The author asserts no conflicts of interest.
Rural Population Estimates: An Analysis of a Large Secondary Data Set
Article first published online: 20 NOV 2012
© 2012 National Rural Health Association
The Journal of Rural Health
Volume 29, Issue 3, pages 233–238, Summer 2013
How to Cite
Bennett, K. J. (2013), Rural Population Estimates: An Analysis of a Large Secondary Data Set. The Journal of Rural Health, 29: 233–238. doi: 10.1111/j.1748-0361.2012.00446.x
- Issue published online: 26 JUN 2013
- Article first published online: 20 NOV 2012
- health disparities;
- health services research;
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.