Economic Evaluation of Enhanced vs Standard Varenicline Treatment for Tobacco Cessation.

Importance: Smoking is the leading preventable cause of death and illness in the US. Identifying cost-effective smoking cessation treatment may increase the likelihood that health systems deliver such treatment to their patients who smoke. Objective: To evaluate the cost-effectiveness of standard vs enhanced varenicline use (extended varenicline treatment or varenicline in combination with nicotine replacement therapy) among individuals trying to quit smoking. Design, Setting, and Participants: This economic evaluation assesses the Quitting Using Intensive Treatments Study (QUITS), which randomized 1251 study participants who smoked into 4 conditions: (1) 12-week varenicline monotherapy (n = 315); (2) 24-week varenicline monotherapy (n = 311); (3) 12-week varenicline combination treatment with nicotine replacement therapy patch (n = 314); or (4) 24-week varenicline combination treatment with nicotine replacement therapy patch (n = 311). Study enrollment occurred in Madison and Milwaukee, Wisconsin, between November 11, 2017, and July 2, 2020. Statistical analysis took place from May to October 2023. Main Outcomes and Measures: The primary outcome was 7-day point prevalence abstinence (biochemically confirmed with exhaled carbon monoxide level ≤5 ppm) at 52 weeks. The incremental cost-effectiveness ratio (ICER), or cost per additional person who quit smoking, was calculated using decision tree analysis based on abstinence and cost for each arm of the trial. Results: Of the 1251 participants, mean (SD) age was 49.1 (11.9) years, 675 (54.0%) were women, and 881 (70.4%) completed the 52-week follow-up. Tobacco cessation at 52 weeks was 25.1% (79 of 315) for 12-week monotherapy, 24.4% (76 of 311) for 24-week monotherapy, 23.6% (74 of 314) for 12-week combination therapy, and 25.1% (78 of 311) for 24-week combination therapy, respectively. The total mean (SD) cost was $1175 ($365) for 12-week monotherapy, $1374 ($412) for 12-week combination therapy, $2022 ($813) for 24-week monotherapy, and $2118 ($1058) for 24-week combination therapy. The ICER for 12-week varenicline monotherapy was $4681 per individual who quit smoking and $4579 per quality-adjusted life-year (QALY) added. The ICER for 24-week varenicline combination therapy relative to 12-week monotherapy was $92 000 000 per additional individual who quit smoking and $90 000 000 (95% CI, $15 703 to dominated or more costly and less efficacious) per additional QALY. Conclusions and Relevance: This economic evaluation of standard vs enhanced varenicline treatment for smoking cessation suggests that 12-week varenicline monotherapy was the most cost-effective treatment option at the commonly cited threshold of $100 000/QALY. This study provides patients, health care professionals, and other stakeholders with increased understanding of the health and economic impact of more intensive varenicline treatment options.

What to do after smoking relapse? A sequential multiple assignment randomized trial of chronic care smoking treatments.

