Why location matters: Associations between county-level characteristics and availability of NCORP and NCI sites

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. While 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 CDC'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 (CI) using modified Poisson regression models to evaluate the association between county-level characteristics and site availability within singular and singular & 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 (SD) increase in the racial and ethnic theme score there was a 22% higher likelihood of NCORP site availability (95% CI 1.10-1.36); for a SD increase in the socioeconomic status theme score there was a 24% lower likelihood of NCORP site availability (95% CI 0.67-0.87). Associations were of smaller magnitude when including contiguous counties. NCI sites were located in more vulnerable counties.

CONCLUSION(S)

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.

Understanding the financial cost of cancer clinical trial participation

BACKGROUND

Though financial hardship is a well-documented adverse effect of standard-of-care cancer treatment, little is known about out-of-pocket costs and their impact on patients participating in cancer clinical trials. This study explored the financial effects of cancer clinical trial participation.

METHODS

This cross-sectional analysis used survey data collected in December 2022 and May 2023 from individuals with cancer previously served by Patient Advocate Foundation, a nonprofit organization providing social needs navigation and financial assistance to US adults with a chronic illness. Surveys included questions on cancer clinical trial participation, trial-related financial hardship, and sociodemographic data. Descriptive and bivariate analyses were conducted using Cramer's V to estimate the in-sample magnitude of association. Associations between trial-related financial hardship and sociodemographics were estimated using adjusted relative risks (aRR) and corresponding 95% confidence intervals (CI) from modified Poisson regression models with robust standard errors.

RESULTS

Of 650 survey respondents, 18% (N = 118) reported ever participating in a cancer clinical trial. Of those, 47% (n = 55) reported financial hardship as a result of their trial participation. Respondents reporting trial-related financial hardship were more often unemployed or disabled (58% vs. 43%; V = 0.15), Medicare enrolled (53% vs. 40%; V = 0.15), and traveled >1 h to their cancer provider (45% vs. 17%; V = 0.33) compared to respondents reporting no hardship. Respondents who experienced trial-related financial hardship most often reported expenses from travel (reported by 71% of respondents), medical bills (58%), dining out (40%), or housing needs (40%). Modeling results indicated that respondents traveling >1 h vs. ≤30 min to their cancer provider had a 2.2× higher risk of financial hardship, even after adjusting for respondent race, income, employment, and insurance status (aRR = 2.2, 95% CI 1.3-3.8). Most respondents (53%) reported needing $200-$1000 per month to compensate for trial-related expenses. Over half (51%) of respondents reported less willingness to participate in future clinical trials due to incurred financial hardship. Notably, of patients who did not participate in a cancer clinical trial (n = 532), 13% declined participation due to cost.

CONCLUSION

Cancer clinical trial-related financial hardship, most often stemming from travel expenses, affected almost half of trial-enrolled patients. Interventions are needed to reduce adverse financial participation effects and potentially improve cancer clinical trial participation.

TBCRC 057: Survey about willingness to participate in cancer clinical trials during the pandemic

BACKGROUND

Breast cancer patients experienced heightened anxiety during the pandemic. Also, modifications to clinical trial activities allowing for virtual platforms, local assessments, and greater flexibility were introduced to facilitate participation. We sought to evaluate the association between pandemic-related anxiety and willingness to participate in trials and how pandemic-era modifications to trial activities affect the decision to participate.

METHODS

We conducted an online survey from August to September, 2021 of patients with breast cancer assessing pandemic-related anxiety; clinical trials knowledge and attitudes; willingness to participate during and before the pandemic; and how each modification affects the decision to participate. Fisher's exact tests evaluated differences in proportions and two-sample t-tests evaluated differences in means. The association of pandemic-related anxiety with a decline in willingness to participate during compared to prior to the pandemic was modeled using logistic regression.

RESULTS

Among 385 respondents who completed the survey, 81% reported moderate-severe pandemic-related anxiety. Mean willingness to participate in a trial was lower during the pandemic than prior [2.97 (SD 1.17) vs. 3.10 (SD 1.09), (p < 0.001)]. Severe anxiety was associated with higher odds of diminished willingness to participate during the pandemic compared to prior (OR 5.07). Each of the modifications, with the exception of opting out of research-only blood tests, were endorsed by >50% of respondents as strategies that would increase their likelihood of deciding to participate.

CONCLUSIONS

While pandemic-related anxiety was associated with diminished willingness to participate in trials, the leading reasons for reluctance to consider trial participation were unrelated to the pandemic but included worries about not getting the best treatment, side effects, and delaying care. Patients view trial modifications favorably, supporting continuation of these modifications, as endorsed by the National Cancer Institute and others.

Cancer-Related Financial Hardship Screening as Part of Practice Transformation

BACKGROUND

Data on financial hardship, an "adverse event" in individuals with cancer, are needed to inform policy and supportive care interventions and reduce adverse economic outcomes.

METHODS

Lay navigator-led financial hardship screening was piloted among University of Alabama at Birmingham oncology patients initiating treatment in October 2020. Financial hardship screening, including reported financial distress and difficulty, was added to a standard-of-care treatment planning survey. Screening feasibility and completion and proportions of reported financial distress and difficulty were calculated overall and by patient race and rurality. The risk of financial distress by patient sociodemographics was estimated.

RESULTS

Patients who completed a treatment planning survey (N=2741) were 18% Black, Indigenous, or persons of color (BIPOC) and 16% rural dwelling. The majority of patients completed financial hardship screening (90%), surpassing the target feasibility completion rate of 75%. The screening revealed 34% of patients were experiencing financial distress, including 49% of BIPOC and 30% of White patients. Adjusted models revealed BIPOC patients had a 48% higher risk of financial distress compared with those who were White (risk ratio 1.48, 95% CI, 1.31-1.66). Large differences in reported financial difficulties were seen comparing patients who were BIPOC and White (utilities: 33% vs. 10%, upfront medical payments: 44% vs. 23%, transportation: 28% vs. 12%, respectively).

CONCLUSIONS

The collection of patient-reported financial hardship data via routine clinical care was feasible and identified racial inequities at treatment initiation. Efforts to collect patient economic data should support the design, implementation, and evaluation of patient-centered interventions to improve equity and reduce the impact of financial hardship.

Factors associated with completeness in documentation of diagnostic work-up and treatment in patients with breast cancer in Sudan

Purpose

This study evaluates the relationship between geography and ethnicity on the completeness of documentation of diagnostic work-up and treatment modalities in Sudan for patients with breast cancer.

Methods

This retrospective study used data abstracted from patients with breast cancer receiving cancer care at Sudan's largest cancer centre (Radiation and Isotopes Center Khartoum) in 2017. Patient demographic and clinical characteristics were abstracted from paper medical records. Odds ratios and 95% confidence intervals were estimated to evaluate complete diagnostic work-up on ethnic group, origin and residence using binomial logistic regression models.

Results

Of 237 patients, the median age was 52 (interquartile range 43-61). Most often patients identified as Arab (68%), originated from Central, Northeastern and Khartoum regions (all 28%) and lived in the Khartoum region (52%). Overall, 49% had incomplete diagnostic work-up, with modest differences by ethnicity and geography. In adjusted analyses, non-statistical differences were found between the ethnic group, geographic origin and residence and having complete diagnostic work-up. For treatment modality, significant differences were observed between receptor status and receiving hormone therapy (p = 0.004). Only 28% of patients with HR+ breast cancer received hormonal therapy. For those with HR- or undocumented breast cancer subtype, 36% and 17% received hormone therapy, respectively.

Conclusion

Approximately half of Sudanese patients with breast cancer had incomplete diagnostic work-up, irrespective of ethnicity and geography. Moreover, a high proportion of patients received inappropriate treatment. This underlines a considerable systems-based quality gap in care delivery, demanding efforts to improve diagnostic work-up for all patients with breast cancer in Sudan.

A nationwide cross-sectional study on the association of patient-level factors with financial anxiety in the context of chronic medical conditions

Patient-level characteristics associated with the prevalence and severity of financial anxiety have yet to be described. We conducted a cross-sectional analysis of survey data assessing financial anxiety in patients with chronic medical conditions in December 2020. 1771 patients (42.6% response rate) participated in the survey. Younger age (19-35 age compared to ≥ 75 age) (β, 5.86; 95% CI 2.10-9.63), male sex (β, - 1.9; 95% CI - 3.1 to - 0.73), Hispanic/Latino race/ethnicity (compared with White patients) (β, 2.55; 95% CI 0.39-4.71), household size ≥ 4 (compare with single household) (β, 4.54; 95% CI 2.44-6.64), household income of ≥ $96,000-$119,999 (compared with ≤ $23,999) (β, - 3.2; 95% CI - 6.3 to 0.04), single marital status (compared with married) (β, 2.18; 95% CI 0.65-3.71), unemployment (β, 2.07; 95% CI 0.39-3.74), high-school education (compared with advanced degrees) (β, 3.10; 95% CI 1.32-4.89), lack of insurance coverage (compared with private insurance) (β, 6.05; 95% CI 2.66-9.45), more comorbidities (≥ 3 comorbidities compared to none) (β, 2.95; 95% CI 1.00-4.90) were all independently associated with financial anxiety. Patients who are young, female, unmarried, and representing vulnerable sub-populations are at elevated risk for financial anxiety.

Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer

PURPOSE

Remote symptom monitoring (RSM) using electronic patient-reported outcomes enables patients with cancer to communicate symptoms between in-person visits. A better understanding of key RSM implementation outcomes is crucial to optimize efficiency and guide implementation efforts. This analysis evaluated the association between the severity of patient-reported symptom alerts and time to response by the health care team.

METHODS

This secondary analysis included women with stage I-IV breast cancer who received care at a large academic medical center in the Southeastern United States (October 2020-September 2022). Symptom surveys with at least one severe symptom alert were categorized as severe. Response time was categorized as optimal if the alert was closed by a health care team member within 48 hours. Odds ratios (ORs), predicted probabilities, and 95% CIs were estimated using a patient-nested logistic regression model.

RESULTS

Of 178 patients with breast cancer included in this analysis, 63% of patients identified as White and 85% of patients had a stage I-III or early-stage cancer. The median age at diagnosis was 55 years (IQR, 42-65). Of 1,087 surveys included, 36% reported at least one severe symptom alert and 77% had an optimal response time by the health care team. When compared with surveys that had no severe symptom alerts, surveys with at least one severe symptom alert had similar odds of having an optimal response time (OR, 0.97; 95% CI, 0.68 to 1.38). The results were similar when stratified by cancer stage.

CONCLUSION

Response times to symptom alerts were similar for alerts with at least one severe symptom compared with alerts with no severe symptoms. This suggests that alert management is being incorporated into routine workflows and not prioritized based on disease or symptom alert severity.

Associations Between Patient-Perceived Cancer Curability and Advance Directive Completion

Background: Despite Advance Care Planning recommendations for patients with cancer, many lack Advance Directives (ADs). AD disparities persist among Black, Indigenous, or People of Color (BIPOC) patients. Based on a hypothesized correlation, we examined the association between patient-perceived cancer incurability and AD completion. Methods: This cross-sectional study obtained self-reported AD completion and incurability perception from routine care surveys. AD completion by incurability perception was estimated using modified Poisson regression. Subgroup analyses examined patients who were BIPOC, White, and had solid organ malignancies. Results: Our sample (N = 1209) was predominantly female (70%), White (73%) with early-stage disease (60%), and solid organ malignancies (82%). AD completion was 42%, and 40% of patients reported their cancer incurable. Patient-perceived incurability was not associated with increased AD completion (likelihood ratio 0.94, 95% confidence interval 0.78-1.13) in overall or subgroup analyses. Conclusion: Patient-perceived cancer incurability was not associated with AD completion, even accounting for race/ethnicity and cancer type.

Abstract P4-07-20: Effect of prior treatments on post-CDK 4/6 inhibitor overall survival in hormone receptor positive breast cancer

Purpose: There are multiple treatment options for patients with metastatic breast cancer (MBC); however, there is minimal data on the optimal sequencing. Furthermore, limited information is available to understand the influence of prior treatment duration and class on novel therapies in real-world settings, such as cyclin-dependent kinase 4/6 inhibitors (CDK 4/6i) for patients with hormone receptor-positive, human epidermal growth factor receptor 2- negative (HR+ HER2-) MBC. Our study sought to identify the effect of prior treatments on post-CDK 4/6i survival.

Methods: This retrospective study used the nationwide, de-identified electronic health record-derived Flatiron Health database of women with HR+ HER2- MBC who received at least one CDK 4/6i between 2011 and 2020. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated for the association between duration and class of all cancer treatments prior to receipt of CDK 4/6i and overall survival (OS) adjusting for age at diagnosis, race and ethnicity, site of metastasis, and metastatic diagnosis year. Time dependent HRs were used to compare the OS for patients receiving one versus multiple CDK 4/6i.

Results: Of 5,363 patients, most were aged 55-64 (29%), White (69%), and had visceral metastasis (70%). The median survival from receipt of first CDK 4/6 inhibitor was 3.3 years. When compared to patients with no prior treatments, patients with up to one year of prior treatments had a 30% increased hazard of death ((HR, 1.30; 95% CI 1.15-1.46; Table 1). Similarly, patients with one to less than three years of prior treatment had a 68% increased hazard of death (HR 1.68; 95% CI 1.49-1.88) and those with three or more years had a 55% increased hazard of death (HR 1.55; 95% CI 1.36-1.76). Furthermore, patients who received prior endocrine therapy alone experienced a 29% increased hazard of death (HR, 1.29; 95% CI 1.16-1.44), while patients receiving prior chemotherapy experienced a 72% increased hazard of death (HR, 1.72; 95% CI 1.54-1.93) when compared with patients who did not receive a prior treatment. Finally, patients who received a different CDK 4/6i after their first had a 17% decreased hazard of death compared to patients who received subsequent endocrine or chemotherapy after their first CDK 4/6i (HR, 0.83; 95% CI 0.71-0.96).

Conclusion: Prior treatment duration and class are associated with a decreased overall survival after CDK 4/6 inhibitor administration. However, patients receiving more than one CDK 4/6 inhibitor in their sequence saw survival benefits. This highlights the importance for clinicians to consider prior treatment and duration in treatment decision-making and for trialists to stratify by these factors when reporting results of future studies.

Table 1: The association between OS and treatment duration and class before CDK 4/6

Citation Format: Jeffrey Franks, Nicole E. Caston, Ahmed Elkhanany, Travis Gerke, Andres Azuero, Gabrielle B. Rocque. Effect of prior treatments on post-CDK 4/6 inhibitor overall survival in hormone receptor positive breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-07-20.

Effect of prior treatments on post-CDK 4/6 inhibitor survival in hormone receptor-positive breast cancer

PURPOSE

Multiple treatment options exist for patients with metastatic breast cancer (MBC). However, limited information is available on the impact of prior treatment duration and class on survival outcome for novel therapies, such as cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) for patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+ HER2-) MBC.

METHODS

This study used a nationwide, de-identified electronic health record-derived database to identify women with HR+ HER2- MBC who received at least one CDK 4/6i between 2011 and 2020. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated for the association between prior duration and class of cancer treatment (both early-stage and metastatic) and prior CDK 4/6i survival as well as for those with multiple CDK 4/6i.

RESULTS

Of 5363 patients, the median survival from first CDK 4/6 inhibitor administration was 3.3 years. When compared to patients with no prior treatments, patients with < 1 year of prior treatment duration had a 30% increased hazard of death (HR, 1.30; 95% CI 1.15-1.46), those with 1 to < 3 years a 68% increased hazard of death (HR 1.68; 95% CI 1.49-1.88), and those with 3 or more years a 55% increased hazard of death (HR 1.55; 95% CI 1.36, 1.76). Patients who received prior therapy (endocrine or chemotherapy) before their CDK 4/6i had worse outcomes than those who received no prior therapy. Similar results were seen when comparing patients in the metastatic setting alone. Finally, patients who received a different CDK 4/6i after their first saw a lower hazard of death compared to patients who received subsequent endocrine or chemotherapy after their first CDK 4/6i.

CONCLUSION

Prior treatment duration and class are associated with a decreased overall survival after CDK 4/6 inhibitor administration. This highlights the importance for clinicians to consider prior treatment and duration in treatment decision-making and for trialists to stratify by these factors when randomizing patients or reporting results of future studies.

Patient-reported discrimination among limited-resourced cancer survivors: a brief report

PURPOSE

Healthcare discrimination has been associated with health disparities including lower cancer screenings, higher medical mistrust, and strained patient-provider relationships. Our study sought to understand patient-reported discrimination among cancer survivors with limited resources living in the United States.

DESIGN AND METHODS

We used cross-sectional survey data distributed by the Patient Advocate Foundation (PAF) in 12/2020 and 07/2021. Respondents reported source and reason of healthcare discrimination. Age, sex, race and ethnicity, annual household income, Rural-Urban Commuting Area (RUCA), Area Deprivation Index (ADI), employment status, cancer type, and number of comorbidities were independent variables of interest. The association between these variables and patient-reported healthcare discrimination was estimated using risk ratios (RRs) and 95% confidence intervals (CIs) from a multivariable modified Poisson regression model with robust standard errors.

FINDINGS

A total of 587 cancer survivors were included in our analysis. Most respondents were female (72%) and aged ≥56 (62%); while 33% were Black, Indigenous, or Person of Color. Overall, 23% reported receipt of discrimination, with the majority reporting doctor, nurse, or healthcare provider as the source (58%). Most common reasons for discrimination included disease status (42%), income/ability to pay (36%), and race and ethnicity (17%). In the adjusted model, retired survivors were 62% less likely to report discrimination compared to those employed (RR 0.38; 95% CI 0.23-0.64). Additionally, survivors with ≥3 comorbidities were 86% more likely to report discrimination compared to those survivors with no non-cancer comorbidities (RR 1.86; 95% CI 1.26-2.72).

