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IntroductionAvailable evidence is mixed concerning associations between smoking status and COVID-19 clinical outcomes. Effects of nicotine replacement therapy (NRT) and vaccination status on COVID-19 outcomes in smokers are unknown.MethodsElectronic health record data from 104 590 COVID-19 patients hospitalized February 1, 2020 to September 30, 2021 in 21 U.S. health systems were analyzed to assess associations of smoking status, in-hospital NRT prescription, and vaccination status with in-hospital death and ICU admission.ResultsCurrent (n = 7764) and never smokers (n = 57 454) did not differ on outcomes after adjustment for age, sex, race, ethnicity, insurance, body mass index, and comorbidities. Former (vs never) smokers (n = 33 101) had higher adjusted odds of death (aOR, 1.11; 95% CI, 1.06-1.17) and ICU admission (aOR, 1.07; 95% CI, 1.04-1.11). Among current smokers, NRT prescription was associated with reduced mortality (aOR, 0.64; 95% CI, 0.50-0.82). Vaccination effects were significantly moderated by smoking status; vaccination was more strongly associated with reduced mortality among current (aOR, 0.29; 95% CI, 0.16-0.66) and former smokers (aOR, 0.47; 95% CI, 0.39-0.57) than for never smokers (aOR, 0.67; 95% CI, 0.57, 0.79). Vaccination was associated with reduced ICU admission more strongly among former (aOR, 0.74; 95% CI, 0.66-0.83) than never smokers (aOR, 0.87; 95% CI, 0.79-0.97).ConclusionsFormer but not current smokers hospitalized with COVID-19 are at higher risk for severe outcomes. SARS-CoV-2 vaccination is associated with better hospital outcomes in COVID-19 patients, especially current and former smokers. NRT during COVID-19 hospitalization may reduce mortality for current smokers.ImplicationsPrior findings regarding associations between smoking and severe COVID-19 disease outcomes have been inconsistent. This large cohort study suggests potential beneficial effects of nicotine replacement therapy on COVID-19 outcomes in current smokers and outsized benefits of SARS-CoV-2 vaccination in current and former smokers. Such findings may influence clinical practice and prevention efforts and motivate additional research that explores mechanisms for these effects.

BackgroundThere is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics.MethodsElectronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses. Moderation by patient characteristics, vaccination status, cancer type, and year of the pandemic was examined.Results6.8% of the patients had current (n = 7,141) and 6.5% had past (n = 6,749) cancer diagnoses. Current cancer predicted both severe outcomes but past cancer did not; adjusted odds ratios (aOR) for mortality were 1.58 [95% confidence interval (CI), 1.46-1.70] and 1.04 (95% CI, 0.96-1.13), respectively. Mortality rates decreased over the pandemic but the incremental risk of current cancer persisted, with the increment being larger among younger vs. older patients. Prior COVID-19 vaccination reduced mortality generally and among those with current cancer (aOR, 0.69; 95% CI, 0.53-0.90).ConclusionsCurrent cancer, especially among younger patients, posed a substantially increased risk for death and ICU admission among patients with COVID-19; prior COVID-19 vaccination mitigated the risk associated with current cancer. Past history of cancer was not associated with higher risks for severe COVID-19 outcomes for most cancer types.ImpactThis study clarifies the characteristics that modify the risk associated with cancer on severe COVID-19 outcomes across the first 20 months of the COVID-19 pandemic. See related commentary by Egan et al., p. 3.

