Importance: The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known.

Objective: To estimate outcomes of various mammography screening strategies.

Design, Setting, and Population: Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses.

Exposures: Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and “real-world” treatment.

Main Outcomes and Measures: Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women.

Results: Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women.

Conclusions: This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.

Since 2009, the US Preventive Services Task Force (USPSTF) has recommended biennial mammography screening at ages 50 to 74 years, with clinical recommendations for discussion between patients and their primary care clinicians about individual risks and preferences for starting screening before age 50.^1,2 The USPSTF concluded in 2016 that the evidence was insufficient to assess the benefits and harms of digital breast tomosynthesis (DBT) as a primary screening method. In contrast to digital mammography, which uses a single radiograph projection per view, DBT involves multiple projections that are used to construct image slices, reducing tissue overlap. Screening facilities have been transitioning from digital mammography to DBT because of lower false-positive recall rates and higher cancer detection rates for DBT compared with digital mammography,^3,4 even though data do not show a reduction in rates of advanced cancer diagnosis.^5,6 Other changes since the 2016 recommendation include increasing breast cancer incidence among younger women and advances in treatment.⁷ Importantly, Black and African American women (hereafter referred to as Black women) continue to experience higher breast cancer mortality than White women despite similar rates of mammography screening and lower (but steadily increasing) rates of breast cancer incidence.⁸ The impact of these new data on the net benefit of screening mammography is unknown.

Population simulation models are a valuable tool for synthesizing evidence from observational and trial data to estimate the impact of different screening strategies. We used well-established Cancer Intervention and Surveillance Modeling Network (CISNET) models to estimate the benefits and harms of breast cancer screening strategies that varied by the ages to start and stop screening, modality, and interval for women overall and for Black women, including the impact of screening strategies on breast cancer mortality disparities for Black women. The results are provided to inform discussions about US breast cancer screening strategies by the USPSTF and other groups.

Model Overview

Six CISNET breast cancer models were used to estimate benefits and harms of mammography screening: Dana-Farber Cancer Institute (model D), Erasmus University Medical Center (model E), Georgetown Lombardi Comprehensive Cancer Center-Albert Einstein College of Medicine (model GE), University of Texas MD Anderson Cancer Center (model M), Stanford University (model S), and University of Wisconsin-Madison–Harvard Medical School (model W). These models were included in the 2 previous decision analyses conducted for the USPSTF.^9,10 Since the 2016 analysis, the models have incorporated several updates to inputs including screening performance characteristics for digital mammography and DBT, current breast cancer incidence trends, updated breast cancer stage and hormone receptor distributions, “real-world” treatment assignment and effects for women overall and for Black women. Detailed descriptions of each model are available elsewhere^11-17 and in an online technical report.¹⁸ The University of Wisconsin Health Sciences institutional review board determined that this study was not human subjects research.

Population for Analysis

These analyses modeled a single cohort of US women with no personal history of breast cancer born in 1980 (ie, age 40 years in 2020) excluding women at the highest risk (ie, genetic susceptibility mutations or chest radiation at a young age). The models began with women at birth or age 20 or 25 years (since breast cancer is rare before this age; the initiation age varied by model) and accumulated all outcomes until death. The models evaluated women overall and Black women, and strata according to breast density, elevated risk, or comorbidity level. The term “women” was used while recognizing that not all individuals eligible for mammography screening self-identify as women.¹⁹ Since model results are based on data for sex (ie, female) rather than gender identity, models apply to cisgender women and may not accurately reflect breast cancer risk for transgender men and nonbinary persons. This modeling analysis treated race as a social construct and aimed to provide evidence regarding the trade-offs of mammography screening strategies for self-identified Black women as an approach to reduce the observed disparities in breast cancer mortality.²⁰

Model Input Parameters

All 6 models used a common set of data inputs for women overall and 4 models included race-specific inputs for Black women for breast cancer incidence, breast density, digital mammography and DBT performance, treatment assignment and efficacy, and causes of death other than breast cancer (Table 1).¹⁸ In addition, model-specific parameters were used to represent preclinical detectable times, lead-time, and age- and estrogen receptor (ER)/human epidermal growth factor receptor 2 (HER2)–specific stage distribution in screen-detected vs non–screen-detected cases on the basis of each model’s structure.

