Rural Population Estimates: An Analysis of a Large Secondary Data Set


  • Kevin J. Bennett PhD

    Corresponding author
    1. Arnold School of Public Health, South Carolina Rural Health Research Center, University of South Carolina, Columbia, South Carolina
    • Department of Family and Preventive Medicine, University of South Carolina School of Medicine, Columbia, South Carolina
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  • The author asserts no conflicts of interest.

For further information, contact: Kevin J. Bennett, PhD, Department of Family and Preventive Medicine, University of South Carolina School of Medicine, 3209 Colonial Drive, Columbia, SC 29203; e-mail:



Health services research often utilizes secondary data sources such as the Behavioral Risk Factor Surveillance System (BRFSS). Since 2006, the released BRFSS data do not include respondents who live in counties with 10,000 or fewer residents, and the CDC no longer offers the opportunity to access the unrestricted data set. As a result, rural residents can be underrepresented in BRFSS data after 2005. The purpose of this analysis is to examine the potential for bias introduced by rural underestimation.


We utilized 6 BRFSS data sets; the 2005 full data and the 2005-2009 restricted data. We estimated population sizes for each survey year, and we compared these estimates to comparable data from the US Census intercensal estimates. We also compared estimates of preventive service utilization (mammography, Pap tests, colorectal cancer screening, and influenza vaccinations) between the two 2005 data versions.


Rural populations were underrepresented, particularly with the smaller counties excluded. Remote rural residents were the most consistently underrepresented. Preventive service delivery estimates differed between the full and restricted 2005 data versions. Mammography and Pap test estimates tended to be higher in the restricted data, while colorectal cancer screening and influenza vaccinations were similar or inconsistent. These results indicate that restricting by county size introduced bias in these estimates.


Having quality, nationally representative data is important to study disparities in service delivery. The potential bias introduced by the BRFSS county restriction may result in rural research being less effective for policy recommendations and interventions.