BackgroundThe Cancer Center Cessation Initiative (C3I) is a National Cancer Institute (NCI) Cancer Moonshot Program that supports NCI-designated cancer centers developing tobacco treatment programs for oncology patients who smoke. C3I-funded centers implement evidence-based programs that offer various smoking cessation treatment components (e.g., counseling, Quitline referrals, access to medications). While evaluation of implementation outcomes in C3I is guided by evaluation of reach and effectiveness (via RE-AIM), little is known about technical efficiency-i.e., how inputs (e.g., program costs, staff time) influence implementation outcomes (e.g., reach, effectiveness). This study demonstrates the application of data envelopment analysis (DEA) as an implementation science tool to evaluate technical efficiency of C3I programs and advance prioritization of implementation resources.MethodsDEA is a linear programming technique widely used in economics and engineering for assessing relative performance of production units. Using data from 16 C3I-funded centers reported in 2020, we applied input-oriented DEA to model technical efficiency (i.e., proportion of observed outcomes to benchmarked outcomes for given input levels). The primary models used the constant returns-to-scale specification and featured cost-per-participant, total full-time equivalent (FTE) effort, and tobacco treatment specialist effort as model inputs and reach and effectiveness (quit rates) as outcomes.ResultsIn the DEA model featuring cost-per-participant (input) and reach/effectiveness (outcomes), average constant returns-to-scale technical efficiency was 25.66 (SD = 24.56). When stratified by program characteristics, technical efficiency was higher among programs in cohort 1 (M = 29.15, SD = 28.65, n = 11) vs. cohort 2 (M = 17.99, SD = 10.16, n = 5), with point-of-care (M = 33.90, SD = 28.63, n = 9) vs. no point-of-care services (M = 15.59, SD = 14.31, n = 7), larger (M = 33.63, SD = 30.38, n = 8) vs. smaller center size (M = 17.70, SD = 15.00, n = 8), and higher (M = 29.65, SD = 30.99, n = 8) vs. lower smoking prevalence (M = 21.67, SD = 17.21, n = 8).ConclusionMost C3I programs assessed were technically inefficient relative to the most efficient center benchmark and may be improved by optimizing the use of inputs (e.g., cost-per-participant) relative to program outcomes (e.g., reach, effectiveness). This study demonstrates the appropriateness and feasibility of using DEA to evaluate the relative performance of evidence-based programs.

Main objectiveThere is limited information on how patient outcomes have changed during the COVID-19 pandemic. This study characterizes changes in mortality, intubation, and ICU admission rates during the first 20 months of the pandemic.Study design and methodsUniversity of Wisconsin researchers collected and harmonized electronic health record data from 1.1 million COVID-19 patients across 21 United States health systems from February 2020 through September 2021. The analysis comprised data from 104,590 adult hospitalized COVID-19 patients. Inclusion criteria for the analysis were: (1) age 18 years or older; (2) COVID-19 ICD-10 diagnosis during hospitalization and/or a positive COVID-19 PCR test in a 14-day window (+/- 7 days of hospital admission); and (3) health system contact prior to COVID-19 hospitalization. Outcomes assessed were: (1) mortality (primary), (2) endotracheal intubation, and (3) ICU admission.Results and significanceThe 104,590 hospitalized participants had a mean age of 61.7 years and were 50.4% female, 24% Black, and 56.8% White. Overall risk-standardized mortality (adjusted for age, sex, race, ethnicity, body mass index, insurance status and medical comorbidities) declined from 16% of hospitalized COVID-19 patients (95% CI: 16% to 17%) early in the pandemic (February-April 2020) to 9% (CI: 9% to 10%) later (July-September 2021). Among subpopulations, males (vs. females), those on Medicare (vs. those on commercial insurance), the severely obese (vs. normal weight), and those aged 60 and older (vs. younger individuals) had especially high mortality rates both early and late in the pandemic. ICU admission and intubation rates also declined across these 20 months.ConclusionsMortality, intubation, and ICU admission rates improved markedly over the first 20 months of the pandemic among adult hospitalized COVID-19 patients although gains varied by subpopulation. These data provide important information on the course of COVID-19 and identify hospitalized patient groups at heightened risk for negative outcomes.Trial registrationClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).

