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.

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.

Smoking Cessation, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology.

Although the harmful effects of smoking after a cancer diagnosis have been clearly demonstrated, many patients continue to smoke cigarettes during treatment and beyond. The NCCN Guidelines for Smoking Cessation emphasize the importance of smoking cessation in all patients with cancer and seek to establish evidence-based recommendations tailored to the unique needs and concerns of patients with cancer. The recommendations contained herein describe interventions for cessation of all combustible tobacco products (eg, cigarettes, cigars, hookah), including smokeless tobacco products. However, recommendations are based on studies of cigarette smoking. The NCCN Smoking Cessation Panel recommends that treatment plans for all patients with cancer who smoke include the following 3 tenets that should be done concurrently: (1) evidence-based motivational strategies and behavior therapy (counseling), which can be brief; (2) evidence-based pharmacotherapy; and (3) close follow-up with retreatment as needed.

An Examination of Cardiac Vagal Control Indices and Cognitive Stress Appraisal in Cigarette Smokers.

Identifying factors that influence how individuals who smoke cigarettes respond to stress is important as stress is a risk factor for smoking and its maintenance. This study examined the modulatory role of cardiac vagal control (CVC), a physiological correlate of self-regulation, on cognitive stress appraisal processes of adults who smoke. Sixty daily cigarette smokers were randomized to receive positive or negative feedback during a modified Trier Social Stress Test. Pre- and post-task stress appraisals were assessed and resting and reactivity CVC measures were computed. Moderated regression models assessed if the relation between feedback condition and post-task stress appraisal varied as a function of CVC. We hypothesized that participants receiving negative feedback would report greater post-task stress appraisal compared to participants receiving positive feedback, and the strength of the effect of both feedback groups would be greater at higher levels of CVC. All models showed significant main effects of feedback condition (b = - 0.42, p = 0.01; b = - 0.45, p = 0.01) on post-task stress appraisal: participants receiving negative feedback reported greater post-task stress appraisal. No significant main or interactive effects of CVC and feedback condition on post-task stress appraisal were observed. This study demonstrates that stress appraisals of daily cigarette smokers are sensitive to social feedback, but are not moderated by individual differences in CVC. Future investigations are needed to clarify whether this finding is explained by smoking-specific impairments in CVC as well as the distinct and interactive effects of physiological and psychological processes implicated in stress and smoking risk.

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.

A comprehensive electronic health record-enabled smoking treatment program: Evaluating reach and effectiveness in primary care in a multiple baseline design.

Effective treatments for smoking cessation exist but are underused. Proactive chronic care approaches may enhance the reach of cessation treatment and reduce the prevalence of smoking in healthcare systems. This pragmatic study evaluated a population-based Comprehensive Tobacco Intervention Program (CTIP) implemented in all (6) adult primary care clinics in a Madison, Wisconsin, USA healthcare cooperative, assessing treatment reach, reach equity, and effectiveness in promoting smoking cessation. CTIP launched in 3 waves of 2 clinics each in a multiple baseline design. Electronic health record (EHR) tools facilitated clinician-delivered pharmacotherapy and counseling; guiding tobacco care managers in phone outreach to all patients who smoke; and prompting multimethod bulk outreach to all patients on a smoking registry using an opt-out approach. EHR data were analyzed to assess CTIP reach and effectiveness among 6894 adult patients between January 2018 and February 2020. Cessation treatment reach increased significantly after CTIP launch in 5 of 6 clinics and was significantly higher when clinics were active vs. inactive in CTIP [Odds Ratio (OR) range = 2.0-3.0]. Rates of converting from current to former smoking status were also higher in active vs. inactive clinics (OR range = 2.2-10.5). Telephone treatment reach was particularly high in historically underserved groups, including African-American, Hispanic, and Medicaid-eligible patients. Implementation of a comprehensive, opt-out, chronic-care program aimed at all patients who smoke was associated with increases in the rates of pharmacotherapy and counseling delivery and quitting smoking. Proactive outreach may help reduce disparities in treatment access.

