Decomposing the educational inequalities in the factors associated with severe acute malnutrition among under-five children in low- and middle-income countries

BACKGROUND: Low- and Middle-Income Countries (LMIC) have remained plagued with the burden of severe acute malnutrition (SAM). The decomposition of the educational inequalities in SAM across individual, household and neighbourhood characteristics in LMIC has not been explored. This study aims to decompose educational-related inequalities in the development of SAM among under-five children in LMIC and identify the risk factors that contribute to the inequalities. METHODS: We pooled successive secondary data from the Demographic and Health Survey conducted between 2010 and 2018 in 51 LMIC. We analysed data of 532,680 under-five children nested within 55,823 neighbourhoods. Severe acute malnutrition was the outcome variable while the literacy status of mothers was the main exposure variable. The explanatory variables cut across the individual-, household- and neighbourhood-level factors of the mother-child pair. Oaxaca-Blinder decomposition method was used at p = 0.05. RESULTS: The proportion of children whose mothers were not educated ranged from 0.1% in Armenia and Kyrgyz Republic to as much as 86.1% in Niger. The overall prevalence of SAM in the group of children whose mothers had no education was 5.8% compared with 4.2% among those whose mothers were educated, this varied within each country. Fourteen countries (Cameroon(p < 0.001), Chad(p < 0.001), Comoro(p = 0.047), Burkina Faso(p < 0.001), Ethiopia(p < 0.001), India(p < 0.001), Kenya(p < 0.001), Mozambique(p = 0.012), Namibia(p = 0.001), Nigeria(p < 0.001), Pakistan(p < 0.001), Senegal(p = 0.003), Togo(p = 0.013), and Timor Leste(p < 0.001) had statistically significant pro-illiterate inequality while no country showed statistically significant pro-literate inequality. We found significant differences in SAM prevalence across child’s age (p < 0.001), child’s sex(p < 0.001), maternal age(p = 0.001), household wealth quintile(p = 0.001), mother’s access to media(p = 0.001), birth weight(p < 0.001) and neighbourhood socioeconomic status disadvantage(p < 0.001). On the average, neighbourhood socioeconomic status disadvantage, location of residence were the most important factors in most countries. Other contributors to the explanation of educational inequalities are birth weight, maternal age and toilet type. CONCLUSIONS: SAM is prevalent in most LMIC with wide educational inequalities explained by individual, household and community-level factors. Promotion of women education should be strengthened as better education among women will close the gaps and reduce the burden of SAM generally. We recommend further studies of other determinate causes of inequalities in severe acute malnutrition in LMIC.