3. Results: Environmental Indicators, Geographic Categories, and County Obesity Rates
3.1. Regression Using Geographic Categories Only
shows the parameter estimates for Regression 1 (Geographic Categories Only). This regression controls for interdependency between geographic categories, so not all contrasts apparent in Table 1
, Table 2
and Table 3
are significant when expressed in terms of regression coefficients. Significant variations for obesity are associated with particular regions: higher obesity rates in the East South Central states for all persons, males and females; and higher male obesity also in West North Central, Middle Atlantic, and West South Central states. There is also a significant poverty gradient apparent for all persons obesity. The obesity excess in extreme poverty areas is higher for females, as is the excess for black majority counties. There is also a marked obesity excess in counties where other ethnic groups are in the majority, most apparently for female obesity [49
]. These are counties with American Indian or Alaska native majorities.
For males, excess obesity is also linked to NCHS urban category, including higher obesity in Categories 4, 6, and 8, namely towns adjacent to metropolitan areas. Variations such as this may be linked to differences in active commuting patterns, access to exercise opportunities, and so on, and may be reduced to insignificance when environmental factors are controlled for.
3.2. Extended Regression
shows the results of the extended regression, namely Regression 2 (Geographic Categories and Environmental Indicators), including the seven environmental indicators. To enable comparison of the relative impacts of the indicators on obesity, they are all converted to a [0, 1] scale, (x − min(x))/range(x).
Comparison of Regression 2 against Regression 1 shows the following features: (a) considerably increased levels of explained variation when environmental indicators are included, for example, from 38% to 62% for female obesity; (b) a marked reduction in variation linked to area poverty status; (c) a diminution in the effects of county majority ethnic group; (d) main effect urban-rural and regional categories mostly have insignificant effects in Table 5
(i.e., the 95% credible intervals include zero); (e) considerable reduction in variances associated with interactions between geographic categories; and (f) reductions in the proportion of residual variation that are spatially structured/clustered.
As an exception to the generally reduced significance for regional effects in (d), the Mountain Census division (Idaho, Montana, Wyoming Arizona, Colorado, Nevada, New Mexico, Utah) emerges as having significantly lower obesity after environmental influences are allowed for. Obesity in the East South Central states is no longer significant after allowing for environmental indicators, though for males a significant excess obesity in the West South Central states remains.
Regarding the relative impacts of the environmental indicators, these are mostly significant. Among the highest impacts are the positive effects on obesity of inactivity, with a smaller negative effect of exercise access. There is also a high negative impact on obesity of the concentration score, which is a negative index of settlement dispersal and car dependence. Relative to measures of activity and settlement patterns, impacts of the food environment are smaller, but still mostly significant, with effects in the expected direction, and with impacts tending to be stronger for females. The food environment index (which ranges from 0, for worst environments, to 10, for best environments) has an expected negative effect, and has the strongest impact of the four food environment indicators among females, and also among all persons.
Features (a) to (f) noted in Section 3.2
are important as they show clearly how far geographic differences in obesity can to a large degree be explained by indicators of activity and food environments, and of settlement and commuting patterns. Feature (d) is important since many studies of US obesity differences note major regional contrasts [63
], and contrasts according to urban-rural status [65
]; for example [63
] refer to “pronounced regional concentrations of obesity prevalence”. Feature (b) is also important, since there are wide obesity contrasts according to area poverty level (see Figure 2
Both regional contrasts and poverty effects lose much of their relevance in explaining area obesity differences (in regression terms) when indicators of food and activity environment, and of settlement and commuting patterns, are used as predictors. This does not mean that regional contrasts or poverty gradients in obesity are unimportant per se, but that their regression effects on obesity operates mainly via environmental differences, and direct effects of poverty and region are much diminished once environment is allowed for. Alternatively expressed, the major part of the poverty effect on obesity seems to be explained by the disproportionate exposure to, and burden of, obesogenic environments (i.e., the environmental injustice) experienced by low-income populations, and the impacts of these environments on behavior [67
]. For example, inactivity rates of 28.4% in the 20% of counties with highest poverty rates contrast with rates of 21.6% in the lowest poverty quintile, so allowing for inactivity diminishes the direct poverty effect on obesity rates. Similarly, the major part of the regional effects on obesity seems to be explained by the disproportionate exposure to obesogenic environments in certain regions. Thus [64
] mention that “spatial clusters of both higher and lower obesity levels [..] are indicative of regional variations in obesogenic environments and associated risk factors”.
