Determining the risk factors of under-five morbidity in Bangladesh: a Bayesian logistic regression approach

Abstract

Purpose Child morbidity prevents Bangladesh from reaching the target for the Sustainable Development Goals (target 3.2) despite the country’s success in reducing child mortality rates. As a result, it's crucial to consider a child's health-related issues. Therefore, this study aims to explore the prevalence and factors associated with under-five child morbidity in Bangladesh. Methods The Bangladesh Demographic and Health Survey, 2017–2018, a secondary cross-sectional survey data, was used in this study, which collected information using a two-stage systematic sampling design. After association test, Bayesian estimation of binary logistic regression model was used to identify the significant risk factors of morbidity among under-five children, and a trace plot was used to try to figure out the convergence of simulation. Results According to the prevalence analysis of this study, it can be noted that more than one-thirds of under-five children in Bangladesh suffered from at least one of the child health-related problems, and of these, the highest prevalence of child morbidity was found in the Barisal division of Bangladesh (~ 42%). According to the Bayesian logistic regression results, higher child age and maternal education are associated with a potential decrease in the risk of child morbidity in Bangladesh. Again, male children had a 7% higher risk of morbidity than female children. Another finding was that underweight children suffered 31% more fever/diarrhea/ acute respiratory infection (ARI) related problems than others. Conclusions According to this study’s findings, child morbidity is still a significant issue in Bangladesh. Therefore, progress on risk factors, such as maternal education, child nutrition, etc., should be the focus of policymakers' intervention.