Health services research · 2014
Identification of abnormal screening mammogram interpretation using Medicare claims data.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Hubbard Rebecca A, Zhu Weiwei, Balch Steven, Onega Tracy, Fenton Joshua J
Аннотация
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.
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.