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Spatial analysis of undernutrition of children in léogâne Commune, Haiti

Andrea L Spray1, Brittany Eddy2, James Aaron Hipp3

  • 1George Warren Brown School of Social Work, Washington University in St. Louis, St. Louis, Missouri, USA. andrealspray@gmail.com

Food and Nutrition Bulletin
|March 11, 2014
PubMed

Insights

Child malnutrition in Haiti is widespread. Geographically weighted regression revealed pockets of undernutrition, highlighting the need for localized interventions to address child health disparities.

Area of Science:

  • Public Health
  • Geospatial Analysis
  • Child Nutrition

Background:

  • Haiti faces severe child malnutrition, with over 21.9% of children under five chronically malnourished.
  • Léogâne Commune exhibits high under-five mortality rates, indicating critical health challenges.
  • Traditional regression methods may obscure localized variations in child undernutrition causes.

Purpose of the Study:

  • To characterize the nutrition and health status of children aged 6-35 months in Léogâne Commune, Haiti.
  • To apply geographically weighted regression (GWR) to understand spatial variations in child undernutrition.
  • To compare GWR with ordinary least squares (OLS) regression for analyzing undernutrition determinants.

Main Methods:

  • A representative cross-sectional household survey (N=150) was conducted in July 2008.
  • Data collection included caregiver questionnaires and anthropometric measurements for children 6-35 months.
  • Geographically weighted regression (GWR) was used to model spatial patterns of undernutrition (weight-for-age).

Main Results:

  • OLS regression residuals showed significant spatial autocorrelation (Moran's I = 0.08, p=.058), indicating localized undernutrition clusters.
  • Undernutrition was not evenly distributed, appearing in distinct pockets within the population.
  • GWR did not demonstrate improved performance over OLS regression in this specific analysis.

Conclusions:

  • Geospatial data analysis, including GWR, offers a valuable approach to understanding regional variations in child nutrition.
  • This methodology can provide deeper insights into the localized causes of undernutrition.
  • Despite limitations, the study demonstrates the potential of geospatial techniques for targeted public health interventions.
Abstract

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