Literature DB >> 22186768

Facility characteristics do not explain higher false-positive rates in diagnostic mammography at facilities serving vulnerable women.

L Elizabeth Goldman1, Rod Walker, Diana L Miglioretti, Rebecca Smith-Bindman, And Karla Kerlikowske.   

Abstract

BACKGROUND: Facilities serving vulnerable women have higher false-positive rates for diagnostic mammography than facilities serving nonvulnerable women. False positives lead to anxiety, unnecessary biopsies, and higher costs.
OBJECTIVE: Examine whether availability of on-site breast ultrasound or biopsy services, academic medical center affiliation, or profit status explains differences in false-positive rates.
DESIGN: We examined 78,733 diagnostic mammograms performed to evaluate breast problems at Breast Cancer Surveillance Consortium facilities from 1999 to 2005. We used logistic-normal mixed effects regression to determine if adjusting for facility characteristics accounts for observed differences in false-positive rates. MEASURES: Facilities were characterized as serving vulnerable women based on the proportion of mammograms performed on racial/ethnic minorities, women with lower educational attainment, limited household income, or rural residence.
RESULTS: Although the availability of on-site ultrasound and biopsy services was associated with greater odds of a false positive in most models [odds ratios (OR) ranging from 1.24 to 1.88; P<0.05], adjustment for these services did not attenuate the association between vulnerability and false-positive rates. Estimated ORs for the effect of vulnerability indexes on false-positive rates unadjusted for facility services were: lower educational attainment [OR 1.33; 95% confidence intervals (CI), 1.03-1.74]; racial/ethnic minority status (OR 1.33; 95% CI, 0.98-1.80); rural residence (OR 1.56; 95% CI, 1.26-1.92); limited household income (OR 1.38; 95% CI, 1.10-1.73). After adjustment, estimates remained relatively unchanged.
CONCLUSIONS: On-site diagnostic service availability may contribute to unnecessary biopsies, but does not explain the higher diagnostic mammography false-positive rates at facilities serving vulnerable women.

Entities:  

Mesh:

Year:  2012        PMID: 22186768      PMCID: PMC3422679          DOI: 10.1097/MLR.0b013e3182407c8a

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  20 in total

1.  Predictive margins with survey data.

Authors:  B I Graubard; E L Korn
Journal:  Biometrics       Date:  1999-06       Impact factor: 2.571

2.  Differences in screening mammography outcomes among White, Chinese, and Filipino women.

Authors:  Karla Kerlikowske; Jennifer Creasman; Jessica W T Leung; Rebecca Smith-Bindman; Virginia L Ernster
Journal:  Arch Intern Med       Date:  2005-09-12

3.  Performance benchmarks for screening mammography.

Authors:  Robert D Rosenberg; Bonnie C Yankaskas; Linn A Abraham; Edward A Sickles; Constance D Lehman; Berta M Geller; Patricia A Carney; Karla Kerlikowske; Diana S M Buist; Donald L Weaver; William E Barlow; Rachel Ballard-Barbash
Journal:  Radiology       Date:  2006-10       Impact factor: 11.105

4.  A comparison of Medicare reimbursement and results for various imaging-guided breast biopsy techniques.

Authors:  R L Howisey; M B Acheson; R K Rowbotham; A Morgan
Journal:  Am J Surg       Date:  1997-05       Impact factor: 2.565

5.  Influence of annual interpretive volume on screening mammography performance in the United States.

Authors:  Diana S M Buist; Melissa L Anderson; Sebastien J P A Haneuse; Edward A Sickles; Robert A Smith; Patricia A Carney; Stephen H Taplin; Robert D Rosenberg; Berta M Geller; Tracy L Onega; Barbara S Monsees; Lawrence W Bassett; Bonnie C Yankaskas; Joann G Elmore; Karla Kerlikowske; Diana L Miglioretti
Journal:  Radiology       Date:  2011-02-22       Impact factor: 11.105

6.  Analysis of covariance and standardization as instances of prediction.

Authors:  P W Lane; J A Nelder
Journal:  Biometrics       Date:  1982-09       Impact factor: 2.571

7.  The implications of regional variations in Medicare spending. Part 1: the content, quality, and accessibility of care.

Authors:  Elliott S Fisher; David E Wennberg; Thérèse A Stukel; Daniel J Gottlieb; F L Lucas; Etoile L Pinder
Journal:  Ann Intern Med       Date:  2003-02-18       Impact factor: 25.391

8.  Current medicolegal and confidentiality issues in large, multicenter research programs.

Authors:  P A Carney; B M Geller; H Moffett; M Ganger; M Sewell; W E Barlow; N Stalnaker; S H Taplin; C Sisk; V L Ernster; H A Wilkie; B Yankaskas; S P Poplack; N Urban; M M West; R D Rosenberg; S Michael; T D Mercurio; R Ballard-Barbash
Journal:  Am J Epidemiol       Date:  2000-08-15       Impact factor: 4.897

9.  Performance benchmarks for diagnostic mammography.

Authors:  Edward A Sickles; Diana L Miglioretti; Rachel Ballard-Barbash; Berta M Geller; Jessica W T Leung; Robert D Rosenberg; Rebecca Smith-Bindman; Bonnie C Yankaskas
Journal:  Radiology       Date:  2005-06       Impact factor: 11.105

10.  Performance of diagnostic mammography for women with signs or symptoms of breast cancer.

Authors:  William E Barlow; Constance D Lehman; Yingye Zheng; Rachel Ballard-Barbash; Bonnie C Yankaskas; Gary R Cutter; Patricia A Carney; Berta M Geller; Robert Rosenberg; Karla Kerlikowske; Donald L Weaver; Stephen H Taplin
Journal:  J Natl Cancer Inst       Date:  2002-08-07       Impact factor: 13.506

View more
  4 in total

1.  Availability of Advanced Breast Imaging at Screening Facilities Serving Vulnerable Populations.

Authors:  Christoph I Lee; Andy Bogart; Jessica C Germino; L Elizabeth Goldman; Rebecca A Hubbard; Jennifer S Haas; Deirdre A Hill; Anna Na Tosteson; Jennifer A Alford-Teaster; Wendy B DeMartini; Constance D Lehman; Tracy L Onega
Journal:  J Med Screen       Date:  2015-06-15       Impact factor: 2.136

2.  Racial differences in false-positive mammogram rates: results from the ACRIN Digital Mammographic Imaging Screening Trial (DMIST).

Authors:  Anne Marie McCarthy; Philip Yamartino; Jianing Yang; Mirar Bristol; Emily F Conant; Katrina Armstrong
Journal:  Med Care       Date:  2015-08       Impact factor: 2.983

3.  Do mammographic technologists affect radiologists' diagnostic mammography interpretative performance?

Authors:  Louise M Henderson; Thad Benefield; J Michael Bowling; Danielle D Durham; Mary W Marsh; Bruce F Schroeder; Bonnie C Yankaskas
Journal:  AJR Am J Roentgenol       Date:  2015-04       Impact factor: 3.959

4.  TPM2 as a potential predictive biomarker for atherosclerosis.

Authors:  Ling-Bing Meng; Meng-Jie Shan; Yong Qiu; Ruomei Qi; Ze-Mou Yu; Peng Guo; Chen-Yi Di; Tao Gong
Journal:  Aging (Albany NY)       Date:  2019-09-05       Impact factor: 5.682

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.