A Pilot Study to Evaluate the Use of Automated Nicotine Metabolite Ratio Reporting within Primary Care as an Implementation Strategy to Increase the Use of Tobacco Treatments.

INTRODUCTION: Concerns about safety and effectiveness of tobacco treatments reduce their use. We explored integrating the nicotine metabolite ratio (NMR), and messaging about its potential for improving safety and effectiveness, as a strategy to increase use of tobacco treatments within primary care. METHODS: Through a prospective cohort design, we explored the effects of integrating NMR testing within primary care on the provision of tobacco treatment; 65 patients completed assessments including NMR before a clinic visit. At the clinic visit, patients' clinicians received an electronic health record alert about the patient's NMR and personalized treatment recommendations to improve effectiveness and safety. Being asked about smoking and advised to quit, and a referral for tobacco treatment or medication prescription, were assessed within 30 days of the appointment and were compared to a usual care cohort (N=85). RESULTS: The NMR and usual care cohorts reported similar rates of being asked about smoking (92.3% vs. 92.9%, p=1.0), being advised to quit (72.3% vs. 74.1%, p=0.85), being referred for tobacco treatment (23.1% vs. 36.5%, p=0.11), and receiving tobacco use medications (20% vs. 27.1%, p=0.34). In the NMR cohort, fast vs. slow metabolizers were more likely to receive medication (26% vs. 0%, p=0.003) and all patients who received varenicline (n=8) were fast metabolizers. CONCLUSIONS: NMR results and treatment recommendations did not increase tobacco treatment rates in primary care, although it may increase treatment rates and use of varenicline for fast metabolizers. Future studies could test ways to use the NMR to increase tobacco treatment rates in clinical settings. IMPLICATIONS: This study generated a novel implementation strategy, namely an electronic health record alert about patients' NMR and personalized treatment recommendations, in an effort to increase tobacco treatment rates in primary care. While the strategy did not increase tobacco treatment rates, it may have boosted the rate of varenicline prescription for patients who metabolize nicotine faster, aligning with evidence-based practice.

Effect of early medication adherence on behavioral treatment utilization and smoking cessation among individuals with current or past major depressive disorder.

SIGNIFICANCE: Little is known about the mechanisms by which medication adherence promotes smoking cessation among adults with MDD. We tested the hypothesis that early adherence promotes abstinence by increasing behavioral treatment (BT) utilization. METHODS: Data for this post-hoc analysis were from a randomized trial of 149 adults with current or past MDD treated with BT and either varenicline (n = 81) or placebo (n = 68). Arms were matched on medication regimen. Early medication adherence was measured by the number of days in which medication was taken at the prescribed dose during the first six of 12 weeks of pharmacological treatment (weeks 2-7). BT consisted of eight 45-minute sessions (weeks 1-12). Bioverified abstinence was assessed at end-of-treatment (week 14). A regression-based approach was used to test whether the effect of early medication adherence on abstinence was mediated by BT utilization. RESULTS: Among 141 participants who initiated the medication regimen, BT utilization mediated the effect of early medication adherence on abstinencea) an interquartile increase in early medication days from 20 to 42 predicted a 4.2 times increase in abstinence (Total Risk Ratio (RR) = 4.24, 95% CI = 2.32-13.37; p <.001); b) increases in BT sessions predicted by such an increase in early medication days were associated with a 2.7 times increase in abstinence (Indirect RR = 2.73, 95% CI = 1.54-7.58; p <.001); and c) early medication adherence effects on abstinence were attenuated, controlling for BT (Direct RR = 1.55, 95% CI = 0.83-4.23, p =.17). CONCLUSIONS: The effect of early medication adherence on abstinence in individuals with current or past MDD is mediated by intensive BT utilization.

Nicotine Metabolite Ratio Decreases After Switching Off Efavirenz-Based Therapy in People With HIV Who Smoke.

