Effect of Individual or Neighborhood Disadvantage on the Association Between Neighborhood Walkability and Body Mass Index

Objectives. We sought to test whether the association between walkable environments and lower body mass index (BMI) was stronger within disadvantaged groups that may be particularly sensitive to environmental constraints.Methods. We measured height and weight in a diverse sample of 13 102 adults living throughout New York City from 2000–2002. Each participant''s home address was geocoded and surrounded by a circular buffer with a 1-km radius. The composition and built environment characteristics of these areas were used to predict BMI through the use of generalized estimating equations. Indicators of individual or area disadvantage included low educational attainment, low household income, Black race, and Hispanic ethnicity.Results. Higher population density, more mixed land use, and greater transit access were most consistently associated with a lower BMI among those with more education or higher incomes and among non-Hispanic Whites. Significant interactions were observed for education, income, race, and ethnicity.Conclusions. Contrary to expectations, built environment characteristics were less consistently associated with BMI among disadvantaged groups. This pattern may be explained by other barriers to maintaining a healthy weight encountered by disadvantaged groups.Urban environments have recently been studied for their effects on diet, physical activity, obesity, and obesity-related health conditions.^1–4 Metropolitan areas and counties characterized as compact, rather than sprawling, may facilitate walking for transportation and may curb obesity.^5,6 Yet, metropolitan areas are far from homogeneous, and neighborhood-level measures of the built environment have also been associated with obesity.¹ Within New York City, for example, higher population density, more mixed land uses, and access to public transit near the home were associated with lower body mass index (BMI).⁷Neighborhood socioeconomic disadvantage and racial or ethnic composition have also been examined as predictors of obesity⁸ and as potential confounders in studies of other environmental features. Research on the built environment and obesity has recently begun to consider whether population characteristics might modify the effect of built environment characteristics on obesity and related behaviors.^9–11 Speculation as to which populations would be most sensitive to the obesity-related effects of the built environment has yielded competing hypotheses. Several authors have posited that poor or disadvantaged groups would be most likely to respond to their residential environment, lacking means to go elsewhere.^1,8 On the other hand, some disadvantaged individuals may be “captive walkers” who rely on walking for transportation,¹² thus being unable to respond to the local environment by retreating into their vehicles. Meanwhile, the often discussed problem of “self-selection” could bias observational studies of neighborhoods and health¹³ and could imply that relatively affluent and advantaged individuals will have the strongest (albeit noncausal) associations between their neighborhood environment and obesity because more resources facilitate the selection of an environment to fit one''s preferred lifestyle. Finally, measurement error or unmeasured environmental characteristics may differ depending on the characteristics of the population studied. Ostensibly “walkable” neighborhoods may be less safe or attractive in more deprived areas.^14–17 Also, the use of paid gyms and the norms related to food or activity may differ by population,¹⁸ and the culturally mediated response to environmental features may differ as well.^19,20We report stratified analyses from a large, diverse population in New York City to shed light on these ideas. We a priori identified 4 population characteristics that identify disadvantaged individuals and neighborhoods: low educational attainment, low household income, Black race, and Hispanic ethnicity. Competing hypotheses from the published literature predict stronger or weaker associations for disadvantaged populations.