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Cost-effectiveness analyses of new cancer treatments in real-world settings (e.g., post-clinical trials) inform healthcare decision makers about their healthcare investments for patient populations. The results of these analyses are often, though not always, presented with statistical uncertainty. This paper identifies five ways to characterize statistical uncertainty: (1) a 95% confidence interval (CI) for the incremental cost-effectiveness ratio (ICER); (2) a 95% CI for the incremental net benefit (INB); (3) an INB by willingness-to-pay (WTP) plot; (4) a cost-effectiveness acceptability curve (CEAC); and (5) a cost-effectiveness scatterplot. It also explores their usage in 22 articles previously identified by a rapid review of real-world cost effectiveness of novel cancer treatments. Seventy-seven percent of these articles presented uncertainty results. The majority those papers (59%) used administrative data to inform their analyses while the remaining were conducted using models. Cost-effectiveness scatterplots were the most commonly used method (34.3%), with 40% indicating high levels of statistical uncertainty, suggesting the possibility of a qualitatively different result from the estimate given. Understanding the necessity for and the meaning of uncertainty in real-world cost-effectiveness analysis will strengthen knowledge translation efforts to improve patient outcomes in an efficient manner.

Abstract Background Due to advancements in methods of cancer treatment, the population of people living with and beyond cancer is dramatically growing. The number of cancer survivors developing cardiovascular diseases and heart failure is also rising, due in part to the cardiotoxic nature of many cancer treatments. Guidelines are being increasingly released, emphasising the need for interdisciplinary action to address this gap in survivorship care. However, the extent to which interventions exist, incorporating the recommendations of cardio-oncology research, remains undetermined. Objective The aim of this scoping review is to assess the nature, extent and remit of existing cancer care interventions and their integration of cardio-oncology principles. Methods The review was conducted in accordance with the PRISMA Extension for Scoping Reviews Guidelines. Databases were independently searched for articles from 2010 to 2022, by two members of the research team. Data were charted and synthesised using the following criteria: (a) the focus of the intervention (b) the medium of delivery (c) the duration (d) the modalities included in the interventions (e) the research articles associated with each intervention (f) the type of studies conducted (g) key measures used (h) outcomes reported. Results Interventions encompassed six key modalities: Psychological Support, Physical Activity, Nutrition, Patient Education, Lifestyle and Caregiver Support. The focus, medium of delivery and duration of interventions varied significantly. While a considerable number of study protocols and pilot studies exist documenting HSMIs, only 25% appear to have progressed beyond this stage of development. Of those that have, the present review did not identify any ‘feasible’ interventions that covered each of the six modalities, while being generalisable to all cancer survivors and incorporating the recommendations from cardio-oncology research. Conclusion Despite the substantial volume of research and evidence from the field of cardio-oncology, the findings of this scoping review suggest that the recommendations from guidelines have yet to be successfully translated from theory to practice. There is an opportunity, if not necessity, for cardiac rehabilitation to expand to meet the needs of those living with and beyond cancer.

Cost-effectiveness analysis (CE Analysis) provides evidence about the incremental gains in patient outcomes costs from new treatments and interventions in cancer care. The utilization of "real-world" data allows these analyses to better reflect differences in costs and effects for actual patient populations with comorbidities and a range of ages as opposed to randomized controlled trials, which use a restricted population. This rapid review was done through PubMed and Google Scholar in July 2022. Relevant articles were summarized and data extracted to summarize changes in costs (in 2022 CAD) and effectiveness in cancer care once funded by the Canadian government payer system. We conducted statistical analyses to examine the differences between means and medians of costs, effects, and incremental cost effectiveness ratios (ICERs). Twenty-two studies were selected for review. Of those, the majority performed a CE Analysis on cancer drugs. Real-world cancer drug studies had significantly higher costs and effects than non-drug therapies. Studies that utilized a model to project longer time-horizons saw significantly smaller ICER values for the treatments they examined. Further, differences in drug costs increased over time. This review highlights the importance of performing real-world CE Analysis on cancer treatments to better understand their costs and impacts on a general patient population.