Rates of cigarette smoking in people with HIV (PWH) are two to three times higher than in people without HIV. Nicotine is metabolized by CYP2A6 and the nicotine metabolite ratio (NMR; 3-hydroxycotinine/cotinine) is a measure of nicotine clearance. Higher NMR has been observed in PWH and is associated with lower quit rates. Efavirenz, a mainstay antiretroviral therapy (ART) globally, partially upregulates its own metabolism through CYP2A6. We hypothesized that efavirenz also upregulates nicotine metabolism by CYP2A6, resulting in a higher NMR, and switching to non-efavirenz ART would decrease the NMR, potentially leading to improved quit rates. We compared the NMR during and after efavirenz use among PWH in a longitudinal, multisite cohort. Eligibility criteria included: (i) active cigarette smoking, (ii) ART switched from efavirenz-based to non-efavirenz-based regimen, (iii) plasma available at pre- and post-ART switch, and (iv) viral suppression during study period. Plasma cotinine and 3-hydroxycotinine were measured by liquid chromatography-tandem mass spectrometry. T-tests compared the NMR on and off efavirenz. Samples were collected between 2010 and 2019 in 72 PWH. The mean NMR difference after switching to a non-efavirenz-based regimen was -0.24 (SD: 0.37, P < 0.001); 44 PWH had at least a 0.1 decrease in NMR. Effect modification by race was present; Black PWH had a larger mean decrease. Our findings suggest that previously observed higher NMR among PWH may be due to direct pharmacologic effects of ART. Assessing the effect of ART on the NMR suggests that avoiding nicotine metabolism inducers could potentially increase quit rates.

Predictors of improved clinician screening, assessment, and treatment for tobacco use for clients in community mental healthcare following training.

Introduction: People with mental illness (MI) are more likely to smoke cigarettes and less likely to receive treatment for tobacco use than the general population. Understanding factors associated with improved staff treatment of tobacco use in community mental health settings has received limited study. Methods: We used data from a completed cluster-randomized clinical trial that tested two interventions designed to increase treatment for tobacco use in mental health clinics. Among 222 clinic staff, we examined demographic and employment characteristics, changes in perceived skills, knowledge, and beliefs using the S-KAP (i.e., perceptions of staff responsibility to treat tobacco use; client quit motivation; client outcomes; and barriers) as predictors of change in clinician reported delivery of tobacco use treatment following training. Results: Clinician reported treatment of client tobacco use significantly increased from baseline to week 52 across both study arms (p<0.001). This increase in reported treatment for tobacco use was associated with increases from baseline to week 52 in clinician reported skills to treat tobacco use, perceptions of responsibility to treat client tobacco use, and perceptions about client motivation to quit smoking (p's<0.05). Conclusions: Training clinicians in community mental healthcare to address client tobacco use may improve outcomes by helping them to develop the needed skills, convincing them that treating tobacco use is part of their role as clinicians, and by helping clinicians to recognize that clients are motivated to quit smoking. These may be targets to improve how clinicians in community health settings address client tobacco use.

Protocol for a pragmatic stepped wedge cluster randomized clinical trial testing behavioral economic implementation strategies to increase supplemental breast MRI screening among patients with extremely dense breasts.

