What question did this study set out to answer?

The study aims to reliably estimate childhood malnutrition while accounting for socioeconomic determinants and measurement errors.

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

View Full Paper

Ask AI

Bookmark

Authors

Modeling predicts childhood malnutrition outcomes using socioeconomic factors, suggesting error adjustment is crucial.

The study aims to reliably estimate childhood malnutrition while accounting for socioeconomic determinants and measurement errors.
Developed a hierarchical Bayesian logistic regression model
Adjusted for measurement error and clustering by region and survey year
Incorporated known sensitivity and specificity for outcome adjustment
Compared corrected and uncorrected models using simulated DHS-like data
Bayesian correction improved predictive accuracy and reduced bias in prevalence estimates
Corrected model achieved higher AUC values (0.882-0.930) than uncorrected (0.878-0.928)
Exhibited lower mean squared error (0.121 vs. 0.137)
Identified significant socioeconomic predictors affecting child malnutrition

BOY-NGBOGBELE et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark