External validation of medicare claims codes for digital mammography and computer-aided detection

Joshua J Fenton, Weiwei Zhu, Steven Balch, Rebecca Smith-Bindman, Karen K Lindfors, Rebecca A. Hubbard

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)1344-1347
Number of pages4
JournalCancer Epidemiology Biomarkers and Prevention
Volume21
Issue number8
DOIs
StatePublished - Aug 2012

Fingerprint

Mammography
Medicare
Breast Neoplasms
Quality Improvement
Research
Registries
Technology

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 journalArticle

Fenton, Joshua J ; Zhu, Weiwei ; Balch, Steven ; Smith-Bindman, Rebecca ; Lindfors, Karen K ; Hubbard, Rebecca A. / External validation of medicare claims codes for digital mammography and computer-aided detection. In: Cancer Epidemiology Biomarkers and Prevention. 2012 ; Vol. 21, No. 8. pp. 1344-1347.
@article{ccb6b899710740eb85550e4a836de975,
title = "External validation of medicare claims codes for digital mammography and computer-aided detection",
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.",
author = "Fenton, {Joshua J} and Weiwei Zhu and Steven Balch and Rebecca Smith-Bindman and Lindfors, {Karen K} and Hubbard, {Rebecca A.}",
year = "2012",
month = "8",
doi = "10.1158/1055-9965.EPI-12-0406",
language = "English (US)",
volume = "21",
pages = "1344--1347",
journal = "Cancer Epidemiology Biomarkers and Prevention",
issn = "1055-9965",
publisher = "American Association for Cancer Research Inc.",
number = "8",

}

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.

UR - http://www.scopus.com/inward/record.url?scp=84864818898&partnerID=8YFLogxK

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

VL - 21

SP - 1344

EP - 1347

JO - Cancer Epidemiology Biomarkers and Prevention

JF - Cancer Epidemiology Biomarkers and Prevention

SN - 1055-9965

IS - 8

ER -