BACKGROUND: Increased breast density augments breast cancer risk and reduces mammography sensitivity. Supplemental breast MRI screening can significantly increase cancer detection among women with dense breasts. However, few women undergo this exam, and screening is consistently lower among racially minoritized populations. Implementation strategies informed by behavioral economics ("nudges") can promote evidence-based practices by improving clinician decision-making under conditions of uncertainty. Nudges directed toward clinicians and patients may facilitate the implementation of supplemental breast MRI. METHODS: Approximately 1600 patients identified as having extremely dense breasts after non-actionable mammograms, along with about 1100 clinicians involved with their care at 32 primary care or OB/GYN clinics across a racially diverse academically based health system, will be enrolled. A 2 × 2 randomized pragmatic trial will test nudges to patients, clinicians, both, or neither to promote supplemental breast MRI screening. Before implementation, rapid cycle approaches informed by clinician and patient experiences and behavioral economics and health equity frameworks guided nudge design. Clinicians will be clustered into clinic groups based on existing administrative departments and care patterns, and these clinic groups will be randomized to have the nudge activated at different times per a stepped wedge design. Clinicians will receive nudges integrated into the routine mammographic report or sent through electronic health record (EHR) in-basket messaging once their clinic group (i.e., wedge) is randomized to receive the intervention. Independently, patients will be randomized to receive text message nudges or not. The primary outcome will be defined as ordering or scheduling supplemental breast MRI. Secondary outcomes include MRI completion, cancer detection rates, and false-positive rates. Patient sociodemographic information and clinic-level variables will be examined as moderators of nudge effectiveness. Qualitative interviews conducted at the trial's conclusion will examine barriers and facilitators to implementation. DISCUSSION: This study will add to the growing literature on the effectiveness of behavioral economics-informed implementation strategies to promote evidence-based interventions. The design will facilitate testing the relative effects of nudges to patients and clinicians and the effects of moderators of nudge effectiveness, including key indicators of health disparities. The results may inform the introduction of low-cost, scalable implementation strategies to promote early breast cancer detection. TRIAL REGISTRATION: ClinicalTrials.gov NCT05787249. Registered on March 28, 2023.

Depression Severity Moderates Reward Learning Among Smokers with Current or Past Major Depressive Disorder in a Smoking Cessation Randomized Clinical Trial.

INTRODUCTION: Behavioral and pharmacological smoking cessation treatments are hypothesized to increase patients' reward learning to reduce craving. Identifying changes in reward learning processes that support effective tobacco dependence interventions among smokers who experience depression may guide patients towards efficient treatment strategies. The objective was to investigate the extent to which adult daily cigarette smokers with current or past major depressive disorder (MDD) learned to seek reward during 12 weeks of treatment combining behavioral activation and varenicline. We hypothesized that a decline in reward learning would be attenuated (least to most) in the following order: 1) Behavioral activation integrated with ST (BASC) + varenicline, 2) BASC + placebo, 3) Standard behavioral cessation treatment (ST) + varenicline, 4) ST + placebo. METHODS: We ran a Phase 4, placebo-controlled, randomized clinical trial with 300 participants receiving 12 weeks of one of four conditions across two urban medical centers. Depressive symptoms were measured using the Beck Depression Inventory-II (BDI). Reward learning was ascertained at Weeks 1, 7, and 14 using the Probabilistic Reward Task (PRT), a laboratory task that uses an asymmetric reinforcement schedule to assess (a) learning to seek reward (response bias), (b) differentiate between stimuli, and (c) time to react to cues. RESULTS: There was a significant interaction of BDI group x PRT response bias. Response bias declined from Week 7 to 14 among participants with high baseline depression symptoms. The other two BDI groups showed no change in response bias. CONCLUSIONS: Controlling for baseline depression, participants showed a decrease in response bias from Week 1 to 14, and from Weeks 7 to 14. Treatment condition and abstinence status were unassociated with change in reward learning. IMPLICATIONS: Smokers who report greater depression severity show a decline in reward learning despite their participation in smoking cessation treatments, suggesting that depressed populations pose unique challenges with standard smoking cessation approaches.

Seeing Through the Blind: Belief about Treatment Randomization and Smoking Cessation Outcome among People with Current or Past Major Depressive Disorder who Smoke in a Placebo-Controlled Trial of Varenicline.

INTRODUCTION: Blinding participants to randomization is a cornerstone of science. However, participant beliefs about their allocation can influence outcomes. We examined blind integrity, the association between trial arm belief and cessation, and potential mechanisms linking treatment arm and treatment arm belief among people with major depressive disorder (MDD) who smoke receiving varenicline in a placebo-controlled trial. METHODS: 175 participants were asked at the end of treatment (EOT) if they thought they received placebo, varenicline, or were not sure. We assessed the relationship between treatment arm belief and actual treatment allocation, examined the association between treatment arm belief and EOT cessation, and evaluated changes in craving, withdrawal, side effects, depression symptoms, and smoking reward as mediators through which treatment arm was believed. RESULTS: Treatment arm belief was significantly associated with actual arm assignment (χ2(2)=13.0, p=0.002). Participants in the varenicline arm were >3 times as likely to believe they were taking varenicline, vs. "not sure" (RR=3.05 [1.41-6.60], p=0.005). Participants in the placebo arm were just as likely to believe they were taking placebo vs. "not sure" (χ2[2]=0.75, p=0.69). Controlling for treatment arm, belief that one received varenicline was significantly associated with an increase in cessation rate (OR=5.91 [2.06-16.92], p=0.001). Change in the rewarding experience of smoking may mediate participant ability to discern getting varenicline B=0.077 [0.002-0.192], p <0.05). CONCLUSIONS: Participants receiving varenicline can discern that they received varenicline and this belief is associated with higher cessation rates. Research is needed to continue to examine how participants correctly identify their allocation to varenicline.

Protocol to evaluate sequential electronic health record-based strategies to increase genetic testing for breast and ovarian cancer risk across diverse patient populations in gynecology practices.

BACKGROUND: Germline genetic testing is recommended by the National Comprehensive Cancer Network (NCCN) for individuals including, but not limited to, those with a personal history of ovarian cancer, young-onset (< 50 years) breast cancer, and a family history of ovarian cancer or male breast cancer. Genetic testing is underused overall, and rates are consistently lower among Black and Hispanic populations. Behavioral economics-informed implementation strategies, or nudges, directed towards patients and clinicians may increase the use of this evidence-based clinical practice. METHODS: Patients meeting eligibility for germline genetic testing for breast and ovarian cancer will be identified using electronic phenotyping algorithms. A pragmatic cohort study will test three sequential strategies to promote genetic testing, two directed at patients and one directed at clinicians, deployed in the electronic health record (EHR) for patients in OB-GYN clinics across a diverse academic medical center. We will use rapid cycle approaches informed by relevant clinician and patient experiences, health equity, and behavioral economics to optimize and de-risk our strategies and methods before trial initiation. Step 1 will send patients messages through the health system patient portal. For non-responders, step 2 will reach out to patients via text message. For non-responders, Step 3 will contact patients' clinicians using a novel "pend and send" tool in the EHR. The primary implementation outcome is engagement with germline genetic testing for breast and ovarian cancer predisposition, defined as a scheduled genetic counseling appointment. Patient data collected through the EHR (e.g., race/ethnicity, geocoded address) will be examined as moderators of the impact of the strategies. DISCUSSION: This study will be one of the first to sequentially examine the effects of patient- and clinician-directed strategies informed by behavioral economics on engagement with breast and ovarian cancer genetic testing. The pragmatic and sequential design will facilitate a large and diverse patient sample, allow for the assessment of incremental gains from different implementation strategies, and permit the assessment of moderators of strategy effectiveness. The findings may help determine the impact of low-cost, highly transportable implementation strategies that can be integrated into healthcare systems to improve the use of genomic medicine. TRIAL REGISTRATION: ClinicalTrials.gov. NCT05721326. Registered February 10, 2023. https://www. CLINICALTRIALS: gov/study/NCT05721326.

Participatory logic modeling in a multi-site initiative to advance implementation science.

