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Neighbourhood food environments and body mass index among New York City adults
  1. James H Stark1,
  2. Kathryn Neckerman2,
  3. Gina S Lovasi3,
  4. Kevin Konty1,
  5. James Quinn3,
  6. Peter Arno4,
  7. Deborah Viola4,
  8. Tiffany G Harris1,
  9. Christopher C Weiss5,
  10. Michael D M Bader6,
  11. Andrew Rundle3
  1. 1Division of Epidemiology, New York City Department of Health and Mental Hygiene, New York, New York, USA
  2. 2Columbia Population Research Center, Columbia University, New York, NY, USA
  3. 3Department of Epidemiology, Mailman School of Public Health Columbia University, New York, New York, USA
  4. 4Department of Health Policy and Management, School of Health Sciences and Practice, New York Medical College, Valhalla, New York, USA
  5. 5Department of Sociology, New York University, New York, NY, USA
  6. 6Department of Sociology, Center on Health, Risk and Society, American University, Washington, District of Columbia, USA
  1. Correspondence to Dr Andrew Rundle, Department of Epidemiology, Mailman School of Public Health Columbia University, 722 West 168th Street, Rm 730, New York, NY 10032, USA; agr3{at}columbia.edu

Abstract

Background Studies evaluating the impact of the neighbourhood food environment on obesity have summarised the density or proximity of individual food outlets. Though informative, there is a need to consider the role of the entire food environment; however, few measures of whole system attributes have been developed. New variables measuring the food environment were derived and used to study the association with body mass index (BMI).

Methods Individual data on BMI and sociodemographic characteristics were collected from 48 482 respondents of the 2002–2006 community health survey in New York City and linked to residential zip code-level characteristics. The food environment of each zip code was described in terms of the diversity of outlets (number of types of outlets present in a zip code), the density of outlets (outlets/km2) and the proportion of outlets classified as BMI-unhealthy (eg, fast food, bodegas).

Results Results of the cross-sectional, multilevel analyses revealed an inverse association between BMI and food outlet density (−0.32 BMI units across the IQR, 95% CI −0.45 to −0.20), a positive association between BMI and the proportion of BMI-unhealthy food outlets (0.26 BMI units per IQR, 95% CI 0.09 to 0.43) and no association with outlet diversity. The association between BMI and the proportion of BMI-unhealthy food outlets was stronger in lower (<median for % poverty) poverty zip codes than in high-poverty zip codes.

Conclusions These results support a more nuanced assessment of the impact of the food environment and its association with obesity.

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