A mixed-effects multilevel Poisson regression analysis of diarrhea prevalence among under-five children in Indonesia

Diarrheal disease is the second leading cause of death in under-five children. This study assessed the prevalence and risk factors of diarrhea among under-five Indonesian children using data from the 2017 Indonesia Demography and Health Survey.The analysis included 17,263 children living with their mothers and was based on a multistage stratified sampling design. Diarrhea was defined as maternal report of an episode within the two weeks preceding the survey. Descriptive statistics and chi-square tests were used for preliminary analysis. Determinants of diarrhea were examined using mixed-effects multilevel Poisson regression to account for the hierarchical data structure, with region specified as a random effect. All analyses were conducted using Stata. The model included region as a random effect, allowing baseline diarrhea prevalence to vary across regions. Female children had a lower prevalence of diarrhea than males (APR = 0.883; 95%CI:0.815-0.956), while children aged 1-2 years had a higher prevalence compared with infants. Higher household wealth was strongly protective, with children from the richest households showing a substantially lower prevalence (APR = 0.720; 95%CI:0.611-0.812). Environmental factors remained significant in the multilevel framework, as households with standard sanitation facilities had lower diarrhea prevalence (APR = 0.871; 95%CI:0.776-0.976), and rural residence was associated with higher prevalence. The small random-effect variance indicated limited residual regional heterogeneity after adjustment. Childhood diarrhea in Indonesia is shaped by interacting individual, environmental, and community-level factors. Socioeconomic disadvantage and inadequate sanitation remain major contributors to diarrhea prevalence among under-five children. The limited residual regional variation suggests that improvements in household living conditions and sanitation, particularly in rural areas, could substantially reduce disease burden. Multilevel analytical approaches are crucial for informing targeted and effective public health interventions.