Decomposing the educational inequalities in the factors associated with severe acute malnutrition among under-five children in Low- and Middle-Income Countries

Abstract

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, neighbourhood and national level characteristics in LMIC have 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 (DHS) 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: Mothers with no formal education ranged from 0.1 % in Armenia and Kyrgyz 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=0.000), Chad(p=0.000), Comoro(p=0.047), Burkina Faso(p=0.000), Ethiopia(p=0.000), India(p=0.000), Kenya(p=0.000), Mozambique(p=0.012), Namibia(p=0.001), Nigeria(p=0.000), Pakistan(p=0.000), Senegal(p=0.003), Togo(p=0.013), and Timor Leste(p=0.000) 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=0.000), child’s sex(p=0.000), maternal age(p=0.001), household wealth quintile(p=0.001), mother’s access to media(p=0.001), birth weight(p=0.000) and neighbourhood socioeconomic status disadvantage(p=0.000). 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. Educational inequalities in SAM was 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.

Mind the gap: What explains the poor-non-poor inequalities in severe wasting among under-five children in low- and middle-income countries? Compositional and structural characteristics

“…Keywords including low and middle-income countries, childhood morbidity, undernutrition, malnutrition, severe acute malnutrition, severe wasting, were used to search for factors associated with wealth-based inequality in SW across literature database such as PubMed, Medline, Hinari. The individual-and neighbourhood level factors were identified empirically from the literature [11][12][13][14][15][16][17][18][19][20][21][22][23]41] are:…”

“…The individual-level factors are the sex of the children (male versus female): to determine if the biological differences could explain susceptibility to SW; children age in years (under 1 year and 12-59 months): SW has been reported to differ by children ages; maternal education (none, primary or secondary plus): better education could lead to better access to information and enhance earnings, and reduced risk of SW; maternal age (15 to 24, 25 to 34, 35 to 49): younger mothers may have limited education and earnings and thereby increase risk of SW among their children. Others are marital status (never, currently and formerly married): currently married may have spousal support that may reduce the risk of SW; occupation (currently employed or not): capability of providing necessary nutritional intakes; access to media (at least one of radio, television or newspaper): access to information could enhance prevention of SW; sources of drinking water (improved or unimproved), toilet type (improved or unimproved), weight at birth (average+, small and very small), birth interval (firstborn, <36 months and >36 months): children with short birth interval are at higher risk of SW and may have higher experience of wealth-related inequality in SW; and birth order (1, 2, 3 and 4+), children with high birth order are at higher risk of SW and experience higher wealth-related inequality in SW [11][12][13][14][15][16][17][18][19][20][21][22][23]41].…”

“…We considered neighbourhood socioeconomic disadvantage as a neighbourhood-level variable in this study. Neighbourhood socioeconomic disadvantage was operationalized with a principal component comprised of the proportion of respondents without education (poor), unemployed, living in rural areas, and living below the poverty level [11][12][13][14][15][16][17][18][19][20][21][22][23]41].…”

“…It has been associated with myriads of interconnected factors across several individual-, household-and community-levels [11][12][13][14][15][16][17][18][19][20]. According to the literature, household food security, adequacy of health care and feeding, environmental sanitation, maternal education, parental employment status, and media access are some of the risk factors of malnutrition among children [11,12,[21][22][23][13][14][15][16][17][18][19][20]. These factors are all connected to household wealth status.…”

A Bivariate Analysis of the Spatial Distributions of Stunting and Wasting Among Children Under-Five in Nigeria

“…A lot of research efforts have gone into understanding possible determinants of childhood undernutrition especially in Nigeria. In their contributions, Fagbamigbe et al (2020) consider a decomposition of educational inequalities in the possible factors associated with severe acute malnutrition in low and middle-income countries including Nigeria. Findings from the study reveal that most of the countries have high prevalence of severe acute malnutrition and there is high proilliterate inequality in the malnutrition indicator such that countries with wider educational gaps among women experience high severe acute malnutrition.…”