Stunting and Anemia in Children from Urban Poor Environments in 28 Low and Middle-income Countries: A Meta-analysis of Demographic and Health Survey Data

Nutrients. 2020 Nov 18;12(11):3539. doi: 10.3390/nu12113539.

Abstract

Child malnutrition remains a global concern with implications not only for children's health and cognitive function, but also for countries' economic growth. Recent reports suggest that global nutrition targets will not be met by 2025. Large gaps are evident between and within countries. One of the largest disparities in child malnutrition within counties is between urban and rural children. Large disparities also exist in urban areas that have higher rates of child malnutrition in the urban poor areas or slums. This paper examines stunting and anemia related to an urban poverty measure in children under age 5 in 28 low and middle-income countries with Demographic and Health Survey data. We used the United Nations Human Settlements Programme (UN-HABITAT) definition to define urban poor areas as a proxy for slums. The results show that in several countries, children had a higher risk of stunting and anemia in urban poor areas compared to children in urban non-poor areas. In some countries, this risk was similar to the risk between the rural and urban non-poor. Tests of heterogeneity showed that these results were not homogeneous across countries. These results help to identify areas of greater disadvantage and the required interventions for stunting and anemia.

Keywords: Demographic and Health Surveys (DHS); anemia; child malnutrition; meta-analysis; stunting; urban poor; urbanicity; urbanization; urban–rural residence.

Publication types

  • Meta-Analysis

MeSH terms

  • Anemia / epidemiology*
  • Child Nutrition Disorders / epidemiology*
  • Child, Preschool
  • Comorbidity
  • Developing Countries / statistics & numerical data*
  • Female
  • Growth Disorders / epidemiology*
  • Health Surveys / statistics & numerical data*
  • Humans
  • Infant
  • Male
  • Poverty / statistics & numerical data*
  • Urban Population / statistics & numerical data