Five of the 6 models adapted an age-period-cohort modeling approach to estimate breast cancer incidence in the absence of screening among the overall and Black female population;^21,22 model M used Surveillance, Epidemiology, and End Results (SEER) rates with a linear model based on rates in 1975 and calibrated over time.¹² Incidence was increased for subgroups with elevated risk or with greater breast density. Density was modeled by Breast Imaging Reporting and Data Systems (BI-RADS) categories: almost entirely fatty (“a”), scattered fibroglandular densities (“b”), heterogeneously dense (“c”), and extremely dense (“d”).⁴⁰ Density category was assigned at age 40 years and remained the same or decreased by 1 level at age 50 years and again at age 65, based on observed age-specific prevalence rates in the Breast Cancer Surveillance Consortium (BCSC).¹⁸ Density was related to breast cancer risk and screening performance but was assumed to not affect molecular subtype or disease natural history (eg, tumor growth rates). Models incorporated screening sensitivity applied to each mammogram a woman received. Age-specific sensitivity values for digital mammography and DBT (hereafter referred to collectively as mammography) overall and by density category were also based on data from the BCSC.¹⁸ Data for the BCSC reflects breast imaging in community practice across the US.⁴¹

With treatment, screen detection at an earlier stage could lead to improved survival, reduced risk of death, and/or greater chance of cure with a smaller tumor size, depending on model. Treatment was assigned based on age, stage, and molecular subtype. To reflect real-world patterns of breast cancer care, the probability of receiving specific types of systemic treatment was based on data from the National Comprehensive Cancer Network as previously reported and, for newer therapies, expert opinion.^25,26 Efficacy of systemic therapy was based on the most recent published meta-analysis of clinical trials and, for newer therapies, clinical trial reports;^28,29 treatment efficacy (in the setting of optimal stage–based and tumor subtype–based treatment) was assumed to be equal by race.⁴² In contrast to efficacy, treatment effectiveness was modeled as lower for Black women due to multiple factors that may arise from systemic racism and lead to worse treatment quality (eg, delayed initiation, suboptimal regimens, dose reductions, and incomplete cycles).^43-46 Based on published data, treatment benefit was therefore reduced by 28% for ER-negative tumors and 56% for ER-positive tumors in models restricted to Black women.²⁷

Probability of death from non–breast cancer causes was derived from Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) and the Human Mortality Database; these values were replaced by comorbidity-specific values in subgroup analyses.^35,36

Screening Strategies

We compared model results for 36 mammography screening scenarios that varied by modality (digital mammography or DBT performed with concurrent or synthetic digital mammography),^47-52 starting age (40, 45, or 50 years) and stopping age (74 or 79 years), and interval (annual, biennial, or hybrid intervals). The 3 hybrid screening scenarios were (1) annual from ages 40 to 49 then biennial at age 50; (2) annual from ages 45 to 54 then biennial at age 55; and (3) annual from ages 45 to 49 then biennial at age 50. The models assumed 100% adherence to screening.

Outcomes

Benefits included percent reduction in breast cancer mortality, breast cancer deaths averted, and life-years gained (LYG) over the lifetimes of 1000 women screened compared with no screening. We also examined quality-adjusted life-years (QALYs) gained, which were calculated using age-specific utilities for women in the general population,^38,53 with disutilities applied for undergoing screening, diagnostic evaluation, and breast cancer treatment based on the stage at diagnosis (eTable 1 in the JAMA Supplement^37,39).

Harms accumulated over the lifetime included recalls for additional imaging in women without cancer (hereafter referred to as false-positive recalls), benign results from biopsies recommended for findings on screening mammography (hereafter referred to as benign biopsies), and overdiagnosed cases of ductal carcinoma in situ (DCIS) and invasive breast cancer. Overdiagnosis was defined as the excess breast cancer cases diagnosed in the presence of screening that were not diagnosed in the absence of screening over the lifetime. The harm of overtreatment after overdiagnosis was captured by the treatment-related decrement in utility without a change in life expectancy.

