A roadmap to reduce stunting

Stunting, defined as a low height for age z-score (HAZ), begins in the prenatal environment leading to low birth weight and continues with growth faltering in the first 2 y of life, after which it is generally irreversible. Although stunting prevalence has decreased worldwide from 1990 to 2018, stunting continues to afflict 21.3% of children aged <5 y worldwide (1). The burden of stunting falls almost entirely on low-income countries as it is tied to poverty, an excess of childhood infections, and an inadequate diet. The World Health Assembly (2) and the UN’s Sustainable Development Goals (3) call for a 40% reduction in childhood stunting by 2025 with the ultimate goal of eradicating all childhood malnutrition. Although the destructive power of stunting is well known, the actions which will operationalize its reductions are not. A several part series on stunting in this issue of the American Journal of Clinical Nutrition (4–12) elucidates factors and policies that allow for a robust decline in stunting, by using a mixed method approach to identify how “exemplar countries” reduced stunting despite only modest improvements in economic growth. Stunting in childhood: an overview of global burden, trends, determinants, and drivers of decline (4) is a systematic review of 89 studies from which basic, underlying, and immediate determinants of stunting were identified. Basic determinants are: 1) an asset index of household income and 2) parental education, particularly maternal education. Underlying determinants are numerous: 1) sanitary disposal of stool, 2) clean water, 3) bed nets, 4) vaccination coverage, 5) attendance of antenatal clinic visits, 6) optimal breastfeeding practices, and 7) household food security. Immediate stunting determinants are: 1) reduction in fertility, 2) birth spacing, 3) maternal height, 4) infant birthweight, 5) dietary diversity, and 6) diarrhea incidence. In order to understand how these determinants affected the stunting reduction in exemplar countries, an in-depth analysis of quantitative and qualitative data was employed (5). After potential exemplar countries that had a rapid decline in stunting relative to their economic growth were identified, 5 countries were studied in-depth based on a minimum population threshold of 5 million people and representation of different global regions; Peru, the Kyrgyz Republic, Nepal, Senegal, and Ethiopia. A quantitative data analysis of each country’s Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), which are nationally conducted, standardized household surveys collecting health and nutrition data, was performed to discern changes in HAZ with respect to the basic, underlying, and immediate determinants. For each country, HAZ kernel density plots showing population level shifts in HAZ over time as well as changes in kurtosis, a measure of variability in the data, were prepared. Descriptive analyses evaluated inequality of HAZ changes by wealth quintile, geographic region, rural or urban setting, maternal education, and child gender. Victora curves, which plot predicted HAZ by child’s age in months for each country, determined the critical ages of growth faltering as well as the maternal effect on HAZ at birth. These data formed the basis of the multivariable analyses used to identify factors that predicted an improvement in stunting. Robust qualitative data from key stakeholders at the national, regional, and community levels were collected to gain insight into the programmatic changes and contextual factors associated with stunting reduction. These included surveys of community health workers and teachers and focus groups with mothers. This qualitative data helped to inform a timeline of key policies, programs, and practices that led to stunting reduction. The most important data in this stunting project are the anthropometric measurements, and thus great care was taken to assess their quality (6). A composite score was created based on the proportion of collected data with incomplete demographic information or anthropometric measurements, tendency for a digit to appear more often than by chance, large differences in HAZ by month indicative of bias in age reporting, and extreme/implausible values for HAZ and weight for height z-score (WHZ). The 5 countries used for the analyses had highquality anthropometric data, and this methodology is a tool by which other datasets can be judged. The next 5 articles of this series are in-depth country case studies for the 5 exemplar countries, Peru, the Kyrgyz Republic, Nepal, Senegal, and Ethiopia (7–11). Baseline stunting was 25– 66% and the reduction over 16–25 years was 15 to 30 percentage points (Table 1). Change in kurtosis in each HAZ kernel density plot was analyzed as a surrogate for changes in the distribution of stunting across the population (Table 1). A decrease in kurtosis suggests greater equity in stunting across determinant groups but could also be a marker of greater precision in data collection. Therefore, separate equity analyses were conducted to determine change in disparities among wealth quintile, education level,