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Validation of a Medicare Claims-based Algorithm for Identifying Breast Cancers Detected at Screening Mammography
Joshua J Fenton1, Tracy Onega, Weiwei Zhu
1*Department of Family and Community Medicine, Center for Healthcare Research and Policy, and the Comprehensive Cancer Center, University of California, Davis, Sacramento, CA †Section of Biostatistics and Epidemiology, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, NH ‡Group Health Research Institute, Seattle, WA Departments of §Radiology ∥Epidemiology and Biostatistics, University of California, San Francisco, CA ¶Department of Radiology, University of North Carolina, Chapel Hill, NC #Health Promotion Research, University of Vermont, Burlington, VT **Department of Medicine, University of California, San Francisco, CA ††Department of Biostatistics, University of Washington, Seattle, WA.
Background:
The breast cancer detection rate is a benchmark measure of screening mammography quality, but its computation requires linkage of mammography interpretive performance information with cancer incidence data. A Medicare claims-based measure of detected breast cancers could simplify measurement of this benchmark and facilitate mammography quality assessment and research.
Objectives:
To validate a claims-based algorithm that can identify with high positive predictive value (PPV) incident breast cancers that were detected at screening mammography.
Research Design:
Development of a claims-derived algorithm using classification and regression tree analyses within a random half-sample of Medicare screening mammography claims followed by validation of the algorithm in the remaining half-sample using clinical data on mammography results and cancer incidence from the Breast Cancer Surveillance Consortium (BCSC).
Subjects:
Female fee-for-service Medicare enrollees aged 68 years and older who underwent screening mammography from 2001 to 2005 within BCSC registries in 4 states (CA, NC, NH, and VT), enabling linkage of claims and BCSC mammography data (N=233,044 mammograms obtained by 104,997 women).
Measures:
Sensitivity, specificity, and PPV of algorithmic identification of incident breast cancers that were detected by radiologists relative to a reference standard based on BCSC mammography and cancer incidence data.
Results:
An algorithm based on subsequent codes for breast cancer diagnoses and treatments and follow-up mammography identified incident screen-detected breast cancers with 92.9% sensitivity [95% confidence interval (CI), 91.0%-94.8%], 99.9% specificity (95% CI, 99.9%-99.9%), and a PPV of 88.0% (95% CI, 85.7%-90.4%).
Conclusions:
A simple claims-based algorithm can accurately identify incident breast cancers detected at screening mammography among Medicare enrollees. The algorithm may enable mammography quality assessment using Medicare claims alone.