Analysis

Outcomes were tallied from age 40 years (the youngest age to start screening across strategies) to death and expressed per 1000 women. Results were summarized by the median and range across models for each outcome. We also generated efficiency frontiers by plotting the sequence of strategies that represented the largest incremental percent breast cancer mortality reduction (or LYG) per mammogram performed. Screening strategies on this frontier were considered the most efficient (ie, no alternative existed that provided equal or greater benefit with fewer screens or harms). Because a strategy providing outcomes that was very similar to an efficient strategy may be still be considered by decision-makers for other reasons (eg, consistency of starting and stopping ages across screening modalities),⁵⁴ we also identified “near-efficient” strategies⁵⁵ defined as a strategy within 5% of the value for screening biennially from ages 50 to 74 with DBT. Strategies that had more harms and/or fewer benefits were referred to as “inferior” to (inefficient or dominated by) other strategies.

Analyses were repeated for Black women and for strata according to density category, elevated relative risk of breast cancer, or comorbidity level.

In sensitivity analyses, for comparison with previous modeling in 2009 and 2016, we repeated the analysis assuming all women with cancer received the most effective therapy (vs the real-world patterns used in the primary analyses).

Screening Strategies for the Overall Population

The 6 models produced consistent results for the screening strategies (eTables 2 and 3 in the JAMA Supplement). For instance, biennial screening with DBT from ages 40 to 74 years yielded a median 30.0% (range, 24.0%-33.7%) reduction in breast cancer mortality vs no screening, with 1376 (range, 1354-1384) false-positive recalls per 1000 women screened (Table 2). Compared with biennial screening with DBT from ages 50 to 74 years, starting at age 40 averted 1.3 (range, 0.9-3.2) additional breast cancer deaths, with 503 (range, 493-506) additional false-positive recalls, 65 (range, 62-66) additional benign biopsies, and 2 (range, 0-4) more overdiagnosed cases per 1000 women screened (Table 3).

Annual screening led to greater reductions in mortality than biennial strategies, with a 37.0% median reduction (range, 33.6%-38.9%) (Table 2) with screening annually from ages 40 to 74 years with DBT but resulted in more false-positive recalls, benign biopsies, and overdiagnosed cases.

With biennial screening from ages 40 to 74 years, digital mammography resulted in 1540 false-positive recalls and 210 benign biopsies per 1000 women screened vs 1376 and 201, respectively, with DBT (Table 2). Use of DBT instead of digital mammography further decreased breast cancer mortality by approximately 1 percentage point and averted less than 1 additional breast cancer death per 1000 women and reduced false-positive recalls by approximately 150-300 per 1000 women over their lifetimes among 9 screening strategies stopping at age 74 (eTable 4 in the JAMA Supplement).

Stopping screening at age 79 vs 74 years generally resulted in an additional 3– to 5–percentage point mortality reduction, 1 additional breast cancer death averted, 64 to 172 more false-positive recalls per 1000 women, and 2 to 4 additional overdiagnosed cases, depending on strategy (eTable 5 in the JAMA Supplement).

Among all possible strategies, 5 DBT screening strategies were identified as efficient or near-efficient for both percent mortality reduction and LYG in at least 5 of 6 models, including one with stopping age 74 years (biennial starting at age 50) and 4 with stopping age 79 (biennial starting at age 40; biennial starting at age 45; annual from ages 40 to 49 with biennial thereafter; and annual starting at age 40) (Figure 1; eFigures 1 and 2 and eTable 6 in the JAMA Supplement). Efficient strategies ranged from 1.7 to 4.3 more breast cancer deaths averted and 41 to 168 more benign biopsies than screening biennially from ages 50 to 74 years per 1000 women (Figure 2). Five similar strategies were identified as efficient when limited to the 18 options with stopping age 74 (biennial starting at age 40, biennial starting at age 45, biennial starting at age 50, annual at ages 40 to 49 with biennial at ages 50 to 74, and annual at ages 40 to 74; eFigure 3 in the JAMA Supplement).

Screening Strategies for Black Women

Seven screening strategies were efficient or near-efficient for LYG or breast cancer mortality reduction among Black women (Figure 1; eFigures 4 and 5 and eTable 7 in the JAMA Supplement). Three strategies were efficient or near-efficient for both metrics among most models, including biennial from ages 40 to 79 years, biennial from ages 45 to 79, and annual from ages 40 to 79. Expanding biennial screening with DBT from ages 50 to 74 to ages 40 to 74 or 79 averted a median of 1.8 and 3.0 additional breast cancer deaths across models, respectively (Figure 2).

