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.
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).
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.
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.
From the aHealthCore, Inc., Alexandria, VA
bHealthCore, Inc., Wilmington, DE
cNational Cancer Institute, NIH, DHHS, Bethesda, MD.
Submitted August 3, 2018; accepted February 18, 2019.
HealthCore researchers operated under an NCI/NIH contract specific to this study; NCI/NIH researchers performed research under their normal governmental duties.
The authors report no conflicts of interest
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Obtaining data/code: These data are not available for request due to patient privacy rules.
Correspondence: Lauren E. Parlett, 1925 Ballenger Avenue, Suite 540, Alexandria, VA 22134. E-mail: firstname.lastname@example.org.