Cost-effectiveness analyses of new cancer treatments in real-world settings (e.g., post-clinical trials) inform healthcare decision makers about their healthcare investments for patient populations. The results of these analyses are often, though not always, presented with statistical uncertainty. This paper identifies five ways to characterize statistical uncertainty: (1) a 95% confidence interval (CI) for the incremental cost-effectiveness ratio (ICER); (2) a 95% CI for the incremental net benefit (INB); (3) an INB by willingness-to-pay (WTP) plot; (4) a cost-effectiveness acceptability curve (CEAC); and (5) a cost-effectiveness scatterplot. It also explores their usage in 22 articles previously identified by a rapid review of real-world cost effectiveness of novel cancer treatments. Seventy-seven percent of these articles presented uncertainty results. The majority those papers (59%) used administrative data to inform their analyses while the remaining were conducted using models. Cost-effectiveness scatterplots were the most commonly used method (34.3%), with 40% indicating high levels of statistical uncertainty, suggesting the possibility of a qualitatively different result from the estimate given. Understanding the necessity for and the meaning of uncertainty in real-world cost-effectiveness analysis will strengthen knowledge translation efforts to improve patient outcomes in an efficient manner.

Cost-effectiveness analysis (CE Analysis) provides evidence about the incremental gains in patient outcomes costs from new treatments and interventions in cancer care. The utilization of “real-world” data allows these analyses to better reflect differences in costs and effects for actual patient populations with comorbidities and a range of ages as opposed to randomized controlled trials, which use a restricted population. This rapid review was done through PubMed and Google Scholar in July 2022. Relevant articles were summarized and data extracted to summarize changes in costs (in 2022 CAD) and effectiveness in cancer care once funded by the Canadian government payer system. We conducted statistical analyses to examine the differences between means and medians of costs, effects, and incremental cost effectiveness ratios (ICERs). Twenty-two studies were selected for review. Of those, the majority performed a CE Analysis on cancer drugs. Real-world cancer drug studies had significantly higher costs and effects than non-drug therapies. Studies that utilized a model to project longer time-horizons saw significantly smaller ICER values for the treatments they examined. Further, differences in drug costs increased over time. This review highlights the importance of performing real-world CE Analysis on cancer treatments to better understand their costs and impacts on a general patient population.

Introduction: Kentucky ranks first in the U.S. in overall cancer incidence and mortality rates. Areas of the state that fall within the Appalachian Region, along Kentucky's eastern border, experience disproportionately high rates of cancer compared to non-Appalachian counties. Purpose: This pilot study investigates whether oral history interviews can be used to understand perspectives on cancer among residents of Appalachian Kentucky., Methods: In 2020, participants (n = 5) who identified as being from and/or having strong connections to Appalachian Kentucky were recruited to participate in this pilot study. Participants included individuals working in cancer-related fields, oncology professionals, and those with personal cancer experience. Using an oral history approach, subjects were asked about challenges within Appalachia that contribute to high rates of cancer regionally. Interviews were analyzed using qualitative content analysis, and data were condensed into themes, subthemes, and subtopics. Relational content analysis was then used to illustrate relationships between the problems being faced in Appalachia and their contributing factors, with potential solutions to those problems., Results: Six key themes emerged from analysis of the oral history interviews: (1) problems being faced in Appalachia; (2) contributing factors; (3) potential solutions; (4) Appalachian disposition; (5) experiences with and thoughts on cancer; and (6) defining success v. the future without changes (intervention). A further 25 subthemes were identified from within these themes. Taken together, these themes and subthemes point to potential areas for specific intervention to shift Appalachia's cancer burden., Implications: This pilot study demonstrates potential benefit in using oral history interviews to elucidate Appalachian Kentuckians' perspectives on cancer. From the nuanced insights gained through this method, a set of culturally appropriate interventions were identified that could address the disproportionate cancer burden in the region. Future studies using an oral history approach could aim to reveal other specific aspects of how cancer impacts individuals, families, and communities., Competing Interests: Cover Page Footnote This study was supported by the University of Kentucky’s Appalachian Career Training in Oncology (ACTION) Program (NCI R25 CA221765). No competing financial or editorial interests were reported by the authors of this paper., (Copyright © 2023 Courtney Martin, Lauren Hudson, and Nathan L. Vanderford.)