AIM: To compare effects of three post-relapse interventions on smoking abstinence. DESIGN: Sequential three-phase multiple assignment randomized trial (SMART). SETTING: Eighteen Wisconsin, USA, primary care clinics. PARTICIPANTS: A total of 1154 primary care patients (53.6% women, 81.2% White) interested in quitting smoking enrolled from 2015 to 2019; 582 relapsed and were randomized to relapse recovery treatment. INTERVENTIONS: In phase 1, patients received cessation counseling and 8 weeks nicotine patch. Those who relapsed and agreed were randomized to a phase 2 relapse recovery group: (1) reduction counseling + nicotine mini-lozenges + encouragement to quit starting 1 month post-randomization (preparation); (2) repeated encouragement to quit starting immediately post-randomization (recycling); or (3) advice to call the tobacco quitline (control). The first two groups could opt into phase 3 new quit treatment [8 weeks nicotine patch + mini-lozenges plus randomization to two treatment factors (skill training and supportive counseling) in a 2 × 2 design]. Phase 2 and 3 interventions lasted ≤ 15 months. MEASUREMENTS: The study was powered to compare each active phase 2 treatment with the control on the primary outcome: biochemically confirmed 7-day point-prevalence abstinence 14 months post initiating phase 2 relapse recovery treatment. Exploratory analyses tested for phase 3 counseling factor effects. FINDINGS: Neither skill training nor supportive counseling (each on versus off) increased 14-month abstinence rates; skills on versus off 9.3% (14/151) versus 5.2% (8/153), P = 0.19; support on versus off 6.6% (10/152) versus 7.9% (12/152), P = 0.73. Phase 2 preparation did not produce higher 14-month abstinence rates than quitline referral; 3.6% (8/220) versus 2.1% [3/145; risk difference = 1.5%, 95% confidence interval (CI) = -1.8-5.0%, odds ratio (OR) = 1.8, 95% CI = 0.5-6.9]. Recycling, however, produced higher abstinence rates than quitline referral; 6.9% (15/217) versus 2.1% (three of 145; risk difference, 4.8%, 95% CI = 0.7-8.9%, OR = 3.5, 95% CI = 1.0-12.4). Recycling produced greater entry into new quit treatment than preparation: 83.4% (181/217) versus 55.9% (123/220), P < 0.0001. CONCLUSIONS: Among people interested in quitting smoking, immediate encouragement post-relapse to enter a new round of smoking cessation treatment ('recycling') produced higher probability of abstinence than tobacco quitline referral. Recycling produced higher rates of cessation treatment re-engagement than did preparation/cutting down using more intensive counseling and pharmacotherapy.

Cost-Effectiveness of a Comprehensive Primary Care Smoking Treatment Program.

INTRODUCTION: Smoking is the leading preventable cause of death and disease in the U.S. This study evaluates the cost-effectiveness from a healthcare system perspective of a comprehensive primary care intervention to reduce smoking rates. METHODS: This pragmatic trial implemented electronic health record prompts during primary care visits and employed certified tobacco cessation specialists to offer proactive outreach and smoking cessation treatment to patients who smoke. The data, analyzed in 2022, included 10,683 patients in the smoking registry from 2017 to 2020. Pre-post analyses compared intervention costs to treatment engagement, successful self-reported smoking cessation, and acute health care utilization (urgent care, emergency department visits, and inpatient hospitalization). Cost per quality-adjusted life year was determined by applying conversion factors obtained from the tobacco research literature to the cost per patient who quit smoking. RESULTS: Tobacco cessation outreach, medication, and counseling costs increased from $2.64 to $6.44 per patient per month, for a total post-implementation intervention cost of $500,216. Smoking cessation rates increased from 1.3% pre-implementation to 8.7% post-implementation, for an incremental effectiveness of 7.4%. The incremental cost-effectiveness ratio was $628 (95% CI: $568, $695) per person who quit smoking, and $905 (95% CI: $822, $1,001) per quality-adjusted life year gained. Acute health care costs decreased by an average of $42 (95% CI: -$59, $145) per patient per month for patients in the smoking registry. CONCLUSIONS: Implementation of a comprehensive and proactive smoking cessation outreach and treatment program for adult primary care patients who smoke meets typical cost-effectiveness thresholds for healthcare.

Genomic medicine to reduce tobacco and related disorders: Translation to precision prevention and treatment.

Genomic medicine can enhance prevention and treatment. First, we propose that advances in genomics have the potential to enhance assessment of disease risk, improve prognostic predictions, and guide treatment development and application. Clinical implementation of polygenic risk scores (PRSs) has emerged as an area of active research. The pathway from genomic discovery to implementation is an iterative process. Second, we provide examples on how genomic medicine has the potential to solve problems in prevention and treatment using two examples: Lung cancer screening and evidence-based tobacco treatment are both under-utilized and great opportunities for genomic interventions. Third, we discuss the translational process for developing genomic interventions from evidence to implementation by presenting a model to evaluate genomic evidence for clinical implementation, mechanisms of genomic interventions, and patient desire for genomic interventions. Fourth, we present potential challenges in genomic interventions including a great need for evidence in all diverse populations, little evidence on treatment algorithms, challenges in accommodating a dynamic evidence base, and implementation challenges in real world clinical settings. Finally, we conclude that research to identify genomic markers that are associated with smoking cessation success and the efficacy of smoking cessation treatments is needed to empower people of all diverse ancestry. Importantly, genomic data can be used to help identify patients with elevated risk for nicotine addiction, difficulty quitting smoking, favorable response to specific pharmacotherapy, and tobacco-related health problems.

