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Intricate links between aquatic animals and their environment expose them to chemical and pathogenic hazards, which can disrupt seafood supply. Here we outline a risk schema for assessing potential impacts of chemical and microbial hazards on discrete subsectors of aquaculture—and control measures that may protect supply. As national governments develop strategies to achieve volumetric expansion in seafood production from aquaculture to meet increasing demand, we propose an urgent need for simultaneous focus on controlling those hazards that limit its production, harvesting, processing, trade and safe consumption. Policies aligning national and international water quality control measures for minimizing interaction with, and impact of, hazards on seafood supply will be critical as consumers increasingly rely on the aquaculture sector to supply safe, nutritious and healthy diets.

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.

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 (SEER, 2000-2016) was used to assess risk of cancer mortality and mortality from any cause across four definitions of rurality: Urban Influence codes (UIC), National Center for Health Statistics (NCHS), Rural-Urban continuum codes (RUCC), and Index of Relative Rurality (IRR). Binary (urban vs. rural) and ternary (urban, micropolitan, rural) definitions were evaluated. Multivariable parametric survival models estimated hazards of mortality overall and among three 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 three-level. Adjusted models using binary definitions revealed 15% to 17% greater hazard of cancer mortality in rural compared to urban. At three-levels when comparing rural to metropolitan, RUCC and NCHS saw similarly increased hazard ratios; however, IRR 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.

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.