Trade-offs between benefits and harms of different screening strategies for Black women followed similar patterns as for all women combined (eTables 8-10 in the JAMA Supplement). All strategies resulted in more breast cancer deaths averted and LYG for Black women compared with the same strategies for women overall. However, this gain in averted breast cancer deaths was insufficient to reduce breast cancer mortality disparities for Black women compared with women overall. Specifically, if Black women were screened with the same strategy as for women overall, breast cancer mortality for Black women would remain more than 40% greater than for women overall (Table 4). Alternatively, if Black women were screened annually from ages 40 to 49 years with biennial screening from ages 50 to 79 and the overall population was screened biennially from ages 40 to 74, the ratio of breast cancer mortality rate for Black women vs women overall would be reduced from 1.44 (28.8/20.0) to 1.34 (26.8/ 20.0; a disparity reduction of 23%). Notably, Black women screened annually at ages 40 to 49 and biennially at ages 50 to 79 would experience fewer false-positives and mammograms per breast cancer death averted with greater life-years gained than women overall screened biennially at ages 40 to 74 (eTable 10 in JAMA Supplement).

Density, Elevated Risk, and Comorbidity Subgroups

Only 3 strategies were efficient in most models for women with dense breasts (BI-RADS category c and d), including biennial screening from ages 50 to 74 years, biennial screening from ages 40 to 79, and annual screening at ages 40 to 79 (eTable 11 in the JAMA Supplement). Across all strategies efficient in at least 1 density category, breast cancer deaths averted using DBT for women with almost entirely fatty breasts ranged from 4.9 for biennial screening at ages 50 to 74 to 7.6 with annual screening at ages 40 to 79 and increased among women with extremely dense breasts from 8.3 to 14.6 (eTable 12 in the JAMA Supplement).

Models showed greater benefits and fewer harms as breast cancer risk increased to 150% and 200% of average risk, with the same 3 screening strategies efficient for both elevated risk levels as for dense breasts (eTable 13 in the JAMA Supplement). Incremental benefits of screening after age 74 years were reduced in the presence of severe comorbidities (eTable 14 in the JAMA Supplement).

Sensitivity Analysis

When all breast cancer cases received the most effective treatment for their cancer subtype and screening stopped at age 74 years, the percent reduction in breast cancer mortality increased as compared with the primary analysis, in which cases received treatment based on real-world treatment patterns (eTable 15 in the JAMA Supplement).

This study used 6 well-established models to estimate the potential benefits and harms of different breast cancer screening strategies in the US. The models demonstrated that screening initiation at age 40 years had superior benefit-to-harm tradeoffs compared with no screening and other screening strategies. Benefits of DBT were comparable with those of digital mammography but resulted in fewer false-positive recalls and similar benign biopsies. Annual screening would lead to greater reductions in breast cancer mortality than biennial strategies but correspondingly more false-positive recalls and overdiagnosed cases. Since breast cancer death rates are higher for Black women, all screening strategies generated greater survival and mortality benefits for Black women than for women overall. However, to reduce racial disparities in breast cancer mortality in the absence of improved equity in the treatment setting, an increase in screening intensity such as annual screening of Black women from ages 40 to 49 would also be needed. Benefits for women with elevated risk or higher breast density were higher than for women overall, but the rankings of strategies were similar to those for women overall. In addition, several strategies with a stopping age of 79 were efficient. For women aged 75 to 79, comorbidities may be an important factor in decisions about when to cease breast cancer screening.

Compared with our 2016 analysis,¹⁰ the predicted benefit-to-harm ratios with biennial strategies starting at age 40 or 45 years have modestly improved. Due to recent increases in breast cancer incidence among women aged 40-49 (154.1 to 160.5 per 100,000 from 1999 to 2018), life-years gained were notably higher for screening strategies that started at age 40 or 45.^7,56 Past analyses assumed optimal treatment selection; starting screening earlier partially compensated for less-than-optimal real-world treatment uptake in the current analysis. Also, with the growing evidence for lower false-positive recall rates with DBT than with digital mammography,^3,4 fewer harms were associated with earlier ages of screening initiation than occurred in prior analyses.