Data envelopment analysis to evaluate the efficiency of tobacco treatment programs in the NCI Moonshot Cancer Center Cessation Initiative.

BACKGROUND: The 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. METHODS: DEA 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. RESULTS: In 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). CONCLUSION: Most 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.

Cardiovascular and Pulmonary Responses to Acute Use of Electronic Nicotine Delivery Systems and Combustible Cigarettes in Long-Term Users.

BACKGROUND: The acute cardiovascular and pulmonary effects of contemporary electronic nicotine delivery systems (ENDS) in long-term users are not known. RESEARCH QUESTION: What are the cardiovascular and pulmonary responses to an acute 15-min product use challenge with ENDS and combustible cigarettes in regular nicotine-containing product users compared with control participants who do not use tobacco or vape? STUDY DESIGN AND METHODS: Observational challenge study before and after nicotine-containing product use of 395 individuals who used ENDS exclusively (n = 164; exhaled carbon monoxide level, < 5 parts per million [ppm]; positive urine NicCheck I [Mossman Associates] results, 82%; fourth-generation ENDS), participants who smoked cigarettes exclusively (n = 117; carbon monoxide level, > 5 ppm; positive urine NicCheck I results), and control participants (n = 114; carbon monoxide level, < 5 ppm; negative urine NicCheck I results). RESULTS: During the 15-min product challenge, cigarette users took a median of 14.0 puffs (interquartile range [IQR], 9.3 puffs); ENDS users took 9.0 puffs (IQR, 7.5 puffs; P < .001). After product challenge, compared with control participants, ENDS users showed greater increases in adjusted mean differences in systolic BP (5.6 mm Hg [95% CI, 4.4-6.8 mm Hg] vs 2.3 mm Hg [95% CI, 0.8-3.8 mm Hg]; P = .001), diastolic BP (4.2 mm Hg [95% CI, 3.3-5.0 mm Hg] vs 2.0 mm Hg [95% CI, 1.1-3.0 mm Hg; P = .003), and heart rate (4.8 beats/min [95% CI, 4.0-5.6 beats/min] vs -1.3 beats/min [95% CI, -2.2 to -0.3 beats/min]; P < .001) and greater reductions in brachial artery diameter (-0.011 cm [95% CI, -0.013 to 0.009 cm] vs -0.006 cm [95% CI, -0.004 to -0.009 cm]; P = .003), time-domain heart rate variability (-7.2 ms [95% CI, -10.5 to -3.7 ms] vs 3.6 ms [95% CI, 1.6-9.3 ms]; P = .001), and FEV1 (ENDS: -4.1 [95% CI, -5.4 to -2.8] vs control participants: -1.1 [95% CI, -2.7 to 0.6]; P = .005) with values similar to those of cigarette users. ENDS users performed worse than control participants on all exercise parameters, notably metabolic equivalents (METs; adjusted mean difference, 1.28 METs [95% CI, 0.73-1.83 METs]; P < .001) and 60-s heart rate recovery (adjusted mean difference, 2.9 beats/min [95% CI, 0.7-5.0 beats/min]; P = .008). INTERPRETATION: ENDS users had acute worsening of blood pressure, heart rate, and heart rate variability, as well as vasoconstriction, impaired exercise tolerance, and increased airflow obstruction after vaping, compared to control participants. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT03863509; URL: www. CLINICALTRIALS: gov.

A machine learning analysis of correlates of mortality among patients hospitalized with COVID-19.

