Clinical and sociodemographic determinants of older breast cancer survivors' reports of receiving advice about exercise.

PURPOSE: Exercise offers various clinical benefits to older breast cancer survivors. However, studies report that healthcare providers may not regularly discuss exercise with their patients. We evaluated clinical and sociodemographic determinants of receiving advice about exercise from healthcare providers among older breast cancer survivors (aged ≥65 years). METHODS: We used data from the Surveillance, Epidemiology, and End Results cancer registries linked to the Medicare Health Outcomes Survey (MHOS) from 2008 to 2015. We included female breast cancer survivors, aged ≥65 years, who completed the MHOS survey ≥2 years after a breast cancer diagnosis in a modified Poisson regression to identify clinical and sociodemographic determinants of reportedly receiving advice about exercise from healthcare providers. RESULTS: The sample included 1,836 breast cancer survivors. The median age of the sample was 76 years (range: 72-81). Overall, 10.7% of the survivors were non-Hispanic Black, 10.1% were Hispanic, and 69.3% were non-Hispanic White. Only 52.3% reported receiving advice about exercise from a healthcare provider. Higher body mass index (BMI) and comorbid medical history that included diabetes, cardiovascular, or musculoskeletal disease were each associated with a higher likelihood of receiving exercise advice. Lower education levels, lower BMI, and never having been married were each associated with a lower likelihood of receiving exercise advice. CONCLUSIONS: Nearly half of breast cancer survivors aged ≥65 years did not report receiving exercise advice from a healthcare provider, suggesting interventions are needed to improve exercise counseling between providers and survivors, especially with women with lower educational attainment who have never been married.

Benefits and Harms of Mammography Screening in 75 + Women to Inform Shared Decision-making: a Simulation Modeling Study.

BACKGROUND: Guidelines recommend shared decision-making (SDM) around mammography screening for women ≥ 75 years old. OBJECTIVE: To use microsimulation modeling to estimate the lifetime benefits and harms of screening women aged 75, 80, and 85 years based on their individual risk factors (family history, breast density, prior biopsy) and comorbidity level to support SDM in clinical practice. DESIGN, SETTING, AND PARTICIPANTS: We adapted two established Cancer Intervention and Surveillance Modeling Network (CISNET) models to evaluate the remaining lifetime benefits and harms of screening U.S. women born in 1940, at decision ages 75, 80, and 85 years considering their individual risk factors and comorbidity levels. Results were summarized for average- and higher-risk women (defined as having breast cancer family history, heterogeneously dense breasts, and no prior biopsy, 5% of the population). MAIN OUTCOMES AND MEASURES: Remaining lifetime breast cancers detected, deaths (breast cancer/other causes), false positives, and overdiagnoses for average- and higher-risk women by age and comorbidity level for screening (one or five screens) vs. no screening per 1000 women. RESULTS: Compared to stopping, one additional screen at 75 years old resulted in six and eight more breast cancers detected (10% overdiagnoses), one and two fewer breast cancer deaths, and 52 and 59 false positives per 1000 average- and higher-risk women without comorbidities, respectively. Five additional screens over 10 years led to 23 and 31 additional breast cancer cases (29-31% overdiagnoses), four and 15 breast cancer deaths avoided, and 238 and 268 false positives per 1000 average- and higher-risk screened women without comorbidities, respectively. Screening women at older ages (80 and 85 years old) and high comorbidity levels led to fewer breast cancer deaths and a higher percentage of overdiagnoses. CONCLUSIONS: Simulation models show that continuing screening in women ≥ 75 years old results in fewer breast cancer deaths but more false positive tests and overdiagnoses. Together, clinicians and 75 + women may use model output to weigh the benefits and harms of continued screening.

The population-level effects of omitting chemotherapy guided by a 21-gene expression assay in node-positive breast cancer: a simulation modeling study.

BACKGROUND: A recent trial showed that postmenopausal women diagnosed with hormone receptor-positive, human epidermal growth factor receptor-2 (HER2)-negative, lymph node-positive (1-3 nodes) breast cancer with a 21-gene recurrence score of ≤ 25 could safely omit chemotherapy. However, there are limited data on population-level long-term outcomes associated with omitting chemotherapy among diverse women seen in real-world practice. METHODS: We adapted an established, validated simulation model to generate the joint distributions of population-level characteristics of women diagnosed with early-stage breast cancer in the U.S. Input parameters were derived from cancer registry, meta-analyses, and clinical trial data. The effects of omitting chemotherapy on 10-year distant recurrence-free survival, life-years, and quality adjusted life-years (QALYs) were modeled for premenopausal and postmenopausal women. QALYs were discounted at 3%. Results were evaluated for subgroups stratified by race and ethnicity. Sensitivity analyses included testing results across a range of inputs. The model was validated using the published RxPONDER trial data. RESULTS: In premenopausal women, the 10-year distant recurrence-free survival rates were 85.3% with chemo-endocrine and 80.1% with endocrine therapy. The estimated life-years and QALYs gained with chemotherapy in premenopausal women were 2.1 and 0.6, respectively. There was no chemotherapy benefit in postmenopausal women. There was no variation in the absolute benefit of chemotherapy across racial or ethnic subgroups. However, there were differences in distant recurrence-free survival rates, life-years, and QALYs across groups. Sensitivity analysis showed similar results. The model closely replicated the RxPONDER trial. CONCLUSIONS: Modeled population-level outcomes show a small chemotherapy benefit in premenopausal women, but no benefit among postmenopausal women. Simulation modeling provides a useful tool to extend trial data and evaluate population-level outcomes.

