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Personalizing Mammography by Breast Density and Other Risk Factors for Breast Cancer: Analysis of Health Benefits and Cost-Effectiveness

John T. Schousboe, MD, PhD; Karla Kerlikowske, MD, MS; Andrew Loh, BA; and Steven R. Cummings, MD
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From Park Nicollet Health Services and University of Minnesota, Minneapolis, Minnesota, and University of California, San Francisco, and San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California.

Acknowledgment: The authors thank the BCSC investigators, participating mammography facilities, and radiologists for the data they provided for this study.

Grant Support: By an unrestricted grant from Eli Lilly and by the Da Costa Family Foundation for Research in Breast Cancer Prevention of the California Pacific Medical Center. Data collection for this work was supported by grants U01CA63740, U01CA86076, U01CA86082, U01CA63736, U01CA70013, U01CA69976, U01CA63731, and U01CA70040 from the National Cancer Institute BCSC. The collection of cancer incidence data used in this study was supported by several state public health departments and cancer registries throughout the United States; a full description of these sources can be found at http://breastscreening.cancer.gov/work/acknowledgement.html.

Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M10-2871.

Reproducible Research Statement:Study protocol, statistical code, and data set: Procedures for requesting these data for research purposes are provided at http://breastscreening.cancer.gov/.

Requests for Single Reprints: John T. Schousboe, MD, PhD, Park Nicollet Institute, 3800 Park Nicollet Boulevard, Minneapolis, MN 55416; e-mail, scho0600@umn.edu.

Current Author Addresses: Dr. Schousboe: Park Nicollet Institute, 3800 Park Nicollet Boulevard, Minneapolis, MN 55416.

Dr. Kerlikowske: Veterans Affairs Medical Center, 4150 Clement Street, San Francisco, CA 94121.

Mr. Loh: 342 Beresford Avenue, Redwood City, CA 94062.

Dr. Cummings: California Pacific Medical Center Research Institute Coordinating Center, 185 Berry Street, Lobby 5, Suite 5700, San Francisco, CA 94107.

Author Contributions: Conception and design: J.T. Schousboe, K. Kerlikowske, S.R. Cummings.

Analysis and interpretation of the data: J.T. Schousboe, K. Kerlikowske, A. Loh, S.R. Cummings.

Drafting of the article: J.T. Schousboe, K. Kerlikowske, A. Loh, S.R. Cummings.

Critical revision of the article for important intellectual content: J.T. Schousboe, K. Kerlikowske, A. Loh.

Final approval of the article: J.T. Schousboe, K. Kerlikowske, A. Loh, S.R. Cummings.

Provision of study materials or patients: J.T. Schousboe, K. Kerlikowske, A. Loh.

Statistical expertise: J.T. Schousboe, K. Kerlikowske.

Obtaining of funding: K. Kerlikowske.

Administrative, technical, or logistic support: J.T. Schousboe, K. Kerlikowske.

Collection and assembly of data: J.T. Schousboe, K. Kerlikowske, A. Loh.

Ann Intern Med. 2011;155(1):10-20. doi:10.7326/0003-4819-155-1-201107050-00003
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Background: Current guidelines recommend mammography every 1 or 2 years starting at age 40 or 50 years, regardless of individual risk for breast cancer.

Objective: To estimate the cost-effectiveness of mammography by age, breast density, history of breast biopsy, family history of breast cancer, and screening interval.

Design: Markov microsimulation model.

Data Sources: Surveillance, Epidemiology, and End Results program, Breast Cancer Surveillance Consortium, and the medical literature.

Target Population: U.S. women aged 40 to 49, 50 to 59, 60 to 69, and 70 to 79 years with initial mammography at age 40 years and breast density of Breast Imaging Reporting and Data System (BI-RADS) categories 1 to 4.

Time Horizon: Lifetime.

Perspective: National health payer.

Intervention: Mammography annually, biennially, or every 3 to 4 years or no mammography.

Outcome Measures: Costs per quality-adjusted life-year (QALY) gained and number of women screened over 10 years to prevent 1 death from breast cancer.