It is vital to determine how patient characteristics that precede COVID-19 illness relate to COVID-19 mortality. This is a retrospective cohort study of patients hospitalized with COVID-19 across 21 healthcare systems in the US. All patients (N = 145,944) had COVID-19 diagnoses and/or positive PCR tests and completed their hospital stays from February 1, 2020 through January 31, 2022. Machine learning analyses revealed that age, hypertension, insurance status, and healthcare system (hospital site) were especially predictive of mortality across the full sample. However, multiple variables were especially predictive in subgroups of patients. The nested effects of risk factors such as age, hypertension, vaccination, site, and race accounted for large differences in mortality likelihood with rates ranging from about 2-30%. Subgroups of patients are at heightened risk of COVID-19 mortality due to combinations of preadmission risk factors; a finding of potential relevance to outreach and preventive actions.

The Relationship of COVID-19 Vaccination with Mortality Among 86,732 Hospitalized Patients: Subpopulations, Patient Factors, and Changes over Time.

BACKGROUND: Information on COVID-19 vaccination effects on mortality among patients hospitalized with COVID-19 could inform vaccination outreach efforts and increase understanding of patient risk. OBJECTIVE: Determine the associations of vaccination status with mortality in adult patients hospitalized with COVID-19. DESIGN: This retrospective cohort study assessed the characteristics and mortality rates of adult patients hospitalized with COVID-19 across 21 healthcare systems in the USA from January 1, 2021, to January 31, 2022. PARTICIPANTS: Adult patients admitted to participating hospitals who had COVID-19 diagnoses and/or positive PCR tests and completed their hospital stay via discharge or death. MAIN MEASURE: In-hospital mortality vs. discharge (outcome) and patient age, sex, race, ethnicity, BMI, insurance status, comorbidities, and vaccination status extracted from the electronic health record (EHR). KEY RESULTS: Of 86,732 adult patients hospitalized with COVID-19, 45,082 (52%) were female, mean age was 60 years, 20,800 (24%) were Black, and 22,792 (26.3%) had one or more COVID-19 vaccinations. Statistically adjusted mortality rates for unvaccinated and vaccinated patients were 8.3% (95% CI, 8.1-8.5) and 5.1% (95% CI, 4.8-5.4) respectively (7.9% vs. 4.5% with no immune compromise). Vaccination was associated with especially large reductions in mortality for obese (OR = 0.67; 95% CI 0.56-0.80) and severely obese (OR = 0.52; 95% CI, 0.41-0.67) patients and for older patients (OR = 0.99; 95% CI, 0.98-0.99). Mortality likelihood was higher later in the study period (August 2021-January 31, 2022) than earlier (January 1, 2021-July 30, 2021) (OR = 1.10; 95% CI = 1.04-1.17) and increased significantly for vaccinated patients from 4.6% (95% CI, 3.9-5.2%) to 6.5% (95% CI, 6.2-6.9%). CONCLUSIONS: Patients vaccinated for COVID-19 had reduced mortality, especially for obese/severely obese and older individuals. Vaccination's protective effect against mortality declined over time and hospitalized obese and older individuals may derive especially great benefit from prior vaccination against SARS-CoV-2.

Integrating Tobacco Treatment Into Oncology Care: Reach and Effectiveness of Evidence-Based Tobacco Treatment Across National Cancer Institute-Designated Cancer Centers.

PURPOSE: Quitting smoking improves patients' clinical outcomes, yet smoking is not commonly addressed as part of cancer care. The Cancer Center Cessation Initiative (C3I) supports National Cancer Institute-designated cancer centers to integrate tobacco treatment programs (TTPs) into routine cancer care. C3I centers vary in size, implementation strategies used, and treatment approaches. We examined associations of these contextual factors with treatment reach and smoking cessation effectiveness. METHODS: This cross-sectional study used survey data from 28 C3I centers that reported tobacco treatment data during the first 6 months of 2021. Primary outcomes of interest were treatment reach (reach)-the proportion of patients identified as currently smoking who received at least one evidence-based tobacco treatment component (eg, counseling and pharmacotherapy)-and smoking cessation effectiveness (effectiveness)-the proportion of patients reporting 7-day point prevalence abstinence at 6-month follow-up. Center-level differences in reach and effectiveness were examined by center characteristics, implementation strategies, and tobacco treatment components. RESULTS: Of the total 692,662 unique patients seen, 44,437 reported current smoking. Across centers, a median of 96% of patients were screened for tobacco use, median smoking prevalence was 7.4%, median reach was 15.4%, and median effectiveness was 18.4%. Center-level characteristics associated with higher reach included higher smoking prevalence, use of center-wide TTP, and lower patient-to-tobacco treatment specialist ratio. Higher effectiveness was observed at centers that served a larger overall population and population of patients who smoke, reported a higher smoking prevalence, and/or offered electronic health record referrals via a closed-loop system. CONCLUSION: Whole-center TTP implementation among inpatients and outpatients, and increasing staff-to-patient ratios may improve TTP reach. Designating personnel with tobacco treatment expertise and resources to increase tobacco treatment dose or intensity may improve smoking cessation effectiveness.

