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Introduction: Cancer‐related stigma is a key driver of advanced breast cancer stage in Sub‐Saharan Africa (SSA). We developed and tested the impact of a breast cancer survivor‐led Stigma reduction intervention (SRI) on stigma and treatment adherence of newly diagnosed patients with breast cancer in Tanzania. Methods: Breast cancer survivors were trained on breast cancer knowledge and motivational interviewing. A total of 4 trained survivors delivered a SRI (standardized flipchart breast education talk, personal testimony, and motivational interviewing) to 30 newly diagnosed patients with breast cancer before treatment. Pre‐ and post‐intervention knowledge surveys and stigma scale surveys were analyzed via Fisher's exact test and Wilcoxon rank‐sum tests. A discussion was held with a group of survivors after the intervention period to elicit feedback on their intervention experience. Results: Among the 30 patients, breast cancer knowledge (median overall percent correct) increased from 28% (IQR: 18%–45%) to 85% (IQR: 79%–88%) (p < 0.001) and stigma (median score) decreased from 75 (IQR: 57–81) to 53 (IQR: 44–66) (p < 0.01) following the intervention. All participants were willing to pursue hospital‐based treatment after undergoing the intervention. Eighty‐seven percent (n = 26) initiated treatment at 8‐week follow‐up after the intervention. All survivors endorsed feeling empowered and valued in their role in this intervention. Conclusions: Breast cancer survivors are a powerful group to combat the lack of knowledge and stigma in community and healthcare settings. Expanding the scope and scale of this intervention holds promise for improving treatment‐seeking behavior and ultimately breast cancer outcomes in SSA. [ABSTRACT FROM AUTHOR]

PURPOSE: Previous randomized controlled trials have demonstrated benefit from remote symptom monitoring (RSM) with electronic patient-reported outcomes. However, the racial diversity of enrolled patients was low and did not reflect the real-world racial proportions for individuals with cancer. METHODS: This secondary, cross-sectional analysis evaluated engagement of patients with cancer in a RSM program. Patient-reported race was grouped as Black, Other, or White. Patient address was used to map patient residence to determine rurality using Rural-Urban Commuting Area Codes and neighborhood disadvantage using Area Deprivation Index. Key outcomes included (1) being approached for RSM enrollment, (2) declining enrollment, (3) adherence with RSM via continuous completion of symptom surveys, and (4) withdrawal from RSM participation. Risk ratios (RR) and 95% CI were estimated from modified Poisson models with robust SEs. RESULTS: Between May 2021 and May 2023, 883 patients were approached to participate, of which 56 (6%) declined RSM. Of those who enrolled in RSM, a total of 27% of patients were Black or African American and 67% were White. In adjusted models, all patient population subgroups of interest had similar likelihoods of being approached for RSM participation; however, Black or African American patients were more than 3× more likely to decline participation than White participants (RR, 3.09 [95% CI, 1.73 to 5.53]). Patients living in more disadvantaged neighborhoods were less likely to decline (RR, 0.49 [95% CI, 0.24 to 1.02]), but less likely to adhere to surveys (RR, 0.81 [95% CI, 0.68 to 0.97]). All patient populations had a similar likelihood of withdrawing. CONCLUSION: Black patients and individuals living in more disadvantaged neighborhoods are at risk for lower engagement in RSM. Further work is needed to identify and overcome barriers to equitable participation. Black patients and individuals living in more disadvantaged neighborhoods are at risk for lower engagement in remote symptom monitoring [ABSTRACT FROM AUTHOR]