IMPLICATIONS

Cancer survivors with limited resources reported substantial discrimination most often from a healthcare provider and most commonly for disease status and income. Discrimination should be mitigated to provide equitable and high-quality cancer care.

Adaptation of Remote Symptom Monitoring Using Electronic Patient-Reported Outcomes for Implementation in Real-World Settings

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.

Ineligible, Unaware, or Uninterested? Associations Between Underrepresented Patient Populations and Retention in the Pathway to Cancer Clinical Trial Enrollment

PURPOSE

Cancer clinical trials can benefit current and future patients; however, Black patients, rural residents, and patients living in disadvantaged areas are often underrepresented. Using an adapted version of Unger and colleagues' model of the process of clinical trial enrollment, we evaluated the relationship between underrepresented patient populations and trial end points.

METHODS

This retrospective study included 512 patients with breast or ovarian cancer who were prescribed a therapeutic drug at the University of Alabama at Birmingham from January 2017 to February 2020. Patient eligibility was assessed using open clinical trials. We estimated odds ratios and 95% CIs using logistic regression models to examine the relationship between underrepresented patient populations and trial enrollment end points: eligibility, interest, offer, enrollment, and declining enrollment.

RESULTS

Of the patients in our sample, 27% were Black, 18% were rural residents, and 19% lived in higher disadvantaged neighborhoods. In adjusted models, each comparison group had similar odds of being eligible for a clinical trial. Black versus White patients had 0.40 times the odds of interest in clinical trials and 0.56 times the odds of enrollment. Patients living in areas of higher versus lower disadvantage had 0.46 times the odds of enrolling and 3.40 times the odds of declining enrollment when offered.

CONCLUSION

Eligibility did not drive clinical trial enrollment disparities in our sample; however, retention in the clinical trial enrollment process appears to vary by group. Additional work is needed to understand how interventions can be tailored to each population's specific needs.

Interest and enrollment in clinical trials by race and ethnicity, rurality, and insurance status in patients with ovarian cancer

83

Background: Enrollment in Phase III clinical trials in gynecologic cancer patients has decreased by approximately 90% since 2011. Efforts to increase enrollment are needed. Previous research showed that not all eligible patients are approached, and those who are do not consistently enroll. Data regarding enrollment by patient characteristics, such as race/ethnicity, rurality, and insurance status is limited. We aimed to identify how these characteristics affect enrollment in ovarian cancer patients. Methods: We conducted retrospective chart review for patients with incident ovarian cancer presenting to the University of Alabama at Birmingham from 1/2017-3/2020. We abstracted patient race, ethnicity, Rural-Urban Commuting Area (RUCA; rural vs urban) and Area Deprivation Index (ADI; most vs least disadvantaged) based on Census tract codes, insurance status, eligibility for available trials, and trial participation from medical records. Patient interest in participation was abstracted from a patient-reported outcomes database. We calculated descriptive statistics and estimated enrollment as a multivariate function of age, race, ethnicity, insurance, RUCA and ADI using binomial logistic regression. We reported associations as odds ratios with 95% confidence intervals. Results: Of 156 patients, 25% were Black, Indigenous, or Persons of Color (BIPOC). 19% lived in a rural area. Mean age was 62 (SD 11.7). Most (95%) patients were insured; 49% Medicare, 40% private insurance, and 6% Medicaid. 126 (81%) were eligible for a trial during their treatment course. Of 102 patients who completed the question on clinical trial interest, 58% were interested; 42% were not. Ultimately, 36% of the 102 enrolled in a trial including 47% of those initially interested and 21% of those not. 39% of white patients (n = 117) initially expressed interest in a trial compared to 33% of BIPOC (n = 39); 48% of white patients ultimately enrolled vs 23% BIPOC. Of patients living in urban vs rural areas with known interest, patients in urban areas had higher interest (44% vs 10%) and higher enrollment (44% vs 31%). Among insurance types, interest and enrollment differed (Medicare (n = 76) 33% and 1%, Private (n = 63) 46% and 46%, Medicaid (n = 9) 33% and 22%, no insurance (n = 8) 25% and 36%). In our adjusted analysis, BIPOC patients had lower odds of enrolling onto clinical trials compared to white patients (OR 0.32, 95% CI 0.13-0.76). Additionally, as age increased by 1 year, odds of enrollment decreased (OR 0.96, 95% CI 0.92-0.99). Conclusions: BIPOC identity and older age were associated with lower rates of clinical trial enrollment. Comprehensive eligibility screening and early introduction could improve enrollment, particularly among BIPOC and older patients. These efforts have potential to improve enrollment as a greater percentage of patients ultimately enrolled on trial than initially expressed interest.

Associations between insurance status and the cancer clinical trial enrollment process

85

Background: Most patients with cancer experience multi-leveled barriers to clinical trial participation, potentially including financial concerns due to the complexity surrounding trial-related insurance coverage. Our study sought to understand the association between insurance status and cancer clinical trial eligibility, offer, and enrollment. Methods: This retrospective cohort study included patients with breast or ovarian cancer receiving a therapeutic cancer drug at the University of Alabama at Birmingham between January 2017 and February 2020. Available clinical trials and eligibility criteria were abstracted from OnCore and ClinicalTrials.gov. Patient trial eligibility, offer from provider, demographics, and clinical characteristics were abstracted from electronic medical records. Patient trial enrollment was determined via OnCore. Odds of clinical trial eligibility, offer, and enrollment by insurance status (private, public [Medicaid, Medicare]) were estimated using logistic regression models. Models estimating odds of trial offer and enrollment contained only eligible patients. Models were adjusted for patient age at diagnosis, race and ethnicity, rural-urban residence, Area Deprivation Index, cancer type, and cancer stage (early, late). Results: A total of 513 patients with breast (71%) or ovarian (29%) cancer were included in our analyses. Median age at diagnosis was 60 (interquartile range: 49-67) years; the majority were White (69%) and had early stage cancer (65%). Half of patients had private insurance (54%), and 46% of patients had public insurance (38% Medicare, 8% Medicaid). Patients with private insurance more often had early stage cancer compared to patients with public insurance (73% vs 57%). Almost two-thirds of patients (65%) were eligible for clinical trial enrollment. Of eligible patients (n = 333), 68% were offered a trial and 47% enrolled onto a trial. In adjusted analyses, patients with public vs private insurance had similar odds of clinical trial eligibility (odds ratio [OR] 0.95, 95% confidence intervals [CI] 0.61-1.48), being offered to participate (OR 1.23, 95% CI 0.71-2.14), and clinical trial enrollment (OR 1.13, 95% CI 0.68-1.89). Conclusions: Our results suggest oncologists do not assess trial eligibility or offering a trial based on insurance status, and patients do not differentially participate based on their insurance coverage. Further research is needed to understand implications of trial participation (e.g., out-of-pocket and time costs) for patients covered by differing insurance.

Identification of target population in the implementation of navigator-delivered home ePRO for patients with cancer receiving treatment

351

Background: One key challenge of practice transformation activities, such as remote symptom monitoring (RSM) using electronic patient reported outcomes (ePROs), is identification of patients starting treatment. In real-world settings, reliance on referrals is likely to miss patients. We describe the difficulties encountered in patient identification and the subsequent changes implemented in protocol to remediate this. Methods: We conducted two PDSA cycles focused on identification and engagement of patients for RSM at the Mitchel Cancer Institute (MCI). Target patient capture was > 75%. Modifications to the patient identification process were documented. Schedules of physicians participating in the RSM program were reviewed from 6/2021 – 5/2022 to identify eligible patients. Patients were considered eligible if they were starting chemotherapy, targeted therapy, or immunotherapy. Patients seeking a second opinion were excluded. Patient demographics, cancer type, cancer stage, and PROs were abstracted from electronic health records and the PRO platform (Carevive). Initial clinic roll-out was conducted in gynecologic oncology, with expansion to breast and thoracic oncology in 10/2021 and 3/2022, respectively. The proportion of eligible patients approached per month was reported.Results: In the first PDSA cycle, the eligibility criteria was defined. Although clinical trials included advanced disease, non-clinical staff screening expressed concern about determining advanced vs. early-stage disease. Thus, inclusion criteria was broadened to include all patients starting treatments. From 6/2021 –8/2021, navigators identified patients by screening patients who presented for chemo-education visits. The navigation team approached 23 patients during this period. However, this process didn’t identify all eligible patients as not all patients beginning treatment received chemo-education visits. In PDSA Cycle 2, the process for new patient contact from initial call for appointment through treatment was reviewed. The implementation team screened all patients in a physician’s schedule a week prior to the office visit as well as on the day of visit. This updated process identified all eligible patients starting either intravenous or oral chemotherapy. The recruitment process was modified to screen the physician schedules rather than chemo educator visits. From 9/2022-5/22, the proportion of eligible patients identified and approached remained high at 100%. This methodological screening process helped the navigation team identify all eligible patients in an efficient manner and they reported comfort in expanding to additional disease teams. Conclusions: Systematic screening of physician schedules can be successfully leveraged for patient identification and reduce time spent manually screening for eligible patients by non-clinical navigators. Clinical trial information: NCT04809740.

