What Community Factors Predict the Risk for Childhood Obesity?

Analysis of geographic patterns of childhood obesity

From December 2006 to January 2008, a research team at the Urban Institute in Washington analyzed the relationship between community characteristics and the prevalence of childhood obesity, and used the results to predict the risk for childhood obesity in states and communities across the country.

Among the community factors included in the analysis were racial/ethnic makeup, income and education levels, employment characteristics, family composition, housing stock and access to supermarkets.

The purpose was to increase understanding of childhood obesity at the state and local levels so that policy-makers and funders could more readily target resources to geographic areas where children are most at risk.

Key Results:

  • The Urban Institute produced a report, available on its website, with a series of maps and tables predicting the relative risk for childhood obesity in all 50 states and within communities across each state.

Key Findings: Among the report's findings:

  • Children ages 6 to 17 are estimated to be at above-average risk for obesity in 34 percent of the nation's census tracts and are at the highest risk for obesity in 6.5 percent of the tracts.
  • Communities where childhood obesity is predicted to be highest face disadvantages across multiple dimensions. For example, 40 percent of the children living in census tracts at the highest risk for childhood obesity were living in poor households, compared to 31 percent in tracts at above-average risk and less than 10 percent in tracts at average or below-average risk.
  • The community indicators that proved most influential in determining a child's probability of being obese were demographic characteristics, household structure and the education and English language proficiency of the population in the child's community.

Conclusions: In their report, the researchers conclude:

  • Strategies to address childhood obesity in communities at greatest risk will be most effective if they recognize the context of the underlying community problems and tailor programs to local circumstances.

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