Background: Given the high risk of COVID-19 mortality, patients with cancer may be vulnerable to fear of COVID-19, adverse psychological outcomes, and health care delays.Methods: This longitudinal study surveyed the pandemic's impact on patients with cancer (N= 1529) receiving Patient Advocate Foundation services during early and later pandemic. Generalized estimating equation with repeated measures was conducted to assess the effect of COVID-19 on psychological distress. Logistic regression with repeated measures was used to assess the effect of COVID-19 on any delays in accessing health care (e.g., specialty care doctors, laboratory, or diagnostic testing, etc.).Results: Among 1199 respondents, 94% considered themselves high risk for COVID-19. Respondents with more fear of COVID-19 had a higher mean psychological distress score (10.21; 95% confidence intervals [CI] 9.38-11.03) compared to respondents with less fear (7.55; 95% CI 6.75-8.36). Additionally, 47% reported delaying care. Respondents with more fear of COVID-19 had higher percentages of delayed care than those with less (56; 95% CI 39%-72% vs. 44%; 95% CI 28%-61%). These relationships persisted throughout the pandemic. For respondents with a COVID-19 diagnosis in their household (n = 116), distress scores were similar despite higher delays in care (58% vs. 27%) than those without COVID-19.Conclusions: Fear of COVID-19 is linked to psychological distress and delays in care among patients with cancer. Furthermore, those who are personally impacted see exacerbated cancer care delays. Timely psychosocial support and health care coordination are critical to meet increased care needs of patients with cancer during the COVID-19 pandemic.

PURPOSE: Despite evidence of clinical benefits, widespread implementation of remote symptom monitoring has been limited. We describe a process of adapting a remote symptom monitoring intervention developed in a research setting to a real-world clinical setting at two cancer centers. METHODS: This formative evaluation assessed core components and adaptations to improve acceptability and fit of remote symptom monitoring using Stirman's Framework for Modifications and Adaptations. Implementation outcomes were evaluated in pilot studies at the two cancer centers testing technology (phase I) and workflow (phase II and III) using electronic health data; qualitative evaluation with semistructured interviews of clinical team members; and capture of field notes from clinical teams and administrators regarding barriers and recommended adaptations for future implementation. RESULTS: Core components of remote symptom monitoring included electronic delivery of surveys with actionable symptoms, patient education on the intervention, a system to monitor survey compliance in real time, the capacity to generate alerts, training nurses to manage alerts, and identification of personnel responsible for managing symptoms. In the pilot studies, while most patients completed > 50% of expected surveys, adaptations were identified to address barriers related to workflow challenges, patient and clinician access to technology, digital health literacy, survey fatigue, alert fatigue, and data visibility. CONCLUSION: Using an implementation science approach, we facilitated adaptation of remote symptom monitoring interventions from the research setting to clinical practice and identified key areas to promote effective uptake and sustainability.

Purpose Telemedicine use during the COVID-19 pandemic among financially distressed patients with cancer, with respect to the determinants of adoption and patterns of utilization, has yet to be delineated. We sought to systematically characterize telemedicine utilization in financially distressed patients with cancer during the COVID-19 pandemic. Methods We conducted a cross-sectional analysis of nationwide survey data assessing telemedicine use in patients with cancer during the COVID-19 pandemic collected by Patient Advocate Foundation (PAF) in December 2020. Patients were characterized as financially distressed by self-reporting limited financial resources to manage out-of-pocket costs, psychological distress, and/or adaptive coping behaviors. Primary study outcome was telemedicine utilization during the pandemic. Secondary outcomes were telemedicine utilization volume and modality preferences. Multivariable and Poisson regression analyses were used to identify factors associated with telemedicine use. Results A convenience sample of 627 patients with cancer responded to the PAF survey. Telemedicine adoption during the pandemic was reported by 67% of patients, with most (63%) preferring video visits. Younger age (19-35 age compared to ≥ 75 age) (OR, 6.07; 95% CI, 1.47-25.1) and more comorbidities (≥ 3 comorbidities compared to cancer only) (OR, 1.79; 95% CI, 1.13-2.65) were factors associated with telemedicine adoption. Younger age (19-35 years) (incidence rate ratios [IRR], 1.78; 95% CI, 24-115%) and higher comorbidities (≥ 3) (IRR; 1.36; 95% CI, 20-55%) were factors associated with higher utilization volume. As area deprivation index increased by 10 units, the number of visits decreased by 3% (IRR 1.03, 95% CI, 1.03-1.05). Conclusions The rapid adoption of telemedicine may exacerbate existing inequities, particularly among vulnerable financially distressed patients with cancer. Policy-level interventions are needed for the equitable and efficient provision of this service.