Development of a Pharmacist-Led Opt-Out Cessation Treatment Protocol for Combustible Tobacco Smoking Within Inpatient Settings.

Background: Although people who smoke cigarettes are overrepresented among hospital inpatients, few are connected with smoking cessation treatment during their hospitalization. Training, accountability for medication use, and monitoring of all patients position pharmacists well to deliver cessation interventions to all hospitalized patients who smoke. Methods: A large Midwestern University hospital implemented a pharmacist-led smoking cessation intervention. A delegation protocol for hospital pharmacy inpatients who smoked cigarettes gave hospital pharmacists the authority to order nicotine replacement therapy (NRT) during hospitalization and upon discharge, and for referral to the Wisconsin Tobacco Quit Line (WTQL) at discharge. Eligible patients received the smoking cessation intervention unless they actively refused (ie, "opt-out"). The program was pilot tested in phases, with pharmacist feedback between phases, and then implemented hospital-wide. Interviews, surveys, and informal mechanisms identified ways to improve implementation and workflows. Results: Feedback from pharmacists led to changes that improved workflow, training and patient education materials, and enhanced adoption and reach. Refining implementation strategies across pilot phases increased the percentage of eligible smokers offered pharmacist-delivered cessation support from 37% to 76%, prescribed NRT from 2% to 44%, and referred to the WTQL from 3% to 32%. Conclusion: Hospitalizations provide an ideal opportunity for patients to make a tobacco quit attempt, and pharmacists can capitalize on this opportunity by integrating smoking cessation treatment into existing inpatient medication reconciliation workflows. Pharmacist-led implementation strategies developed in this study may be applicable in other inpatient settings.

Practice Quit Attempts: Scoping Review of a Novel Intervention Strategy.

OBJECTIVE: Fostering practice quit attempts (PQAs)--that is, attempts to not smoke for a few hours or days, without pressure to permanently quit--represents a potential means to engage more individuals who smoke in efforts to change their smoking. However, little is known about interventions designed to foster PQAs. We aimed to identify the available evidence on PQA-focused intervention strategies and their impact on quit attempt and cessation outcomes. METHOD: We conducted a scoping review of behavioral and pharmacological treatment studies targeting PQAs among adult cigarette smokers. RESULTS: The systematic literature search yielded 3,879 articles, and the full-text review was narrowed to 86. Twenty-three studies were deemed relevant, and 5 were added through other sources, yielding 28 studies total. Fifteen studies included behavioral intervention techniques focused on the development and rehearsal of individualized coping skills, whereas eight studies provided brief advice/instruction. More than half of the PQA-focused interventions incorporated sampling of nicotine replacement products, through either guided or ad lib use. Five studies reported on PQA-focused digital health interventions that prompted brief abstinence challenges. Of eight large-scale controlled trials, six demonstrated an increase in quit attempt and cessation outcomes among the PQA-focused intervention group. CONCLUSIONS: Fostering PQAs through behavioral and pharmacological interventions offers a promising technique for cessation induction that warrants future research.

A pilot randomized controlled trial of smartphone-assisted mindfulness-based intervention with contingency management for smokers with mood disorders.