PURPOSE: Health care contact days—days spent receiving health care outside the home—represent an intuitive, practical, and person-centered measure of time consumed by health care. METHODS: We linked 2019 Medicare Current Beneficiary Survey and traditional Medicare claims data for community-dwelling older adults with a history of cancer. We identified contact days (ie, spent in a hospital, emergency department, skilled nursing facility, or inpatient hospice or receiving ambulatory care including an office visit, procedure, treatment, imaging, or test) and described patterns of total and ambulatory contact days. Using weighted Poisson regression models, we identified factors associated with contact days. RESULTS: We included 1,168 older adults representing 4.51 million cancer survivors (median age, 76.4 years, 52.8% women). The median (IQR) time from cancer diagnosis was 65 (27-126) months. In 2019, these adults had mean (standard deviation) total contact days of 28.4 (27.6) and ambulatory contact days of 24.2 (23.6). These included days for tests (8.0 [8.8]), imaging (3.6 [4.1]), visits with any clinicians (12.4 [11.5]), and visits with primary care clinicians (4.4 [4.7]), and nononcology specialists (7.1 [9.4]) specifically. Sixty-four percent of days with a nonvisit ambulatory service (eg, a test) were not on the same day as a clinician visit. Factors associated with more total contact days included younger age, lower income, more chronic conditions, poor self-rated health, and tendency to "go to doctor as soon as feel bad." CONCLUSION: Older adult cancer survivors spent nearly 1 month of the year receiving health care outside the home. This care was largely ambulatory, often delivered by nononcologists, and varied by factors beyond clinical characteristics. These results highlight the need to recognize patient burdens and improve survivorship care delivery, including through care coordination. Older cancer survivors spend nearly 30 days of a calendar year receiving health care outside their home! [ABSTRACT FROM AUTHOR]

Dr Kaufmann and Dr Rocque envision how #ePRO symptom monitoring can link patients to supportive care services. #pallonc #supponc #patientreportedoutcomes #PROS #cancer #oncology [ABSTRACT FROM AUTHOR]

Background Inequities in guideline-concordant treatment receipt contribute to worse survival in Black patients with breast cancer. Inequity-reduction interventions (eg, navigation, bias training, tracking dashboards) can close such treatment gaps. We simulated the population-level impact of statewide implementation of inequity-reduction interventions on racial breast cancer inequities in North Carolina. Methods Using registry-linked multipayer claims data, we calculated inequities between Black and White patients receiving endocrine therapy (n = 12 033) and chemotherapy (n = 1819). We then built cohort-stratified (endocrine therapy and chemotherapy) and race-stratified Markov models to simulate the potential increase in the proportion of patients receiving endocrine therapy or chemotherapy and subsequent improvements in breast cancer outcomes if inequity-reducing intervention were implemented statewide. We report uncertainty bounds representing 95% of simulation results. Results In total, 75.6% and 72.1% of Black patients received endocrine therapy and chemotherapy, respectively, over the 2006-2015 and 2004-2015 periods (vs 79.3% and 78.9% of White patients, respectively). Inequity-reduction interventions could increase endocrine therapy and chemotherapy receipt among Black patients to 89.9% (85.3%, 94.6%) and 85.7% (80.7%, 90.9%). Such interventions could also decrease 5-year and 10-year breast cancer mortality gaps from 3.4 to 3.2 (3.0, 3.3) and from 6.7 to 6.1 (5.9, 6.4) percentage points in the endocrine therapy cohorts and from 8.6 to 8.1 (7.7, 8.4) and from 8.2 to 7.8 (7.3, 8.1) percentage points in the chemotherapy cohorts. Conclusions Inequity-focused interventions could improve cancer outcomes for Black patients, but they would not fully close the racial breast cancer mortality gap. Addressing other inequities along the cancer continuum (eg, screening, pre- and postdiagnosis risk factors) is required to achieve full equity in breast cancer outcomes. [ABSTRACT FROM AUTHOR]