The associations of muscle-strengthening exercise with recurrence and mortality among breast cancer survivors: a systematic review.

BACKGROUND: Our systematic review aimed to critically evaluate empirical literature describing the association of muscle-strengthening exercise (MSE) with recurrence and/or mortality among breast cancer survivors. METHODS: We included English-language empirical research studies examining the association between MSE and recurrence and/or mortality among females diagnosed with breast cancer. Seven databases (MEDLINE, PsycINFO, Embase, Scopus, Web of Science, Cochrane CENTRAL, and CINAHL) were searched in September 2023. Quality was appraised using the Mixed Methods Appraisal Tool. Results are summarized descriptively. RESULTS: Five sources were identified. MSE measurement differed in relation to the description of the MSE (i.e., muscle-strengthening vs. strength training), examples of activities (e.g., sit-ups or push-ups vs. calisthenics vs. circuit training), and exercise frequency (i.e., days vs. times/week). Findings offer provisional evidence that some MSE may lower the hazards of recurrence and mortality. This association may vary by race, weight status, and menopausal status. CONCLUSIONS: In summary, limited available evidence suggests that MSE may lower the hazards of recurrence and mortality. More consistent measurement and analyses would help generate findings that are more readily comparable and applicable to inform clinical practice. Further research is needed to improve understanding of the strength and differences of these associations among underserved and underrepresented women.

A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States.

Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools. Highlights: There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.

A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care.

PURPOSE: We reviewed existing personalized, web-based, interactive decision-making tools available to guide breast cancer treatment and survivorship care decisions in clinical settings. METHODS: The study was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We searched PubMed and related databases for interactive web-based decision-making tools developed to support breast cancer treatment and survivorship care from 2013 to 2023. Information on each tool's purpose, target population, data sources, individual and contextual characteristics, outcomes, validation, and usability testing were extracted. We completed a quality assessment for each tool using the International Patient Decision Aid Standard (IPDAS) instrument. RESULTS: We found 54 tools providing personalized breast cancer outcomes (e.g., recurrence) and treatment recommendations (e.g., chemotherapy) based on individual clinical (e.g., stage), genomic (e.g., 21-gene-recurrence score), behavioral (e.g., smoking), and contextual (e.g., insurance) characteristics. Forty-five tools were validated, and nine had undergone usability testing. However, validation and usability testing included mostly White, educated, and/or insured individuals. The average quality assessment score of the tools was 16 (range: 6-46; potential maximum: 63). CONCLUSIONS: There was wide variation in the characteristics, quality, validity, and usability of the tools. Future studies should consider diverse populations for tool development and testing. IMPLICATIONS FOR CANCER SURVIVORS: There are tools available to support personalized breast cancer treatment and survivorship care decisions in clinical settings. It is important for both cancer survivors and physicians to carefully consider the quality, validity, and usability of these tools before using them to guide care decisions.

Opportunities, challenges, and future directions for simulation modeling the effects of structural racism on cancer mortality in the United States: a scoping review.

PURPOSE: Structural racism could contribute to racial and ethnic disparities in cancer mortality via its broad effects on housing, economic opportunities, and health care. However, there has been limited focus on incorporating structural racism into simulation models designed to identify practice and policy strategies to support health equity. We reviewed studies evaluating structural racism and cancer mortality disparities to highlight opportunities, challenges, and future directions to capture this broad concept in simulation modeling research. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Articles published between 2018 and 2023 were searched including terms related to race, ethnicity, cancer-specific and all-cause mortality, and structural racism. We included studies evaluating the effects of structural racism on racial and ethnic disparities in cancer mortality in the United States. RESULTS: A total of 8345 articles were identified, and 183 articles were included. Studies used different measures, data sources, and methods. For example, in 20 studies, racial residential segregation, one component of structural racism, was measured by indices of dissimilarity, concentration at the extremes, redlining, or isolation. Data sources included cancer registries, claims, or institutional data linked to area-level metrics from the US census or historical mortgage data. Segregation was associated with worse survival. Nine studies were location specific, and the segregation measures were developed for Black, Hispanic, and White residents. CONCLUSIONS: A range of measures and data sources are available to capture the effects of structural racism. We provide a set of recommendations for best practices for modelers to consider when incorporating the effects of structural racism into simulation models.