Disparities in obesity among rural and urban residents in a health disparate region

Abstract

Background: The burden of obesity and obesity-related conditions is not borne equally and disparities in prevalence are well documented for low-income, minority and rural adults in the United States. The current literature on rural versus urban disparities is largely derived from national surveillance data which may not reflect regional nuances. There is little practical research that supports the reality of local service providers such as county health departments that may serve both urban and rural residents in a given area. Conducted through a community-academic partnership, the primary aim of this study is to quantify the current levels of obesity (BMI), fruit and vegetable (FV) intake and physical activity (PA) in a predominately rural health disparate region. Secondary aims are to determine if a gradient exists within the region in which rural residents have poorer outcomes on these indicators compared to urban residents.

Methods: Conducted as part of a larger ongoing community-based participatory research (CBPR) initiative, data were gathered through a random digit dial telephone survey using previously validated measures (n = 784). Linear, logistic and quantile regression models are used to determine if residency (i.e. rural, urban) predicts outcomes of FV intake, PA and BMI.

Results: The majority (72%) of respondents were overweight (BMI = 29 ± 6 kg/m2), with 29% being obese. Only 9% of residents met recommendations for FV intake and 38% met recommendations for PA. Statistically significant gradients between urban and rural and race exist at the upper end of the BMI distribution. In other words, the severity of obesity is worse among black compared to white and for urban residents compared to rural residents.

Conclusions: These results will be used by the community-academic partnership to guide the development of culturally relevant and sustainable interventions to increase PA, increase FV intake and reduce obesity within this health disparate region. In particular, local stakeholders may wish to address disparities in BMI by allocating resources to the vulnerable groups identified.

Figure 1

Quantile regression models demonstrating effects of residency and demographic factors along the BMI distribution. Note: The dependent variable is continuous BMI. The vertical axis shows the associated covariates while the horizontal axis shows the continuous BMI quantiles. The dashed lines denote the OLS regression coefficients estimates for the covariate shown in each panel; the solid lines denote the quantile regression coefficient estimates; the shaded areas are the 95% confidence intervals for the quantile estimates. Take the first panel for example: the dashed line shows the OLS estimates of the BMI differences between urban and rural (it shows that on average urban population is relatively heavier than rural but it is not statistically significant); the solid lines shows the quantile regression estimates of the BMI differences between urban and rural across the distribution of the BMI (it shows that the only statistically significant urban/rural gradient exists among those who had relatively smaller BMI).