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BackgroundBiennial screening mammography retains most of the benefits of annual breast cancer screening with reduced harms. Whether screening guidelines based on race/ethnicity and age would be more effective than age-based guidelines is unknown.MethodsMammography data from the Breast Cancer Surveillance Consortium were linked to pathology and tumor databases. The authors identified women aged 40 to 74 years who underwent annual, biennial, or triennial screening mammography between 1994 and 2008. Logistic regression was used to estimate adjusted odds ratios (OR) and 95% confidence intervals (95% CI) of adverse tumor characteristics among 14,396 incident breast cancer cases and 10-year cumulative risks of false-positive recall and biopsy recommendation among 1,276,312 noncases.ResultsNo increased risk of adverse tumor characteristics associated with biennial versus annual screening were noted in white women, black women, Hispanic women aged 40 to 49 years, or Asian women aged 50 to 74 years. Hispanic women aged 50 to 74 years who screened biennially versus annually were found to have an increased risk of late-stage disease (OR, 1.6; 95% CI, 1.0-2.5) and large tumors (OR, 1.6; 95% CI, 1.1-2.4). Asian women aged 40 to 49 years who underwent biennial screening had an elevated risk of positive lymph nodes (OR, 3.1; 95% CI, 1.3-7.1). No elevated risks were associated with triennial versus biennial screening. Cumulative false-positive risks decreased markedly with a longer screening interval.ConclusionsThe authors found limited evidence of elevated risks of adverse tumor characteristics with biennial versus annual screening, whereas cumulative false-positive risks were lower. However, elevated risks of late-stage disease in Hispanic women and lymph node-positive disease in younger Asian women who screened less often than annually warrant consideration and replication.

ImportanceControversy exists about the frequency women should undergo screening mammography and whether screening interval should vary according to risk factors beyond age.ObjectiveTo compare the benefits and harms of screening mammography frequencies according to age, breast density, and postmenopausal hormone therapy (HT) use.DesignProspective cohort.SettingData collected January 1994 to December 2008 from mammography facilities in community practice that participate in the Breast Cancer Surveillance Consortium (BCSC) mammography registries.ParticipantsData were collected prospectively on 11,474 women with breast cancer and 922,624 without breast cancer who underwent mammography at facilities that participate in the BCSC.Main outcomes and measuresWe used logistic regression to calculate the odds of advanced stage (IIb, III, or IV) and large tumors (>20 mm in diameter) and 10-year cumulative probability of a false-positive mammography result by screening frequency, age, breast density, and HT use. The main predictor was screening mammography interval.ResultsMammography biennially vs annually for women aged 50 to 74 years does not increase risk of tumors with advanced stage or large size regardless of women's breast density or HT use. Among women aged 40 to 49 years with extremely dense breasts, biennial mammography vs annual is associated with increased risk of advanced-stage cancer (odds ratio [OR], 1.89; 95% CI, 1.06-3.39) and large tumors (OR, 2.39; 95% CI, 1.37-4.18). Cumulative probability of a false-positive mammography result was high among women undergoing annual mammography with extremely dense breasts who were either aged 40 to 49 years (65.5%) or used estrogen plus progestogen (65.8%) and was lower among women aged 50 to 74 years who underwent biennial or triennial mammography with scattered fibroglandular densities (30.7% and 21.9%, respectively) or fatty breasts (17.4% and 12.1%, respectively).Conclusions and relevanceWomen aged 50 to 74 years, even those with high breast density or HT use, who undergo biennial screening mammography have similar risk of advanced-stage disease and lower cumulative risk of false-positive results than those who undergo annual mammography. When deciding whether to undergo mammography, women aged 40 to 49 years who have extremely dense breasts should be informed that annual mammography may minimize their risk of advanced-stage disease but the cumulative risk of false-positive results is high.

Background Uncertainty exists about the appropriate use of screening mammography among older women because comorbid illnesses may diminish the benefit of screening. We examined the risk of adverse tumor characteristics and false positive rates according to screening interval, age, and comorbidity. Methods From January 1999 to December 2006, data were collected prospectively on 2993 older women with breast cancer and 137 949 older women without breast cancer who underwent mammography at facilities that participated in a data linkage between the Breast Cancer Surveillance Consortium and Medicare claims. Women were aged 66 to 89 years at study entry to allow for measurement of 1 year of preexisting illnesses. We used logistic regression analyses to calculate the odds of advanced (IIb, III, IV) stage, large (>20 millimeters) tumors, and 10-year cumulative probability of false-positive mammography by screening frequency (1 vs 2 years), age, and comorbidity score. The comorbidity score was derived using the Klabunde approximation of the Charlson score. All statistical tests were two-sided. Results Adverse tumor characteristics did not differ statistically significantly by comorbidity, age, or interval. Cumulative probability of a false-positive mammography result was higher among annual screeners than biennial screeners irrespective of comorbidity: 48.0% (95% confidence interval [CI] = 46.1% to 49.9%) of annual screeners aged 66 to 74 years had a false-positive result compared with 29.0% (95% CI = 28.1% to 29.9%) of biennial screeners. Conclusion Women aged 66 to 89 years who undergo biennial screening mammography have similar risk of advanced-stage disease and lower cumulative risk of a false-positive recommendation than annual screeners, regardless of comorbidity.

