Evidence map›Paper›PMID 23929404›Full record

ArticleMedical care2016

Validation of a Medicare Claims-based Algorithm for Identifying Breast Cancers Detected at Screening Mammography.

Joshua J Fenton, Tracy Onega, Weiwei Zhu, Steven Balch, Rebecca Smith-Bindman, Louise Henderson, Brian L Sprague, Karla Kerlikowske, Rebecca A Hubbard

Abstract read
In one paragraph

Article in Medical care, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Diagnostic yield of surveillance mammography among older women.Journal of the National Cancer Institute · 2026
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  15. Breast Cancer Diagnosis and Treatment After High-Deductible Insurance Enrollment.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2018
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Joshua J Fenton*Department of Family and Community Medicine, Center for Healthcare Research and Policy, and the Comprehensive Cancer Center, University of California, Davis, Sacramento, CA †Section of Biostatistics and Epidemiology, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, NH ‡Group Health Research Institute, Seattle, WA Departments of §Radiology ∥Epidemiology and Biostatistics, University of California, San Francisco, CA ¶Department of Radiology, University of North Carolina, Chapel Hill, NC #Health Promotion Research, University of Vermont, Burlington, VT **Department of Medicine, University of California, San Francisco, CA ††Department of Biostatistics, University of Washington, Seattle, WA.
Tracy Onega
Weiwei Zhu
Steven Balch
Rebecca Smith-Bindman
Louise Henderson
Brian L Sprague
Karla Kerlikowske
Rebecca A Hubbard

Funding

Statistical Coordinating Center for the Breast Cancer Surveillance ConsortiumU01CA086076 · NCI · KAISER FOUNDATION HEALTH PLAN OF WASHINGTON · PI MIGLIORETTI, DIANA L · 2000 to 2011
$8.5M
IMPROVING THE QUALITY OF MAMMOGRAPHIC INTERPRETATIONU01CA063731 · NCI · KAISER FOUNDATION HEALTH PLAN OF WASHINGTON · PI BUIST, DIANA SM · 1994 to 2011
$6.7M
VERMONT BREAST CANCER SURVEILLANCE SYSTEMU01CA070013 · NCI · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI GELLER, BERTA M · 1995 to 2011
$6.7M
UTILITY AND OUTCOMES FROM MAMMOGRAPHY IN NORTH CAROLINAU01CA070040 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YANKASKAS, BONNIE C · 1995 to 2011
$6.4M
San Francisco Mammography Registry-A Research SourceU01CA063740 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KERLIKOWSKE, KARLA M · 1994 to 2011
$6.2M
STRATEGIC STUDIES IN BREAST CANCER SURVEILLANCEU01CA086082 · NCI · DARTMOUTH COLLEGE · PI ONEGA, TRACY · 2000 to 2011
$4.9M
REFERRAL PATTERNS AND COST ANALYSIS OF SCREENING MAMMOGRAPHY--FOLLOW-UP/BIOPSYU01CA063736 · NCI · COOPER INSTITUTE · PI CUTTER, GARY · 1994 to 2004
$2.7M
NEW MEXICO MAMMOGRAPHY PROJECTU01CA069976 · NCI · UNIVERSITY OF NEW MEXICO · PI ROSENBERG, ROBERT D · 1995 to 2005
$2.7M
ADA3: a novel target for HPV16 E6 oncoproteinR01CA094143 · NCI · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI BAND, VIMLA · 2002 to 2006
$1.7M
Validation of Medicare Claims Data for MammographyR21CA158510 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI FENTON, JOSHUA J · 2011 to 2012
$390k
NCI NIH HHS HHSN261201100031CNCI NIH HHS R01 CA094143NCI NIH HHS R21 CA158510NCI NIH HHS R21CA158510NCI NIH HHS U01 CA063731NCI NIH HHS U01 CA063736NCI NIH HHS U01 CA063740NCI NIH HHS U01 CA069976NCI NIH HHS U01 CA070013NCI NIH HHS U01 CA070040NCI NIH HHS U01 CA086076NCI NIH HHS U01 CA086082NCI NIH HHS U01CA63731NCI NIH HHS U01CA63736NCI NIH HHS U01CA63740NCI NIH HHS U01CA69976NCI NIH HHS U01CA70013NCI NIH HHS U01CA70040NCI NIH HHS U01CA86076NCI NIH HHS U01CA86082
6 · The paper itself

Abstract

backgroundThe breast cancer detection rate is a benchmark measure of screening mammography quality, but its computation requires linkage of mammography interpretive performance information with cancer incidence data. A Medicare claims-based measure of detected breast cancers could simplify measurement of this benchmark and facilitate mammography quality assessment and research.

objectivesTo validate a claims-based algorithm that can identify with high positive predictive value (PPV) incident breast cancers that were detected at screening mammography. RESEARCH

designDevelopment of a claims-derived algorithm using classification and regression tree analyses within a random half-sample of Medicare screening mammography claims followed by validation of the algorithm in the remaining half-sample using clinical data on mammography results and cancer incidence from the Breast Cancer Surveillance Consortium (BCSC). SUBJECTS: Female fee-for-service Medicare enrollees aged 68 years and older who underwent screening mammography from 2001 to 2005 within BCSC registries in 4 states (CA, NC, NH, and VT), enabling linkage of claims and BCSC mammography data (N=233,044 mammograms obtained by 104,997 women). MEASURES: Sensitivity, specificity, and PPV of algorithmic identification of incident breast cancers that were detected by radiologists relative to a reference standard based on BCSC mammography and cancer incidence data.

resultsAn algorithm based on subsequent codes for breast cancer diagnoses and treatments and follow-up mammography identified incident screen-detected breast cancers with 92.9% sensitivity [95% confidence interval (CI), 91.0%-94.8%], 99.9% specificity (95% CI, 99.9%-99.9%), and a PPV of 88.0% (95% CI, 85.7%-90.4%).

conclusionsA simple claims-based algorithm can accurately identify incident breast cancers detected at screening mammography among Medicare enrollees. The algorithm may enable mammography quality assessment using Medicare claims alone.

Indexed as

AlgorithmsAgedAged, 80 and overBreast NeoplasmsEarly Detection of CancerFee-for-Service PlansFemaleHumansIncidenceInsurance Claim ReviewMammographyMedicareReproducibility of ResultsResidence CharacteristicsSocioeconomic FactorsUnited States

Identifiers

PMID23929404
PMCPMC3865072

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.