Over one-half of incident breast cancers occur among Medicare-enrolled women, and Medicare enrollees receive approximately one-third of all screening mammograms nationwide (~13 million annual mammograms). Thus, improving the quality of screening mammography received by Medicare enrollees remains a public health priority. In 2001, Congress extended Medicare coverage to digital mammography and the application of computer-aided detection (CAD) during screening mammography. Rigorous evaluation of the clinical and economic impact of dissemination these technologies within the Medicare population have substantial policy significance. Although Medicare claims could be fruitful data source for such evaluations, uncertainty remains about the validity of key data elements. The Program Announcement, """"""""Cancer Surveillance Using Health Claims-based Data System,"""""""" calls for research to expand the scientific utility of Medicare claims files. In response, this study will use the newly linked Breast Cancer Surveillance Consortium (BCSC)-Medicare data to validate critical data elements of Medicare mammography claims and to evaluate the performance of claims-based algorithms for distinguishing screening from diagnostic mammograms and normal from abnormal radiologist interpretations.
Specific aims are: 1) To refine and validate a promising claims-based algorithm to distinguish screening from diagnostic mammograms. 2) To externally validate claims procedure codes for digital mammography and computer-aided detection (CAD). 3) To develop and validate a claims-based algorithm to distinguish whether screening mammograms were interpreted as normal vs. abnormal. Analyses will demonstrate the potential utility of the claims algorithms for comparative effectiveness research or radiologist-level quality assessment in the context of mammography technology diffusion.

Public Health Relevance

This study may provide essential methodological support for subsequent claims-based comparative effectiveness, economic, quality improvement research related to mammography. By elucidating the strengths and weaknesses of the claims data, study findings may also direct programmatic efforts to improve the quality of Medicare claims data.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
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Health Services Organization and Delivery Study Section (HSOD)
Program Officer
Warren, Joan
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University of California Davis
Family Medicine
Schools of Medicine
United States
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Fenton, Joshua J; Onega, Tracy; Zhu, Weiwei et al. (2016) Validation of a Medicare Claims-based Algorithm for Identifying Breast Cancers Detected at Screening Mammography. Med Care 54:e15-22
Hubbard, Rebecca A; Zhu, Weiwei; Balch, Steven et al. (2015) Identification of abnormal screening mammogram interpretation using Medicare claims data. Health Serv Res 50:290-304
Hubbard, Rebecca A; Benjamin-Johnson, Rhondee; Onega, Tracy et al. (2015) Classification accuracy of claims-based methods for identifying providers failing to meet performance targets. Stat Med 34:93-105
Fenton, Joshua J; Zhu, Weiwei; Balch, Steven et al. (2014) Distinguishing screening from diagnostic mammograms using Medicare claims data. Med Care 52:e44-51
Fenton, Joshua J; Zhu, Weiwei; Balch, Steven et al. (2012) External validation of Medicare claims codes for digital mammography and computer-aided detection. Cancer Epidemiol Biomarkers Prev 21:1344-7