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.

Explaining COVID-19 related mortality disparities in American Indians and Alaska Natives.

American Indian and Alaska Native (AI/AN) individuals are more likely to die with COVID-19 than other groups, but there is limited empirical evidence to explain the cause of this inequity. The objective of this study was to determine whether medical comorbidities, area socioeconomic deprivation, or access to treatment can explain the greater COVID-19 related mortality among AI/AN individuals. The design was a retrospective cohort study of harmonized electronic health record data of all inpatients with COVID-19 from 21 United States health systems from February 2020 through January 2022. The mortality of AI/AN inpatients was compared to all Non-Hispanic White (NHW) inpatients and to a matched subsample of NHW inpatients. AI/AN inpatients were more likely to die during their hospitalization (13.2% versus 7.1%; odds ratio [OR] = 1.98, 95% confidence interval [CI] = 1.48, 2.65) than their matched NHW counterparts. After adjusting for comorbidities, area social deprivation, and access to treatment, the association between ethnicity and mortality was substantially reduced (OR 1.59, 95% CI 1.15, 2.22). The significant residual relation between AI/AN versus NHW status and mortality indicate that there are other important unmeasured factors that contribute to this inequity. This will be an important direction for future research.

Using information technology to integrate tobacco use treatment in routine oncology care: Lessons learned from the U.S. Cancer Center Cessation Initiative Cancer Centers.

Background: Cancer patients who receive evidence-based tobacco-dependence treatment are more likely to quit and remain abstinent, but tobacco treatment programs (TTPs) are not consistently offered. In 2017, the U.S. National Cancer Institute, through the Cancer Moonshot, funded the Cancer Center Cessation Initiative (C3I). C3I supports 52 cancer centers to implement and expand evidence-based tobacco treatment in routine oncology care. Integration into routine care involves the use of health information technology (IT), including modifying electronic health records and clinical workflows. Here, we examine C3I cancer centers' IT leadership involvement and experiences in tobacco-dependence treatment implementation. Method: This qualitative study of C3I-funded cancer centers integrated data from online surveys and in-person, semistructured interviews with IT leaders. We calculated descriptive statistics of survey data and applied content analysis to interview transcripts. Results: Themes regarding IT personnel included suggestions to involve IT early, communicate regularly, understand the roles and influence of the IT team, and match program design with IT funding and resources. Themes regarding electronic health record (EHR) modifications included beginning modifications early to account for long lead time to make changes, working with IT to identify and adapt existing EHR tools for TTP or designing tools that will support a desired workflow developed with end-users, and working with IT personnel to make sure TTPs comply with system and state policies (e.g., privacy laws). Conclusions: The experiences of C3I cancer centers regarding the use of health IT to enhance tobacco-dependence treatment program implementation can guide cancer centers and community oncology practices to potentially enhance TTP implementation and patient outcomes.

Almost a quarter of patients first diagnosed with cancer report current cigarette smoking. There are tobacco treatment programs (TTPs) that effectively help patients quit smoking to improve cancer treatment response, survival, and quality-of-life. In 2017, the U.S. National Cancer Institute (NCI) funded the Cancer Center Cessation Initiative (C3I) and supported 52 cancer centers to implement these TTPs. A key component of these programs is the information technology (IT) necessary to refer patients to the program and document their progress. As coordinators of C3I, our team conducted interviews with IT leaders at these cancer centers to learn about the implementation of the programs. IT leaders suggested that IT teams be involved early in the program implementation process and that leaders communicate with the IT team regularly to address necessary changes to referral and documentation systems. IT teams are important to involve early and regularly throughout the TTP implementation process because they have unique knowledge of how funding, policy, and existing technological tools will impact the implementation and success of the program. Our findings emphasize the importance of involving IT teams early in the planning process for such programs. Studies such as this focusing on the experiences and knowledge of specific team members, such as the IT team, enhance tobacco-dependence treatment program implementation and can guide cancer centers and community oncology practices to implement these programs to improve patient outcomes.

Factors associated with 60-day readmission among inpatients with COVID-19 at 21 United States health systems.

Identifying patients at risk for readmission after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection could facilitate care planning and prevention. This retrospective cohort study of 60-day readmission included 105 543 COVID-19 patients at 21 US healthcare systems who were discharged alive between February 2020 and November 2021. Generalized linear mixed regression analyses tested predictors of 60-day readmission and severity. The all-cause readmission rate was 15% (95% confidence interval [CI] = 10%-21%), with 22% (95% CI = 18%-26%) of readmitted patients needing intensive care, and 6% (95% CI = 05%-07%) dying. Factors associated with readmission included male sex, government insurance, positive smoking history, co-morbidity burden, longer index admissions, and diagnoses at index admission (e.g., cancer, chronic kidney disease, and liver disease). Death and intensive care rates at readmission declined postvaccine availability. Receiving at least two COVID-19 vaccine doses, which were more common among older patients and those with comorbid conditions, was not independently associated with readmission but predicted a reduced risk of death at readmission. This retrospective cohort study identified factors associated with all-cause readmission for patients re-admitted to the same health system after hospitalization with SARS-CoV-2 infection. Patients who are male, who smoke, who have a higher comorbidity burden, and have government insurance may benefit from additional postacute care planning.

