Identification of abnormal screening mammogram interpretation using Medicare claims data
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Published on: August 30, 2013
Rebecca A Hubbard1, Weiwei Zhu, Steven Balch
1Group Health Research Institute and Department of Biostatistics, University of Washington, 1730 Minor Ave, Suite 1600, Seattle, WA, 98101.
Objective:
To develop and validate Medicare claims-based approaches for identifying abnormal screening mammography interpretation.
Data Sources:
Mammography data and linked Medicare claims for 387,709 mammograms performed from 1999 to 2005 within the Breast Cancer Surveillance Consortium (BCSC).
Study Design:
Split-sample validation of algorithms based on claims for breast imaging or biopsy following screening mammography.
Data Extraction Methods:
Medicare claims and BCSC mammography data were pooled at a central Statistical Coordinating Center.
Principal Findings:
Presence of claims for subsequent imaging or biopsy had sensitivity of 74.9 percent (95 percent confidence interval [CI], 74.1-75.6) and specificity of 99.4 percent (95 percent CI, 99.4-99.5). A classification and regression tree improved sensitivity to 82.5 percent (95 percent CI, 81.9-83.2) but decreased specificity (96.6 percent, 95 percent CI, 96.6-96.8).
Conclusions:
Medicare claims may be a feasible data source for research or quality improvement efforts addressing high rates of abnormal screening mammography.
