Journal Articles

Improving population representation through geographic health information systems: mapping the MURDOCK study

July 18, 2014

Improving population representation through geographic health information systems: mapping the MURDOCK study. American Journal of  Translational Research. July 2014. Ben W Strauss1, Ellis M Valentiner1, Sayanti Bhattacharya2,3, Michelle M Smerek2, Ashley A Dunham2, L Kristin Newby4,5, Marie Lynn Miranda1,6,7

Author Affiliations: 1National Center for Geospatial Medicine, School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI, USA; 2Duke Translational Research Institute, Duke University, Durham, NC, USA; 3Duke Global Health Institute, Duke University Medical Center, Durham, NC, USA; 4Division of Cardiovascular Medicine, Duke University Medical Center, Durham, NC, USA; 5Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, USA; 6Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA; 7Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA

Abstract: This paper highlights methods for using geospatial analysis to assess, enhance, and improve recruitment efforts to ensure representativeness in study populations. We apply these methods to the Measurement to Understand Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) study, a longitudinal population health study focused on the city of Kannapolis and Cabarrus County, NC. Although efforts have been made to recruit a participant registry that is representative of the 18 ZIP code catchment region inclusive of Cabarrus County and Kannapolis, bias in such recruitment is inevitable. Participants in the MURDOCK study are geospatially referenced at entry, providing information that can be used to monitor and guide recruitment efforts. MURDOCK participant population representativeness was assessed using chi-squared tests to compare the MURDOCK population with 2010 Census data, relative to both the entire 18 ZIP code catchment area and for individual Census tracts. A logistic regression model was fit to characterize Census tracts with low recruitment, defined by fewer than 56 participants from that tract. The distance to the site at which participants enrolled was calculated, and median distance to enrollment site was used in the logistic regression. Tracts with low recruitment rates contained higher minority and younger populations, suggesting specific strategies for improving recruitment in these areas. Areal units farther away from enrollment sites were also not well-sampled, despite being in the specified study area, indicating that distance traveled to enrollment may be a barrier. These results have implications for targeting recruitment efforts and representative samples more generally, including in other population-based studies.

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