Estimating the Impact of Rurality in Disparities in Cancer Mortality.

PURPOSE: Estimation of the independent effect of rurality on cancer mortality requires causal inference methodology and consideration of area-level socioeconomic status and rural designations. METHODS: Using SEER data, we identified key incident cancers diagnosed between 2000 and 2016 at age ≥20 years (N = 3,788,273), examining a 20% random sample (n = 757,655). Standardized competing risk and survival models estimated the association between rural residence, defined by Rural-Urban Continuum Codes, and cancer-specific and all-cause mortality, controlling for age at cancer diagnosis, sex, race/ethnicity, year of diagnosis, and Area Deprivation Index (ADI). We estimated the attributable fraction (AF) of rurality and high ADI (ADI > median) to the probability of mortality. Finally, we examined county measurement issues contributing to mortality rates discordant from hypothesized rates. RESULTS: The 5-year standardized failure probability for cancer mortality for rural patients was 33.9% versus 31.56% for urban. The AF for rural residence was 1.04% at year 1 (0.89% by year 5), the highest among local stage disease (Y1 2.1% to Y5 1.9%). The AF for high ADI was 3.33% in Y1 (2.87% in Y5), while the joint effect of rural residence and high ADI was 4.28% in Y1 (3.71% in Y5). Twenty-two percent of urban counties and 30% of rural were discordant. Among discordant urban counties, 30% were only considered urban because of adjacency to metro area. High ADI was associated with urban discordance and low ADI with rural discordance. CONCLUSION: Rural residence independently contributes to cancer mortality. The rural impact is the greatest among those with localized disease and in high deprivation areas. Rural-urban county designations may mask high-need urban counties, limiting eligibility to state and federal resources dedicated to rural areas.

Contribution of County Characteristics to Disparities in Rural Mortality After Cancer Diagnosis.

INTRODUCTION: Rural disparities in cancer outcomes have been widely evaluated, but limited evidence is available to describe what characteristics of rural environments contribute to the increased risk of poor outcomes. Therefore, this manuscript sought to assess the mediating effects of county characteristics on the relationship between urban/rural status and mortality among patients with cancer, characterize county profiles, and determine at-risk county profiles alongside rural settings. METHODS: Patients diagnosed with cancer between 2000 and 2016 were assessed using Surveillance, Epidemiology and End Results data linked to the 2010 Rural-Urban Commuting Codes and 2010 County Health Rankings. There were 757,655 patients representing 596 counties (of 3,143 in the U.S.) and 12 states. Mediation analyses, conducted in 2023, estimated the direct contribution of rurality to 5-year all-cause survival and the contribution of the rural effect indirectly through County Health Ranking domains. Latent class analysis and survival models identified county groupings and estimated the hazard of mortality associated with class membership. RESULTS: Rankings for premature death, clinical care, and physical environment resulted in rural patients having 17.9%-20.2% less survival time than urban patients. Of this, 4.1%-12.6% of the total excess risk was mediated by these characteristics. Patients living in rural and high-risk county classes saw higher all-cause mortality than those in urban lower-risk counties (hazard ratio=1.04, 95% CI=1.01, 1.08 and 1.07, 95% CI=1.03, 1.11). CONCLUSIONS: Counties with poorer health rankings had increased mortality risks regardless of rurality; however, the poor rankings, notably health behaviors and social and economic factors, elevated the risk for rural counties.

Urban-rural differences in cancer mortality: Operationalizing rurality.

OBJECTIVE: To assess urban-rural differences in cancer mortality across definitions of rurality as (1) established binary cut-points, (2) data-driven binary cut-points, and (3) continuous. METHODS: We used Surveillance, Epidemiology, and End Results (SEER) data between 2000 and 2016 to identify incident adult screening-related cancers. Analyses were based on one testing and four validation cohorts (all n = 26,587). Urban-rural status was defined by Rural-Urban Continuum Codes, National Center for Health Statistics codes, and the Index of Relative Rurality. Each was modeled using established binary cut-points, data-driven cut-points, and as continuous. The primary outcome was 5-year cancer-specific mortality. RESULTS: Compared to established cut-points, data-driven cut-points classified more patients as rural, resulted in larger White populations in rural areas, and yielded 7%-14% lower estimates of urban-rural differences in cancer mortality. Further, hazard of cancer mortality increased 4%-67% with continuous rurality measures, revealing important between-unit differences. CONCLUSIONS: Different cut-points introduce variation in urban-rural differences in mortality across definitions, whereas using urban-rural measures as continuous allows rurality to be conceptualized as a continuum, rather than a simple aggregation. POLICY IMPLICATIONS: Findings provide alternative cut-points for multiple measures of rurality and support the consideration of utilizing continuous measures of rurality in order to guide future research and policymakers.

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.