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.

Trends in E-cigarette Use in Callers to the Wisconsin Tobacco Quit Line.

INTRODUCTION: E-cigarette use has been increasing for years with a limited understanding of how to help users quit. Quit lines are a potential resource for e-cigarette cessation. Our objective was to characterize e-cigarette users who call state quit lines and to examine trends in e-cigarette use by callers. METHODS: This retrospective study examined data from adult callers to the Wisconsin Tobacco Quit Line from July 2016 through November 2020, including demographics, tobacco product use, motivations for use, and intentions to quit. Descriptive analyses were performed by age group with pairwise comparisons. RESULTS: A total of 26,705 encounters were handled by the Wisconsin Tobacco Quit Line during the study period. E-cigarettes were used by 11% of callers. Young adults aged 18-24 had the highest rates of use at 30%, and their use rose significantly from 19.6% in 2016 to 39.6% in 2020. E-cigarette use among young adult callers peaked at 49.7% in 2019, coinciding with an outbreak of e-cigarette-related lung injury. Only 53.5% of young adult callers used e-cigarettes to "cut down on other tobacco," compared to 76.3% of adult callers aged 45-64 (P <0.05). Of all callers using e-cigarettes, 80% were interested in quitting. CONCLUSIONS: E-cigarette use among callers to the Wisconsin Tobacco Quit Line has increased, driven largely by young adults. Most e-cigarette users who call the quit line want to quit. Thus, quit lines can serve an important role in e-cigarette cessation. A better understanding of strategies to help e-cigarette users quit is needed, particularly in young adult callers.

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.

The Impact of the COVID-19 Pandemic on Tobacco Treatment Program Implementation at National Cancer Institute-Designated Cancer Centers.

INTRODUCTION: The COVID-19 pandemic disrupted cancer screening and treatment delivery, but COVID-19's impact on tobacco cessation treatment for cancer patients who smoke has not been widely explored. AIMS AND METHODS: We conducted a sequential cross-sectional analysis of data collected from 34 National Cancer Institute (NCI)-designated cancer centers participating in NCI's Cancer Center Cessation Initiative (C3I), across three reporting periods: one prior to COVID-19 (January-June 2019) and two during the pandemic (January-June 2020, January-June 2021). Using McNemar's Test of Homogeneity, we assessed changes in services offered and implementation activities over time. RESULTS: The proportion of centers offering remote treatment services increased each year for Quitline referrals (56%, 68%, and 91%; p = .000), telephone counseling (59%, 79%, and 94%; p = .002), and referrals to Smokefree TXT (27%, 47%, and 56%; p = .006). Centers offering video-based counseling increased from 2020 to 2021 (18% to 59%; p = .006), Fewer than 10% of centers reported laying off tobacco treatment staff. Compared to early 2020, in 2021 C3I centers reported improvements in their ability to maintain staff and clinician morale, refer to external treatment services, train providers to deliver tobacco treatment, and modify clinical workflows. CONCLUSIONS: The COVID-19 pandemic necessitated a rapid transition to new telehealth program delivery of tobacco treatment for patients with cancer. C3I cancer centers adjusted rapidly to challenges presented by the pandemic, with improvements reported in staff morale and ability to train providers, refer patients to tobacco treatment, and modify clinical workflows. These factors enabled C3I centers to sustain evidence-based tobacco treatment implementation during and beyond the COVID-19 pandemic. IMPLICATIONS: This work describes how NCI-designated cancer centers participating in the Cancer Center Cessation Initiative (C3I) adapted to challenges to sustain evidence-based tobacco use treatment programs during the COVID-19 pandemic. This work offers a model for resilience and rapid transition to remote tobacco treatment services delivery and proposes a policy and research agenda for telehealth services as an approach to sustaining evidence-based tobacco treatment programs.

The Bucket Approach: Developing and Implementing an On-line Training Program in Tobacco Dependence Interventions Tailored for Behavioral Health Clinicians.

People coping with a mental illness and/or addictive disorders have a very high prevalence of smoking cigarettes. The Bucket Approach, a free online training, tailors evidence-based tobacco dependence interventions for behavioral health clinicians to increase the likelihood that they will also address the tobacco use of their patients. From October 2019 through August 2021, 999 people enrolled in and 447 people completed the training. Individuals who completed the training evaluated it highly with an overall mean score of 8.4 (scale = 1 for very poor to 10 for very good). 3- and 6-month follow-up surveys documented continued impact. The training resulted in substantial changes in beliefs about treating tobacco dependence. For example, before training, 18.3% of trainees strongly agreed with the statement, "The skills currently possessed by behavioral health clinicians can be easily applied to the treatment of tobacco dependence." This increased to 40.7% at the end of training.

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.

The Tobacco Industry and Harm Reduction.

This Viewpoint discusses the ways in which the tobacco industry can advance their stated goals of harm reduction and a smoke-free future.

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).