We analysed data of 532,680 under-five children nested within 55,823 neighbourhoods from 51 LMIC who participated in the DHS between 2010 and 2018. The regions of the world, countries, year of data collection, numbers of neighbourhoods, number of under-five children, percentage of the uneducated mothers and the weighted prevalence of SAM among children of uneducated and educated mothers are listed in Table 1. The proportions of mothers with no formal education ranged from 0.1 % in Armenia and Kyrgyz to 86.1 % in Niger and a median of 20.1 % in Haiti.
Prevalence of SAM by countries and maternal education
We found wide variations in the prevalence of SAM among children of educated and uneducated mothers across the 51 LMIC studied (Table 1 and Figure 1). The overall SAM prevalence was 4.7% with a median prevalence of 1.8% ranging from 0.1 % in Guatemala to 9.9 % in Timor-Leste as shown in Table 1. The prevalence of SAM among children of uneducated mothers ranged from 0.0 % in Lesotho, Zimbabwe, Kyrgyz, Armenia and Guatemala to 12.7 % in Timor-Leste, while it ranged from 0.1 % in Peru and Guatemala to 9.4% in Timor-Leste among children of the educated mothers. Mantel Haenszel test of homogeneity of odds ratio was used to test statistical significance with literacy level as an effect modifier. We found significant pro-illiterate inequalities in 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) but no country has pro-educated inequalities as shown in Table 1.
Prevalence of SAM by children characteristics and maternal education
Table 2 presents the descriptive statistics for the characteristics of the pooled sample of children across the 51 LMIC. About 51 % of the children were males while only 20% were infants. About 53% of the mothers were aged 25 to 34 years old and about 31% had no formal education. Nearly one-third of the mothers were not working at the time of the survey. 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. The Mantel Haenszel test of homogeneity of odds ratio used to test statistical significance with literacy level as an effect modifier showed that all the characteristics considered were independently significant. For instance 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) had significant differences in SAM prevalence viz-a-viz mothers’ literacy (Table 2). Infants, male children and mothers at extreme age intervals; 15 to 24 and 34 to 49 had overall higher SAM prevalence. For wealth index, births of women from lowest wealth quintile had the highest rate of SAM within the “uneducated” group compared with those from richest wealth quintile (6.8 % vs 3.4%) but the margins were closer within the “educated” group.
Magnitude and variations in educational inequality in SAM
Figures 1 and 2 show the risk differences (a measure of inequality) between children of uneducated and educated women across the 51 countries studied. Among the 51 countries included in this analysis, 14 countries showed statistically significant pro-illiterate inequality (i.e. SAM is more prevalent among children from uneducated mothers). None of the countries showed statistically significant pro-literacy (i.e. when SAM is more prevalent among children from educated mothers) while 37 countries showed no statistically significant inequality. As illustrated by Fig. 1, in Eastern Africa, the educational difference was largest for Ethiopia (20.55 per 1000 children) and lowest for Malawi (− 0.50). In Western Africa, the largest educational difference was in Nigeria (48.22) and lowest for Cote d’Ivoire (-6.41). In the Caribbean, the difference was largest for Timor Leste (32.60) and lowest for Myanmar (-0.96). Burundi and Senegal with 2.5 % weight each had the largest contribution to the pooled result. In the pooled analysis, Nigeria still had the highest pro-illiterate inequality (48.22) and followed by Namibia (44.75) as shown in Figure 2. Overall, there was significant pro-illiterate in the total pooled sample of children in this study. The risk difference was 7.18 (95% Confidence Interval (CI): 3-12) per 1000 children among children of uneducated mothers compared with those of educated mothers as shown in the random effects in Figure 1. The random effect shows the overall risk difference among all children born to educated and uneducated mothers irrespective of their countries. In Figure 2, we used the colours blue, yellow and red to indicate statistically significant pro-illiterate inequality, no significant inequality and statistically significant pro-literate inequality respectively.
Two of the nine countries in Eastern Africa inequality, 2 of the countries in Middle Africa, none in Northern Africa, and only Namibia in Southern Africa showed statistically significant pro-illiterate. In Western Africa, 2 of the 13 countries while only 2 of the five countries in Southern Asia showed statistically significant pro-illiterate inequality compared with only one country among the four countries studied in the Caribbean.
Relationship between prevalence of SAM and magnitude of the educational inequality
Figure 3 shows the relationship between the prevalence of SAM and the magnitude of inequality for all the 51 countries in this study. The 51 countries were categorized into 4 distinct categories: (1) High severe acute malnutrition and high pro-illiterate inequality such as Timor-Leste and Nigeria; (2) High severe acute malnutrition and high pro-literate inequality was not found in any country; (3) Low severe acute malnutrition and high pro-illiterate inequality such as Namibia and Kenya; and (4) Low severe acute malnutrition and high pro-literate inequality was not found in any country. In Figure 3, colours cyan, orange and red were used to depict statistically significant pro-illiterate inequality, no significant inequality and statistically significant pro-literate inequality respectively.
Decomposition of educational inequality in the prevalence of SAM
Figure 4 shows the detailed decomposition of the part of the inequality that was caused by compositional effects of the determinants of SAM among under-five children. There were variations in the important factors responsible for the educational inequalities across the countries. The “explained” (compositional component) and the “unexplained” (structural component) portions of the educational inequalities are depicted by red and blue colours respectively; the lighter the red colour the lower the percentage contribution of the “explained” portion and the lighter the blue colour, the lower the percentage contribution of the “unexplained” portion.
On the average, neighbourhood socioeconomic status disadvantage and, location of residence were the most important factors in most countries. In Senegal, the largest contributions to the educational inequality in the prevalence of SAM was by neighbourhood socioeconomic disadvantage, followed by the location of residence, wealth index and access to media. Wealth index and media access narrowed the inequality in the development of SAM between children from educated and uneducated mothers. In Togo, location of residence had the largest contribution to the educational inequality followed by neighbourhood socioeconomic status disadvantage and followed by media access. Marital status, child age and sex, birth weight and mother’s employment status did not show any significant contribution to educational inequality in the development of SAM in any of the 13 countries.