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

Abstract

PurposeChild 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.MethodsThe 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.ResultsAccording 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.ConclusionsAccording 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.