Why aren’t more patients with breast cancer enrolled in clinical trials?

84

Background: Only 2-8% of adult patients with cancer participate in clinical trials, likely due to strict exclusion criteria as well as financial and access issues. The primary objective of this study is to understand the population of patients with breast cancer who are and are not offered a clinical trial and the impact of exclusion criteria on enrollment. Methods: Inclusion and exclusion criteria from study protocols housed in OnCore and ClinicalTrials.gov were obtained for breast cancer-specific, therapeutic clinical trials open at the University of Alabama at Birmingham (UAB) from 2016 to 2020. Patients with breast cancer receiving oncology services at UAB from 2016 to 2020 were identified from electronic health records. Race and ethnicity and address were abstracted. Address was utilized to characterize patients as living in areas of higher vs. lower deprivation (Area Deprivation Index) and rural communities (Rural-Urban Commuting Area). Chart abstraction was conducted to assess if patients were offered a trial, eligible for a trial, reason for ineligibility, and enrollment in trial vs standard of care treatment. Results: 518 patients were included; 387 were offered a trial and 131 were not. The median age of patients offered a trial was 57 years old, whereas the median age of patients who were not offered a trial was 61. The majority of patients offered a trial were more often White (72% vs. 24% African American), resided in areas of lower disadvantage (70% vs 17% most disadvantaged), and urban residents (75% vs 13% rural). Of the 387 patients offered a trial, 319 (82%) enrolled, 34 (9%) declined enrollment and chose standard of care, and the remaining 34 (9%) were interested in enrollment but later found to be ineligible. Reasons for ineligibility of the 34 patients who were offered a trial included comorbidities (n = 9), tumor size (n = 7), metastases (n = 5), and previous cancer history (n = 4). Additionally, 9 patients were ineligible for miscellaneous reasons (abnormal labs, age, prescription, trial closed to accrual, tumor characteristics). Of the 131 patients that were not offered a clinical trial, 77 (59%) were ineligible for enrollment. Reasons for ineligibility included: stage 1 disease (n = 35), tumor size and characteristics (n = 24), and comorbidities and abnormal labs (n = 18). The remaining 54 patients would have been eligible, but their provider did not offer a clinical trial. Conclusions: Most patients who are offered a clinical trial are willing to participate; physicians not offering a trial to patients appears to be a driver for low enrollment. Strict exclusion criteria related to comorbidities limit trial participation. Further work is needed to understand the relative importance of these eligibility criteria in relation to validity. Efforts should be made to include patients in clinical trials that reflect the diverse patient population that will receive the drug in the future.

Associations between patient-perceived cancer curability and advance directive completion based on race and ethnicity and cancer type

200

Background: Despite Advance Care Planning (ACP) recommendations for patients with cancer for over 20 years, uptake remains low, and many such patients lack Advance Directives (AD). Our previous data showed a lack of association between patient-perceived cancer incurability and AD completion, however less is known about whether this relationship remains for key subgroups where perceptions may differ, such as BIPOC (Black, Indigenous, or People of Color) patients and patients with incurable hematologic malignancies. Methods: This cross-sectional study used Patient Reported Outcomes (PRO) from surveys administered during routine outpatient care at the University of Alabama at Birmingham from 12/2016 to 08/2021. Patients self-reported AD completion and perception of curability. Demographics consisting of age, sex, race and ethnicity, and marital status, and clinical characteristics, namely cancer type, cancer stage (stage 0-III grouped as early vs. IV/progression/recurrence as late) and phase of care (initial being the first 12 months, survivorship starting after this, and end of life being the last 6 months of life) were abstracted from the electronic medical record. Descriptive statistics were calculated using frequencies and percentages for categorical variables and median and interquartile ranges (IQR) for continuous variables. Likelihood ratios (LR) and 95% confidence intervals (CI) were estimated using a modified Poisson regression with robust error variance to evaluate the relationships between patient-perceived incurability and AD completion. Models were subset for White patients (n = 879), BIPOC patients (n = 330), and patients with solid organ malignancies (n = 987). Models were adjusted for demographics and clinical characteristics. Results: Of 1209 patients, 73% were White, and 82% had solid organ malignancies, most commonly gynecologic (32%), breast (17%), and gastrointestinal (13%). The sample was predominantly female (70%) with early-stage disease (60%) and a median age of 66 (IQR 58-72). AD completion was 46%, 32%, and 41% for patients who were White, BIPOC, or had solid organ malignancies, respectively. In adjusted analyses, patient-perceived incurability was not associated with AD completion for any of the patient subgroups (White LR 1.06, 95% CI 0.89-1.35; BIPOC LR 0.93, 95% CI 0.60-1.44; solid organ LR 1.10, 95% CI 0.88-1.36). Conclusions: Patient-perception of incurability does not appear to be associated with AD completion even in subgroup analyses based on race and ethnicity or cancer type.

Sociodemographic difference in patients who enroll and decline remote symptom monitoring (RSM)

268

Background: Remote symptom monitoring (RSM) using patient-reported outcomes has been shown to reduce symptom burden and hospitalizations in clinical trials. However, little is known about how willing patients are to participate in remote symptom monitoring in real-world settings, particularly for vulnerable patient populations. This study aims to compare characteristics of cancer patients enrolled vs. patients who declined enrollment into RSM. Methods: This prospective study used data that assessed the characteristics of patients who enrolled vs. patients who declined enrollment into RSM. Inclusion criteria included participants’ age ≥18 with cancer who received chemotherapy, targeted therapy, or immunotherapy at the University of Alabama at Birmingham. Race and ethnicity (Black or African American, White, Asian, other and unknown), sex, cancer type (breast, gastrointestinal [GI], genitourinary [GU], gynecological [GYNX], head and neck, leukemia, lymphoma, melanoma, myeloma and other), urban/rural residence, Area Deprivation Index (ADI), and insurance type (Medicaid, Medicare, none, other and private) were abstracted from electronic medical records (EMR) and PRO platform (Carevive). Descriptive statistics were calculated using frequencies and percentages for categorical variables and medians and interquartile ranges for continuous variables. Differences in enrollment status characteristics were calculated using measures of effect size such as Cramer’s V. Results: Of the 307 patients, two thirds of patients were female (71%); 25% were Black or African American and 66% were White patients; 15% lived in an area of higher disadvantage. For insurance, 46%, 26%, 10%, 8%, and 9% of patients had Private, Medicare, Medicaid, other insurance, and no insurance, respectively. The proportion of patients who declined enrollment was higher for males than females (22% vs. 10%), Black or African American than White (18% vs 13%); and having Medicare than private insurance (22% vs. 10%). Compared to those who enrolled, patients who declined enrollment were more often to be male (V:0.2), Black or African American (V:0.1); and have Medicare insurance (V:0.2). Patients enrolled vs. declined in RSM had similar ADI scores (V:0.01). Conclusions: This study demonstrates that potentially vulnerable patients, including Black patients and those with public insurance, have lower RSM engagement. Future analysis is needed to understand participation barriers and how to better engage diverse populations to ensure optimal healthcare delivery to all patients.

Evaluating nurses' time to response by severity and cancer stage in a remote symptom monitoring program

341

Background: Remote symptom monitoring (RSM) using electronic patient reported outcomes (ePROs) allow for patients with cancer to communicate symptoms to their clinical team between clinic visits. Prior randomized control trials of RSM focused on advanced cancer, and less data are available for patient with early stage cancers. The University of Alabama at Birmingham (UAB) implemented RSM for early stage (I-III) and advanced stage (IV) patients on active treatment. This study evaluates nurses’ real-world response time to alerts by varying severity and by patients cancer stages. Methods: This study included women with stage I-IV breast cancer who received care at UAB from October 2020 through May 2022. The program was first implemented in the breast clinic allowing for larger patient numbers with early and advanced stage breast cancer. A composite score for symptom severity is automatically calculated in the Carevive® platform for moderate, severe, or worsening symptoms using patient responses for frequency, severity, and interference. The nurse receives an alert if a symptom is moderate or severe. Surveys with at least one severe alert were categorized as severe and response time was categorized as optimal if the survey was closed within 48 hours (goal time for phone message follow-up). Odds ratios (OR), predicted probabilities, and 95% confidence intervals (CI) were estimated using a patient nested logistic regression evaluating time to response comparing surveys with at least one severe alert notification to those with no severe, adjusting for age at enrollment, race, cancer stage, provider who closed the surveys, and quarter from study start and date. An interaction between severity and cancer stage was evaluated. Results: Of 137 patients included in this study, 64% were White; 86% were diagnosed with early-stage breast cancer. The median age at diagnosis was 54 (27-79). Of 802 surveys included, 38% reported at least one severe symptom and 70% had an optimal response time. Similar results were seen when stratified by early vs. advanced stage with 39% and 38% reporting at least one severe alert and 68% and 71% an optimal response time, respectively. In our adjusted analysis, when compared with surveys that had no severe alerts, surveys with at least one severe alert had similar odds of having an optimal response time (OR, 1.29; 95%CI, 0.88, 1.89). No significant interaction between severity and stage was observed on the odds of optimal response time. Conclusions: Response times to alerts were similar regardless of the severity of the alert and cancer stage, suggesting alert management is incorporated into routine workflows and not prioritized based on disease or alert severity. Additional research is needed to understand factors contributing to non-optimal response times.