Associations of Demographics, Dependence, and Biomarkers With Transitions in Tobacco Product Use in a Cohort of Cigarette Users and Dual Users of Cigarettes and E-cigarettes.

INTRODUCTION: It is uncertain whether e-cigarettes facilitate smoking cessation in the real world. We aimed to understand whether and how transitions among cigarette, e-cigarette, and dual use are associated with sociodemographics, dependence measures, and biomarkers. AIMS AND METHODS: We followed 380 adult daily cigarette users and dual users every 2 months for up to 2 years. We estimated transition rates between noncurrent, cigarette-only, e-cigarette-only, and dual use states using a multistate transition model. We estimated univariable hazard ratios (HR) for demographics, dependence measures for cigarettes and e-cigarettes, biomarkers, spousal or partner behaviors, and other measures. RESULTS: We estimated that participants transitioned from cigarette-only to e-cigarette-only through a period of dual use. Dual users ceased smoking (transitioning to e-cigarette-only use) at a greater rate than cigarette-only users did (HR 2.44, 95% CI: 1.49, 4.02). However, of the 60% of dual users estimated to transition to single product use in 1 year, 83% would transition to cigarette-only use and only 17% to e-cigarette-only use. E-cigarette dependence measures were generally associated with reduced e-cigarette cessation rather than enhanced cigarette cessation. E-cigarette users motivated by harm or toxicity reduction or because of restrictions on where or when they could smoke had reduced rates of smoking relapse. Cigarette dependence and spousal smoking were barriers to cigarette cessation for dual users, while using e-cigarettes first in the morning, motivation to quit smoking, and sensory, social, and emotional enjoyment of e-cigarettes (secondary dependence motives) were facilitators of smoking cessation among dual users. CONCLUSIONS: Tobacco control policy and interventions may be informed by the barriers and facilitators of product transitions. IMPLICATIONS: Although e-cigarettes have the potential to promote smoking cessation, their real-world impact is uncertain. In this cohort, dual users were more likely to quit smoking than cigarette-only users, but the overall impact was small because most dual users returned to cigarette-only use. Moreover, e-cigarette dependence promoted continued dual use rather than smoking cessation. Yet, high motivation to quit smoking and the sensory, social, and emotional enjoyment of e-cigarettes facilitated smoking cessation in dual users. Better understanding the barriers and facilitators of transitions can help to develop regulations and interventions that lead to more effective use of e-cigarettes for smoking cessation.

Relations of Current and Past Cancer with Severe Outcomes among 104,590 Hospitalized COVID-19 Patients: The COVID EHR Cohort at the University of Wisconsin.

BACKGROUND: There 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. METHODS: Electronic 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. RESULTS: 6.8% 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). CONCLUSIONS: Current 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. IMPACT: This 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.

Smoking Status, Nicotine Medication, Vaccination, and COVID-19 Hospital Outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) Study.

INTRODUCTION: Available 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. METHODS: Electronic 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. RESULTS: Current (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). CONCLUSIONS: Former 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. IMPLICATIONS: Prior 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.

Treating Smoking in Cancer Patients: An Essential Component of Cancer Care-The New National Cancer Institute Tobacco Control Monograph.