BackgroundThe Cancer Center Cessation Initiative (C3I) was launched in 2017 as a part of the NCI Cancer Moonshot program to assist NCI-designated cancer centers in developing tobacco treatment programs for oncology patients. Participating centers have implemented varied evidence-based programs that fit their institutional resources and needs, offering a wide range of services including in-person and telephone-based counseling, point of care, interactive voice response systems, referral to the quitline, text- and web-based services, and medications.MethodsWe used a mixed methods comparative case study design to evaluate system-level implementation costs across 15 C3I-funded cancer centers that reported for at least one 6-month period between July 2018 and June 2020. We analyzed operating costs by resource category (e.g., personnel, medications) concurrently with transcripts from semi-structured key-informant interviews conducted during site visits. Personnel salary costs were estimated using Bureau of Labor Statistics wage data adjusted for area and occupation, and non-wage benefits. Qualitative findings provided additional information on intangible resources and contextual factors related to implementation costs.ResultsMedian total monthly operating costs across funded centers were $11,045 (range: $5129-$20,751). The largest median operating cost category was personnel ($10,307; range: $4122-$19,794), with the highest personnel costs attributable to the provision of in-person program services. Monthly (non-zero) cost ranges for other categories were medications ($17-$573), materials ($6-$435), training ($96-$516), technology ($171-$2759), and equipment ($10-$620). Median cost-per-participant was $466 (range: $70-$2093) and cost-per-quit was $2688 (range: $330-$9628), with sites offering different combinations of program components, ranging from individually-delivered in-person counseling only to one program that offered all components. Site interviews provided context for understanding variations in program components and their cost implications.ConclusionsAmong most centers that have progressed in tobacco treatment program implementation, cost-per-quit was modest relative to other prevention interventions. Although select centers have achieved similar average costs by offering program components of various levels of intensity, they have varied widely in program reach and effectiveness. Evaluating implementation costs of such programs alongside reach and effectiveness is necessary to provide decision makers in oncology settings with the important additional information needed to optimize resource allocation when establishing tobacco treatment programs.

As collaborative biomedical research has increased in size and scope, so, too, has the need to facilitate the disparate work being done by investigators across institutional, geographic and, often, disciplinary boundaries. Yet we know little about what facilitation is on a day-to-day basis or what types of facilitation work contribute to the success of collaborative science. Here, we report on research investigating facilitation by examining the work of two coordinating centers (CCs), central bodies tasked with coordination and operations management of multi-site research. Based at the Fred Hutchinson Cancer Research Center, both CCs were run by the same team and part of National Cancer Institute-funded consortia engaged in what we call "Coordinated Collaborative Science." These CCs were charged with facilitating the collaborative work of their projects, with the aim of helping each cancer-epidemiology consortium achieve its scientific objective. This paper presents the results of a qualitative, interview-based study of the coordinating centers of two National Cancer Institute-funded consortia. Participants were observed in meetings and interviewed about their work in the consortium. A grounded-theory approach was used to analyze field notes and interview transcripts. We found that each CC engaged in four types of facilitation work: (a) structural work; (b) collaboration-development work; (c) operational work; and (d) data work. Managerial and scientific experience and expertise have been institutionalized in processes and procedures developed over decades of managing consortia. By applying collective decades of experience and expertise in the facilitation of collaborative work, the CC PIs and staff were able to provide the consortium with a neutral, third-party view of the project, keeping it on track toward its scientific objectives, and providing leadership and support when needed. The CCs also helped the consortia avoid some of the pitfalls of collaborative research that have been well documented in the literature on team science. As such, the CC saved research-site personnel time, effort, and money. Further research on the development of facilitation standards is crucial to the success of Coordinated Collaborative Science.

As collaborative biomedical research has increased in size and scope, so, too, has the need to facilitate the disparate work being done by investigators across institutional, geographic and, often, disciplinary boundaries. Yet we know little about what facilitation is on a day-to-day basis or what types offacilitation work contribute to the success of collaborative science. Here, we report on research investigating facilitation by examining the work of two coordinating centers (CCs), central bodies tasked with coordination and operations management of multi-site research. Based at the Fred Hutchinson Cancer Research Center, both CCs were run by the same team and part of National Cancer Institute-funded consortia engaged in what we call 'Coordinated Collaborative Science,' These CCs were charged with facilitating the collaborative work of their projects, with the aim of helping each cancer-epidemiology consortium achieve its scientific objective. This paper presents the results of a qualitative, interview-based study of the coordinating centers of two National Cancer Institute-funded consortia. Participants were observed in meetings and interviewed about their work in the consortium. A grounded-theory approach was used to analyze field notes and interview transcripts. We found that each CC engaged in four types of facilitation work: (a) structural work; (b) collaboration-development work; (c) operational work; and (d) data work. Managerial and scientific experience and expertise have been institutionalized in processes and procedures developed over decades of managing consortia. By applying collective decades of experience and expertise in the facilitation of collaborative work, the CC PIs and staff were able to provide the consortium with a neutral, third-party view of the project, keeping it on track toward its scientific objectives, and providing leadership and support when needed. The CCs also helped the consortia avoid some of the pitfalls of collaborative research that have been well documented in the literature on team science. As such, the CC saved research-site personnel time, effort, and money. Further research on the development of facilitation standards is crucial to the success of Coordinated Collaborative Science. Keywords: coordinating centers; team science; cancer epidemiology; coordination; facilitations, Background In recent years, biomedical research has become increasingly collaborative (Falk-Krzesinski et al., 2011; Wuchty, Jones, & Uzzi, 2007). Today's large research challenges such as global climate change and the [...]

