Consistent with the limited previous research investigating the relationship between neighborhood characteristics and BMI (Robert and Reither, 2004
; Morenoff et al., 2006
), we find that there is significant variation in BMI at both individual- and neighborhood-levels—although no significant variation is found at the county-level.18
While most of the variation in BMI is attributable to variation within neighborhoods, there is a significant correlation between neighborhood context and BMI, net of individual-level socioeconomic adjustments. In general, adjusting for neighborhood context resulted in a modest to moderate reduction of the observed ethnic disparity in BMI for men, with less consistent results for women. Significant differences in BMI across ethnicity persisted after adjustments for neighborhood context for both genders.
Our examination into whether there are racial differences in the association between neighborhood context and BMI reveals that the strength and pattern of the association do vary by ethnicity. A significant relationship between neighborhood context and BMI is found to be most consistent for Mexican-Americans. The only neighborhood characteristic examined that did not have a significant association to BMI for Mexican-Americans was proportion Black in a neighborhood for Mexican-American males. However, with the simultaneous inclusion of multiple neighborhood-level controls, only education ICE for Mexican-American females remained significant. The increase in BMI for Mexican-Americans associated with an increase in proportion of Hispanics in a neighborhood is somewhat surprising (Model 5A and 5B), given the literature on the salutatory health effects of ethnic enclaves. However, one anomalous trend to the pattern of advantaged health among immigrants has been the trend towards Type-II diabetes and obesity among Hispanic immigrants that may be the result of the adoption of American lifestyles in combination with childhood deprivation and dietary patterns. Though additional analyses (results not shown) allowing for differential effects between U.S. born and foreign born Hispanics did not change the direction of the association. One potential explanation for the direction of the relationship may be that because of the higher prevalence of obesity among Hispanics – particularly among Mexican-Americans (Thom et al., 2006
) – being overweight may be viewed as a cultural norm and more socially acceptable in areas of high Hispanic concentration, leading to higher rates of obesity in those areas. Although this social model has been hypothesized for Blacks (Boardman et al., 2005
; Robert and Reither, 2004
), our results suggest that this psychosocial interaction may also extend to Hispanics as well.
With respect to gender differences, our findings of stronger neighborhood associations for females than males are consent with previous literature on BMI and on other health outcomes (e.g. Cubbin et al., 2001
; Stafford et al., 2005
; Robert and Reither, 2004
). Several potential mechanisms for the differential relationship have been tested. For example, Cohen et al. (2006)
found that men and women use local parks in Los Angeles differently; men are more likely to engage in physical activity in parks, whereas women are more likely to sit. Morenoff and Sampson (1997)
report that land use types and street connectivity have differential impacts on walking for men and women in Chicago. Some researchers hypothesize that the neighborhood environment may be more important for women than for men because women traditionally spend more time in the home and are thus exposed to the neighborhood for a greater amount of time (Robert, 1999
). The general persistence of education ICE to remain statistically significant for women, but not for men, also suggests that there is a stronger connection between the neighborhood educational capital for women then men. Perhaps women are more likely to use informal networking to obtain and share information regarding health and health risks than men. In sum, women may be more reliant on neighborhood resources or men are more influenced by extra-residential factors, or both. However, other studies have found stronger associations between composite measures of neighborhood disadvantage and mortality among men than among women (Sundquist et al., 2004
; Nordstrom et al., 2004
). Further exploration is required to ascertain mechanisms driving these differences.
In addition, simultaneously adjusting for multiple neighborhood contexts resulted in an increase in observed ethnic disparity for women, with a reduction for men. This is the opposite pattern to results from Morenoff et al.'s (2006)
study which observed a reduction in ethnic BMI disparity for women and an increase for men with the inclusion of multiple neighborhood-level adjustments. Robert and Reither (2004)
also observed a reduction in ethnic BMI disparity for women. This suggests a complex web of relationships between neighborhood characteristics, ethnicity, and BMI which may be particularly sensitive to the specific combination of neighborhood contexts that is being investigated.
