Accuracy of Cancer Registry Primary Payer Information and Implications for Policy Research

Author:

Davidoff Amy J.1,Enewold Lindsey1,Williams Courtney P.12,Bhattacharya Manami1,Sanchez Janeth I.13

Affiliation:

1. Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD

2. Department of Preventive Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL

3. Office of the Director, National Institutes of Health, Bethesda, MD

Abstract

Background: Cancer registry-based “primary payer at diagnosis” (PPDx) data are commonly used to evaluate the effect of insurance on cancer care outcomes, yet little is known about how well they capture Medicaid or Medicare enrollment. Methods: We linked the National Cancer Institute’s Surveillance, Epidemiology, and End Results registry data to monthly Centers for Medicare and Medicaid Services (CMS) Medicaid and Medicare enrollment records, state-year Medicaid policy, and managed care enrollment. We selected adults aged 19–64 years diagnosed between 2007 and 2011. We used bivariate analyses to compare PPDx to CMS enrollment at diagnosis month and assessed underreporting rates by patient characteristics and state-year policy. Results: PPDx reported 7.8% Medicare and 10.1% Medicaid, whereas CMS enrollment indicated 5.5% Medicare, 10.4% Medicaid, and 3.4% dual Medicare-Medicaid (N = 896,031). Positive predictive values for PPDx assignment to Medicaid and Medicare were 65.3% and 75.4%, with false negative rates of 52.0% and 33.8%, respectively. Medicaid underreporting was higher in low (56.5%) versus high (50.8%) poverty areas, for males (56.1%) versus females (48.9%), for Medicaid poverty expansion or waiver enrolled (63.8%) versus cash assistance-related eligibility (47.3%), and in states with large managed care enrollment (all P < 0.001). If Medicaid and Medicare enrollment data were used to edit PPDx, 12.0% of persons would switch primary payer assignment. Conclusions: Registry-reported PPDx fails to fully capture Medicaid and Medicare enrollment, which may result in biased estimates of insurance-related policy impacts. Enhancement with objective enrollment data could reduce measurement error and bias in estimates necessary to support policy assessment.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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