What’s missing? Diagnostic workup for breast cancer in Sudan

110

Background: In Sudan, healthcare access may limit diagnostic workup for breast cancer. This study evaluates the relationship between geography and ethnicity and completeness of documentation of diagnostic workup (staging and receptor testing) in Sudan. Methods: This retrospective study used data abstracted from patients with breast cancer receiving cancer care at Sudan’s largest cancer center (Radiation and Isotopes Center Khartoum [RICK]) in 2017. The patient’s age at diagnosis, sex, breast cancer stage, ethnic subgroup (further categorized as Arab and non-Arab), regions of origin and residence (Central, Northeastern, Western, and Khartoum [where RICK is located]), and receptor status from pathology reports were abstracted from paper medical records. Complete diagnostic workup was defined as having both receptor testing and staging. Descriptive statistics were calculated using frequencies and percentages for categorical variables and median and interquartile range (IQR) for continuous variables. Odds ratios (OR) and 95% confidence intervals (CI) were estimated to evaluate complete diagnostic workup on ethnic group, origin, and residence using binomial logistic regression models (excluding non-Sudanese patients and those with missing demographics). Results: Of 240 patients included, 237 were female, median age was 53 (IQR 43-62). Most often patients were Arab (68%), originated from Northeastern and Khartoum regions (both 28%) and lived in the Khartoum region (53%). Overall, 49% patients were missing receptor testing and/or staging, with modest differences by geographic region and ethnicity (Table). In adjusted analyses, non-Arab patients had similar odds of having complete diagnostic workup when compared to Arab patients (OR 1.22; 95% CI 0.70-2.10). Patients originating from and residing in regions outside the Khartoum region had similar odds of complete diagnostic workup when compared to patients originating from and residing in the Khartoum region. Conclusions: Almost half of breast cancer patients had incomplete diagnostic workup, regardless of region of origin, region of residence, and ethnic group. This highlights a substantial systems-based quality gap in care delivery, warranting efforts to improve completeness in diagnostic workup for all patients with breast cancer in Sudan.[Table: see text]

Trajectory of symptoms reported in remote symptom monitoring over the course of oncology treatment for gynecologic cancers

270

Background: Patients now have the ability to utilize electronic patient reported outcomes (ePROs) for remote symptom monitoring (RSM). This analysis seeks to better understand trajectory of reported symptoms during treatment for patients with gynecologic cancer participating in RSM. Methods: We approached patients with gynecological cancer initiating treatment at the Mitchell Cancer Institute (MCI) between 7/1/21-4/30/2022. Patients were eligible if they were starting chemotherapy, targeted therapy, or immunotherapy for a new cancer. Patients seeking a second opinion were excluded. Enrolled patients received symptom survey (PRO-CTCAE questions) via text or email once per week. Initially, only severe alerts were forwarded to the clinical care team; moderate alerts were forwarded to clinical teams once they were comfortable with alert management. Patients completed symptom assessments for 24 weeks or until withdrawal. Patient age at enrollment, race, sex, cancer type, cancer stage, and PROs were abstracted from electronic health records and the PRO platform (Carevive). Descriptive statistics were calculated using frequencies and percentages for categorical variables and median and interquartile ranges (IQR) for continuous variables. Results: A total of 60 female patients with gynecological cancer were enrolled; 33% were Black or African American and 67% were White; median age was 61 years (IQR 53-68). Seventy-eight percent (47/60) of patients reported 379 symptoms with at least one moderate or severe alert during this time period; 32% considered moderate and 68% considered severe. Overall, the most frequently reported symptom was pain (29%). At baseline (week 0), 14% and 41% of 56 patients reported moderate symptoms and severe symptoms, respectively. Symptom burden decreased over time with 4% and 7% of 27 patients who completed a survey at 12 weeks reporting moderate and severe symptoms. Specific symptom trajectories followed similar patterns. Conclusions: In our sample, patients reported the majority of symptoms during the first three months of treatment. Symptom trajectory decreased with time, suggesting symptoms are being effectively monitored and addressed by the clinical teams engaging in RSM. Future research is needed to understand if symptom improvement translates to increased quality of life, decreased hospitalizations, and increased survival for patients, as well as lessen the burden of call volume on the clinical team.

Adaptation of remote symptom monitoring using electronic patient-reported outcomes for implementation in real-world settings

272

Background: 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 semi-structured 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. Conclusions: 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.

Nursing strategies to improve alert closure for remote symptom monitoring

421

Background: For successful remote symptom monitoring using patient-reported outcomes, nurses should respond to alerts in a timely fashion. Where clinical trials utilized research staff for alert management, the shift to standard-of-care delivery necessitates that this responsibility be added as a task to an already strained nursing workforce. Little is known about strategies to engage nurses to improve timeliness of alert management. Methods: In this quality improvement initiative, we aimed to improve timeliness of alert closures generated by moderate or severe symptoms within a remote symptom monitoring program. Optimal closure was defined as < 48 hours, which was consistent with institutional requirements for response to patient phone calls. A continuous quality improvement approach, with multiple Plan Do Study Act (PDSA) cycles was conducted. Data was captured from the electronic medical record and PRO platform (Carevive). Descriptive statistics included frequencies and percentages. The proportion of alerts closed each month < 48 hours, 48-72 hours, 3-7 days, and > 7 days were reported overall and by disease team (i.e., major cancer types). Surveys not closed were considered > 7 days. The timing of strategies to improve nursing engagement were documented and evaluated for impact on alert closure. Results: From June 1, 2021-May 31, 2022, 1121 moderate or severe alerts were generated from 234 patients. Disease teams had variable remote symptom monitoring start dates: breast, leukemia, and limited gynecologic (prior to 6/2021); myeloma and gastrointestinal (7/2021); genitourinary (10/2021); head and neck (12/2021); melanoma (2/2022); and Lymphoma (4/2022). In 6/2021, the overall alert closure at < 48 hours, 48-72 hours, 3-7 days, and > 7 days was 57%, 4%, 14%, and 25% respectively (n = 28). To improve alert closures, several key strategies were deployed to improve alert closure times including disease-specific reporting and meetings with nursing leadership (10/2021); identification of a nurse champion, creation of “cheat sheets” to remind nurses how to close alerts, and individualized calls with nurses with open alerts (1/2022), and inclusions of requirement to close alerts in nursing newsletters (2/2022). Overall, alert closure less than 48 hours improved to 61% by 12/2021 (n = 97) and to 69% by 5/2022 (n = 167). Disease group alert closure varied, with higher closure more commonly in teams with greater duration of use, such as breast cancer team with an alert closure of 85% < 48 hours in May 2022. Conclusions: Key nursing engagement strategies improve alert closure for remote symptom monitoring programs implemented in real-world settings.

Determinants of telemedicine adoption among financially distressed patients with cancer during the COVID-19 pandemic: insights from a nationwide study

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.

Health insurance and financial hardship in cancer survivors during the COVID-19 pandemic

Uninsured or underinsured individuals with cancer are likely to experience financial hardship, including forgoing healthcare or non-healthcare needs such as food, housing, or utilities. This study evaluates the association between health insurance coverage and financial hardship among cancer survivors during the COVID-19 pandemic. This cross-sectional analysis used Patient Advocate Foundation (PAF) survey data from May to July 2020. Cancer survivors who previously received case management or financial aid from PAF self-reported challenges paying for healthcare and non-healthcare needs during the COVID-19 pandemic. Associations between insurance coverage and payment challenges were estimated using Poisson regression with robust standard errors, which allowed for estimation of adjusted relative risks (aRR). Of 1,437 respondents, 74% had annual household incomes <$48,000. Most respondents were enrolled in Medicare (48%), 22% in employer-sponsored insurance, 13% in Medicaid, 6% in an Affordable Care Act (ACA) plan, and 3% were uninsured. Approximately 31% of respondents reported trouble paying for healthcare during the COVID-19 pandemic. Respondents who were uninsured (aRR 2.58, 95% confidence interval [CI] 1.83–3.64), enrolled in an ACA plan (aRR 1.86, 95% CI 1.28–2.72), employer-sponsored insurance (aRR 1.70, 95% CI 1.23–2.34), or Medicare (aRR 1.49, 95% CI 1.09–2.03) had higher risk of trouble paying for healthcare compared to Medicaid enrollees. Challenges paying for non-healthcare needs were reported by 57% of respondents, with 40% reporting trouble paying for food, 31% housing, 28% transportation, and 20% internet. In adjusted models, Medicare and employer-sponsored insurance enrollees were less likely to have difficulties paying for non-healthcare needs compared to Medicaid beneficiaries. Despite 97% of our cancer survivor sample being insured, 31% and 57% reported trouble paying for healthcare and non-healthcare needs during the COVID-19 pandemic, respectively. Greater attention to both medical and non-medical financial burden is needed given the economic pressures of the COVID-19 pandemic.

