Nicolien T. van Ravesteyn

EFFECTS OF MAMMOGRAPHY SCREENING UNDER DIFFERENT SCREENING SCHEDULES: MODEL ESTIMATES OF POTENTIAL BENEFITS AND HARMS

To inform the USPSTF recommendations about breast cancer screening, Mandelblatt and colleagues developed 6 models of breast cancer incidence and mortality in the United States and estimated benefit...

Risk Factors for Breast Cancer for Women Aged 40 to 49 Years: A Systematic Review and Meta-analysis

BACKGROUND Identifying risk factors for breast cancer specific to women in their 40s could inform screening decisions. PURPOSE To determine what factors increase risk for breast cancer in women aged 40 to 49 years and the magnitude of risk for each factor. DATA SOURCES MEDLINE (January 1996 to the second week of November 2011), Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews (fourth quarter of 2011), Scopus, reference lists of published studies, and the Breast Cancer Surveillance Consortium. STUDY SELECTION English-language studies and systematic reviews of risk factors for breast cancer in women aged 40 to 49 years. Additional inclusion criteria were applied for each risk factor. DATA EXTRACTION Data on participants, study design, analysis, follow-up, and outcomes were abstracted. Study quality was rated by using established criteria, and only studies rated as good or fair were included. Results were summarized by using meta-analysis when sufficient studies were available or from the best evidence based on study quality, size, and applicability when meta-analysis was not possible. Data from the Breast Cancer Surveillance Consortium were analyzed with proportional hazards models by using partly conditional Cox regression. Reference groups for comparisons were set at U.S. population means. DATA SYNTHESIS Sixty-six studies provided data for estimates. Extremely dense breasts on mammography or first-degree relatives with breast cancer were associated with at least a 2-fold increase in risk for breast cancer. Prior breast biopsy, second-degree relatives with breast cancer, or heterogeneously dense breasts were associated with a 1.5- to 2.0-fold increased risk; current use of oral contraceptives, nulliparity, and age 30 years or older at first birth were associated with a 1.0- to 1.5-fold increased risk. LIMITATIONS Studies varied by measures, reference groups, and adjustment for confounders, which could bias combined estimates. Effects of multiple risk factors were not considered. CONCLUSION Extremely dense breasts and first-degree relatives with breast cancer were each associated with at least a 2-fold increase in risk for breast cancer in women aged 40 to 49 years. Identification of these risk factors may be useful for personalized mammography screening. PRIMARY FUNDING SOURCE National Cancer Institute.

Interpreting Overdiagnosis Estimates in Population-based Mammography Screening

Estimates of overdiagnosis in mammography screening range from 1% to 54%. This review explains such variations using gradual implementation of mammography screening in the Netherlands as an example. Breast cancer incidence without screening was predicted with a micro-simulation model. Observed breast cancer incidence (including ductal carcinoma in situ and invasive breast cancer) was modeled and compared with predicted incidence without screening during various phases of screening program implementation. Overdiagnosis was calculated as the difference between the modeled number of breast cancers with and the predicted number of breast cancers without screening. Estimating overdiagnosis annually between 1990 and 2006 illustrated the importance of the time at which overdiagnosis is measured. Overdiagnosis was also calculated using several estimators identified from the literature. The estimated overdiagnosis rate peaked during the implementation phase of screening, at 11.4% of all predicted cancers in women aged 0–100 years in the absence of screening. At steady-state screening, in 2006, this estimate had decreased to 2.8%. When different estimators were used, the overdiagnosis rate in 2006 ranged from 3.6% (screening age or older) to 9.7% (screening age only). The authors concluded that the estimated overdiagnosis rate in 2006 could vary by a factor of 3.5 when different denominators were used. Calculations based on earlier screening program phases may overestimate overdiagnosis by a factor 4. Sufficient follow-up and agreement regarding the chosen estimator are needed to obtain reliable estimates.

Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.