Implicated in several chronic diseases, the gastrointestinal microbiome is hypothesised to influence carcinogenesis. We compared faecal microbiota of newly diagnosed treatment-naïve overweight and obese cancer patients and matched controls. Cases were enrolled in presurgical weight-loss trials for breast (NCT02224807) and prostate (NCT01886677) cancers and had a body mass index (BMI) ≥25 kg/m2. Cancer-free controls were matched 1:1 by age (±5 years), race, gender, and BMI (±5 kg/m2). All participants provided faecal samples; isolated bacterial DNA were PCR amplified at the V4 region of the 16S rRNA gene and analysed using the QIIME pipeline. Tests compared cases versus controls, then separately by gender. Microbial alpha-diversity and beta-diversity were assessed, and relative abundance of Operational Taxonomic Units (OTU’s) were compared at the genus level, with false discovery rate (FDR) correction. 22 overweight and obese cancer patients were matched with 22 cancer-free controls, with an average BMI of 30.5±4.3 kg/m2, age 54.4±5.3 years, and 54.5% were black. Fourteen matches were made between breast cancer cases and healthy female controls, and 8 matches were made with prostate cancer cases and healthy male controls. Comparison of all cases and controls revealed no differences in alpha diversity, though prostate cancer patients had higher Chao1 (P=0.006) and Observed Species (P=0.036) than cancer-free males. Beta-diversity metrics were significantly different between cases and controls (P<0.03 for all tests in whole sample and in men), though only unweighted Unifrac was different in women (P=0.005). Kruskal Wallis tests indicated significant differences among 16 genera in all matches, 9 in female, and 51 in male. This study suggests the faecal microbiota of treatment-naive breast and prostate cancer patients differs from controls, though larger samples are needed to substantiate these findings. Trial registration: NIH Clinical Trials, NCT01886677, NCT02224807, registered 26 June 2013, 25 Aug 2014 (respectively) – retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT01886677 ; https://clinicaltrials.gov/ct2/show/NCT02224807

PURPOSE: Many patient population groups are not proportionally represented in clinical trials, including patients of color, at age extremes, or with comorbidities. It is therefore unclear how treatment outcomes may differ for these patients compared with those who are well-represented in trials. METHODS: This retrospective cohort study included women diagnosed with stage I-III breast cancer between 2005 and 2015 in the national CancerLinQ Discovery electronic medical record–based data set. Patients with comorbidities or concurrent cancer were considered unrepresented in clinical trials. Non-White patients and/or those age < 45 or ≥ 70 years were considered under-represented. Patients who were White, age 45-69 years, and without comorbidities were considered well-represented. Cox proportional hazards models were used to evaluate 5-year mortality by representation group and patient characteristics, adjusting for cancer stage, subtype, chemotherapy, and diagnosis year. RESULTS: Of 11,770 included patients, 48% were considered well-represented in trials, 45% under-represented, and 7% unrepresented. Compared with well-represented patients, unrepresented patients had almost three times the hazard of 5-year mortality (adjusted hazard ratio [aHR], 2.71; 95% CI, 2.08 to 3.52). There were no significant differences in the hazard of 5-year mortality for under-represented patients compared with well-represented patients (aHR, 1.19; 95% CI, 0.98 to 1.45). However, among under-represented patients, those age < 45 years had a lower hazard of 5-year mortality (aHR, 0.63; 95% CI, 0.48 to 0.84) and those age ≥ 70 years had a higher hazard of 5-year mortality (aHR, 2.21; 95% CI, 1.76 to 2.77) compared with those age 45-69 years. CONCLUSION: More than half of the patients were under-represented or unrepresented in clinical trials, because of age, comorbidity, or race. Some of these groups experienced poorer survival compared with those well-represented in trials. Trialists should ensure that study participants reflect the disease population to support evidence-based decision making for all individuals with cancer.