Peer-reviewed article
Multilevel Modelling of the Factors Associated with Stunting among Under-five Children in Tanzania: A Bayesian Approach
Edwin Musheiguza, Tukae Mbegalo, Justine N. Mbukwa ·
Summary
Written by AI, not yet AICS's view · claude-sonnet-5This study looked at data from four national health surveys in Tanzania to understand what factors are linked to stunting (poor growth) in children under five. It used advanced statistical modelling to account for both individual family circumstances and broader regional or community conditions. The study found that stunting rates have been falling over time, from about 41% in 2010 to about 29% in 2022. Poverty levels and household access to water, assets, and infrastructure were strongly linked to stunting risk. The authors suggest that improving household living conditions and providing nutrition education, especially to working mothers and food-insecure families, could help reduce stunting.
Key points
- Stunting prevalence was 41.4% in 2010, 34.1% in 2015/16, and 28.96% in 2022
- A higher multidimensional poverty index (MPI) was linked to 10% higher odds of stunting in 2015/16
- A higher WAMI index (water, assets, maternal education, income) was linked to 64% lower odds of stunting in 2010
- Multiple births (twins etc.) were linked to different stunting risks in different survey years: 17% lower odds in 2004/5 but 68% higher odds in 2010
- Stunting is associated with both individual household factors and wider contextual/regional factors
Limitations
The study only shows associations from survey data, not proof that any single factor causes stunting, and does not test or evaluate any specific programme or intervention.
Abstract
Introduction: In Tanzania, prevalence exceeds 30% and is unevenly distributed across geographical areas. The observed distribution may reflect unobserved contextual factors; however, little literature applies Bayesian multilevel generalised linear mixed (B-MGLM) models, which can account for them. This study aimed to model the determinants of stunting, controlling for contextual factors at both the region and enumeration area levels. Methods: This was a secondary data analysis using four recent Tanzania Demographic and Health Surveys (TDHS) datasets. We studied 7,492, 6,806, 8,929, and 4,797 under-five children in TDHS 2004/5, 2010, 2015/16, and 2022, respectively. We used B-MGLM models to evaluate the determinants of stunting. We used the deviance information criterion to compare the models. Results: About 41.4%, 34.1%, and 28.96% of under-five children were stunted in 2010, 2015/16, and 2022, respectively. Controlling for other factors in the model, a unit increase in MPI increased the odds of stunting by 10% [AOR = 1.1; 95% CI: 1.1, 1.2], while a unit increase in WAMI reduced the odds of stunting by 64% [AOR=0.36; 95% CI: 0.3, 0.4] in 2015/16 and 2010, respectively. Other interesting results were that children with multiple births had 17% [AOR=0.83; 95% CI: 0.7, 0.9] lower odds and 68% [AOR=1.68; 95% CI: 1.5, 1.9] higher odds of stunting in 2004/5 and 2010, respectively. Conclusion: Stunting is associated with individual and contextual factors. Critical efforts should focus on the availability of items that form WAMI and MPI, with greater emphasis on elements that form WAMI because of its consistent negative relationship with stunting across the two surveys. Nutrition education should target working mothers and households with food insecurity.