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Showing content from http://www.ncbi.nlm.nih.gov/pubmed/24872543 below:

Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography

. 2014 May 28;106(6):dju092. doi: 10.1093/jnci/dju092. Print 2014 Jun. Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography Sandra J Lee², Clyde B Schechter², Karla Kerlikowske², Oguzhan Alagoz², Donald Berry², Diana S M Buist², Mucahit Cevik², Gary Chisholm², Harry J de Koning², Hui Huang², Rebecca A Hubbard², Diana L Miglioretti², Mark F Munsell², Amy Trentham-Dietz², Nicolien T van Ravesteyn², Anna N A Tosteson², Jeanne S Mandelblatt²

Affiliations

¹ Affiliations of authors: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA (NKS); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (SJL, HH); Departments of Family & Social Medicine and Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY (CBS); Departments of Epidemiology and Biostatistics, and General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA (KK); Department of Industrial and Systems Engineering (OA, MC) and Department of Population Health Sciences and Carbone Cancer Center (OA, AT-D), University of Wisconsin, Madison, WI; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX (DB, GC, MFM); Group Health Research Institute, Seattle, WA (DSMB, RAH); Department of Public Health, Erasmus MC, Rotterdam, The Netherlands (HJdK, NTvR); Department of Public Health Sciences, School of Medicine, University of California, Davis, California (DLM); Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH (ANAT); Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC (JSM). natasha_stout@hms.harvard.edu.
² Affiliations of authors: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA (NKS); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (SJL, HH); Departments of Family & Social Medicine and Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY (CBS); Departments of Epidemiology and Biostatistics, and General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA (KK); Department of Industrial and Systems Engineering (OA, MC) and Department of Population Health Sciences and Carbone Cancer Center (OA, AT-D), University of Wisconsin, Madison, WI; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX (DB, GC, MFM); Group Health Research Institute, Seattle, WA (DSMB, RAH); Department of Public Health, Erasmus MC, Rotterdam, The Netherlands (HJdK, NTvR); Department of Public Health Sciences, School of Medicine, University of California, Davis, California (DLM); Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH (ANAT); Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC (JSM).

PMID: 24872543
PMCID: PMC4067109
DOI: 10.1093/jnci/dju092

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Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography

Natasha K Stout et al. J Natl Cancer Inst. 2014.

. 2014 May 28;106(6):dju092. doi: 10.1093/jnci/dju092. Print 2014 Jun. Authors Natasha K Stout¹, Sandra J Lee², Clyde B Schechter², Karla Kerlikowske², Oguzhan Alagoz², Donald Berry², Diana S M Buist², Mucahit Cevik², Gary Chisholm², Harry J de Koning², Hui Huang², Rebecca A Hubbard², Diana L Miglioretti², Mark F Munsell², Amy Trentham-Dietz², Nicolien T van Ravesteyn², Anna N A Tosteson², Jeanne S Mandelblatt² Affiliations

¹ Affiliations of authors: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA (NKS); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (SJL, HH); Departments of Family & Social Medicine and Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY (CBS); Departments of Epidemiology and Biostatistics, and General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA (KK); Department of Industrial and Systems Engineering (OA, MC) and Department of Population Health Sciences and Carbone Cancer Center (OA, AT-D), University of Wisconsin, Madison, WI; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX (DB, GC, MFM); Group Health Research Institute, Seattle, WA (DSMB, RAH); Department of Public Health, Erasmus MC, Rotterdam, The Netherlands (HJdK, NTvR); Department of Public Health Sciences, School of Medicine, University of California, Davis, California (DLM); Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH (ANAT); Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC (JSM). natasha_stout@hms.harvard.edu.
² Affiliations of authors: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA (NKS); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (SJL, HH); Departments of Family & Social Medicine and Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY (CBS); Departments of Epidemiology and Biostatistics, and General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA (KK); Department of Industrial and Systems Engineering (OA, MC) and Department of Population Health Sciences and Carbone Cancer Center (OA, AT-D), University of Wisconsin, Madison, WI; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX (DB, GC, MFM); Group Health Research Institute, Seattle, WA (DSMB, RAH); Department of Public Health, Erasmus MC, Rotterdam, The Netherlands (HJdK, NTvR); Department of Public Health Sciences, School of Medicine, University of California, Davis, California (DLM); Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH (ANAT); Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC (JSM).

PMID: 24872543
PMCID: PMC4067109
DOI: 10.1093/jnci/dju092

Item in Clipboard

Abstract

Background: Compared with film, digital mammography has superior sensitivity but lower specificity for women aged 40 to 49 years and women with dense breasts. Digital has replaced film in virtually all US facilities, but overall population health and cost from use of this technology are unclear.

Methods: Using five independent models, we compared digital screening strategies starting at age 40 or 50 years applied annually, biennially, or based on density with biennial film screening from ages 50 to 74 years and with no screening. Common data elements included cancer incidence and test performance, both modified by breast density. Lifetime outcomes included mortality, quality-adjusted life-years, and screening and treatment costs.

Results: For every 1000 women screened biennially from age 50 to 74 years, switching to digital from film yielded a median within-model improvement of 2 life-years, 0.27 additional deaths averted, 220 additional false-positive results, and $0.35 million more in costs. For an individual woman, this translates to a health gain of 0.73 days. Extending biennial digital screening to women ages 40 to 49 years was cost-effective, although results were sensitive to quality-of-life decrements related to screening and false positives. Targeting annual screening by density yielded similar outcomes to targeting by age. Annual screening approaches could increase costs to $5.26 million per 1000 women, in part because of higher numbers of screens and false positives, and were not efficient or cost-effective.

Conclusions: The transition to digital breast cancer screening in the United States increased total costs for small added health benefits. The value of digital mammography screening among women aged 40 to 49 years depends on women's preferences regarding false positives.

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Figures
Figure 1.

Discounted costs and discounted quality-adjusted…

Figure 1.

Discounted costs and discounted quality-adjusted life-years (QALYs) per 1000 women. A ) Six…

Figure 1.

Discounted costs and discounted quality-adjusted life-years (QALYs) per 1000 women. A) Six digital screening scenarios (triangles) under the base-case assumptions for an exemplar model. Those strategies considered efficient form the efficiency frontier (solid line). The base case did not include quality-of-life decrements for participating in screening or for receiving a false-positive mammogram. B) Sensitivity analysis for an exemplar model. Changing the specificity of digital or relative risk of breast cancer by breast density (solid gray lines, diamonds) did not appreciably change results from the base case (solid black lines, triangles) in the middle. Reducing the cost of a digital mammogram improved the efficiency of screening (dashed line, circles), whereas including quality-of-life decrements from screening reduced efficiency (dotted line, squares).