Background Imaging tests are one of the most frequently used diagnostic modalities in healthcare, but the benefits of their direct impacts on clinical decisionmaking have been countered by concerns that they can be overused. Assessing the relative value of imaging tests has largely focused on measures of test accuracy, which overlooks more comprehensive benefits and risks of imaging tests, particularly their impact on patient-centred outcomes (PCOs). We present the findings of the Patient Reported Outcomes of Diagnostics (PROD) research study in response to a methodological gap in the area of diagnostic test comparative effectiveness research. Methods Over a 3-year period, the PROD Study engaged with multiple stakeholders to identify existing conceptual models related to PCOs for imaging testing, conducted primary research and evidence synthesis, and developed consensus recommendations to describe and categorise PCOs related to imaging testing. Results The PROD framework categorises PCOs from imaging studies within four main domains: information or knowledge yielded, physical impact, emotional outcomes and test burden. PCOs interact with each other and influence effects across domains, and can be modified by factors related to the patient, clinical situation, healthcare team and the testing environment. Conclusions Using PCOs to inform healthcare decisionmaking will require ways of collating and presenting information on PCOs in ways that can inform patient- provider decision-making, and developing methods to determine the relative importance of outcomes (including test accuracy) to one another. [ABSTRACT FROM AUTHOR]

Background In the United States, influenza activity during the 2021–2022 season was modest and sufficient enough to estimate influenza vaccine effectiveness (VE) for the first time since the beginning of the coronavirus disease 2019 pandemic. We estimated influenza VE against laboratory-confirmed outpatient acute illness caused by predominant A(H3N2) viruses. Methods Between October 2021 and April 2022, research staff across 7 sites enrolled patients aged ≥6 months seeking outpatient care for acute respiratory illness with cough. Using a test-negative design, we assessed VE against influenza A(H3N2). Due to strong correlation between influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination, participants who tested positive for SARS-CoV-2 were excluded from VE estimations. Estimates were adjusted for site, age, month of illness, race/ethnicity, and general health status. Results Among 6260 participants, 468 (7%) tested positive for influenza only, including 440 (94%) for A(H3N2). All 206 sequenced A(H3N2) viruses were characterized as belonging to genetic group 3C.2a1b subclade 2a.2, which has antigenic differences from the 2021–2022 season A(H3N2) vaccine component that belongs to clade 3C.2a1b subclade 2a.1. After excluding 1948 SARS-CoV-2–positive patients, 4312 patients were included in analyses of influenza VE; 2463 (57%) were vaccinated against influenza. Effectiveness against A(H3N2) for all ages was 36% (95% confidence interval, 20%–49%) overall. Conclusions Influenza vaccination in 2021–2022 provided protection against influenza A(H3N2)-related outpatient visits among young persons. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) approaches facilitate risk prediction model development using high-dimensional predictors and higher-order interactions at the cost of model interpretability and transparency. We compared the relative predictive performance of statistical and ML models to guide modeling strategy selection for surveillance mammography outcomes in women with a personal history of breast cancer (PHBC). Methods: We cross-validated seven risk prediction models for two surveillance outcomes, failure (breast cancer within 12 months of a negative surveillance mammogram) and benefit (surveillance-detected breast cancer). We included 9,447 mammograms (495 failures, 1,414 benefits, and 7,538 nonevents) from years 1996 to 2017 using a 1:4 matched case-control samples of women with PHBC in the Breast Cancer Surveillance Consortium. We assessed model performance of conventional regression, regularized regressions (LASSO and elastic-net), and ML methods (random forests and gradient boosting machines) by evaluating their calibration and, among well-calibrated models, comparing the area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI). Results: LASSO and elastic-net consistently provided well-calibrated predicted risks for surveillance failure and benefit. The AUCs of LASSO and elastic-net were both 0.63 (95% CI, 0.60-0.66) for surveillance failure and 0.66 (95% CI, 0.64-0.68) for surveillance benefit, the highest among well-calibrated models. Conclusions: For predicting breast cancer surveillance mammography outcomes, regularized regression outperformed other modeling approaches and balanced the trade-off between model flexibility and interpretability. Impact: Regularized regression may be preferred for developing risk prediction models in other contexts with rare outcomes, similar training sample sizes, and low-dimensional features. [ABSTRACT FROM AUTHOR]