Bayesian multilevel modelling of the association between socio-economic status and stunting among under-five-year children in Tanzania.

BACKGROUND: Stunting is associated with socioeconomic status (SES) which is multidimensional. This study aimed to compare different SES indices in predicting stunting. METHODS: This was the secondary data analysis using Tanzania Demographics and Health Surveys (TDHS). The study used 7492, 6668, and 8790 under-five-year children from TDHS 2004/5, 2010, and 2015/16, respectively. The Household Wealth Index (HWI); Water and Sanitation, Assets, Maternal education and Income (WAMI); Wealth Assets, Education, and Occupation (WEO); and the Multidimensional Poverty Index (MPI) indices were compared. The summated scores, principal component analysis (PCA), and random forest (RF) approaches were used to construct indices. The Bayesian and maximum likelihood multilevel generalized linear mixed models (MGLMM) were constructed to determine the association between each SES index and stunting. RESULTS: The study revealed that 42.3%, 38.4%, and 32.4% of the studied under-five-year children were stunted in 2004/5, 2010, and 2015/16, respectively. Compared to other indicators of SES, the MPI had a better prediction of stunting for the TDHS 2004/5 and 2015/16, while the WAMI had a better prediction in 2010. For each score increase in WAMI, the odds of stunting were 64% [BPOR = 0.36; 95% CCI 0.3, 0.4] lower in 2010, while for each score increase in MPI there was 1 [BPOR = 1.1; 95% CCI 1.1, 1.2] times higher odds of stunting in 2015/16. CONCLUSION: The MPI and WAMI under PCA were the best measures of SES that predict stunting. Because MPI was the best predictor of stunting for two surveys (TDHS 2004/5 and 2015/16), studies dealing with stunting should use MPI as a proxy measure of SES. Use of BE-MGLMM in modelling stunting is encouraged. Strengthened availability of items forming MPI is inevitable for child growth potentials. Further studies should investigate the determinants of stunting using Bayesian spatial models to take into account spatial heterogeneity.

Inequalities in stunting among under-five children in Tanzania: decomposing the concentration indexes using demographic health surveys from 2004/5 to 2015/6.

BACKGROUND: Child stunting is a global health concern. Stunting leads to several consequences on child survival, growth, and development. The absolute level of stunting has been decreasing in Tanzania from from 50% in 1991/92 to 34% in 2016 although the prevalence is still high (34%)Stunting varyies across socioeconomic determinants with a larger burden among the socioeconomic disadvantaged group. The reduction of inequalities in stunting is very crucial as we aim to reduce stunting to 28% by 2021 and hence attain zero malnutrition by 2030 under Sustainable Development Goal 2.2.This study aimed at determining the trend, contributing factors and changes of inequalities in stunting among children aged 3-59 months from 2004 to 2016. METHODS: Data were drawn from the Tanzania Demographic and Health Surveys. The concentration index (CIX) was used to quantify the magnitude of inequalities in stunting. The pooled Poisson regression model was used to determine the factors for stunting, decision criterion for significant determinants was at 5% level of significance. The CIX was decomposed using the Wagstaff and Watanabe decomposition methods., the percentage contribution of each factor to the toal concentration index was used to rank the factors for socioeconomic inequalities in stutning. RESULTS: Inequalities in stunting were significantly concentrated among the poor; evidenced by CIX = - 0.019 (p < 0.001) in 2004, - 0.018 (p < 0.001) in 2010 and - 0.0096 (p < 0.001) in 2015. There was insignificant decline in inequalities in stunting; the difference in CIX from 2004 to 2010 was 0.0015 (p = 0.7658), from 2010 to 2015/6 was - 0.0081 (p = 0.1145). The overall change in CIX from 2004 to 2015/6 was 0.00965 (p = 0.0538). Disparities in the distribution of wealth index (mean contribution > 84.7%) and maternal years of schooling (mean contribution > 22.4%) had positive impacts on the levels of inequalities in stunting for all surveyed years. Rural-urban differences reduced inequalities in stunting although the contribution changed over time. CONCLUSION: Inequalities in stunting declined, differentials in wealth index and maternal education had increased contribution to the levels of inequalities in stunting. Reducing stunting among the disadvantaged groups requires initiatives which should be embarked on the distribution of social services including maternal and reproductive education among women of reproductive age, water and health infrastructures in remote areas.