PURPOSE: Continued smoking after the diagnosis of cancer can markedly worsen oncology treatment side effects, cancer outcomes, cancer mortality, and all-cause mortality. Conversely, mounting evidence demonstrates that smoking cessation by patients with cancer improves outcomes. A cancer diagnosis often serves as a teachable moment, characterized by high motivation to quit. However, too few patients with cancer who smoke are offered evidence-based smoking cessation treatment, and too few engage in such treatment. METHODS AND MATERIALS: The National Cancer Institute commissioned Tobacco Control Monograph 23, Treating Smoking in Cancer Patients: An Essential Component of Cancer Care, to review and synthesize the evidence that clarifies the need to intervene with smoking in cancer care. RESULTS: Although many patients with newly diagnosed cancer who smoke make quit attempts, many of these are unsuccessful, and among those who successfully quit, relapse is common. Indeed, an estimated 12.2% of adults ever diagnosed with cancer reported they currently smoked (National Health Interview Survey, 2020). Patients with cancer who smoke are likely to benefit from smoking cessation treatments, including counseling and US Food and Drug Administration-approved medications, and there are many effective strategies to increase delivery of smoking cessation treatment in cancer care settings. CONCLUSION: Smoking cessation is among the most effective treatment options for improving the likelihood of survival, quality of life, and overall health of patients with cancer who smoke. It is important for cancer care clinicians and patients to realize that it is never too late to quit smoking and that there are clear benefits to doing so, regardless of cancer type.

Electronic health record closed-loop referral ("eReferral") to a state tobacco quitline: a retrospective case study of primary care implementation challenges and adaptations.

BACKGROUND: Health system change can increase the reach of evidence-based smoking cessation treatments. Proactive electronic health record (EHR)-enabled, closed-loop referral ("eReferral") to state tobacco quitlines increases the rates at which patients who smoke accept cessation treatment. Implementing such system change poses many challenges, however, and adaptations to system contexts are often required, but are understudied. This retrospective case study identified adaptations to eReferral EHR tools and implementation strategies in two healthcare systems. METHODS: In a large clustered randomized controlled trial (C-RCT; NCT02735382) conducted in 2016-2017, 11 primary care clinics in two healthcare systems implemented quitline eReferral, starting with 1 pilot clinic per system followed by 2 phases of implementation (an experimental phase in 5-6 test clinics per system and then a system-wide dissemination phase in both systems). Adaptations were informed by stakeholder input from live trainings, follow-up calls and meetings in the first month after eReferral launch, emails, direct observation by researchers, and clinic staff survey responses. Retrospective, descriptive analysis characterized implementation strategy modifications and adaptations using the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies (FRAME-IS). A pre- and post-implementation survey assessed staff ratings of eReferral acceptability and implementation barriers and facilitators. FINDINGS: Major modifications to closed-loop eReferral implementation strategies included aligning the eReferral initiative with other high-priority health system objectives, modifying eReferral user interfaces and training in their use, modifying eReferral workflows and associated training, and maintaining and enhancing interoperability and clinician feedback functions. The two health systems both used Epic EHRs but used different approaches to interfacing with the quitline vendor and integrating eReferral into clinician workflows. Both health systems engaged in iterative refinement of the EHR alert prompting eReferral, the eReferral order, trainings, and workflows. Staff survey comments suggested moderate acceptability of eReferral processes and identified possible targets for future modifications in eReferral, including reducing clinician burden related to EHR documentation and addressing clinicians' negative beliefs about patient receptivity to cessation treatment. CONCLUSIONS: System-wide implementation of tobacco quitline eReferral in primary care outpatient clinics is feasible but requires extensive coordination across stakeholders, tailoring to local health system EHR configurations, and sensitivity to system- and clinic-specific workflows. TRIAL REGISTRATION: www. CLINICALTRIALS: gov, NCT02735382 . Registered on 12 August 2016.

The first 20 months of the COVID-19 pandemic: Mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems.

MAIN OBJECTIVE: There 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 METHODS: University 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 SIGNIFICANCE: The 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. CONCLUSIONS: Mortality, 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 REGISTRATION: ClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).

Underutilization of guideline-concordant smoking cessation treatments in surgical patients: Lessons from a learning health system.

