Rural populations face numerous barriers to health, including poorer health care infrastructure, access to care, and other sociodemographic factors largely associated with rurality. Multiple measures of rurality used in the biomedical and public health literature can help assess rural–urban health disparities and may impact the observed associations between rurality and health. Furthermore, understanding what makes a place truly “rural” versus “urban” may vary from region to region in the US.
The objectives of this study are to compare and contrast five common measures of rurality and determine how well-correlated these measures are at the national, regional, and divisional level, as well as to assess patterns in the correlations between the prevalence of obesity in the population aged 60+ and each of the five measures of rurality at the regional and divisional level.
Five measures of rurality were abstracted from the US Census and US Department of Agriculture (USDA) to characterize US counties. Obesity data in the population aged 60+ were abstracted from the Behavioral Risk Factor Surveillance System (BRFSS). Spearman’s rank correlations were used to quantify the associations among the five rurality measurements at the national, regional, and divisional level, as defined by the US Census Bureau. Geographic information systems were used to visually illustrate temporal, spatial, and regional variability.
Overall, Spearman’s rank correlations among the five measures ranged from 0.521 (percent urban–urban influence code) to 0.917 (rural–urban continuum code–urban influence code). Notable discrepancies existed in these associations by Census region and by division. The associations between measures of rurality and obesity in the 60+ population varied by rurality measure used and by region.
This study is among the first to systematically assess the spatial, temporal, and regional differences and similarities among five commonly used measures of rurality in the US. There are important, quantifiable distinctions in defining what it means to be a rural county depending on both the geographic region and the measurement used. These findings highlight the importance of developing and selecting an appropriate rurality metric in health research.