Prospective studies that include multiple rounds of breast cancer screening are needed to determine whether, compared with digital mammography, DBT results in a shift toward detecting breast cancer at earlier stages with a concomitant decrease in advanced stage. Initial studies suggest that DBT leads to increased detection of stage I invasive breast cancer as compared with digital mammography, although a reduction in advanced stage has not yet been demonstrated.^6,57-59 Screening benefit related to reductions in breast cancer deaths depends on the advantage of beginning treatment in earlier vs more advanced stages.

This analysis extended findings published in 2021 for a model (GE) that evaluated strategies for reducing breast cancer mortality disparities and improving health equity between Black and White women.⁶⁰ Our models are intended to generate findings for individuals who self-identify as Black, defining race as a social construct where the sociopolitical environment influences biological processes over the life course.^61-63 The current study showed that Black women gained more life-years per mammogram than women overall for each screening strategy. This was due in part to Black women having higher breast cancer mortality, especially among younger women, and gaining less benefit from intended therapy due to worse quality of care. If Black women obtained annual mammography from age 40 to 49 years with biennial screening afterward, mortality disparities were projected to decline while also achieving similar benefit-to-harm tradeoffs as biennial screening starting at age 40 for women overall. These results are similar to those recently published by others using US mortality data that more intensive screening could potentially reduce the Black/White disparity in breast cancer mortality.⁶⁴ If health care systems, policymakers, clinicians, and scientists work to fully eliminate disparities experienced by Black women, the balance of benefits and harms for screening could eventually change to the extent that more intensive screening strategies for Black women are no longer needed to increase equity. However, as described by Chapman et al,⁶⁰ until treatment disparities are substantially decreased or eliminated, screening Black women more intensively represents an immediate possible solution for improving equity. Optimal implementation of any strategy will also require improved equity in DBT access and timely diagnostic workup.⁶⁵

Our analysis considered breast cancer screening strategies using mammography, which has poorer performance in women with dense breasts compared with nondense breasts. Our models estimated that for any given mammography screening strategy, women with dense breasts had more deaths averted and greater life-years gained per mammogram than those with nondense breasts, but false-positive recall rates were higher. Evidence on the impact of supplemental screening with breast magnetic resonance imaging (MRI) or ultrasound for women with dense breasts is limited.^66,67 With federal regulations expanding breast density notification in September 2024 and the absence of consistent clinical guidelines for supplemental screening,⁶⁸ this is a critical area for future research and policymaking.

After accounting for recent trends in life expectancy (prior to the COVID-19 pandemic) and improvements in breast cancer therapies, strategies with screening until age 79 years were identified as efficient. This is consistent with a recent simulation study but contrasts with an emulated trial based on Medicare data showing that breast cancer mortality was not significantly reduced among women screened through age 79.^69,70 Current breast cancer screening trials in progress, including TMIST and WISDOM, are not recruiting women older than 74, and trials testing screening in older women are unlikely to be conducted. Evidence from other types of studies is needed to better understand outcomes of screening for older women.

Relative rankings of strategies were similar across the models. However, the models differ in meaningful ways in structure and assumptions. For example, some models incorporated a benefit from screening due to within-stage shift in detection and subsequent treatment (models E, S, and W) while others required a stage shift (models D and GE) or assigned greater benefit for screen-detected than clinically detected cases within each stage at detection (model M). Among the 5 models that included DCIS as well as invasive breast cancer, 3 models found that the overall number of overdiagnosed cases exceeded the number of breast cancer deaths averted for all screening strategies considered. Underlying incidence in the absence of screening and the proportion of tumors that were nonprogressive are unknown and unobservable; therefore, the different results across models with their respective assumptions about breast cancer natural history provide a range of possible estimates.

Limitations

This research has many important strengths, including the collaboration of 6 independent modeling teams with consistent results and use of the most current data on incidence, screening performance, and modern, real-world therapy. Several caveats should also be considered in interpreting our results. First, the models portray the entire lifetime of women in the 1980 birth cohort and assume that future trends continued along the same trajectories as observed now. Second, we compared results for Black women with the overall female population, which leads to an underestimate of the impact of racism. This was a necessary simplification because these models did not produce estimates for other minoritized groups, non-Black women, or White women. In future research, models will be developed to examine results by racial and ethnic groups as well as interventions to improve health equity. Finally, some analyses were based on findings from fewer than 6 models for pragmatic reasons. In particular, some models were well-poised to examine analyses of racial disparities,⁶⁰ breast density,⁷¹ or comorbidities³⁶ due to programming completed in previous projects.