Cigarette smoking disproportionately affects individuals with mood disorders, but smoking cessation interventions have modest effects in this population. Home mindfulness practice during abstinence incentivized via contingency management (CM) may help those in affective distress quit smoking. METHOD: Adult smokers receiving outpatient psychiatric treatment for mood disorders were randomized to receive a smartphone-assisted mindfulness-based smoking cessation intervention with contingency management (SMI-CM, n = 25) or enhanced standard treatment (EST, n = 24) with noncontingent rewards. Participants in SMI-CM were prompted to practice audio-guided mindfulness five times per day for 38 days (vs. no comparison intervention in EST), and received monetary incentives for carbon monoxide (CO) ≤ 6 ppm. The primary outcome was biochemically verified 7-day point prevalence abstinence rates 2, 4, and 13 weeks after a target quit day. RESULTS: Of the 49 participants, 63.3% were Latinx and 30.6% Black; 75.5% reported household incomes < $25,000. Abstinence rates for SMI-CM were 40.0%, 36.0%, and 16.0% versus 4.2%, 8.3%, and 4.2% in EST at weeks 2, 4, and 13. A generalized estimating equations (GEE) model showed significant overall differences in abstinence rates in SMI-CM versus EST (adjusted odds ratio [AOR] = 8.12, 95% CI = 1.42-46.6, p = .019). Those who received SMI-CM reported significantly greater reduction in smoking-specific experiential avoidance from baseline to 3 days prior to quit date (ß = -7.21, 95% CI = -12.1-2.33, p = .006). CONCLUSIONS: SMI-CM may increase cessation rates among smokers with mood disorders, potentially through reduced smoking-specific experiential avoidance. SMI-CM is a promising intervention, and warrants investigation in a fully powered randomized controlled trial (RCT). (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Can inpatient pharmacists move the needle on smoking cessation? Evaluating reach and representativeness of a pharmacist-led opt-out smoking cessation intervention protocol for hospital settings.

PURPOSE: Hospitalization affords an opportunity to reduce smoking, but fewer than half of patients who smoke receive evidence-based cessation treatment during inpatient stays. This study evaluated a pharmacist-led, electronic health record (EHR)-facilitated opt-out smoking cessation intervention designed to address this need. METHODS: Analyses of EHR records for adult patients who smoked in the past 30 days admitted to an academic medical center in the upper Midwest were conducted using the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework. The reach of a pharmacist-led, EHR-facilitated protocol for smoking cessation treatment was assessed by comparing patients' receipt of nicotine replacement therapy (NRT) and tobacco quitline referral before and after implementation. χ2 tests, t tests, and multiple logistic regression models were used to compare reach across patient demographic groups to assess treatment disparities and the representativeness of reach. Adoption of the program by hospital services was also assessed. RESULTS: Of the 70 hospital services invited to implement the program, 88.6% adopted it and 78.6% had eligible admissions. Treatment reach increased as rates of delivering NRT rose from 43.6% of eligible patients before implementation to 50.4% after implementation (P < 0.0001) and quitline referral rates rose from 0.9% to 11.9% (P < 0.0001). Representativeness of reach by sex and ethnicity improved after implementation, although disparities by race and age persisted after adjustment for demographics, insurance, and primary diagnosis. Pharmacists addressed tobacco use for eligible patients in 62.5% of cases after protocol implementation. CONCLUSION: Smoking cessation treatment reach and representativeness of reach improved after implementation of a proactive, pharmacist-led, EHR-facilitated opt-out smoking cessation treatment protocol in adult inpatient services.

Smoking Treatment: A Report Card on Progress and Challenges.

The impact of tobacco smoking treatment is determined by its reach into the smoking population and the effectiveness of its interventions. This review evaluates the reach and effectiveness of pharmacotherapy and psychosocial interventions for smoking. Historically, the reach of smoking treatment has been low, and therefore its impact has been limited, but new reach strategies such as digital interventions and health care system changes offer great promise. Pharmacotherapy tends to be more effective than psychosocial intervention when used clinically, and newer pharmacotherapy strategies hold great promise of further enhancing effectiveness. However, new approaches are needed to advance psychosocial interventions; progress has stagnated because research and dissemination efforts have focused too narrowly on skill training despite evidence that its core content may be inconsequential and the fact that its mechanisms are either unknown or inconsistent with supporting theory. Identifying effective psychosocial content and its mechanisms of action could greatly enhance the effectiveness of counseling, digital, and web interventions.

Closed-Loop Electronic Referral From Primary Care Clinics to a State Tobacco Cessation Quitline: Effects Using Real-World Implementation Training.