Smoking cessation results in improved cancer treatment outcomes. However, the factors associated with successful implementation of cessation programs in cancer care settings are not well understood. This paper presents the reach the reach and effectiveness of cessation programs implemented in NCI-Designated Cancer Centers in the Cancer Center Cessation Initiative (C3I). An observational, cross-sectional study was conducted among C3I Cancer Centers from July 1, 2019 and December 31, 2019 (N = 38). Reach was calculated as the proportion of patients reporting current smoking that received cessation treatment and was analyzed overall and by organizational characteristics. Smoking abstinence rates were determined by the proportion of participants self-reporting smoking abstinence in the previous 7 and 30 days at 6 months after treatment. On average, nearly 30% of patients who smoked received any cessation treatment. In-person counseling was most implemented but reached an average of only 13.2% of patients who smoked. Although less frequently implemented, average reach was highest for counseling provided via an interactive voice response system (55.8%) and telephone-based counseling (18.7%). Reach was higher at centers with more established programs, electronic health record referral systems, and higher smoking prevalence. At 6-month follow-up, about a fifth of participants on average had not smoked in the past 7 days (21.7%) or past 30 days (18.6%). Variations in reach by organizational characteristics suggest that leadership engagement and investment in technology-facilitated programs may yield higher levels of reach. Understanding which implementation and intervention strategies facilitate greater cessation treatment reach and effectiveness could lead to improved outcomes among cancer patients who smoke. [ABSTRACT FROM AUTHOR]

Background: The University of Wisconsin Institute for Clinical and Translational Research hub supports multiple pilot award programs that engage cross-disciplinary Translational Teams. To support those teams, our Team Science group aims to offer a learning experience that is accessible, active, and actionable. We identified Collaboration Planning as a high-impact intervention to stimulate team-building activities that provide Translational Team members with the skills to lead and participate in high-impact teams. Methods: We adapted the published materials on Collaboration Planning to develop a 90-minute facilitated intervention with questions in 10 areas, presuming no previous knowledge of Science of Team Science (SciTS) or team-science best practices. Attendees received a short follow-up survey and submitted a written collaboration plan with their first quarterly progress report. Results: Thirty-nine participants from 13 pilot teams from a wide range of disciplines engaged in these sessions. We found that teams struggled to know who to invite, that some of our questions were confusing and too grounded in the language of SciTS, and groups lacked plans for managing their information and communications. We identified several areas for improvement including ensuring that the process is flexible to meet the needs of different teams, continuing to evolve the questions so they resonate with teams, and the need to provide resources for areas where teams needed additional guidance, including information and data management, authorship policies, and conflict management. Conclusions: With further development and testing, Collaboration Planning has the potential to support Translational Teams in developing strong team dynamics and team functioning. [ABSTRACT FROM AUTHOR]

The pervasive problem of irreproducibility of preclinical research represents a substantial threat to the translation of CTSA-generated health interventions. Key stakeholders in the research process have proposed solutions to this challenge to encourage research practices that improve reproducibility. However, these proposals have had minimal impact, because they either 1. take place too late in the research process, 2. focus exclusively on the products of research instead of the processes of research, and/or 3. fail to take into account the driving incentives in the research enterprise. Because so much clinical and translational science is team-based, CTSA hubs have a unique opportunity to leverage Science of Team Science research to implement and support innovative, evidence-based, team-focused, reproducibility-enhancing activities at a project's start, and across its evolution. Here, we describe the impact of irreproducibility on clinical and translational science, review its origins, and then describe stakeholders' efforts to impact reproducibility, and why those efforts may not have the desired effect. Based on team-science best practices and principles of scientific integrity, we then propose ways for Translational Teams to build reproducible behaviors. We end with suggestions for how CTSAs can leverage team-based best practices and identify observable behaviors that indicate a culture of reproducible research. [ABSTRACT FROM AUTHOR]