Public Health Nutrition


Maternal–child overweight/obesity and undernutrition in Kenya: a geographic analysis

Lisa R Pawloskia1 c1, Kevin M Curtina2, Constance Gewaa1 and David Attawaya2

a1 Department of Nutrition and Food Studies (MSN 1F8), George Mason University, 4400 University Drive, Fairfax, VA 22030, USA

a2 Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA


The purpose of the study was to examine geographic relationships of nutritional status (BMI), including underweight, overweight and obesity, among Kenyan mothers and children.

Spatial relationships were examined concerning BMI of the mothers and BMI-for-age percentiles of their children. These included spatial statistical measures of the clustering of segments of the population, in addition to inspection of co-location of significant clusters.

Rural and urban areas of Kenya, including the cities of Nairobi and Mombasa, and the Kisumu region.

Mother–child pairs from Demographic and Health Survey data including 1541 observations in 2003 and 1592 observations in 2009. These mother–child pairs were organized into 399 locational clusters.

There is extremely strong evidence that high BMI values exhibit strong spatial clustering. There were co-locations of overweight mothers and overweight children only in the Nairobi region, while both underweight mothers and children tended to cluster in rural areas. In Mombasa clusters of overweight mothers were associated with normal-weight children, while in the Kisumu region clusters of overweight children were associated with normal-weight mothers.

These findings show there is geographic variability as well as some defined patterns concerning the distribution of malnutrition among mothers and children in Kenya, and suggest the need for further geographic analyses concerning the potential factors which influence nutritional status in this population. In addition, the methods used in this research may be easily applied to other Demographic and Health Survey data in order to begin to understand the geographic determinants of health in low-income countries.

(Received January 25 2011)

(Accepted January 03 2012)

(Online publication March 14 2012)


  • Obesity;
  • Clustering;
  • Spatial analysis;
  • Kenya;
  • Geographic information systems


c1 Corresponding author: Email