This is one of the first studies to map the spatial distribution of obesity rates at a fine geographic scale that is more detailed than the state (Holtgrave & Crosby, 2006
; Mokdad, Bowman, Ford, Vinicor, Marks & Koplan, 2001
; Mokdad et al., 2003
), Metropolitan Statistical Area (Ford, Mokdad, Giles, Galuska & Serdula, 2005
) or county level (Ewing, Schmid, Killingsworth, Zlot & Raudenbush, 2003
). The New York City Community Health Survey, mapped the prevalence of obesity at the United Hospital Fund neighborhood level, an aggregate of respondents’ ZIP codes (New York City Department of Health and Mental Hygiene, 2006
The observed disparities in obesity rates across King County ZIP code areas suggest a strong relationship between obesity rates and some area-based indices of SES. Those geographic disparities were much higher than those traditionally ascribed to race or ethnicity. Analyses of local 2000–2005 BRFSS data indicate that obesity rates among African-Americans in King County (26.5%) were 63% higher than among whites (16.3%) (Public Health - Seattle & King County, 2005
). Obesity rates among persons with incomes <$15,000 were 20.0% as compared to 15.1% among persons with incomes >$50,000, a 32% difference (Public Health - Seattle & King County, 2005
). At the national level, Hispanics are 1.2 times more likely to be obese than are whites (23.7% compared to 19.6%) (Mokdad et al., 2001
). Nearly three-fold differences in obesity rates, based on geographic location, have not been reported in the literature.
Traditionally, race/ethnicity as well as education and incomes have been the focus of health disparities research (Isaacs & Schroeder, 2004
). Given recent concerns that individual education and incomes may not adequately reflect social class (Marmot, 2000
), there is a growing emphasis on the roles of occupation, social capital, and social context, all difficult parameters to capture in epidemiologic studies. Area-based measures of SES can provide additional information on poverty and wealth that is only rarely collected in United States health surveys (Krieger, Chen, Waterman, Rehkopf & Subramanian, 2003
). In past studies, percent of residents living below the federal poverty level, based on census data, was identified as the best predictor of health outcomes (Krieger et al., 2003
); whereas the present analyses point to median house values, a shorthand measure of wealth, as a strong predictor of obesity rates. The present findings are thus wholly consistent with past research on social determinants of health (Marmot, 2000
The present study is subject to some serious limitations. First, heights and weights, used to calculate BMI values, were based on telephone self-report. Both men and women under-report weight, and men may over-report height in telephone surveys (Nawaz, Chan, Abdulrahman, Larson & Katz, 2001
). However, the same BRFSS data are said to provide the best picture of the obesity epidemic in the U.S. and are the basis for national policy decisions (Mokdad et al., 2001
; Mokdad et al., 2003
). Second, our exclusive use of aggregate measures of SES and health outcomes does not allow us to generalize the effect of SES on obesity risk among individuals.
In addition, the standard CDC cut-points for obesity may not be appropriate for 18–19 year olds. We did not adjust BMIs for 18–19 year olds because approximately 1.5% of the sample was 18–19 year olds (n=140), and failure to adjust BMIs in the present study would not appreciably bias the results. While the current study evaluated the prevalence of obesity by area, an alternative approach would map mean BMI per area. Such an analysis would reduce potential bias due to misclassification of individuals as obese or not obese; however, data on mean BMI per ZIP code area was not available.
Perhaps more important is the issue that the ZIP code area is a problematic scale for spatial analysis. Because population counts per ZIP code area can vary widely, many ZIP code areas were too small to provide area-based prevalence estimates despite the use of an Empirical Bayes tool. Even for ZIP code areas with a large population, confidence intervals can be quite large as shown in . We were unable to analyze the prevalence of obesity at the census tract level because respondents were only asked to report their ZIP code. Another challenge associated with the use of ZIP code areas is that they are designed to efficiently deliver mail, and the boundaries change subtly on a regular basis (Krieger, Waterman, Chen, Soobader, Subramanian & Carson, 2002
Finally, obesity rates per ZIP code area were calculated with unweighted responses. Although the CDC provides weights for use in county- and state-level calculations, weights created for larger geographic areas are unlikely to adjust for non-response and differential probability of selection at the ZIP code level. Using unweighted data greatly simplified our analyses, since survey design effect for the BRFSS prevalence rates did not have to be taken into account when calculating the significance level of correlations and regression models. Potential sources of bias in state-specific BRFSS data include low response rates and non-response biases within certain demographic groups. Such biases may also occur at the neighborhood level, which might make the present population samples non representative. At state level, BRFSS demographics have been compared to other data sources to determine potential sources of response bias. Fewer external data sources are available in small area studies. This is an important caution, especially since the BRFSS design weights were not devised with small area studies in mind or used in the present analysis.
The present disparities at ZIP code area level stand in contrast to the well-known CDC maps, where the differences in obesity rates between the richer and the poorer states are only weakly apparent (Mokdad et al., 2001
). Whereas the CDC maps have been used to support the argument that obesity rates in the U.S. are unrelated to social class, the present data show – to the contrary - that the obesity problem is concentrated in the most disadvantaged areas.
Studying the geography of obesity will require new maps of the finest spatial and statistical precision. Maps of obesity at finer spatial scales, such as the census tract or ZIP code area scale are preferable to maps at the county or state level. Mapping disease rates by community and neighborhood may very well be the future of public health assessment and surveillance.