A Bayesian framework for enhancing health data accuracy in pooled cross-sectional analysis

The analysis of pooled cross-sectional data plays a vital role in various disciplines, including epidemiology, economics, and the social sciences, by enabling the identification of trends and patterns over time. This study develops statistical models specifically designed to analyze pooled cross-sectional data while accounting for measurement error, with a particular focus on estimating the prevalence of malnutrition among children under five years of age in Cameroon. Measurement error is a persistent issue in surveys, especially in resource-limited settings where data collection accuracy may be compromised. To address this, the research employs logistic regression within a Bayesian framework to reduce the impact of measurement error on malnutrition prevalence estimates, thereby providing more reliable information for policymakers and public health professionals. Through both simulation studies and application to real-world data from Cameroon, the study demonstrates the effectiveness of the proposed models in improving the accuracy and precision of estimates, offering deeper insights into childhood malnutrition in the country. This work advances statistical methodologies for survey data analysis by providing robust tools to address measurement error and support evidence-based interventions to combat malnutrition in Cameroon and similar contexts worldwide.