Overall, this analysis suggests that biennial screening starting at ages 40 or 45 years with digital mammography or DBT and continuing through age 74 or 79 provides gains in life-years and breast cancer mortality reduction per mammogram—and averts more deaths from breast cancer among Black women—than waiting to start screening at age 50. More intensive screening for populations of women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce breast cancer mortality disparities. In the presence of recent changes in breast cancer incidence and improvements in screening technology and breast cancer therapy, mammography screening remains an important strategy to reduce breast cancer burden.

Source: This article was published online in JAMA on April 30, 2024 (JAMA. doi:10.1001/jama.2023.24766).

Conflict of Interest Disclosures: Dr Chapman reported receiving personal fees from ASCO Advantage Program/Daiichi Sankyo outside the submitted work. Dr Caswell-Jin reported receiving grants from Novartis, Effector Therapeutics, and QED Therapeutics outside the submitted work. Dr Li reported holding stock in Agenus Inc and Mink Therapeutics Inc outside the submitted work. Dr Berry reported receiving grants from MD Anderson Cancer Center of the University of Texas during the conduct of the study and being co-owner of Berry Consultants LLC, a company that designs bayesian adaptive clinical trials for pharmaceutical and medical device companies, National Institutes of Health (NIH) cooperative groups, patient advocacy groups, and international consortia, outside the submitted work. Dr Plevritis reported serving as a scientific advisor to Adela Biosciences. Dr X. Huang reported receiving grants from University of Texas MD Anderson Cancer Center during the conduct of the study. Dr van Ravesteyn reported receiving consulting fees (paid to institution) from Wickenstones outside the submitted work. Dr Alagoz reported receiving personal fees from Bristol Myers Squibb, Johnson & Johnson, and Exact Sciences and owning stock in Innovo Analytics LLC outside the submitted work. No other disclosures were reported.

Funding/Support: This report is based on research conducted by the CISNET Breast Cancer Working Group under National Cancer Institute grant U01CA253911. This research was also supported in part by National Cancer Institute (NCI) grant P30CA014520 and P01CA154292 and a Vilas Associate Award to Dr Trentham-Dietz by the University of Wisconsin-Madison. Dr Jayasekera was supported by the Division of Intramural Research at the National Institute on Minority Health and Health Disparities of the National Institutes of Health (NIH) and the NIH Distinguished Scholars Program. The Breast Cancer Surveillance Consortium (http://www.bcsc-research.org/) and its data collection and data sharing activities are funded by the NCI (P01CA154292).

Role of the Funder/Sponsor: Investigators worked with USPSTF members, AHRQ staff, and the EPC review team to define the scope of the project and key questions to be addressed. AHRQ had no role in the conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript findings.

Additional Contributions: We thank the following individuals for their contributions to this project: Tracy Wolff, MD, MPH (US Preventive Services Task Force Program); Howard Tracer, MD (AHRQ); Jillian Henderson, PhD, MPH, Elizabeth Webber, MS, and other members of the Kaiser Permanente Evidence-based Practice Center; members of the US Preventive Services Task Force who contributed to the decision analysis work plan; Doug Owens, MD, MS (Stanford University), Ya-Chen Tina Shih, PhD (MD Anderson Cancer Center), Jennifer Croswell, MD, MPH (Healthcare Delivery Research Program, National Cancer Institute), Sarah M. Temkin, MD (Office of Research on Women’s Health, National Institutes of Health), and 2 additional content experts for their review of the draft report; Julie McGregor, BA, Jennie Martin, MS, and Victoria Foster, MS, for project coordination at the University of Wisconsin-Madison; Linn Abraham, MS (Kaiser Permanente Washington), and Thomas Lawler, PhD (University of Wisconsin-Madison), for assistance with data; and Amy Knudsen, PhD (Massachusetts General Hospital), Claudia Seguin, BA (Massachusetts General Hospital), and Hannah Johnson, MPH (University of Wisconsin-Madison), for assistance with graphing. None of the persons acknowledged received any additional compensation for their contributions.

Additional Information: The small writing committee (Drs Trentham-Dietz, Chapman, Jayasekera, Lowry, Miglioretti, and Mandelblatt) wrote initial drafts and solicited input from the larger group.