INTRODUCTION: Patients who use tobacco are too rarely connected with tobacco use treatment during healthcare visits. Electronic health record enhancements may increase such referrals in primary care settings. This project used the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework to assess the implementation of a healthcare system change carried out in an externally valid manner (executed by the healthcare system). METHODS: The healthcare system used their standard, computer-based training approach to implement the electronic health record and clinic workflow changes for electronic referral in 30 primary care clinics that previously used faxed quitline referral. Electronic health record data captured rates of assessment of readiness to quit and quitline referral 4 months before implementation and 8 months (May-December 2017) after implementation. Data, analyzed from October 2018 to June 2019, also reflected intervention reach, adoption, and maintenance. RESULTS: For reach and effectiveness, from before to after implementation for electronic referral, among adult patients who smoked, assessment of readiness to quit increased from 24.8% (2,126 of 8,569) to 93.2% (11,163 of 11,977), quitline referrals increased from 1.7% (143 of 8,569) to 11.3% (1,351 of 11,977), and 3.6% were connected with the quitline after implementation. For representativeness of reach, electronic referral rates were especially high for women, African Americans, and Medicaid patients. For adoption, 52.6% of staff who roomed at least 1 patient who smoked referred to the quitline. For maintenance, electronic referral rates fell by approximately 60% over 8 months but remained higher than pre-implementation rates. CONCLUSIONS: Real-world implementation of an electronic health record-based electronic referral system markedly increased readiness to quit assessment and quitline referral rates in primary care patients. Future research should focus on implementation methods that produce more consistent implementation and better maintenance of electronic referral.

Machine learning models of tobacco susceptibility and current use among adolescents from 97 countries in the Global Youth Tobacco Survey, 2013-2017.

Adolescents are particularly vulnerable to tobacco initiation and escalation. Identifying factors associated with adolescent tobacco susceptibility and use can guide tobacco prevention efforts. Novel machine learning (ML) approaches efficiently identify interactive relations among factors of tobacco risks and identify high-risk subpopulations that may benefit from targeted prevention interventions. Nationally representative cross-sectional 2013-2017 Global Youth Tobacco Survey (GYTS) data from 97 countries (28 high-income and 69 low-and middle-income countries) from 342,481 adolescents aged 13-15 years (weighted N = 52,817,455) were analyzed using ML regression tree models, accounting for sampling weights. Predictors included demographics (sex, age), geography (region, country-income), and self-reported exposure to tobacco marketing, secondhand smoke, and tobacco control policies. 11.9% (95% CI 11.1%-12.6%) of tobacco-naïve adolescents were susceptible to tobacco use and 11.7% (11.0%-12.5%) of adolescents reported using any tobacco product (cigarettes, other smoked tobacco, smokeless tobacco) in the past 30 days. Regression tree models found that exposure or receptivity to tobacco industry promotions and secondhand smoke exposure predicted increased risks of susceptibility and use, while support for smoke-free air policies predicted decreased risks of tobacco susceptibility and use. Anti-tobacco school education and health warning messages on product packs predicted susceptibility or use, but their protective effects were not evident across all adolescent subgroups. Sex, region, and country-income moderated the effects of tobacco promotion and control factors on susceptibility or use, showing higher rates of susceptibility and use in males and high-income countries, Africa and the Americas (susceptibility), and Europe and Southeast Asia (use). Tobacco policy-related factors robustly predicted both tobacco susceptibility and use in global adolescents, and interacted with adolescent characteristics and other environments in complex ways that stratified adolescents based on their tobacco risk. These findings emphasize the importance of efficient ML modeling of interactions in tobacco risk prediction and suggest a role for targeted prevention strategies for high-risk adolescents.

Time-varying effects of 'optimized smoking treatment' on craving, negative affect and anhedonia.

