Central Data Catalog

Citation Information

Type Journal Article - DHS WP
Title Factors Associated with Stunting in Children under Age 2 in the Cambodia and Kenya 2014 Demographic and Health Surveys
Issue 126
Publication (Day/Month/Year) 2016
URL https://dhsprogram.com/pubs/pdf/WP126/WP126.pdf
Background: This study examined the relationships between child, maternal, household, and gender inequality characteristics and child stunting in Kenya and Cambodia. Globally, an estimated 171 million children are stunted, including 167 million in low- and middle-income countries, with especially high prevalence levels in Africa and Asia. Child stunting reflects chronic undernutrition, which often begins before birth and is almost irreversible after the second year of life. Methods: The study analyzed data from the 2014 Demographic and Health Surveys (DHSsurveys) in Kenya and Cambodia for children under age 2. Bivariate and logistic regression analyses were performed to find associations between the variables and child stunting. Results: The prevalence of stunting among children under age 2 in Kenya was 22%, and in Cambodia, 25%. Child’s age, perceived birth size, family wealth status, and region of residence were significantly associated with stunting. In both countries children from the richest households had 0.4 times lower odds of being stunted compared with those from the poorest households. In Kenya alone, female children had 0.6 times lower odds of being stunted compared with male children. In Cambodia alone, children from rural areas had 0.6 times lower odds of being stunted compared with those from urban areas, while children whose mothers were underweight had 1.7 times higher odds of being stunted than children whose mothers were not underweight. In both countries, there was general lack of a strong and significant relationship between gender inequality and child stunting. Conclusions: Children’s characteristics were more important in predicting stunting than factors related to mothers, households, or gender. More extensive analysis of the DHS data should be done to include other aspects of gender inequality, such as decisions on choice and preparation of food and purchase of household goods.

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