Context Multiple alternative mammography screening strategies exist. Contribution This modeling study estimated outcomes of 8 strategies that differed by starting age and interval. Biennial screening from age 50 to 74 years avoided a median of 7 breast cancer deaths; in contrast, annual screening from age 40 to 74 years avoided an additional 3 deaths but yielded 1988 more false-positive results and 11 more overdiagnoses per 1000 women screened. Annual screening from age 40 years for high-risk women had similar outcomes as screening average-risk women biennially from 50 to 74 years of age. Caution Imaging technologies other than mammography and nonadherence were not modeled. Implication Biennial mammography screening for breast cancer is efficient for average-risk women. Despite decades of mammography screening for early detection of breast cancer, there is no consensus on optimal strategies, target populations, or the magnitude of harms and benefits (111). The 2009 US Preventive Services Task Force (USPSTF) recommended biennial film mammography from age 50 to 74 years and suggested shared decision making about screening for women in their 40s (12). Since that recommendation was formulated, new data on the benefits of screening have emerged (2, 6, 8, 9, 11, 13, 14), digital mammography has essentially replaced plain film (15), and increasingly effective systemic treatment regimens for breast cancer have become standard (16). There has also been growing interest in consumer preferences and personalized screening approaches (1720). These factors could each affect the outcomes of breast cancer screening programs or alter policy decisions about population screening strategies (17). Modeling can inform screening policy decisions because it uses the best available evidence to evaluate a wide range of strategies while holding selected conditions (such as treatment effects) constant, facilitating strategy comparisons (21, 22). Modeling also provides a quantitative summary of outcomes in different groups and assesses how preferences affect results. Collaboration of several models provides a range of plausible effects and illustrates the effects of differences in model assumptions on results (1, 7, 23). We used 6 well-established simulation models to synthesize current data and examine the outcomes of digital mammography screening at various starting ages and intervals among average-risk women. We also examined how breast density, risk, or comorbidity levels affect results and whether preferences for health states related to screening and its downstream consequences affected conclusions. Methods Strategies We evaluated 8 strategies that varied by starting age (40, 45, or 50 years) and interval (annual, biennial, and hybrid [annual for women in their 40s and biennial thereafter]); all strategies stop screening at age 74 years. We included no screening as a baseline. Model Descriptions The models used to evaluate the screening strategies were developed within the Cancer Intervention and Surveillance Modeling Network (CISNET) (2430), and the research was institutional review boardapproved. They were named model D (Dana-Farber Cancer Institute, Boston, Massachusetts), model E (Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands), model GE (Georgetown University Medical Center, Washington, DC, and Albert Einstein College of Medicine, Bronx, New York), model M (MD Anderson Cancer Center, Houston, Texas), model S (Stanford University, Stanford, California), and model W (University of Wisconsin, Madison, Wisconsin, and Harvard Medical School, Boston, Massachusetts). The Appendix provides information on model validation. Since earlier analyses (1), the models have undergone substantial revision to reflect advances in breast cancer control, including portrayal of molecular subtypes based on estrogen receptor (ER) and human epidermal growth factor-2 receptor (HER2) status (23); current population incidence (31) and competing nonbreast cancer mortality; digital screening; and the most current therapies (32). All models except model S include ductal carcinoma in situ (DCIS). The general modeling approach is summarized in this article; full details, including approach, construction, data sources, assumptions, and implementation, are available at https://resources.cisnet.cancer.gov/registry and reference 33. Additional information is available on request, and the models are available for use via collaboration. The models begin with estimates of breast cancer incidence (31) and ER/HER2-specific survival trends without screening or adjuvant treatment, and then overlay data on screening and molecular subtypespecific adjuvant treatment to generate observed incidence and breast cancerspecific mortality trends in the U.S. population (1, 7, 17, 23, 33, 34). Breast cancer has a distribution of preclinical screen-detectable periods (sojourn time) and clinical detection points. Performance characteristics of digital mammography