Validation of a Medicare Claims-based Algorithm for Identifying Breast Cancers Detected at Screening Mammography

Abstract

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.

Figure 1. Algorithm for identifying incident breast cancers detected at screening mammography

Figure shows algorithmic allocation of 116,522 screening mammograms in the test mammogram set with the number allocated and percentage correctly classified in each terminal node. An algorithmic classification of “positive” signifies that the algorithm classified the mammogram as detecting an incident breast cancer, while a “negative” classification signifies that no breast cancer was detected at screening. Timing of all claims events are in relation to the date of screening mammography. To protect patient confidentiality, cell sizes of less than or equal to 11 are suppressed (and related numbers and percentages are given as a range).