The last feature, (f), reflects the fact that control for environmental influences has accounted for much of the spatial clustering apparent in obesity maps. Spatial clustering in residuals occurs when relevant area predictors, which themselves tend to be geographically clustered, have not been controlled for. Some of these factors are observed, and once they are controlled for, spatial patterning of residuals is reduced. Remaining spatial clustering can be attributed to unknown or unmeasurable risk factors, which also tend to vary smoothly in space [16
Regarding the relative impacts of the environmental indicators, effects of both activity indicators are significant. Inactivity effects on obesity are pronounced, and greater for females, this being one source of the overall higher level of explained variation (62%) for females as compared with males (50%). Continuing the environmental justice theme, health-promoting recreation resources may be inequitably distributed across sociodemographic groups, and this is one source of activity differences [68
]. Other area factors (crime rates, perceived neighborhood safety, etc.) may also impact on activity levels [69
In Table 5
, adequate exercise access has a significant negative effect on obesity, but a relatively smaller one compared with the inactivity effect. This may be because much of the impact of exercise access on obesity is mediated by its effect on activity rates (e.g., [70
]). Both inactivity and exercise access are correlated with poverty, namely a correlation of 0.48 (over US counties) between poverty and inactivity, and a correlation of −0.38 between exercise access and poverty. So their inclusion in the regression contributes to explaining the much reduced direct poverty effect apparent in Table 5
. Inactivity rates also vary according to majority ethnic group, being higher in majority black counties (around 30% as compared with the all counties rate of 25%); this may partly account for diminution in the effects of county majority ethnic group in Table 5
as compared with Table 4
There is also a high negative impact on obesity of the concentration score, which is a negative index of settlement dispersal and car dependence. Hence, areas with positive scores (typically metropolitan, higher density areas, with lower car commuting levels and higher active commuting) have lower obesity rates. This impact is greater for males, and is in fact stronger than the effect of inactivity rates, whereas the reverse is true for females. This may be linked to higher car commuting rates and longer car work trips among males [71
Differences in commuting patterns have not figured in the obesity literature as much as food and activity environments, or in the development of sprawl indices. For example, a recent study uses a multivariate factor method to derive a sprawl index without any reference to commuting patterns [72
]. Hence, a more general approach to settlement density and commuting patterns, as used in the present paper, may have explanatory value for area obesity studies.
Relative to measures of activity and settlement patterns, impacts of the food environment are smaller, but still mostly significant, with effects in the expected direction, and with impacts tending to be stronger for females. Table 5
shows the most important predictor among these measures is the Food Environment Index (FEI), a measure of food environment combining limited access to healthy foods with food insecurity. The impact of the FEI is much stronger for females, in line with survey based findings that food insecure adult women were more likely to be obese than food insecure males [73
]. The effects of the two variables measuring exposure to fast food are both positive, while the impact of groceries per head is negative. The impact of the percent of local restaurants that are fast food is greater for women. Subject to the caveat that the present study is ecological, these findings are in line with research showing impacts of food access and security on obesity are mediated by gender [73
A major area of ongoing environmental health research is focused on the effects of place on growing obesity levels [6
]. In this paper, aspects of urban settlement and commuting, healthy food availability, and exercise access have been represented by seven area level indicators. Their impacts on geographic differences in obesity are assessed by regression methods that control for inter-correlations between indicators, and for spatially correlated residuals. The environmental indicators have mostly significant impacts on obesity, account for most of the poverty and regional effects on obesity, provide major increases in the proportion of explained variability, and account for much of the spatial clustering evident in obesity maps. The importance of activity levels/exercise access is supported, and also of settlement patterns (e.g., as expressed in residential density and car dependence). These conclusions have the advantage of being based on county data covering the entire US.
Limitations are, however, present. While the CDC county-level estimates data provide an entire national perspective they are subject to possible biases associated with the BRFSS, namely reliance on self-reported health status, and exclusion of households without phones. Another limitation concerns the available indicators of the environment [9
]: for example, there is a direct measure available of inactivity levels at US county level, but not a direct measure of dietary quality, such as proportions eating five daily servings of fruit and vegetables. This may affect conclusions regarding the relative importance of activity and food environments. There is also a caveat regarding ecological studies, that one cannot make conclusions about individuals from an analysis of aggregate-level data [75
As possible directions for further research, one may mention the need to extend the range of available measures of the food and activity environments, such as a direct county measure of dietary quality. It may also be useful to carry out regression analysis considering both obesity and related diseases (e.g., diabetes), to assess how far environmental impacts on these diseases are direct, or mediated by their impacts on obesity [76
]. Geographical studies also have utility in indicating potential directions for population-based prevention and intervention, that is, not primarily targeted at the individual level or at particular high-risk groups. Such studies may be used to develop area profiles to identify communities where interventions, especially environmental interventions [77
], may be most relevant. In particular, such interventions may include promotion of healthy eating environments, and promoting equity in physical activity, in line with a strategy to promote environmental justice with regard to obesity [67