Exploring under-five child malnutrition in Bangladesh: Analysis using the Extended Composite Index of Anthropometric Failure (ECIEF).

Abstract

Objective: The current study is an attempt to explore under-five child malnutrition in a low-income population setting using the Extended Composite Index of Anthropometric Failure (ECIAF).

Design: Data from the Bangladesh Demographic and Health Survey 2017-18 were analyzed. Malnutrition using ECIAF was estimated using stunting, wasting underweight and overweight. Multilevel logistic regression models identified factors associated with malnutrition. Geospatial analysis was conducted using R programming.

Setting: Bangladesh.

Participants: Children under five years of age.

Results: In Bangladesh, as indicated by the ECIAF, approximately 40.8% (95% Confidence interval (CI): 39.7, 41.9) of children under-five experience malnutrition where about 3.3% (95% CI: 2.9, 3.7) were overweight. Children of parents with no formal education (56.3%, 95% CI: 50.8, 61.8), underweight mothers (53.4%, 95% CI: 50.4, 56.3), belonging to the lowest socio-economic strata (50.6%, 95% CI: 48.3, 53.0), residing in rural areas (43.3%, 95% CI: 41.9, 44.6), and aged below three years (47.7%, 95% CI: 45.2, 50.2) demonstrated a greater age and sex adjusted prevalence of malnutrition. The Sylhet division (Eastern region) exhibited a higher prevalence of malnutrition (>55.0%). Mothers with no formal education (Adjusted odds ratio (AOR): 1.51, 95% CI: 1.08, 2.10), underweight mother (AOR: 1.54, 95% CI: 1.03, 1.83), poorest socio-economic status (AOR: 2.14, 95% CI: 1.64, 2.81), children age 24-35 months of age (AOR: 2.37, 95% CI: 1.97, 2.85), and fourth and above birth order children (AOR: 1.41, 95% CI: 1.16, 1.72) were identified key factors associated with childhood malnutrition while adjusting community and household level variations.

Conclusion: In Bangladesh, two out of five children were malnourished and one in 35 children was overweight. Continuous monitoring of the ECIAF over time would facilitate tracking changes in the prevalence of different forms of malnutrition, helping to plan interventions and assess the effectiveness of interventions aimed at addressing both undernutrition and overweight.