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The line represents the estimated efficiency frontier for model D; labels above line indicate efficient and near-efficient strategies. Efficiency frontier graphs for all models are shown in the JAMA Supplement. Grey shading, in which near-efficient strategies are located, shows area within 5% of the value for screening biennially during ages 50 to 74 years with digital breast tomosynthesis (DBT). For panels A and C, near-efficient strategies included those within 2.20 days of life gained per woman of the efficient frontier for all women and 3.22 days of life per Black woman. For B and D, near-efficient strategies included those within 5% of the efficiency frontier on a relative scale—on an absolute scale, equivalent to 1.27 percentage points for women overall and 1.21 points for Black women. Strategies vary by age at starting and stopping screening, interval between mammograms, and screening modality. A indicates annual; and B, biennial.

^a Near-efficient.

All strategies use digital breast tomosynthesis. Results shown as medians across 6 models of women overall (D [Dana-Farber Cancer Institute], E [Erasmus Medical Center], GE [Georgetown Lombardi Comprehensive Cancer Center-Albert Einstein College of Medicine], M [University of Texas MD Anderson Cancer Center], S [Stanford University], and W [University of Wisconsin-Madison and Harvard Pilgrim Health Care Institute]) and across 4 models of Black women (D, GE, M, and W). Differences in medians calculated by subtracting values in Table 2, Table 3, and eTables 8 and 9 in the JAMA Supplement may not be equivalent to the median of the differences across models, as shown in this figure.
^a Efficient or near-efficient in most models.

Abbreviations: AJCC, American Joint Committee on Cancer; BCSC, Breast Cancer Surveillance Consortium; BI-RADS, Breast Imaging Reporting and Data Systems; CISNET, Cancer Intervention and Surveillance Modeling Network; DBT, digital breast tomosynthesis; ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; NCCN, National Comprehensive Cancer Network; SEER, Surveillance, Epidemiology, and End Results.
^a Additional information regarding model inputs including BCSC data are available in the online Technical Report.^18
b With treatment, screen detection of breast cancer at an earlier stage could lead to improve survival, reduced risk of death, and/or greater chance of cure with a small tumor size, depending on model.

^a Overdiagnosed cases are in situ and invasive breast cancer cases that would not have been clinically detected in the absence of screening. Overdiagnosis is calculated by subtracting the number of cases detected in the screening scenario from the number of cases detected in the no-screening scenario. Model S (Stanford University) is excluded because it does not include ductal carcinoma in situ.
^b Digital mammography strategies show results for models D (Dana-Farber Cancer Institute), E (Erasmus Medical Center), GE (Georgetown Lombardi Comprehensive Cancer Center-Albert Einstein College of Medicine), M (University of Texas MD Anderson Cancer Center), and W (University of Wisconsin-Madison and Harvard Pilgrim Health Care Institute).
^c Digital breast tomosynthesis strategies show results for models D, E, GE, M, S, and W.

Abbreviations: DBT, digital breast tomosynthesis; DCIS, ductal carcinoma in situ; DM, digital mammography; NA, not available.
^a D indicates Dana-Farber Cancer Institute; E, Erasmus Medical Center;  GE, Georgetown Lombardi Comprehensive Cancer Center-Albert Einstein College of Medicine;  M, University of Texas MD Anderson Cancer Center; S, Stanford University; W, University of Wisconsin-Madison and Harvard Pilgrim Health Care Institute.

^a Calculations use the median values for breast cancer deaths from 4 models (D [Dana-Farber Cancer Institute], GE [Georgetown Lombardi Comprehensive Cancer Center-Albert Einstein College of Medicine], M [University of Texas MD Anderson Cancer Center], and W [University of Wisconsin-Madison and Harvard Pilgrim Health Care Institute]). Strategies limited to efficient and near-efficient strategies for both percent breast cancer mortality reduction and life-years gained vs no screening in most models for all women, listed in eTable 6 in the JAMA Supplement, along with selected other strategies.
^b Strategy not efficient nor near-efficient for at least 3 of 4 models for both percent breast cancer mortality reduction and life-years gained vs no screening for Black women as shown in eTable 7 in the JAMA Supplement.
^c Strategy not efficient nor near-efficient for at least 5 of 6 models for both percent breast cancer mortality reduction and life-years gained vs no screening for women overall as shown in eTable 6 in the JAMA Supplement.