Global, regional and national epidemiology and prevalence of child stunting, wasting and underweight in low- and middle-income countries, 2006-2018

Abstract

In 2016, undernutrition, as manifested in childhood stunting, wasting, and underweight were estimated to cause over 1.0 million deaths, 3.9% of years of life lost, and 3.8% of disability-adjusted life years globally. The objective of this study is to estimate the prevalence of undernutrition in low- and middle-income countries (LMICs) using the 2006-2018 cross-sectional nationally representative demographic and health surveys (DHS) data and to explore the sources of regional variations. Anthropometric measurements of children 0-59 months of age from DHS in 62 LMICs worldwide were used. Complete information was available for height-for-age (n = 624,734), weight-for-height (n = 625,230) and weight-for-age (n = 626,130). Random-effects models were fit to estimate the pooled prevalence of stunting, wasting, and underweight. Sources of heterogeneity in the prevalence estimates were explored through subgroup meta-analyses and meta-regression using generalized linear mixed-effects models. Human development index (a country-specific composite index based on life expectancy, literacy, access to education and per capita gross domestic product) and the United Nations region were explored as potential sources of variation in undernutrition. The overall prevalence was 29.1% (95% CI 26.7%, 31.6%) for stunting, 6.3% (95% CI 4.6%, 8.2%) for wasting, and 13.7% (95% CI 10.9%, 16.9%) for underweight. Subgroup analyses suggested that Western Africa, Southern Asia, and Southeastern Asia had a substantially higher estimated prevalence of undernutrition than global average estimates. In multivariable meta-regression, a combination of human development index and United Nations region (a proxy for geographical variation) explained 54%, 56%, and 66% of the variation in stunting, wasting, and underweight prevalence, respectively. Our findings demonstrate that regional, subregional, and country disparities in undernutrition remain, and the residual gaps to close towards achieving the second sustainable development goal-ending undernutrition by 2030.

Figure 1

Prevalence of undernutrition. Countries are shaded according to prevalence (%) of stunting (top row), wasting (middle row) and underweight (bottom row).

Figure 2

Forest plot of stunting prevalence by UN subregions of LMICs: event values represent the number of cases of stunting expressed as a percentage. Blue squares and their corresponding lines are the point estimates and 95% confidence intervals (95% CI). Maroon diamonds represent the pooled estimate of the prevalence for each subgroup (width denotes 95% CI). Weights are from the random-effects meta-analysis model described by DerSimonian and Laird (p for interaction comparing the different subgroups < 0.0001).

Figure 3

Forest plot of wasting prevalence by UN subregions of LMICs: event values represent the number of cases of wasting expressed as a percentage. Blue squares and their corresponding lines are the point estimates and 95% confidence intervals (95% CI). Maroon diamonds represent the pooled estimate of the prevalence for each subgroup (width denotes 95% CI). Weights are from the random-effects meta-analysis model described by DerSimonian and Laird (p for interaction comparing the different subgroups < 0.0001).

Figure 4

Forest plot of underweight prevalence by UN subregions of LMICs: event values represent the number of cases of underweight expressed as a percentage. Blue squares and their corresponding lines are the point estimates and 95% confidence intervals (95% CI). Maroon diamonds represent the pooled estimate of the prevalence for each subgroup (width denotes 95% CI). Weights are from the random-effects meta-analysis model described by DerSimonian and Laird (p for interaction comparing the different subgroups < 0.0001).

Figure 5

Correlation of HDI with undernutrition: A moderate negative correlation exists between HDI and stunting (Spearman’s rho; − 0.65, p value < 0.0001, first column), wasting (Spearman’s rho; − 0.43, p value = 0.0006, second column) and underweight (Spearman’s rho; − 0.67, p value < 0.0001, third column).