Defining rurality: an evaluation of rural definitions and the impact on survival estimates.

BACKGROUND: Patients with cancer living in rural areas have inferior cancer outcomes; however, studies examining this association use varying definitions of "rural," complicating comparisons and limiting the utility of the results for policy makers and future researchers. METHODS: Surveillance, Epidemiology, and End Results data (2000-2016) were used to assess risk of cancer mortality and mortality from any cause across 4 definitions of rurality: Urban Influence codes (UIC), National Center for Health Statistics (NCHS), Rural-Urban continuum codes (RUCC), and Index of Relative Rurality. Binary (urban vs rural) and ternary (urban, micropolitan, rural) definitions were evaluated. Multivariable parametric survival models estimated hazards of mortality overall and among 3 cancer groupings: screening related, obesity related, and tobacco related. Definition agreement was also assessed. RESULTS: Overall, 3 788 273 patients with an incident cancer representing 605 counties were identified. There was little discordance between binary definitions of rural vs urban and moderate agreement at the 3 levels. Adjusted models using binary definitions revealed 15% to 17% greater hazard of cancer mortality in rural compared with urban. At the 3 levels when comparing rural with metropolitan, RUCC and NCHS saw similarly increased hazard ratios; however, Index of Relative Rurality did not. Screening-related cancers saw the highest hazards of mortality and the largest divergence between definitions. Obesity-related and tobacco-related cancers saw similarly increased hazards of mortality at the binary and ternary levels. CONCLUSIONS: Hazard of death is similar across binary definitions; however, this differed when categorized as ternary or continuous, especially among screening-related cancers. Results suggest that study purpose should direct choice of definitions and categorization.

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.

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.

Reopening strategies, mobility and COVID-19 infections in Europe: panel data analysis.

OBJECTIVES: Characterise the reopening policies of European countries after the first wave of infections and evaluate how these policies affected economic activity and subsequent infections. STUDY DESIGN: Using publicly available data, we construct a database of reopening policy announcements by country authorities and develop measures related to the speed and timing of reopening. Using panel data regressions, we then assess how a country's reopening action subsequently affected its mobility and COVID-19 infections. Samples of 22 European countries used in the study comprise: Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Ireland, Israel, Italy, Netherlands, Norway, Poland, Portugal, Romania, Russia, Spain, Switzerland, Turkey, Ukraine and the UK. MAIN OUTCOMES: Mobility index as well as COVID-19 case and death counts. RESULTS: Reopening policies are associated with a 1.5 percentage point increase in mobility and a 4% increase in subsequent infections after 2 weeks. However, some reopening strategies are associated with lower infection risk. In particular, early and fast reopeners saw 5%-10% increases in infections relative to those that opened later and adopted a gradual approach. The sequencing of sectoral reopenings matters, with infection amplification effects larger for some sectors (like retail and events) than others (like schools). CONCLUSIONS: Findings suggest some merit of gradual and late reopening strategies with a careful sequencing of sectoral openings based on their infection amplification risks.

Inpatient Diagnosis of Delirium and Encephalopathy: Coding Trends in 2011-2018.

BACKGROUND: Ten medical societies have called for scientific literature to integrate research on delirium and encephalopathy, while physicians continually debate how to accurately document diagnoses of acute confusional states. OBJECTIVE: To promote this integration, we evaluated trends in diagnoses of delirium and encephalopathy among hospitalized adults and physician specialties, incorporating transitions to the Diagnostic and Statistical Manual of Mental Disorders-5 and the International Classification of Disease, tenth edition. METHODS: Using the 2011-2018 IBM MarketScan datasets, we identified delirium/encephalopathy patients aged ≥18 years using International Classification of Disease 9/10 codes among hospitalized patients. We identified physician specialties associated with the hospitalization and comorbidities within one year before the diagnosis of delirium or encephalopathy. Log-binomial models were used to evaluate diagnostic trends, adjusting for age, gender, insurance, and comorbidities. RESULTS: We identified 10,509 hospitalized patients with a diagnosis of delirium and 94,438 with encephalopathy between 2011 and 2018. Although the number of patients with either diagnosis increased over time, the use of delirium diagnosis was less than it was for encephalopathy compared with 2011 after adjusting for covariates (adjusted risk ratio 0.45; 95% confidence interval 0.43 to 0.48). During the 8 years, neurologists and internists increased their use of both diagnoses, whereas only psychiatrists increased their use of delirium. Family practice physicians and nurse practitioners presented no significant change in either diagnosis for this timeframe. CONCLUSIONS: Our results suggest that refined diagnostic codes and criteria may alter trends among clinicians in diagnosing delirium and/or encephalopathy. Additional diagnostic clarity may be necessary to support refined diagnoses among family practice physicians and nurse practitioners.

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.

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.