Mapping child growth failure in Africa between 2000 and 2015

Abstract

Insufficient growth during childhood is associated with poor health outcomes and an increased risk of death. Between 2000 and 2015, nearly all African countries demonstrated improvements for children under 5 years old for stunting, wasting, and underweight, the core components of child growth failure. Here we show that striking subnational heterogeneity in levels and trends of child growth remains. If current rates of progress are sustained, many areas of Africa will meet the World Health Organization Global Targets 2025 to improve maternal, infant and young child nutrition, but high levels of growth failure will persist across the Sahel. At these rates, much, if not all of the continent will fail to meet the Sustainable Development Goal target-to end malnutrition by 2030. Geospatial estimates of child growth failure provide a baseline for measuring progress as well as a precision public health platform to target interventions to those populations with the greatest need, in order to reduce health disparities and accelerate progress.

Spatial analysis made easy with linear regression and kernels

“…e theoretical properties of RFF estimators are still far from fully understood. e seminal paper by Rahimi and Recht [37] showed that every entry in our kernel matrix is approximated to an error of ± with m = log(N ) 2 Fourier features. A more recent result shows that only √ N log(N ) features can achieve the same learning bounds as full kernel ridge regression with squared loss [38].…”

“…Generally, the output, or response variable, is a variable measured at multiple geolocated points in space (e.g. case counts for a disease under investigation [1], anthropocentric indicators like height and weight [2,3], or socioeconomic indicators such as access to water or education [4,5]). Here, we shall consider response variables as y = (y 1 , y 1 , ..., y N ) ∈ R N , indicating that the metric of interest is a vector of length corresponding to the N locations.…”

Understanding the effects of dichotomization of continuous outcomes on geostatistical inference

“…In other cases, instead, dichotomization is first carried out on a continuous disease indicator variable and a geostatistical model is then developed on the binary outcome. For example, in Zimmerman et al [17], a continuous score quantifying the deviation from normal growth in children is dichotomized in order map stunting prevalence; following a similar approach, Magalhaes et al [18] fit a binomial geostatistical model to dichotomized continuous haemoglobin densities so as to map anaemia prevalence in West Africa.…”

Precision maps for public health

“…In the first paper (page 41), Osgood-Zimmerman et al 2 focused on childhood growth failure. They pooled geolocated information on growth stunting, muscle wasting and weight in children under the age of 5 from several surveys across tens of thousands of villages over 15 years.…”

“…Physician John Snow's map of cholera cases and public wells in London in the 1850s was an early example of this, famously revealing that cholera is spread through contaminated water 1 . Two papers 2,3 in Nature now provide high-resolution maps of health and education levels for children across Africa. Their maps will draw attention to areas most in need of support, and guide future interventions to where they can have the greatest impact.…”