Spatial distribution and predictors of stunting, wasting, and underweight among under-five children in Bangladesh.

backgroundChildhood undernutrition remains a significant public health challenge in Bangladesh, affecting both the physical and cognitive development of children and the country’s economic growth. The study examined the spatial distribution and multilevel determinants of stunting, wasting, and underweight among children under five years of age in Bangladesh.

methodsData were obtained from the nationally representative Bangladesh Demographic and Health Survey (BDHS) 2022. Spatial patterns were analyzed using Moran’s I to assess spatial autocorrelation and the Getis-Ord Gi* statistic to identify hot and cold spots. Three-level Bayesian multilevel logistic regression models were applied to determine the predictors of stunting, wasting, and underweight at the individual, household, and community levels.

resultsOverall, 22.5% of children were stunted, 9.7% wasted, and 19.5% underweight. Although spatial clustering was not statistically significant, distinct hotspot areas observed- stunting were concentrated in Sylhet and Mymensingh, while wasting had higher prevalence in Rajshahi, Rangpur, and Barishal. Poverty in the community was found constantly associated with the three types of undernutrition, with children from communities with high poverty having higher odds of stunting (OR = 1.60; 95% CI: 1.40–1.83), wasting (OR = 1.31; 95% CI: 1.06–1.59), and being underweight (OR = 1.57; 95% CI: 1.31–1.88). Some of the risk factors for wasting and being underweight were undernourished mothers and mothers who are aged ≥ 35 years, while stunting and underweight were associated with older child age, higher birth order, and unsatisfactory sanitation. Lower rates of stunting were found for children whose fathers had some education, employed mothers, and mothers who had at least partial autonomy in making healthcare decisions.

conclusionChild undernutrition in Bangladesh is characterized by a concentration of multiple deprivations at different levels: individual, household and community as well as by the distinct regional hotspot patterns. Integrating national BDHS 2022 with geospatial hotspot mapping and three-level Bayesian multilevel models that account for maternal healthcare decision-making autonomy and contextual neighborhood measures of community poverty and maternal illiteracy, this study provides actionable evidence to prioritize high-burden areas for targeted nutrition, WASH, and poverty-reduction interventions.