AIMS: To identify when smoking cessation treatments affect craving, negative affect and anhedonia, and how these symptoms relate to abstinence, to help evaluate the effects of particular intervention components in multi-component treatments and accelerate treatment refinement. DESIGN: Secondary analysis of data from a two-arm randomized controlled trial. SETTING: Seven primary care clinics in Wisconsin, United States. PARTICIPANTS: Adult primary care patients who smoked daily (n = 574). INTERVENTION AND COMPARATOR: Intervention was abstinence-optimized treatment (A-OT, n = 276) comprising 3 weeks of nicotine mini-lozenges pre-target quit day (TQD), 26 weeks of combination nicotine patch and mini-lozenges post-TQD and extensive psychosocial support. The comparator was recommended usual care (RUC, n = 298), comprising brief counseling and 8 weeks of nicotine patch post-TQD. MEASUREMENTS: Time-varying effect models examined dynamic effects of A-OT (versus RUC) on the primary outcomes of nightly cigarette craving, negative affect and anhedonia from 1 week pre- to 2 weeks post-TQD. Exploratory models examined within-person relations between nicotine medication use and same-day symptom ratings. Secondary logistic regression analyses examined associations between post-TQD craving, negative affect and anhedonia and 1-month post-TQD abstinence. FINDINGS: A-OT significantly suppressed pre- and post-TQD craving (ß = -0.27 to -0.46 across days) and post-TQD anhedonia (ß = -0.24 to -0.38 across days), relative to RUC. Within individuals, using patches was associated with lower negative affect in RUC (ß = -0.42 to -0.52), but not in A-OT. Using more mini-lozenges was associated with greater craving (ß = 0.04-0.07) and negative affect (ß = 0.03-0.05) early, and with lower anhedonia (ß = -0.06 to -0.12) later. Greater post-TQD craving (OR = 0.68) and anhedonia (OR = 0.85) predicted lower odds of abstinence 1 month post-TQD. CONCLUSION: Time-varying effect models showed that a multi-component treatment intervention for smoking cessation suppressed significant withdrawal symptoms more effectively than recommended usual care among daily adult smokers motivated to quit. The intervention reduced craving pre- and post-target quit day (TQD) and anhedonia post-TQD.

Comparative effects of varenicline or combination nicotine replacement therapy versus patch monotherapy on candidate mediators of early abstinence in a smoking cessation attempt.

BACKGROUND AND AIMS: The phase-based model of smoking cessation treatment suggests that treatment needs may vary across phases (e.g. pre-cessation, cessation). This study tested the comparative effects of varenicline and combination nicotine replacement therapy (C-NRT) relative to nicotine patch monotherapy on pre-cessation and cessation phase candidate withdrawal, expectancy and motivation mediators; relations between mediators and abstinence; and indirect effects of enhanced treatments on abstinence via candidate mediators. DESIGN: Secondary mediation analysis of data from the open-label, randomized Wisconsin Smokers' Health Study 2, a comparative effectiveness trial of varenicline or C-NRT, versus patch monotherapy, in adults who smoked, recruited via media and community outreach. SETTING: Research clinics in Madison and Milwaukee, Wisconsin, USA. PARTICIPANTS: A total of 1051 daily smokers motivated to quit smoking (52.5% female; mean age = 48.1, standard deviation = 11.6). INTERVENTIONS: Twelve weeks of varenicline (n = 407) or 12 weeks of combination nicotine patch and nicotine lozenge therapy (n = 421), both compared with 12 weeks of patch control condition (n = 230), with individual smoking cessation counseling. MEASUREMENTS: The primary abstinence outcome was biochemically verified 7-day point-prevalence abstinence 4 weeks post-target quit day (TQD). Candidate mediators (craving, positive smoking expectancies, withdrawal symptoms, and quitting motivation) were assessed via ecological momentary assessment from 1 week prior (pre-cessation phase) to 4 weeks after (cessation phase) the TQD. FINDINGS: Pre-cessation and cessation mean levels and slopes of craving [adjusted odds ratio (aOR) = 0.34-0.79], smoking expectancies (aOR = 0.46-0.79) and quitting motivation (aOR = 1.35-7.21) significantly predicted 4-week post-TQD abstinence (P < 0.05). Significant varenicline mediation occurred via greater suppression in pre-cessation craving [mediated effect (ab) = 0.09, standard error (SE) = 0.03, 95% confidence interval (CI) = 0.04-0.14] and smoking expectancies (ab = 0.06, SE = 0.02, 95% CI = 0.02-0.12). C-NRT mediation occurred via greater reduction in pre-post-TQD changes in craving (ab = 0.04, SE = 0.02, 95% CI = 0.01-0.08) and expectancies (ab = 0.03, SE = 0.02, 95% CI = 0.001-0.07), relative to patch monotherapy. CONCLUSION: Among adult smokers seeking to quit, varenicline seems to work through its effects on suppression of craving and smoking expectancies pre-cessation while combination nicotine replacement therapy mediation seems to work through cessation-related reduction in craving and smoking expectancies changes.

