Validation of an Algorithm for Claims-based Incidence of Prostate Cancer
Published in Epidemiology, May 1, 2019
Author(s): Lauren E Parlett, Daniel C Beachler, Stephen Lanes, Robert Hoover, Michael Cook
DOI: 10.1097/EDE.0000000000001007 | Pubmed ID: 30829831
Abstract
Background: Prostate cancer is a commonly studied outcome in administrative claims studies, but there is a dearth of validated case identifying algorithms. The long-term development of the disease increases the difficulty in separating prevalent from incident prostate cancer. The purpose of this validation study was to assess the accuracy of a claims algorithm to identify incident prostate cancer among men in commercial and Medicare Advantage US health plans.
Methods: We identified prostate cancer in claims as a prostate cancer diagnosis within 28 days after a prostate biopsy and compared case ascertainment in the claims with the gold standard results from the Georgia Comprehensive Cancer Registry (GCCR).
Results: We identified 74,008 men from a large health plan claims database for possible linkage with GCCR. Among the 382 prostate cancer cases identified in claims, 312 were also identified in the GCCR (positive predictive value [PPV] = 82%). Of the registry cases, 91% (95% confidence interval = 88, 94) were correctly identified in claims. Claims and registry diagnosis dates of prostate cancer matched exactly in 254/312 (81%) cases. Nearly half of the false-positive cases also had claims for prostate cancer treatment. Thirteen (43%) false-negative cases were classified as noncases by virtue of having a biopsy and diagnosis >28 days apart as required by the algorithm. Compared to matches, false-negative cases were older men with less aggressive prostate cancer.
Conclusions: Our algorithm demonstrated a PPV of 82% with 92% sensitivity in ascertaining incident PC. Administrative health plan claims can be a valuable and accurate source to identify incident prostate cancer cases.
Funding Transparency
No funding has been identified for this publication.
Tags
Analytic: algorithm validation
Data Source: claims
Research Focus: oncology | hormone supplement/replacement
Study Design: medical record review
Entry last updated (DMY): 28-11-2024.