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Rural Population Estimates: An Analysis of a Large Secondary Data Set
Author(s) -
Bennett Kevin J.
Publication year - 2012
Publication title -
the journal of rural health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.439
H-Index - 57
eISSN - 1748-0361
pISSN - 0890-765X
DOI - 10.1111/j.1748-0361.2012.00446.x
Subject(s) - behavioral risk factor surveillance system , medicine , environmental health , census , population , psychological intervention , rural area , data quality , demography , gerontology , service (business) , business , nursing , pathology , marketing , sociology
Purpose 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. Methods 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. Results 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. Conclusions 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.