Health (Approval Ref. No. 072/CNE/MPH/11) Cameroon DHS 2018: National Ethics Committee, Ministry of Public Health (Approval Ref. No. 108/CNE/MPH/18) Cameroon DHS 2022: National Ethics Committee, Ministry of Public Health (Approval Ref. No. 125/CNE/MPH/22) and ICF IRB (FWA00000845) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Data Availability The data underlying the results presented in this study are publicly available from the Demographic and Health Surveys (DHS) Program. The Cameroon DHS datasets for the years 2004, 2011, 2018, and 2022 can be accessed upon reasonable request from the DHS Program website at https://dhsprogram.com/data/. Researchers must create an account and submit a brief description of their project to obtain permission for data access. The DHS Program then grants access to de-identified datasets for academic and policy research purposes. All data used in this analysis were anonymized prior to access, and no individual identifying information is included. No new data were generated during this study.