Telemedicine adoption and utilization among financially distressed patients with cancer during the COVID-19 pandemic: Insights from a longitudinal nationwide survey

1596

Background: 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 an analysis of survey data assessing the use of telemedicine in patients with cancer during the COVID-19 pandemic collected by Patient Advocate Foundation (PAF) from May 2020 to December 2020. Primary study outcome was telemedicine utilization rate. Secondary outcomes were independent predictors of telemedicine utilization patterns, volume, and utilization preferences. Multivariate and poisson regression analyses were used to identify predictive factors. Results: Of the 1,390 respondents, 627 completed two survey waves and were included in this study. Telemedicine adoption during the pandemic was reported by 67% of patients, with most (63%) preferring video visits. Younger age (odds ratio, 6.07; 95% CI, 1.47-25.1), and higher comorbidities (odds ratio, 1.79; 95% CI, 1.13-2.65) were independent predictors associated with telemedicine adoption. Younger age (19-35 yrs.) (incidence rate ratios [IRR], 1.78; 95%CI, 24-115%) and higher comorbidities (≥3) (IRR; 1.36; 95%CI, 20-55%) were independent predictors 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 under-resourced patients with cancer. Policy-level interventions are needed for the equitable and efficient provision of this service.

Evaluating the implementation and impact of navigator-supported remote symptom monitoring and management: a protocol for a hybrid type 2 clinical trial

Background

Symptoms in patients with advanced cancer are often inadequately captured during encounters with the healthcare team. Emerging evidence demonstrates that weekly electronic home-based patient-reported symptom monitoring with automated alerts to clinicians reduces healthcare utilization, improves health-related quality of life, and lengthens survival. However, oncology practices have lagged in adopting remote symptom monitoring into routine practice, where specific patient populations may have unique barriers. One approach to overcoming barriers is utilizing resources from value-based payment models, such as patient navigators who are ideally positioned to assume a leadership role in remote symptom monitoring implementation. This implementation approach has not been tested in standard of care, and thus optimal implementation strategies are needed for large-scale roll-out.

Methods

This hybrid type 2 study design evaluates the implementation and effectiveness of remote symptom monitoring for all patients and for diverse populations in two Southern academic medical centers from 2021 to 2026. This study will utilize a pragmatic approach, evaluating real-world data collected during routine care for quantitative implementation and patient outcomes. The Consolidated Framework for Implementation Research (CFIR) will be used to conduct a qualitative evaluation at key time points to assess barriers and facilitators, implementation strategies, fidelity to implementation strategies, and perceived utility of these strategies. We will use a mixed-methods approach for data interpretation to finalize a formal implementation blueprint.

Discussion

This pragmatic evaluation of real-world implementation of remote symptom monitoring will generate a blueprint for future efforts to scale interventions across health systems with diverse patient populations within value-based healthcare models.

Trial registration

NCT04809740; date of registration 3/22/2021.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07914-6.

Building Sustainable Practice Transformation Through Payment Reform Initiatives

PURPOSE

Novel value-based payment approaches provide an opportunity to deploy and sustain health care delivery interventions, such as treatment planning documentation. However, limited data are available on implementation costs.

METHODS

We described key factors affecting the cost of implementing care improvements under value-based payments, using treatment planning and Medicare's Oncology Care Model as examples. We estimated expected costs of implementing treatment plans for years 1 and 2-6 under (1) different staffing models, (2) use of technology, and (3) differences in the patients engaged. We compared costs to the payment amounts under the Oncology Care Model.

RESULTS

Team-based models where staffing is aligned with skills needed for key tasks (eg, a combination of lay navigator, nurse, and physician) are more financially feasible when compared with using physicians or nurses alone. When existing staff are at or near capacity, hiring new staff focused on practice transformation activities allows adequate time for new initiatives without negative impacts on existing services. Investments in information technology can enhance staff productivity, but initial costs may be high. Interventions may not be financially feasible if implemented for a small patient volume or only for patients insured by a particular payer. Finally, costs may be higher for disadvantaged populations, and equity in care delivery may require higher payments from payers.

CONCLUSION

Estimating the cost of implementing an intervention in different types of practice settings with various types of patients is essential to ensure that a value-based payment system will adequately support desired improvements in quality of care for all patients.

Examining the association among fear of COVID-19, psychological distress, and delays in cancer care

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.

Fear of COVID-19: Effects on mental health in under-resourced patients with cancer

152

Background: Given the high risk of COVID-19 mortality, patients with cancer are not only vulnerable to physical consequences of COVID-19 infection, but also to adverse psychological outcomes, including fear of COVID-19. Without intervention, psychological distress in patients with cancer can lead to worsening symptoms, poor quality of life, and lower survival. We sought to evaluate the association between fear of COVID-19 and psychological distress for under-resourced patients with cancer during the pandemic. Methods: This observational, longitudinal survey study, fielded during early (May 20- July 11, 2020) and later pandemic (December 2-December 23, 2020), evaluated the pandemic’s impact on patients with cancer receiving Patient Advocate Foundation (PAF) services from July 2019 – April 2020. Questions focused on individual experiences with COVID-19 and psychological, emotional, physical, and material effects from the pandemic. The validated Fear of COVID-19 Scale was used to determine fear of COVID-19. Psychological distress was determined using a four-item questionnaire by Holingue et al. (score range 4 – 16). Means and 95% confidence intervals (CI) were estimated using generalized estimating equation modeling with repeated measures to assess the effect of fear of COVID-19 on psychological distress early and later in the pandemic. Models adjusted for age, sex, race/ethnicity, region, annual household income, household size, marital status, employment status, Area Deprivation Index category, Rural-Urban Commuting Code category, cases per 100,000 in county of residence, cancer type, and number of comorbidities. Results: Amongst 1199 survey respondents, 94% considered themselves high risk for COVID-19. 448 respondents completed both the first and second survey. The majority of respondents were female (72%) and age 56-75 (55%); 40% were Black, Indigenous, or Persons of Color. In adjusted models of respondents who completed the early pandemic survey, respondents with more fear of COVID-19 had a higher mean psychological distress score (10.21; 95% CI 9.38-11.03) compared to respondents with less fear (7.55; 95% CI 6.75-8.36). Among those who completed the later pandemic survey, median fear of COVID-19 decreased (20 vs 19)median distress scores remained the same (8); respondents with more fear of COVID-19 had a higher mean psychological distress score (9.98; 95%CI 9.04-10.92) compared to respondents with less fear (7.87; 95%CI 6.98-8.76). Conclusions: Fear of COVID-19 was linked to psychological distress and persisted throughout the pandemic among under-resourced patients with cancer. Timely psychosocial support is critical to meet increased care needs experienced by patients with cancer during the COVID-19 pandemic. Given these results, fear of COVID-19 could be considered as a trigger to integrate psychological interventions in patients with cancer to treat psychological distress.