Results of Base-Case Analysis: Biennial mammography cost less than $100 000 per QALY gained for women aged 40 to 79 years with BI-RADS category 3 or 4 breast density or aged 50 to 69 years with category 2 density; women aged 60 to 79 years with category 1 density and either a family history of breast cancer or a previous breast biopsy; and all women aged 40 to 79 years with both a family history of breast cancer and a previous breast biopsy, regardless of breast density. Biennial mammography cost less than $50 000 per QALY gained for women aged 40 to 49 years with category 3 or 4 breast density and either a previous breast biopsy or a family history of breast cancer. Annual mammography was not cost-effective for any group, regardless of age or breast density.

Results of Sensitivity Analysis: Mammography is expensive if the disutility of false-positive mammography results and the costs of detecting nonprogressive and nonlethal invasive cancer are considered.

Limitation: Results are not applicable to carriers of BRCA1 or BRCA2 mutations.

Conclusion: Mammography screening should be personalized on the basis of a woman's age, breast density, history of breast biopsy, family history of breast cancer, and beliefs about the potential benefit and harms of screening.

Primary Funding Source: Eli Lilly, Da Costa Family Foundation for Research in Breast Cancer Prevention of the California Pacific Medical Center, and Breast Cancer Surveillance Consortium.


Grahic Jump Location
Figure 1.
Markov model of possible state transitions.

The dotted-and-dashed lines indicate transitions from the healthy state; the dashed lines indicate transitions from the DCIS state; and the solid lines indicate transitions from the invasive breast cancer states. DCIS = ductal carcinoma in situ.

Grahic Jump Location
Grahic Jump Location
Figure 2.
Incidence of invasive breast cancer as a function of age and breast density in U.S. women.

BI-RADS = Breast Imaging Reporting and Data System; SEER = Surveillance, Epidemiology, and End Results.

* Per 10 000 women per year.

Grahic Jump Location
Grahic Jump Location
Figure 3.
Cost-effective mammography screening strategies for women aged 40 to 79 years, by age and breast density.

Strategies assume a willingness-to-pay threshold of $100 000 (top) or $50 000 (bottom) per QALY gained. BI-RADS = Breast Imaging Reporting and Data System; QALY = quality-adjusted life-year.

Grahic Jump Location




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Society (and women's) perspective required
Posted on July 7, 2011
John D. Keen
Cook County John H. Stroger Hospital, Chicago IL
Conflict of Interest: None Declared

Schousboe et al have contributed to the mammographic screening debate by calculating the cost-effectiveness (C/E) ratio of annual screening compared to biennial screening for all ages as more than $340,00/QALY from a "national payer" perspective. (1) The American Cancer Society and some professional groups who benefit from screening advocate this aggressive annual schedule.

Unfortunately, this C/E ratio understates the case by ignoring indirect costs included with the society perspective. For instance, women's time and travel costs are substantial. (2) Screening mammograms as well as recall exams cause anxiety, and this harm should have been included in the base-case and varied in the sensitivity analysis in Table 2. (1) Finally, overdiagnosis of invasive breast cancers of 30% rather than 10% is a more reasonable estimate. (3) Overdiagnosis causes overtreatment, and these excess interventions from surgery and radiation result in disfigurement and increased mortality that the authors should reflect in the QALY analysis.

Most women in the United States do not have a "national payer", they have insurance companies. Medicare fee schedules do not necessarily reflect resource costs (2) and future C/E analyses should use more accurate median reimbursement information from insurance databases. According to the FDA, digital technology penetration is now at 78%(4), so using median Medicare film reimbursement is outdated. Given digital technology linkage with computer-aided detection, direct screening resource cost greater than $200 per mammogram is more appropriate than $108 and should have at least been included in a sensitivity analysis.

C/E analysis is a useful tool and in this case shows the opportunity cost of aggressive annual screening mammography. Medical care resources are limited and have better alternative uses. Ultimately, physicians should take the patient perspective and promote informed medical decision- making along with personalized risk-based screening. In the case of screening mammography, physicians should obtain individual informed consent given the substantial harms from overdiagnosis.


1. Schousboe JT, Kerlikowske K, Loh A, Cummings SR. Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness. Ann Intern Med;155(1):10-20.

2. Tosteson AN, Stout NK, Fryback DG, et al. Cost-effectiveness of digital mammography breast cancer screening. Ann Intern Med. 2008;148(1):1-10.

