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

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.


Events per 100 observations
Prevalence of wasting (%)

Equation for random-effects analysis using the method of DerSimonian and Laird 3
Suppose that there are studies/surveys. The estimated treatment effect for a binary response variable (logarithm of the odds ratio) in the ℎ study, = 1,2, … , , is . The estimated variance of in the ℎ study is 2 . The weight for the estimated treatment effect in the ℎ study in the fixed-effects model is = 1 2 ⁄ . The overall weighted treatment effect in the mixed-effects model is is the observed effect in the ℎ study 2.
is the pooled population parameter of interest (natural logarithm of the population odds ratio) 3.
is the random error term for the ℎ study 4.
It is assumed that 1 , 2 , … , are independent random variables with ~ (0, 2 ), = 1,2, … , . The variance term 2 reflects intra-study variability and its estimate is 2 . Usually, and 2 (or ) are provided as descriptive statistics in the ℎ study report. The overall weighted treatment effect in the mixed -effects model is