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

Abstract

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.

Maternal Factors Promoting Normal Linear Growth of Children from Impoverished Rwandan Households

“…Another study from Bangladesh established a positive relationship between maternal employment and the overall economic well-being of the family with long-term favourable benefits on the child’s development [31]. That being said, some studies have found the opposite when a mother is involved in any activity that can generate income [32,33]. The reason behind this could be the reduced time to allocate to children and hence reduced child care [34].…”

“…That being said, some studies have found the opposite when a mother is involved in any activity that can generate income [32,33]. The reason behind this could be the reduced time to allocate to children and hence reduced child care [34].…”

Maternal Factors Promoting Normal Linear Growth of Children from Impoverished Rwandan Households

“…Another survey from Bangladesh established a positive relationship between maternal employment and the overall economic well-being of the family, which had long-term favourable bene ts for the child's development [31]. Some studies have found the opposite when a mother is involved in any activity that can generate income [32,33]. The reason behind this could be the reduced time allocated to children and, hence, reduced child care [34].…”