3. Keen JD. Promoting screening mammography: insight or uptake? J Am Board Fam Med. 2010;23(6):775-82.

4. U.S Food and Drug Administration. Mammography Quality and Standards Act National Statistics. Silver Spring, MD: U.S. Food and Drug Administration; 2011. Accessed at www.fda.gov/Radiation- EmittingProducts/MammographyQualityStandardsActandProgram/FacilityScorecard/ucm113858.htm on 6 July 2011.

Conflict of Interest:

None declared

Response to Keen
Posted on August 10, 2011
John T Schousboe
Park Nicollet Institute
Conflict of Interest: None Declared

We thank Dr. Keen for his thoughtful comments regarding our cost- effectiveness modeling study of mammography.

We agree that the costs relative to health benefits gained of performing annual compared to biennial screening mammography are very high, and our analyses may have underestimated these costs. From the societal perspective, additional costs of mammography could be included, among them time and travel costs to and from a breast imaging facility. However, advanced breast cancer may result in more disability, and from the societal perspective one must also account for the costs of replacing disabled women in the workplace.(1) If more frequent mammography detects breast cancer at earlier stages, there may be lower labor replacement costs. This exclusion would tend to result in over estimation of the costs per QALY gained.

We are unaware of any evidence that the Medicare reimbursement underestimates the true cost of mammography, or that private insurer reimbursement rates are a more accurate indicator of the true costs of mammography. We agree, however, that if the true cost of screening film mammography is higher than the median Medicare reimbursement, then our calculated costs per QALY gained of more frequent compared to less frequent (or no) mammography are underestimated.

There is no consensus that the rate of overdiagnosis of DCIS and invasive breast cancer is 30%.(2) Recent evidence has shown that overdiagnosis is most prominent in the first few years of mammography when long-standing non-progressive lesions are first discovered and even during that time period the overdiagnosis rate may be 11% to 12%.(3) After a period of repeated mammography, the ongoing overdiagnosis rate may be under 3%.

We chose to model the use of film mammography because the majority of women in the Breast Cancer Surveillance Consortium were screened with that technique between 1996 and 2006 allowing reliable estimates for breast density's influence on stage at diagnosis by screening interval. While digital mammography is more expensive, it may improve detection of breast cancer in women with high breast density and in younger women. As stated in our article, based on the cost-effectiveness study of digital compared to film mammography of Tosteson and colleagues,(4) digital mammography every two years would likely appear to be cost-effective compared to every 3 to 4 years or no mammography for women at all ages with high breast density (Bi-Rads-3 or -4 density), and would appear to be less cost- effective for women with average or low breast density compared to our results. Therefore our conclusions regarding the value of including breast density in decision making regarding frequency of screening mammography would be strengthened.

We agree that anxiety induced by mammography likely represents a transient reduction in quality of life for a subset of women. We chose not to include this in the base case analysis because of the lack of empirical data regarding its duration and the true quantity of the associated quality of life loss. As we noted in our sensitivity analysis, if this anxiety is substantial, then mammography becomes quite expensive. On the other hand, there is the theoretical possibility that true negative mammography results are reassuring to some, and if this were true the cost of mammography relative to the gain in health benefits would be lower. All of these points lead us to agree with Dr. Keen that personalized risk- based mammography screening while providing the patient with full information regarding harms and benefits of screening is paramount.

John T. Schousboe, MD, PhD Karla Kerlikowske, MD, MS Steven R. Cummings, MD


1. Koopmanschap MA, van Ineveld BM. Towards a new approach for estimating indirect costs of disease. Social Science & Medicine. 1992;34(9):1005-10.

2. Biesheuvel C, Barratt A, Howard K, Houssami N, Irwig L. Effects of study methods and biases on estimates of invasive breast cancer overdetection with mammography screening: a systematic review. Lancet Oncol. 2007;8(12):1129-38.

3. de Gelder R, Heijnsdijk EA, van Ravesteyn NT, Fracheboud J, Draisma G, de Koning HJ. Interpreting Overdiagnosis Estimates in Population-based Mammography Screening. Epidemiol Rev. 2011;33(1):111-21.

4. Tosteson AN, Stout NK, Fryback DG, et al. Cost-effectiveness of digital mammography breast cancer screening. Ann Intern Med. 2008;148(1):1-10.

Conflict of Interest:

None declared

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