Our study suggests that neighborhood factors work through both indirect (individual-level covariates) and direct pathways to influence the body mass of residents. Sensitivity analyses – results not presented – with different outcome specifications (e.g. log bmi, binary obesity, binary overweight) yielded the same pattern of findings; further, model diagnostics calculating Cook's Distances did not reveal any potential outliers that would greatly influence model results.19
As such, the significance and pattern of results suggest that consideration of contextual risk factors, as well as individual-level characteristics, may be important in combating the obesity epidemic.
Although this study takes critical steps towards understanding the intersection between ethnicity, BMI, and space, it is not without its limitations. As previously mentioned, the NHANES III, with its over-sampling of Blacks and Hispanics, is especially well-suited to investigating the correlates of racial disparities in BMI. Although the NHANES III is a nationally representative sample of the U.S. population, missing geocode identifiers restricted our analysis sample which resulted in an under-representation of individuals residing in more rural areas. The importance of neighborhood characteristics may vary by degree of residential density and our findings may not be generalizable to more rural areas. It should also be noted that inferences from this study may also not be generalizable to other developed countries. In a recent review, Cummins and Macintyre (2006)
concluded that, although there is consistent evidence for a relationship between neighborhood context and diet and obesity in the U.S., there is much weaker (and inconsistent) evidence for a relationship outside the United States.
Second, causal interpretations of our findings should be tempered. The inclusion of neighborhood-level measures may serve to reduce bias in the ethnicity coefficients, as it may capture additional compositional differences across areas; however, neighborhood estimates may be biased if the measures are correlated with omitted characteristics at the individual-level (e.g. preferences for physical activity) that can affect health. In addition to bias due to omission of individual characteristics, poorly measured and/or poorly specified individual-level factors that were included in the models, may also yield biased neighborhood estimates.
Moreover, the cross-sectional nature of our study precludes any causal inferences for neighborhood effects. Conducting randomized control studies, such as the MTO, are likely the most powerful strategy to recover causal estimates. However, short of costly large-scale experimental studies, survey data with multiple observations across individuals would better lend itself to causal modeling strategies (e.g. fixed-effect models). In addition, because temporal fluctuations in neighborhood context have been found to vary across ethnicity (Quillian, 2003
; Timberlake, 2003
; Do, 2006
), longitudinal data that provide information on the length of exposure to neighborhood context may be especially valuable when investigating the causes and correlates of racial health disparity and teasing out the sources for the differences in neighborhood impact on BMI across ethnicity.
Lastly, census measures are used to characterize neighborhood conditions and resources (e.g. public exercise facilities) and are only rough proxies for theoretically causal neighborhood conditions and processes (e.g. grocery store quality, presence of parks, and walk-ability). The reliance on census data precludes us from testing potential mechanisms and mediators through which neighborhood disadvantage and racial composition affect BMI. Although the study was not designed to investigate mediators, the MTO experiment, for example, found that the experimental group reported consuming significantly higher amounts of fruits and vegetables versus controls at follow-up (Kling et al., 2004
). The change in diet may be a possible contributing factor in the decreased prevalence of obesity in the experimental group.
The finding that census-derived neighborhood measures only account for a small portion of observed ethnic disparities suggests that research on more specific and proximate neighborhood conditions/processes is warranted. Clearly, the high levels of segregation observed in these and other data, and the subsequent disproportionate allocation of toxic environments across social space, may tell us much about disparities in obesity and health—as evidenced by the ability of our rough proxies to explain even a small portion of these disparities. However, specific policy implications resulting from this study are unclear as our global measures of neighborhood disadvantage and racial segregation limit our understanding of the causal mechanisms underlying the connection between neighborhood context and BMI. Future research that examines possible causal mechanisms such as diet, exercise, and availability of exercise facilities, are needed to form policy recommendations and interventions.