Background The majority of patients with cancer seek care at community oncology sites; however, most clinical trials are available at National Cancer Institute (NCI)–designated sites. Although the NCI National Cancer Oncology Research Program (NCORP) was designed to address this problem, little is known about the county-level characteristics of NCORP site locations. Methods This cross-sectional analysis determined the association between availability of NCORP or NCI sites and county-level characteristic theme percentile scores from the Center for Disease Control and Prevention's Social Vulnerability Index themes. Health Resources and Services Administration's Area Health Resource Files were used to determine contiguous counties. We estimated risk ratios and 95% confidence intervals (CIs) using modified Poisson regression models to evaluate the association between county-level characteristics and site availability within singular and singular and contiguous counties. Results Of 3141 included counties, 14% had an NCORP, 2% had an NCI, and 1% had both sites. Among singular counties, for a standard deviation increase in the racial and ethnic theme score, there was a 22% higher likelihood of NCORP site availability (95% CI = 1.10 to 1.36); for a standard deviation increase in the socioeconomic status theme score, there was a 24% lower likelihood of NCORP site availability (95% CI = 0.67 to 0.87). Associations were of smaller magnitude when including contiguous counties. NCI sites were located in more vulnerable counties. Conclusions NCORP sites were more often in racially diverse counties and less often in socioeconomically vulnerable counties. Research is needed to understand how clinical trial representation will increase if NCORP sites strategically increase their locations in more vulnerable counties. [ABSTRACT FROM AUTHOR]

Background In qualitative work, patients report that seemingly short trips to clinic (eg, a supposed 10-minute blood draw) often turn into "all-day affairs." We sought to quantify the time patients with cancer spend attending ambulatory appointments. Methods We conducted a retrospective study of patients scheduled for oncology-related ambulatory care (eg, labs, imaging, procedures, infusions, and clinician visits) at an academic cancer center over 1 week. The primary exposure was the ambulatory service type(s) (eg, clinician visit only, labs and infusion, etc.). We used Real-Time Location System badge data to calculate clinic times and estimated round-trip travel times and parking times. We calculated and summarized clinic and total (clinic + travel + parking) times for ambulatory service types. Results We included 435 patients. Across all service day type(s), the median (IQR) clinic time was 119 (78-202) minutes. The estimated median (IQR) round-trip driving distance and travel time was 34 (17-49) miles and 50 (36-68) minutes. The median (IQR) parking time was 14 (12-15) minutes. Overall, the median (IQR) total time was 197 (143-287) minutes. The median total times for specific service type(s) included: 99 minutes for lab-only, 144 minutes for clinician visit only, and 278 minutes for labs, clinician visit, and infusion. Conclusion Patients often spent several hours pursuing ambulatory cancer care on a given day. Accounting for opportunity time costs and the coordination of activities around ambulatory care, these results highlight the substantial time burdens of cancer care, and support the notion that many days with ambulatory health care contact may represent "lost days." [ABSTRACT FROM AUTHOR]

Background Tumor biomarkers are regularly used to guide breast cancer treatment and clinical trial enrollment. However, there remains a lack of knowledge regarding physicians' perspectives towards biomarkers and their role in treatment optimization, where treatment intensity is reduced to minimize toxicity. Methods Thirty-nine academic and community oncologists participated in semi-structured qualitative interviews, providing perspectives on optimization approaches to chemotherapy treatment. Interviews were audio-recorded, transcribed, and analyzed by 2 independent coders utilizing a constant comparative method in NVivo. Major themes and exemplary quotes were extracted. A framework outlining physicians' conception of biomarkers, and their comfortability with their use in treatment optimization, was developed. Results In the hierarchal model of biomarkers, level 1 is comprised of standard-of-care (SoC) biomarkers, defined by a strong level of evidence, alignment with national guidelines, and widespread utilization. Level 2 includes SoC biomarkers used in alternative contexts, in which physicians expressed confidence, yet less certainty, due to a lack of data in certain subgroups. Level 3, or experimental, biomarkers created the most diverse concerns related to quality and quantity of evidence, with several additional modulators. Conclusion This study demonstrates that physicians conceptualize the use of biomarkers for treatment optimization in successive levels. This hierarchy can be used to guide trialists in the development of novel biomarkers and design of future trials. [ABSTRACT FROM AUTHOR]