Association of fear of COVID-19 with delays in care or treatment interruptions in patients with cancer

98

Background: Patients with cancer are at risk for severe COVID-19 and may be vulnerable to health care delays. Delays or interruptions in care may lead to adverse cancer outcomes. Little is known about the relationship between fear of COVID-19 and disruptions in cancer care delivery. Methods: This longitudinal survey was distributed to individuals with cancer who received services July 2019-April 2020 from Patient Advocate Foundation, a non-profit organization that provides case management and financial aid to patients with chronic illness. Data was collected twice - early pandemic (5/20/20-7/11/20) and later pandemic (12/3/20-12/23/20). Fear of COVID-19 was assessed with the Fear of COVID-19 Scale and dichotomized as more (≥22) vs less (< 22) fearful. Respondents reported delays in care or treatment interruptions due to the pandemic and reasons for delays or interruptions. Respondents rated concern about potential long-term health issues due to delays on a 5-point Likert-like scale. We estimated predicted percentages and 95% confidence intervals (CI) using logistic regression models to assess the association of fear of COVID-19 (more vs less fearful) with delay in care or treatment interruption (any vs none) at each time point. We adjusted models for age, sex, race/ethnicity, region, annual household income, marital status, employment status, household size, Area Deprivation Index category, Rural-Urban Commuting Code category, county-level COVID-cases per 100,000, cancer type and number of comorbidities. Results: Amongst the 1,199 early pandemic survey respondents, the majority were female (72%), had household income < $48,000 (73%), and had ≥1 comorbidity (60%). 448 of the early pandemic survey respondents also completed the later survey. 464 (39%) and 166 (37%) respondents were categorized as more fearful at the early and later time points respectively. 567 (47%) and 191 (43%) reported delays or interruptions at the early and later time points respectively. The most common reported reasons for delays or interruptions were hospital/provider restrictions (early: 27%, later: 19%) and patient choice (early: 13%, later: 15%). Among respondents with delays or interruptions at each time point, > 70% were at least moderately concerned about potential long-term health issues due to delays. In adjusted models, more fearful respondents had higher predicted percentages of delayed care or treatment interruptions compared to less fearful respondents early (more fearful: 56%, 95% CI 39%-72%; less fearful: 44%; 95% CI 28%-61%) and later (more fearful: 55%, 95% CI 35%-73%; less fearful: 38%; 95% CI 22%-57%) in the pandemic. Conclusions: Fear of COVID-19 is common among patients with cancer and is linked with delays in care and treatment interruptions. System-wide strategies are needed to address fear of COVID-19 and to ensure equitable, timely, and safe access to cancer care throughout the pandemic.

Patient-reported unfair treatment within the health care system

115

Background: Patients with cancer who perceive discrimination and unfair treatment from the health care system are at risk for lower health-related quality of life. This study seeks to better understand the characteristics of under-resourced patients who report unfair treatment from the health care system and providers. Methods: This cross-sectional analysis used data from a nationwide survey distributed in December 2020 by Patient Advocate Foundation (PAF), a US non-profit organization providing case management and financial aid to individuals with chronic illness. The survey was fielded via email to those who received PAF services from July 2019-April 2020. Inclusion criteria included a valid e-mail address, aged ≥ 19, and either current or previous cancer treatment. Respondents reported unfair treatment in connection to their health care. Age, sex, race/ethnicity, and annual household income were abstracted from the PAF database. The validated Group-Based Medical Mistrust Scale was used to assess respondents’ level of mistrust in medical providers as it relates to their ethnic group. Scores range from 12-60 and were categorized based on tertiles as high mistrust (scores ≥ 29), neutral (21-28), and low mistrust (≤ 20). Frequencies and percentages were calculated for categorical variables. Results: There were a total of 429 survey respondents with cancer. Most respondents were female (73%) and aged 56-75 (57%); 31% were Black, Indigenous, or Persons of Color (BIPOC). The most common cancer types were hematologic (33%) and breast (33%). Overall, 20% (n = 86) of respondents reported having received unfair treatment. Of those reporting unfair treatment, 56% reported receipt from their doctor, nurse, or health care provider, 51% insurance company, 38% the health care system, and 14% pharmacist. When asked why they felt unfairly treated, the most common responses were related to insurance status (51%), disease or condition (45%), and income (35%). Notably, unfair treatment due to race/ethnicity (6%), sex (9%), and sexual orientation/gender expression (3%) were uncommon. When compared to those who reported objective treatment, respondents reporting unfair treatment were more often unemployed/other (28% vs 11%), privately insured (38% vs 27%), having income < $23,000 (40% vs 25%), having 3+ comorbidities (40% vs 23%) and reporting more mistrust in medical providers (53% vs 27%). There did not appear to be a difference in reporting of unfair treatment by race/ethnicity. Of BIPOC respondents, 51% reported high mistrust in medical providers. Conclusions: This under-resourced population of respondents with cancer reported unfair treatment related to their finances, insurance, and disease status. Our data suggest health care-associated discrimination may occur based on socioeconomic resources. This work identifies a novel equity consideration warranting further evaluation.

Survival in the real world: A national analysis of patients treated for early-stage breast cancer

75

Background: Many patient population groups are not proportionally represented in clinical trials, including patients of color, at age extremes, or with comorbidities. It is unclear how treatment outcomes may differ for these patients compared to those well represented in trials. Methods: This retrospective cohort study included women diagnosed with early-stage (I-III) breast cancer (EBC) between 2005-2015 in the CancerLinQ Discovery electronic medical record-based dataset. Patients with comorbidities or concurrent cancer were considered unrepresented in clinical trials. Non-White patients and/or those aged <45 or ≥70 years were considered underrepresented. Patients who were White and aged 45-69 were considered well represented. Overall and EBC subtype-stratified Cox proportional hazards models estimated hazard ratios (HR) and 95% confidence intervals (CI) for five-year mortality by representation group. The overall model was adjusted for cancer stage, subtype, chemotherapy intensity, and year of EBC diagnosis. Stratified models were adjusted for cancer stage, individual treatment regimen (due to lack of chemotherapy intensity variation within subtype), and year of EBC diagnosis. Results: Of 11,770 patients, most were aged 45-69 (71%), White (72%), diagnosed with stage II (51%), or HR+HER2- EBC (56%). Unrepresented patients (7%) were categorized due to comorbidities (76%), concurrent cancer (22%), or both (2%). Underrepresented patients (45%) were categorized based on age (44%), race/ethnicity (39%), or both (17%). The remaining patients were well represented in trials (48%). In adjusted models, unrepresented patients had almost three times the hazard of death than well-represented patients (HR 2.71, 95% CI 2.08-3.52; Table). The hazard of death for underrepresented versus well-represented patients was similar (HR 1.19, 95% CI 0.98-1.45). Comparable results were seen in EBC subtype-specific models. Conclusions: Over half of patients in this study would be considered underrepresented or unrepresented in clinical trials due to age, comorbidity, or race/ethnicity. Patients considered unrepresented in trials experienced poorer survival compared to those well-represented. Trialists should ensure study participants reflect the real-world disease population to support evidence-based decision making for all individuals with cancer.[Table: see text]

Effect of limited reporting in clinical trials on the ability to guide treatment decisions for real-world patients with cancer

18

Background: National Comprehensive Cancer Network (NCCN) guidelines are derived from reviews of clinical trials. Many of these trials have strict inclusion criteria, resulting in trial samples that are not representative of the larger cancer population. We therefore sought to understand how clinical trials, referenced in the NCCN guidelines, report key patient baseline demographics relating to age, race/ethnicity, and country or geographic region. Methods: NCCN guidelines for four cancer types were reviewed: prostate, colon, breast, and lung. We abstracted race/ethnicity, age, country/geographic region, and hazard ratios (HRs) from references indexed in the NCCN Guidelines. Race/ethnicity and age information was obtained from baseline characteristics tables in reported studies. The country/geographic region from which participants were recruited was acquired from each individual trial’s National Clinical Trial (NCT) number, linked to clinicaltrial.gov or the main manuscript. Each study was also assessed for its reporting of survival outcomes based on race/ethnicity, age, and country. Results: A total of 31 studies reporting on 36 regimens were examined for this review. While all studies reported age, only 39% (n=12) included characterization of older adults (60 years or older). 52% provided information on the racial and ethnic makeup of the study sample. Countries where participants were recruited were mostly not reported in the main papers, rather they were identified from ClinicalTrials.gov. Also, while 67% of all studies (n=25) included an international sample, only 5% reported the country or geographic location in the main manuscript. Few studies reported efficacy by patient sub-population. 12 of the 31 (39%) manuscripts reported HRs by age. Of the 16 manuscripts reporting race/ethnicity, 16% included HRs by race/ethnicity. Only one study reported efficacy outcomes by country. Conclusions: There is a need to have a standardized system for reporting baseline characteristics as well as trial outcomes for clinical trials. Including information on subgroup-specific baseline and efficacy outcomes in clinical trial results is an inexpensive way of improving the quality of information available to oncologists and will aid them in making evidence-based treatment decisions for the entirety of their patient populations.