depend on age, first versus subsequent screen, time since last mammogram, and breast density. ER/HER2 status is assigned at diagnosis on the basis of stage and age. Molecular subtype and stage-specific treatment reduces the hazard of breast cancer death (models D, GE, M, and S) or results in a cure for some cases (models E and W). Women can die of breast cancer or other causes. Screen detection of cancer during the preclinical screen-detectable period can result in the identification and treatment of earlier-stage or smaller tumors than might occur via clinical detection, with a corresponding reduction in breast cancer mortality. We used a cohort of women born in 1970 with average-risk and average breast density and followed them from age 25 years (because breast cancer is rare before this age [0.08% of cases]) until death or age 100 years. Model Input Parameters The models used a common set of age-specific variables for breast cancer incidence, performance of digital mammography, treatment effects, and average and comorbidity-specific nonbreast cancer causes of death (20, 35). The parameter values are available at www.uspreventiveservicestaskforce.org/Page/Document/modeling-report-collaborative-modeling-of-us-breast-cancer-1/breast-cancer-screening1 (33). In addition, each group included model-specific inputs (or intermediate outputs) to represent preclinical detectable times, lead time, and age- and ER/HER2-specific stage distribution in screen- versus nonscreen-detected women on the basis of their specific model structure (1, 7, 2330). These model-specific parameters were based on assumptions about combinations of values that reproduced U.S. trends in incidence and breast cancerspecific mortality, including proportions of DCIS that were nonprogressive and would not be detected without screening. Models M and W also assumed some small nonprogressive invasive cancers. The models adopted an ageperiodcohort modeling approach to project incidence rates of breast cancer in the absence of screening (31, 36); model M used 19751979 rates from the Surveillance, Epidemiology, and End Results program. The models assumed 100% adherence to screening and receipt of the most effective treatment to isolate the effect of varying screening strategies. Four models used age-specific sensitivity values for digital mammography that were observed in the Breast Cancer Surveillance Consortium (BCSC) for detection of invasive and DCIS cancers combined (model S uses data for invasive cancers only). Separate values were used for initial and subsequent mammography by screening interval, using standard BCSC definitions: Annual includes data from screens occurring within 9 to 18 months of the prior screen, and biennial includes data on screens within 19 to 30 months (37, 38). Model D used these data as input variables (28), and models GE, S, and W used the data for calibration (24, 25, 27). Models E and M fit estimates from the BCSC and other data (26, 29). Women with ER-positive tumors received 5 years of hormone therapy and an anthracycline-based regimen accompanied by a taxane. Women with ER-negative invasive tumors received anthracycline-based regimens with a taxane. Those with HER2-positive tumors also received trastuzumab. Women with ER-positive DCIS received hormonal therapy (16). Treatment effectiveness was based on clinical trials and was modeled as a reduction in breast cancerspecific mortality risk or increase in the proportion cured compared with ER/HER2-specific survival in the absence of adjuvant treatment (32). Benefits Screening benefits (vs. no screening or incremental to other strategies) included percentage of reduction in breast cancer mortality, breast cancer deaths averted, and life-years and quality-adjusted life-years (QALYs) gained because of averted or delayed breast cancer death. Benefits (and harms) were accumulated from age 40 to 100 years to capture the lifetime effect of screening. We considered preferences, or utilities, to account for morbidity from screening and treatment. A disutility for age- and sex-specific general population health was first applied to quality-adjust the life-years (39). These were further adjusted to account for additional decrements in life-years related to undergoing screening (0.006 for 1 week, or 1 hour), evaluating a positive screen (0.105 for 5 weeks, or 3.7 days), undergoing initial treatment by stage (for the first 2 years after diagnosis), and having distant disease (for the last year of life for all women who die of breast cancer) (Appendix Table 1) (33, 40, 41). Appendix Table 1. Utility Input Parameter Values Use and Harms Use of services focused on the number of mammograms required for the screening strategy. Harms included false-positive mammograms, benign biopsies, and overdiagnosis. Rates of false-positive mammograms were calculated as mammograms read as abnormal or needing further work-up in women without cancer divided by the total number of screening

Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts

Background At least nineteen states have laws that require telling women with dense breasts and a negative screening mammogram to consider supplemental screening. The most readily available supplemental screening modality is ultrasound, yet little is known about its effectiveness.

Benefits, Harms, and Costs for Breast Cancer Screening After US Implementation of Digital Mammography

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

Race-Specific Impact of Natural History, Mammography Screening, and Adjuvant Treatment on Breast Cancer Mortality Rates in the United States

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

Personalizing Age of Cancer Screening Cessation Based on Comorbid Conditions: Model Estimates of Harms and Benefits

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 womens preferences regarding false positives.

Effects of Screening and Systemic Adjuvant Therapy on ER-Specific US Breast Cancer Mortality

Background: U.S. Black women have higher breast cancer mortality rates than White women despite lower incidence. The aim of this study is to investigate how much of the mortality disparity can be attributed to racial differences in natural history, uptake of mammography screening, and use of adjuvant therapy. Methods: Two simulation models use common national race, and age-specific data for incidence, screening and treatment dissemination, stage distributions, survival, and competing mortality from 1975 to 2010. Treatment effectiveness and mammography sensitivity are assumed to be the same for both races. We sequentially substituted Black parameters into the White model to identify parameters that drive the higher mortality for Black women in the current time period. Results: Both models accurately reproduced observed breast cancer incidence, stage and tumor size distributions, and breast cancer mortality for White women. The higher mortality for Black women could be attributed to differences in natural history parameters (26–44%), use of adjuvant therapy (11–19%), and uptake of mammography screening (7–8%), leaving 38% to 46% unexplained. Conclusion: Black women appear to have benefited less from cancer control advances than White women, with a greater race-related gap in the use of adjuvant therapy than screening. However, a greater portion of the disparity in mortality appears to be due to differences in natural history and undetermined factors. Impact: Breast cancer mortality may be reduced substantially by ensuring that Black women receive equal adjuvant treatment and screening as White women. More research on racial variation in breast cancer biology and treatment utilization is needed. Cancer Epidemiol Biomarkers Prev; 20(1); 112–22. ©2011 AACR.

Radiation-Induced Breast Cancer Incidence and Mortality from Digital Mammography Screening: A Modeling Study

BACKGROUND Harms and benefits of cancer screening depend on age and comorbid conditions, but reliable estimates are lacking. OBJECTIVE To estimate the harms and benefits of cancer screening by age and comorbid conditions to inform decisions about screening cessation. DESIGN Collaborative modeling with 7 cancer simulation models and common data on average and comorbid condition level-specific life expectancy. SETTING U.S. population. PATIENTS U.S. cohorts aged 66 to 90 years in 2010 with average health or 1 of 4 comorbid condition levels: none, mild, moderate, or severe. INTERVENTION Mammography, prostate-specific antigen testing, or fecal immunochemical testing. MEASUREMENTS Lifetime cancer deaths prevented and life-years gained (benefits); false-positive test results and overdiagnosed cancer cases (harms). For each comorbid condition level, the age at which harms and benefits of screening were similar to that for persons with average health having screening at age 74 years. RESULTS Screening 1000 women with average life expectancy at age 74 years for breast cancer resulted in 79 to 96 (range across models) false-positive results, 0.5 to 0.8 overdiagnosed cancer cases, and 0.7 to 0.9 prevented cancer deaths. Although absolute numbers of harms and benefits differed across cancer sites, the ages at which to cease screening were consistent across models and cancer sites. For persons with no, mild, moderate, and severe comorbid conditions, screening until ages 76, 74, 72, and 66 years, respectively, resulted in harms and benefits similar to average-health persons. LIMITATION Comorbid conditions influenced only life expectancy. CONCLUSION Comorbid conditions are an important determinant of harms and benefits of screening. Estimates of screening benefits and harms by comorbid condition can inform discussions between providers and patients about personalizing screening cessation decisions. PRIMARY FUNDING SOURCE National Cancer Institute and Centers for Disease Control and Prevention.