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Leonard CE, Brensinger CM, Nam YH, Bilker WB, Barosso GM, Mangaali MJ, Hennessy S. The quality of Medicaid and Medicare data obtained from CMS and its contractors: implications for pharmacoepidemiology. BMC Health Serv Res 2017; 17:304. [PMID: 28446159 PMCID: PMC5406992 DOI: 10.1186/s12913-017-2247-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Accepted: 04/19/2017] [Indexed: 11/23/2022] Open
Abstract
Background Administrative claims of United States Centers for Medicare and Medicaid Services (CMS) beneficiaries have long been used in non-experimental research. While CMS performs in-house checks of these claims, little is known of their quality for conducting pharmacoepidemiologic research. We performed exploratory analyses of the quality of Medicaid and Medicare data obtained from CMS and its contractors. Methods Our study population consisted of Medicaid beneficiaries (with and without dual coverage by Medicare) from California, Florida, New York, Ohio, and Pennsylvania. We obtained and compiled 1999–2011 data from these state Medicaid programs (constituting about 38% of nationwide Medicaid enrollment), together with corresponding national Medicare data for dually-enrolled beneficiaries. This descriptive study examined longitudinal patterns in: dispensed prescriptions by state, by quarter; and inpatient hospitalizations by federal benefit, state, and age group. We further examined discrepancies between demographic characteristics and disease states, in particular frequencies of pregnancy complications among men and women beyond childbearing age, and prostate cancers among women. Results Dispensed prescriptions generally increased steadily and consistently over time, suggesting that these claims may be complete. A commercially-available National Drug Code lookup database was able to identify the dispensed drug for 95.2–99.4% of these claims. Because of co-coverage by Medicare, Medicaid data appeared to miss a substantial number of hospitalizations among beneficiaries ≥ 45 years of age. Pregnancy complication diagnoses were rare in males and in females ≥ 60 years of age, and prostate cancer diagnoses were rare in females. Conclusions CMS claims from five large states obtained directly from CMS and its contractors appeared to be of high quality. Researchers using Medicaid data to study hospital outcomes should obtain supplemental Medicare data on dual enrollees, even for non-elders. Trial Registration Not applicable. Electronic supplementary material The online version of this article (doi:10.1186/s12913-017-2247-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Charles E Leonard
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA. .,Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA.
| | - Colleen M Brensinger
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA.,Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA
| | - Young Hee Nam
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA.,Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA
| | - Warren B Bilker
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA.,Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA
| | - Geralyn M Barosso
- Division of Health Policy and Management, School of Public Health, University of Minnesota, 420 Delaware Street SE, Mayo D355, Minneapolis, MN, 55455-0381, USA
| | - Margaret J Mangaali
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA.,Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA.,Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104-4865, USA.,Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, 34th Street & Civic Center Boulevard, Philadelphia, PA, 19104-5158, USA
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