Measuring prevalence and incidence of chronic conditions in claims and electronic health record databases

Abstract

Background

Health care databases are natural sources for estimating prevalence and incidence of chronic conditions, but substantial variation in estimates limits their interpretability and utility. We evaluated the effects of design choices when estimating prevalence and incidence in claims and electronic health record databases.

Methods

Prevalence and incidence for five chronic diseases at increasing levels of expected frequencies, from cystic fibrosis to COPD, were estimated in the Clinical Practice Research Datalink (CPRD) and MarketScan databases from 2011 to 2014. Estimates were compared using different definitions of lookback time and contributed person-time.

Results

Variation in lookback time substantially affected estimates. In 2014, for CPRD, use of an all-time vs a 1-year lookback window resulted in 4.3–8.3 times higher prevalence (depending on disease), reducing incidence by 1.9–3.3 times. All-time lookback resulted in strong temporal trends. COPD prevalence between 2011 and 2014 in MarketScan increased by 25% with an all-time lookback but stayed relatively constant with a 1-year lookback. Varying observability did not substantially affect estimates.

Conclusion

This framework draws attention to the underrecognized potential for widely varying incidence and prevalence estimates, with implications for care planning and drug development. Though prevalence and incidence are seemingly straightforward concepts, careful consideration of methodology is required to obtain meaningful estimates from health care databases.

Keywords:

• epidemiology
• epidemiologic methods
• epidemiological monitoring
• sentinel surveillance
• pharmacoepidemiology
• cross-sectional studies
• secondary databases
• prevalence
• prevalence studies
• incidence

Acknowledgments

We would like to thank Abby Case, Pattra Mattox, and Christina Raabe for their excellent assistance with this study. An earlier version of this article was presented as a poster at the 34th International Conference on Pharmacoepidemiology and Therapeutic Risk Management (ICPE) in Prague, Czech Republic, August 22–26, 2018. This study was partially supported by Boehringer Ingelheim.

Author contributions

Jeremy A Rassen designed the study and led the writing and editing of all sections of the text, figures, and tables. Dorothee B Bartels directed the choice of disease areas, designed the study’s analytic strategy, led implementation, and contributed to the writing of all sections of the text. Sebastian Schneeweiss advised on the study’s analytic strategy and contributed to the writing of all sections of the text. Amanda Patrick advised on the study parameters and analytic strategy and contributed to the writing of all sections of the text. William Murk advised on design and implementation, created the figures and tables, and contributed to the writing and editing of all sections of the text. All authors contributed to data analysis, drafting and revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Disclosure

Jeremy A Rassen is an employee of and has an ownership interest in Aetion, Inc, a technology company that provides analytic software and services to the health care industry. Dorothee B Bartels is an employee of Boehringer Ingelheim, which is a customer of Aetion, Inc. Sebastian Schneeweiss is a consultant to World Health Information Science Consultants (WHISCON), LLC, and to Aetion, Inc, in which he also owns equity. He is the principal investigator of investigator-initiated grants to the Brigham and Women’s Hospital from Bayer, Genentech, and Boehringer Ingelheim. Amanda R Patrick is an employee of and has ownership in Aetion, Inc. At the time of writing, William Murk was an employee of and had ownership in Aetion, Inc, in which he has an ownership interest. The authors report no other conflicts of interest in this work.