Enrollment factors and bias of disease prevalence estimates in administrative claims data
| Autor: | Michael D. Kappelman, Evan S. Dellon, Suzanne F. Cook, MA Brookhart, Jeffery K. Allen, John Logie, Elizabeth T. Jensen |
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| Rok vydání: | 2015 |
| Předmět: |
Adult
Male Prescription drug Adolescent Gastrointestinal Diseases Epidemiology Cross-sectional study media_common.quotation_subject Population Insurance Claim Review Article Young Adult Bias Health care Prevalence Humans Medicine Child education media_common Selection bias education.field_of_study Insurance Health Actuarial science business.industry Operational definition Patient Selection Infant Newborn Infant Health Services Middle Aged Insurance Pharmaceutical Services United States Cross-Sectional Studies Child Preschool Cohort Female business Demography |
| Zdroj: | Annals of Epidemiology. 25:519-525.e2 |
| ISSN: | 1047-2797 |
| DOI: | 10.1016/j.annepidem.2015.03.008 |
| Popis: | 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bias, where overly restrictive inclusion criteria definitions may result in selection of a source population that may no longer represent the population from which cases arose. |
| Databáze: | OpenAIRE |
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