Abstract
Background: While Medicare claims are a potential resource for clinical mammography research or quality monitoring, the validity of key data elements remains uncertain. Claims codes for digital mammography and computer-aided detection (CAD), for example, have not been validated against a credible external reference standard. Methods: We matched Medicare mammography claims for women who received bilateral mammograms from 2003 to 2006 to corresponding mammography data from the Breast Cancer Surveillance Consortium (BCSC) registries in four U.S. states (N = 253,727 mammograms received by 120,709 women). We assessed the accuracy of the claims-based classifications of bilateral mammograms as either digital versus film and CAD versus non-CAD relative to a reference standard derived from BCSC data. Results: Claims data correctly classified the large majority of film and digital mammograms (97.2% and 97.3%, respectively), yielding excellent agreement beyond chance (κ = 0.90). Claims data correctly classified the large majority of CAD mammograms (96.6%) but a lower percentage of non-CAD mammograms (86.7%). Agreement beyond chance remained high for CAD classification (κ = 0.83). From 2003 to 2006, the predictive values of claims-based digital and CAD classifications increased as the sample prevalences of each technology increased. Conclusion: Medicare claims data can accurately distinguish film and digital bilateral mammograms and mammograms conducted with and without CAD. Impact: The validity of Medicare claims data regarding film versus digital mammography and CAD suggests that these data elements can be useful in research and quality improvement.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1344-1347 |
| Number of pages | 4 |
| Journal | Cancer Epidemiology Biomarkers and Prevention |
| Volume | 21 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 2012 |
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ASJC Scopus subject areas
- Epidemiology
- Oncology
Cite this
External validation of medicare claims codes for digital mammography and computer-aided detection. / Fenton, Joshua J; Zhu, Weiwei; Balch, Steven; Smith-Bindman, Rebecca; Lindfors, Karen K; Hubbard, Rebecca A.
In: Cancer Epidemiology Biomarkers and Prevention, Vol. 21, No. 8, 08.2012, p. 1344-1347.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - External validation of medicare claims codes for digital mammography and computer-aided detection
AU - Fenton, Joshua J
AU - Zhu, Weiwei
AU - Balch, Steven
AU - Smith-Bindman, Rebecca
AU - Lindfors, Karen K
AU - Hubbard, Rebecca A.
PY - 2012/8
Y1 - 2012/8
N2 - Background: While Medicare claims are a potential resource for clinical mammography research or quality monitoring, the validity of key data elements remains uncertain. Claims codes for digital mammography and computer-aided detection (CAD), for example, have not been validated against a credible external reference standard. Methods: We matched Medicare mammography claims for women who received bilateral mammograms from 2003 to 2006 to corresponding mammography data from the Breast Cancer Surveillance Consortium (BCSC) registries in four U.S. states (N = 253,727 mammograms received by 120,709 women). We assessed the accuracy of the claims-based classifications of bilateral mammograms as either digital versus film and CAD versus non-CAD relative to a reference standard derived from BCSC data. Results: Claims data correctly classified the large majority of film and digital mammograms (97.2% and 97.3%, respectively), yielding excellent agreement beyond chance (κ = 0.90). Claims data correctly classified the large majority of CAD mammograms (96.6%) but a lower percentage of non-CAD mammograms (86.7%). Agreement beyond chance remained high for CAD classification (κ = 0.83). From 2003 to 2006, the predictive values of claims-based digital and CAD classifications increased as the sample prevalences of each technology increased. Conclusion: Medicare claims data can accurately distinguish film and digital bilateral mammograms and mammograms conducted with and without CAD. Impact: The validity of Medicare claims data regarding film versus digital mammography and CAD suggests that these data elements can be useful in research and quality improvement.
AB - Background: While Medicare claims are a potential resource for clinical mammography research or quality monitoring, the validity of key data elements remains uncertain. Claims codes for digital mammography and computer-aided detection (CAD), for example, have not been validated against a credible external reference standard. Methods: We matched Medicare mammography claims for women who received bilateral mammograms from 2003 to 2006 to corresponding mammography data from the Breast Cancer Surveillance Consortium (BCSC) registries in four U.S. states (N = 253,727 mammograms received by 120,709 women). We assessed the accuracy of the claims-based classifications of bilateral mammograms as either digital versus film and CAD versus non-CAD relative to a reference standard derived from BCSC data. Results: Claims data correctly classified the large majority of film and digital mammograms (97.2% and 97.3%, respectively), yielding excellent agreement beyond chance (κ = 0.90). Claims data correctly classified the large majority of CAD mammograms (96.6%) but a lower percentage of non-CAD mammograms (86.7%). Agreement beyond chance remained high for CAD classification (κ = 0.83). From 2003 to 2006, the predictive values of claims-based digital and CAD classifications increased as the sample prevalences of each technology increased. Conclusion: Medicare claims data can accurately distinguish film and digital bilateral mammograms and mammograms conducted with and without CAD. Impact: The validity of Medicare claims data regarding film versus digital mammography and CAD suggests that these data elements can be useful in research and quality improvement.
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UR - http://www.scopus.com/inward/citedby.url?scp=84864818898&partnerID=8YFLogxK
U2 - 10.1158/1055-9965.EPI-12-0406
DO - 10.1158/1055-9965.EPI-12-0406
M3 - Article
C2 - 22695737
AN - SCOPUS:84864818898
VL - 21
SP - 1344
EP - 1347
JO - Cancer Epidemiology Biomarkers and Prevention
JF - Cancer Epidemiology Biomarkers and Prevention
SN - 1055-9965
IS - 8
ER -