Elizabeth T Jensen1, Suzanne F Cook2, Jeffery K Allen2, John Logie2, Maurice Alan Brookhart3, Michael D Kappelman4, Evan S Dellon5. 1. Center for Esophageal Diseases and Swallowing, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill; Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill. Electronic address: elizabeth_jensen@med.unc.edu. 2. World Wide Epidemiology, GlaxoSmithKline, Research Triangle Park. 3. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill. 4. Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill; Division of Pediatric Gastroenterology and Hepatology, Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill. 5. Center for Esophageal Diseases and Swallowing, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill; Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill.
Abstract
PURPOSE: Considerations for using administrative claims data in research have not been well-described. To increase awareness of how enrollment factors and insurance benefit use may contribute to prevalence estimates, we evaluated how differences in operational definitions of the cohort impact observed estimates. METHODS: We conducted a cross-sectional study estimating the prevalence of five gastrointestinal conditions using MarketScan claims data for 73.1 million enrollees. We extracted data obtained from 2009 to 2012 to identify cohorts meeting various enrollment, prescription drug benefit, or health care utilization characteristics. Next, we identified patients meeting the case definition for each of the diseases of interest. We compared the estimates obtained to evaluate the influence of enrollment period, drug benefit, and insurance usage. RESULTS: As the criteria for inclusion in the cohort became increasingly restrictive the estimated prevalence increased, as much as 45% to 77% depending on the disease condition and the definition for inclusion. Requiring use of the insurance benefit and a longer period of enrollment had the greatest influence on the estimates observed. CONCLUSIONS: Individuals meeting case definition were more likely to meet the more stringent definition for inclusion in the study cohort. This may be considered a form of selection bias, where overly restrictive inclusion criteria definitions may result in selection of a source population that may no longer represent the population from which cases arose.
PURPOSE: Considerations for using administrative claims data in research have not been well-described. To increase awareness of how enrollment factors and insurance benefit use may contribute to prevalence estimates, we evaluated how differences in operational definitions of the cohort impact observed estimates. METHODS: We conducted a cross-sectional study estimating the prevalence of five gastrointestinal conditions using MarketScan claims data for 73.1 million enrollees. We extracted data obtained from 2009 to 2012 to identify cohorts meeting various enrollment, prescription drug benefit, or health care utilization characteristics. Next, we identified patients meeting the case definition for each of the diseases of interest. We compared the estimates obtained to evaluate the influence of enrollment period, drug benefit, and insurance usage. RESULTS: As the criteria for inclusion in the cohort became increasingly restrictive the estimated prevalence increased, as much as 45% to 77% depending on the disease condition and the definition for inclusion. Requiring use of the insurance benefit and a longer period of enrollment had the greatest influence on the estimates observed. CONCLUSIONS: Individuals meeting case definition were more likely to meet the more stringent definition for inclusion in the study cohort. This may be considered a form of selection bias, where overly restrictive inclusion criteria definitions may result in selection of a source population that may no longer represent the population from which cases arose.
Authors: Robert Dufour; Ashish V Joshi; Margaret K Pasquale; David Schaaf; Jack Mardekian; George A Andrews; Nick C Patel Journal: Pain Pract Date: 2013-12-01 Impact factor: 3.183
Authors: Pornthep Tanpowpong; Sarabeth Broder-Fingert; Joshua C Obuch; David O Rahni; Aubrey J Katz; Daniel A Leffler; Ciaran P Kelly; Carlos A Camargo Journal: Ann Epidemiol Date: 2013-01-10 Impact factor: 3.797
Authors: Patrick P Gleason; G Caleb Alexander; Catherine I Starner; Stephen T Ritter; Holly K Van Houten; Brent W Gunderson; Nilay D Shah Journal: J Manag Care Pharm Date: 2013-09
Authors: Shuling Li; Yi Peng; Eric D Weinhandl; Anne H Blaes; Karynsa Cetin; Victoria M Chia; Scott Stryker; Joseph J Pinzone; John F Acquavella; Thomas J Arneson Journal: Clin Epidemiol Date: 2012-04-10 Impact factor: 4.790
Authors: Elizabeth C Ailes; Regina M Simeone; April L Dawson; Emily E Petersen; Suzanne M Gilboa Journal: Birth Defects Res A Clin Mol Teratol Date: 2016-11