Exclusion criteria of breast cancer research protocols: A descriptive analysis

82

Background: Clinical trials play an important role in advancing cancer treatments. Unfortunately, only about 3% of adults with cancer are enrolled in a clinical trial in the United States due to various barriers to enrollment. This includes restrictive eligibility criteria, which currently have no standard guidelines. The purpose of this study is to evaluate the variability of eligibility criteria. Methods: This descriptive analysis utilized all therapeutic breast protocols offered at the University of Alabama at Birmingham (UAB) between 2004-2020. Exclusion criteria (e.g., laboratory values and comorbidities) were extracted from protocols using OnCore, an online dataset used to manage clinical trials, and ClinicalTrials.gov. Laboratory values or vital signs analyzed included liver function tests, hematologic labs, Eastern Cooperative Oncology Group (ECOG) performance status, and hypertension. Comorbid conditions included congestive heart failure, cardiovascular disease, presence of central nervous system (CNS) metastases, and history of prior cancer. Comorbid conditions were further analyzed by amount of time protocols required participants to be from initial diagnosis or exacerbation-free. Results: There were a total of 102 eligible protocols. Substantial heterogeneity was observed in exclusion criteria across liver/hematologic laboratory values and demographic/comorbidity variables. Among liver laboratory values, most protocols included an upper limit of acceptable for bilirubin (78%): 9% used the institutional upper limit of normal (ULN), 2% used 1.2xULN, 3% used 1.25xULN, 56% used 1.5xULN, 6% used 2xULN, and 2% used 3xULN. Similar variability was observed in protocols that included alanine transaminase and aspartate transaminase. Among hematological labs, 82% of protocols defined a lower limit of acceptable absolute neutrophil count: 1% 500mcL, 11% used 1,000mcL, 4% used 1,200mcL, 1% used 1,250mcL, 64% used 1,500mcL, and 1% used 1,800mcL. Of the comorbid conditions, exclusion criteria varied for congestive heart failure (49%), an acute exacerbation of cardiovascular disease (80%), CNS metastases (59%) and a prior cancer (66%). While most protocols included cardiovascular disease, the allowable timeframe varied between protocols: 4% did not allow an acute exacerbation within the previous 3 months, 32% did not allow within the previous 6 months, 5% did not allow within the previous 12 months, and 38 % did not specify a time frame. Protocols including history of a prior cancer as a criterion similarly had varied definitions based on timeline. Conclusions: Substantial heterogeneity was observed among clinical trial protocols. While exclusion criteria are necessary for patient safety, there is lack of evidence for current parameters. Future research should focus on defining standardized eligibility criteria while allowing for deviation based on drug specificity.

Hesitancy and malignancy: Vaccine hesitancy among individuals with cancer

148

Background: The World Health Organization cited vaccine hesitancy as one of 2019’s top ten threats to global health, a threat that has been further exacerbated by COVID-19 pandemic. Existing COVID-19 vaccine hesitancy research focuses on the general population, but less is known about the specific concerns of medically vulnerable populations, including individuals with cancer. Methods: This cross-sectional analysis used data that assessed likelihood of COVID-19 vaccination (likely vs unlikely/unsure) among past or current patients with cancer from a nationwide survey administered in December 2020 by the Patient Advocate Foundation (PAF), a non-profit organization that provides case management and financial aid to patients diagnosed with a chronic illness. Inclusion criteria included previous or current cancer treatment, aged ≥ 19, and a valid e-mail address. Age, sex, race/ethnicity, and urban/rural residence were abstracted from the PAF database. Respondents self-reported education level, employment status, trust in media regarding COVID-19 pandemic, and media viewership on COVID-19 vaccine development. The Group-Based Medical Mistrust Scale assessed respondents’ level of mistrust in medical providers based on ethnicity. Likelihood of COVID-19 vaccine acceptance was evaluated using risk ratios (RR) and 95% confidence intervals (CI) from modified Poisson regression models with robust error variance. All variables were included in our model. Results: Of 429 respondents, 48% were unlikely/unsure about accepting the COVID-19 vaccine, primarily due to concerns about vaccine safety (32%) and worry about health conditions (12%). When compared to those likely to accept COVID-19 vaccine, respondents who were unlikely/unsure were more often Black, Indigenous, or People of Color (40% vs. 23%), aged 36-55 (40% vs. 29%), and female (80% vs. 65%). In adjusted analysis, Black respondents were 55% less likely to accept a COVID-19 vaccine, when compared to White respondents (RR 0.55; 95% CI 0.4-0.8). When compared to those who did not follow the media regarding COVID-19 vaccine development, those who followed the media very closely were 4.5 times more likely to accept a COVID-19 vaccine (RR 4.5; 95% CI 1.6-13.2). Respondents who reported below average trust in the media were 60% less likely to accept a COVID-19 vaccine (RR 0.6; 95% CI 0.5-0.8), compared to those who reported above average trust in the media. Conclusions: Despite being at high risk of COVID-19 morbidity and mortality, a substantial proportion of under-resourced individuals with cancer were unlikely/unsure about vaccination, exposing a significant disconnect between risk of severe disease and vaccine acceptance. Our analysis also reveals a need to assess for and debunk misinformation to increase vaccine enthusiasm among medically vulnerable populations.

Clinical trial representativeness and treatment intensity in a real-world sample of women with early stage breast cancer

PURPOSE

The extent to which evidence-based treatments are applied to populations not well represented in early stage breast cancer (EBC) trials remains unknown. This study evaluated treatment intensity for patients traditionally well represented, underrepresented, and unrepresented in clinical trials.

METHODS

This retrospective cohort study used real-world data to evaluate the intensity (high or low) of EBC chemotherapy by patient characteristics (age, race and ethnicity, presence of comorbidity) denoting clinical trial representation status (well represented, underrepresented, unrepresented) for patients diagnosed from 2011 to 2020. Odds ratios (OR) from a logistic regression model was used to evaluate the association between receipt of high-intensity chemotherapy and clinical trial representation status characteristics adjusting for cancer stage and subtype.

RESULTS

Of 970 patients with EBC, 41% were characterized as well represented, 45% as underrepresented, and 13% as unrepresented in clinical trials. In adjusted models, patients aged ≥ 70 versus 45-69 had lower odds of receiving a high-intensity treatment (OR 0.40, 95% CI 0.26-0.60), while those aged < 45 versus 45-69 had higher odds of receiving high-intensity treatment (OR 1.82, 95% CI 1.10-3.01). In predicted estimates, the proportion of patients receiving a high-intensity treatment was 87% for patients aged < 45, 79% for patients aged 45-69, and 60% for patients aged ≥ 70.

CONCLUSION

59% of the EBC population is not well represented in clinical trials. Age was associated with differential treatment intensity. Widening clinical trial eligibility criteria should be considered to better understand survival outcomes, toxicity effects, and ultimately make evidence-based treatment decisions using a more diverse sample.

Survival in the Real World: A National Analysis of Patients Treated for Early-Stage Breast Cancer

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.

Clinical trial representativeness and treatment intensity in a real-world sample of women with early-stage breast cancer

6584

Background: Early stage breast cancer (EBC) treatment is used in women of all ages, races, and health states. However, as clinical trials often do not represent real-world populations, the extent to which evidence-based treatments are prescribed to populations not well represented in these trials is not known. This study evaluated treatment intensity for patients traditionally well represented, underrepresented, and unrepresented in clinical trials. Methods: This retrospective cohort study used the nationwide de-identified electronic health record derived Flatiron Health database for patients diagnosed with EBC between 2011-2020. We categorized treatments as either high- (AC-TH [doxorubicin, cyclophosphamide followed by paclitaxel or docetaxel, trastuzumab]; ACT [paclitaxel or docetaxel, doxorubicin, cyclophosphamide]; TCH [paclitaxel or docetaxel, carboplatin, trastuzumab]; TCHP [paclitaxel or docetaxel, carboplatin, trastuzumab, pertuzumab]) or low-intensity (AC [doxorubicin, cyclophosphamide]; TC [paclitaxel or docetaxel, cyclophosphamide]; TH [paclitaxel or docetaxel, trastuzumab]). Unrepresented patients often have one or more comorbidities and/or prior cancer; underrepresented patients are typically Black, Indigenous, people of color, or of age extremes ( < 45, 70+); well represented patients are White and between the ages of 45-69. Odds ratios (OR), predicted proportions, and 95% confidence intervals (CI) from a two-level (patients nested in practice) hierarchical logistic regression model evaluated associations between receipt of high-intensity chemotherapy and patient characteristics of clinical trial representation (age, race/ethnicity, presence of comorbidity). Results: Our study included 970 patients with EBC with 13%, 45%, and 41% characterized as unrepresented, underrepresented, and well represented in clinical trials, respectively. In the adjusted model, those aged ≥ 70 vs 45-69 had lower odds of receiving a high-intensity treatment (OR 0.40, 95% CI 0.26-0.60), while those aged < 45 vs 45-69 had higher odds of receiving high-intensity treatment (OR 1.82, 95% CI 1.10-3.01). The predicted proportion of patients receiving a high-intensity treatment was 87% (95% CI: 80%-92%) for patients aged < 45, 79% (95% CI: 74%-84%) for patients aged 45-69, and 60% (95% CI: 50%-70%) for patients aged ≥ 70. Neither race/ethnicity nor comorbidity status were associated with odds of receiving high-intensity chemotherapy. Conclusions: Over half of the EBC population is not well represented in clinical trials. Age was associated with differential treatment intensity, despite a lack of evidence that these differences are appropriate. Widening clinical trial eligibility criteria is one way to better understand survival outcomes, identify potential toxicities, and ultimately make evidence-based treatment decisions using a more diverse sample.

Gut microbial differences in breast and prostate cancer cases from two randomised controlled trials compared to matched cancer-free controls

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.