Obesity and the built environment among Massachusetts children

Clin Pediatr (Phila). 2009 Nov;48(9):904-12. doi: 10.1177/0009922809336073. Epub 2009 Jun 1.

Abstract

Background: The built environment may influence weight status.

Method: Using cross-sectional data for children aged 2 to 18 years, the authors linked clinical and spatial data using geographic information systems and analyzed for associations between body mass index (BMI) and density of and distance to nearest built environment variable (schools, sidewalks, subway stations, bicycle trails, open space, and fast-food restaurants) using bivariate and multilevel analyses.

Results: The study sampled 21 008 children; 54% were white, 26% Hispanic, 37% overweight, and 20% obese. In bivariate analysis, distance to nearest fast-food restaurant was inversely associated with BMI, whereas density of fast-food restaurants was positively associated with BMI. Distance to school and subway station, amount of open space, and density of subway stations were inversely associated with BMI. Controlling for sociodemographic factors, only living near a greater density of subway stations was inversely associated with overweight (odds ratio, 0.87; 95% confidence interval, 0.81-0.94) and obesity (odds ratio, 0.90; 95% confidence interval, 0.82-0.99).

Conclusion: Distance to nearest subway station is associated with BMI among Massachusetts children.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Age Factors
  • Body Mass Index*
  • Child
  • Child, Preschool
  • Confidence Intervals
  • Cross-Sectional Studies
  • Environment
  • Environment Design*
  • Exercise
  • Feeding Behavior
  • Female
  • Geographic Information Systems
  • Health Behavior
  • Humans
  • Male
  • Massachusetts / epidemiology
  • Obesity / epidemiology*
  • Obesity / etiology*
  • Obesity / physiopathology
  • Odds Ratio
  • Overweight / epidemiology
  • Overweight / etiology
  • Overweight / physiopathology
  • Probability
  • Residence Characteristics / statistics & numerical data*
  • Risk Factors
  • Sex Factors
  • Socioeconomic Factors