Electronically Monitored Nicotine Gum Use Before and After Smoking Lapses: Relationship With Lapse and Relapse.

INTRODUCTION: Greater use of nicotine replacement therapy (NRT) is related to smoking cessation success, but the causal direction is unclear. This study characterized the relationship between NRT use and smoking lapse and relapse. METHODS: Participants (N = 416 smokers; 57% female, 85% White) were recruited from primary care for a smoking cessation factorial experiment and analyzed if abstaining ≥1 day in the first 2 weeks post-target quit day (TQD). Participants were randomized to counseling and 8 versus 26 weeks of nicotine patch plus nicotine gum post-TQD. Participants carried electronic dispensers that timestamped each gum use. Participants who lapsed (smoked after abstaining) within 6 weeks post-TQD were matched with nonlapsers (n = 146 pairs) on multiple variables. We compared lapsers' versus matched nonlapsers' gum use in the 5 days before and after the lapsers' first lapse. RESULTS: By week 6 post-TQD, 63% of participants lapsed. Compared with nonlapsers, lapsers used less gum 1 and 2 days pre-"lapse" and on the 5 days post-lapse. Lapsers used less gum during the 5 days post-lapse than the 5 days pre-lapse. Univariate survival analyses with lapsers showed greater gum use during both pre- and post-lapse periods predicted longer latency to relapse in the first 6 weeks. CONCLUSIONS: In a smoking cessation attempt using nicotine patch plus gum, lapsers versus matched nonlapsers used less gum immediately preceding and following their first lapse. Lower mean gum use before and after lapses predicted a more rapid escalation to relapse. Decreased nicotine gum use both precedes and follows returns to smoking during cessation attempts. IMPLICATIONS: This research examined electronically monitored nicotine gum use collected in real time and found that among smokers engaged in a quit attempt, lapsers (vs. matched nonlapsers) tended to decrease their gum use 1-2 days prior to lapsing and to further decrease their gum use from pre- to post-lapse. Decreased gum use pre-lapse may signal heightened lapse risk in 1-2 days, with lower level of gum use predicting a more precipitous course of relapse. These results encourage further exploration of objective measures of smoking medication use patterns to examine their signaling properties and to inform understanding of cessation failure. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT01120704.

Closed-loop electronic referral to SmokefreeTXT for smoking cessation support: a demonstration project in outpatient care.

Too few smokers who present for outpatient healthcare receive evidence-based interventions to stop smoking. Referral to nationally available smoking cessation support may enhance tobacco intervention reach during healthcare visits. This study evaluated the feasibility of outpatient electronic health record (EHR)-enabled, closed-loop referral (eReferral) to SmokefreeTXT, a National Cancer Institute text message smoking cessation program. SmokefreeTXT eReferral for adult patients who smoke was implemented in a family medicine clinic and an allergy and asthma clinic in an integrated Midwestern healthcare system. Interoperable, HIPAA-compliant eReferral returned referral outcomes to the EHR. In Phase 1 of implementation, clinicians were responsible for eReferral; in Phase 2 this responsibility shifted to Medical Assistants and/or nurses. EHR data were extracted to compute eReferral rates among adult smokers and compare demographics among those eReferred versus not referred. SmokefreeTXT data were used to compute SmokefreeTXT enrollment rates among those eReferred. Descriptive analyses of clinic staff surveys assessed implementation context and staff attitudes toward and adaptations of eReferral processes. During clinician implementation, 43 of 299 adult smokers (14.4%) were eReferred. During medical assistant (MA) implementation, 36 of 401 adult smokers (9.0%) were eReferred. Overall, among those eReferred, 25.7% completed SmokefreeTXT enrollment (3.1% of patients eligible for eReferral). Staff survey responses indicated that eReferral was efficient and easy. eReferral rates and relevant attitudes varied meaningfully by clinic. Thus, interoperable eReferral via outpatient EHR to SmokefreeTXT is feasible and acceptable to clinic staff and enrolls roughly 3.0% of smokers. Clinic context and implementation approach may influence reach.