While smoking is a well-established risk factor for surgical complications, it is unclear how frequently guideline-concordant tobacco treatments are prescribed to surgical patients. In this cross-sectional study including 164673 unique patients evaluated in outpatient surgery clinics at a single institution in 2020, despite a relatively high smoking prevalence (14.7%), guideline-concordant treatment rates were very low, with only 12.7% of patients receiving pharmacotherapy and 31.7% receiving any treatment. Addressing disparities in smoking cessation treatments are critical given the disproportionate impact of smoking on surgical outcomes.

Assessment of formal tobacco treatment and smoking cessation in dual users of cigarettes and e-cigarettes.

BACKGROUND: The utility of electronic cigarettes ('e-cigarettes') as a smoking cessation adjunct remains unclear. Similarly, it is unclear if formal tobacco treatment (pharmacotherapy and/or behavioural support) augments smoking cessation in individuals who use both cigarettes and e-cigarettes. METHODS: We performed a longitudinal cohort study of adult outpatients evaluated in our tertiary care medical centre (6/2018-6/2020). E-cigarette use, smoking status and formal tobacco treatment (deterrent pharmacotherapy and/or behavioural support) were assessed in 6-month blocks (eg, cohort 1 (C1)=6/2018-12/2018, C2=1/2019-6/2019 and so on) using our electronic health record. We assessed the relationship between e-cigarette use (either with or without formal tobacco treatment) and point prevalence of smoking cessation at 6 and 12 months. RESULTS: 111 823 unique patients were included in the study. The prevalence of dual use of cigarettes and e-cigarettes increased significantly over the study period (C1=0.8%; C2=1.1%; C3=1.8%; C4=2.3%; p<0.001). The prevalence of smoking cessation at 12 months was higher among e-cigarette users (20.8%) compared with non-users (16.8%) (risk difference, 4.0% (95% CI 2.5% to 5.5%); adjusted relative risk (aRR) 1.354, 95% CI 1.252 to 1.464, p<0.0001). Further, among dual users of cigarettes and e-cigarettes, the prevalence of smoking cessation at 12 months was higher among individuals who received tobacco treatment (29.1%) compared with individuals who did not receive tobacco treatment (19.6%) (risk difference, 9.5% (95% CI, 4.6% to 14.4%); aRR 1.238, 95% CI 1.071 to 1.432, p=0.004). INTERPRETATION: These results suggest that dual users of cigarettes and e-cigarettes benefit from formal tobacco treatment. Clinicians should consider offering formal tobacco treatment to such patients, though future trials are needed.

Understanding what changes adults in a smoking cessation study believe they need to make to quit smoking: A qualitative analysis of pre- and post-quit perceptions.

OBJECTIVE: Most individuals who try to quit smoking will not succeed even if they use evidence-based treatment. Qualitative methods can help identify cessation treatments' limitations and suggest adaptations to increase treatment success. METHOD: Rapid qualitative analysis was conducted on data from 125 adults who smoked daily (48% female; 44% White) and participated in a smoking cessation trial and completed qualitative interviews 2 weeks prequit, reporting on changes they needed to make to quit, and 100 adults (50% female; 49% White) who completed a second interview 2 weeks postquit, reporting changes they had made. RESULTS: The anticipated changes reported prequit (in order of frequency) were as follows: identify smoking triggers (without a coping plan), focus on benefits of quitting, reduce exposure to others smoking, make other health changes, reduce exposure to nonsocial smoking cues, and reduce alcohol consumption. Many participants were unable to identify specific changes that would aid their cessation success. Changes reported postquit included the following: use the 4 D strategies (delay, drink water, deep breathing, distract), reduce exposure to nonsocial smoking cues, focus on benefits of quitting, change daily routine, make other health changes, reduce exposure to others smoking, and get support from loved ones. Most changes reported postquit were consistent with clinical practice guidelines; however, use of cessation medication was the least reported theme. CONCLUSION: Prior to quitting, over a third of participants were unable to identify changes to increase cessation success. Those who could focus on triggers and cues for smoking. Postquit, participants reported using cessation strategies encouraged during study cessation counseling. (PsycInfo Database Record (c) 2022 APA, all rights reserved).