Dataset Information
Identification of abnormal screening mammogram interpretation using Medicare claims data.
Ontology highlight
ABSTRACT: 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.
SUBMITTER: Hubbard RA
PROVIDER: S-EPMC4319883 | biostudies-literature | 2015 Feb
REPOSITORIES: biostudies-literature
Publications
Identification of abnormal screening mammogram interpretation using Medicare claims data.
Health services research 20140628 1
<h4>Objective</h4>To develop and validate Medicare claims-based approaches for identifying abnormal screening mammography interpretation.<h4>Data sources</h4>Mammography data and linked Medicare claims for 387,709 mammograms performed from 1999 to 2005 within the Breast Cancer Surveillance Consortium (BCSC).<h4>Study design</h4>Split-sample validation of algorithms based on claims for breast imaging or biopsy following screening mammography.<h4>Data extraction methods</h4>Medicare claims and BCS ...[more]