Predictors of adherence to nicotine replacement therapy: Machine learning evidence that perceived need predicts medication use.

BACKGROUND: Nonadherence to smoking cessation medication is a frequent problem. Identifying pre-quit predictors of nonadherence may help explain nonadherence and suggest tailored interventions to address it. AIMS: Identify and characterize subgroups of smokers based on adherence to nicotine replacement therapy (NRT). METHOD: Secondary classification tree analyses of data from a 2-arm randomized controlled trial of Recommended Usual Care (R-UC, n = 315) versus Abstinence-Optimized Treatment (A-OT, n = 308) were conducted. R-UC comprised 8 weeks of nicotine patch plus brief counseling whereas A-OT comprised 3 weeks of pre-quit mini-lozenges, 26 weeks of nicotine patch plus mini-lozenges, 11 counseling contacts, and 7-11 automated reminders to use medication. Analyses identified subgroups of smokers highly adherent to nicotine patch use in both treatment conditions, and identified subgroups of A-OT participants highly adherent to mini-lozenges. RESULTS: Varied facets of nicotine dependence predicted adherence across treatment conditions 4 weeks post-quit and between 4- and 16-weeks post-quit in A-OT, with greater baseline dependence and greater smoking trigger exposure and reactivity predicting greater medication use. Greater quitting motivation and confidence, and believing that stop smoking medication was safe and easy to use were associated with greater adherence. CONCLUSION: Adherence was especially high in those who were more dependent and more exposed to smoking triggers. Quitting motivation and confidence predicted greater adherence, while negative beliefs about medication safety and acceptability predicted worse adherence. Results suggest that adherent use of medication may reflect a rational appraisal of the likelihood that one will need medication and will benefit from it.

Understanding quit patterns from a randomized clinical trial: Latent classes, predictors, and long-term abstinence.

OBJECTIVE: Tobacco dependence treatment is recognized as a dynamic, chronic process comprised of several specific phases. Of these phases, the Cessation phase is the most critical as it has demonstrated the strongest relation to quit success. Yet, little is understood about smoking trajectories during this period. The current study aimed to address gaps in the smoking research literature and advance understanding of the dynamic quit process unique to completing an integrated smoking treatment by evaluating quit behavior during the Cessation phase. METHOD: Two hundred and sixty-seven treatment seeking smokers enrolled in a clinical trial to evaluate the efficacy of a novel, integrated smoking cessation treatment (46.1% male; Mage = 39.25, SD = 13.70) were included in the present study. Repeated-measure latent class analysis was employed to evaluate quit patterns from quit day through day 14 post-quit. RESULTS: Results supported a four-class solution: Consistent Quitters, Non-Quitters, Relapsers, and Delayed Quitters. Predictors of class membership included age, number of prior quit attempts, motivation to quit smoking, and quit day smoking urges. Moreover, class membership was significantly associated with 6-month abstinence. CONCLUSION: Results suggest that there are four relevant classes of quit behavior, each with specific predictor variables including age, motivation to quit, smoking urges, and number of quit attempts, and that these classes relate to long-term abstinence. These results have the potential to inform manualized smoking cessation treatment interventions based on relevant subgroups of quit behavior.