BACKGROUND: Logic models map the short-term and long-term outcomes that are expected to occur with a program, and thus are an essential tool for evaluation. Funding agencies, especially in the United States (US), have encouraged the use of logic models among their grantees. They also use logic models to clarify expectations for their own funding initiatives. It is increasingly recognized that logic models should be developed through a participatory approach which allows input from those who carry out the program being evaluated. While there are many positive examples of participatory logic modeling, funders have generally not engaged grantees in developing the logic model associated with their own initiatives. This article describes an instance where a US funder of a multi-site initiative fully engaged the funded organizations in developing the initiative logic model. The focus of the case study is Implementation Science Centers in Cancer Control (ISC3), a multi-year initiative funded by the National Cancer Institute. METHODS: The reflective case study was collectively constructed by representatives of the seven centers funded under ISC3. Members of the Cross-Center Evaluation (CCE) Work Group jointly articulated the process through which the logic model was developed and refined. Individual Work Group members contributed descriptions of how their respective centers reviewed and used the logic model. Cross-cutting themes and lessons emerged through CCE Work Group meetings and the writing process. RESULTS: The initial logic model for ISC3 changed in significant ways as a result of the input of the funded groups. Authentic participation in the development of the logic model led to strong buy-in among the centers, as evidenced by their utilization. The centers shifted both their evaluation design and their programmatic strategy to better accommodate the expectations reflected in the initiative logic model. CONCLUSIONS: The ISC3 case study demonstrates how participatory logic modeling can be mutually beneficial to funders, grantees and evaluators of multi-site initiatives. Funded groups have important insights about what is feasible and what will be required to achieve the initiative's stated objectives. They can also help identify the contextual factors that either inhibit or facilitate success, which can then be incorporated into both the logic model and the evaluation design. In addition, when grantees co-develop the logic model, they have a better understanding and appreciation of the funder's expectations and thus are better positioned to meet those expectations.

Cluster Randomized Pragmatic Clinical Trial Testing Behavioral Economic Implementation Strategies to Improve Tobacco Treatment for Patients With Cancer Who Smoke.

PURPOSE: Few cancer centers systematically engage patients with evidence-based tobacco treatment despite its positive effect on quality of life and survival. Implementation strategies directed at patients, clinicians, or both may increase tobacco use treatment (TUT) within oncology. METHODS: We conducted a four-arm cluster-randomized pragmatic trial across 11 clinical sites comparing the effect of strategies informed by behavioral economics on TUT engagement during oncology encounters with cancer patients. We delivered electronic health record (EHR)-based nudges promoting TUT across four nudge conditions: patient only, clinician only, patient and clinician, or usual care. Nudges were designed to counteract cognitive biases that reduce TUT engagement. The primary outcome was TUT penetration, defined as the proportion of patients with documented TUT referral or a medication prescription in the EHR. Generalized estimating equations were used to estimate the parameters of a linear model. RESULTS: From June 2021 to July 2022, we randomly assigned 246 clinicians in 95 clusters, and collected TUT penetration data from their encounters with 2,146 eligible patients who smoke receiving oncologic care. Intent-to-treat (ITT) analysis showed that the clinician nudge led to a significant increase in TUT penetration versus usual care (35.6% v 13.5%; OR = 3.64; 95% CI, 2.52 to 5.24; P < .0001). Completer-only analysis (N = 1,795) showed similar impact (37.7% clinician nudge v 13.5% usual care; OR = 3.77; 95% CI, 2.73 to 5.19; P < .0001). Clinician type affected TUT penetration, with physicians less likely to provide TUT than advanced practice providers (ITT OR = 0.67; 95% CI, 0.51 to 0.88; P = .004). CONCLUSION: EHR nudges, informed by behavioral economics and aimed at oncology clinicians, appear to substantially increase TUT penetration. Adding patient nudges to the implementation strategy did not affect TUT penetration rates.

Participatory Logic Modeling in a Multi-Site Initiative to Advance Implementation Science.

Background: It is increasingly being recognized that logic models should be developed through a participatory approach which allows input from those who carry out the program being evaluated. While there are many positive examples of participatory logic modeling, funders have generally not used this approach in the context of multi-site initiatives. This article describes an instance where the funder and evaluator of a multi-site initiative fully engaged the funded organizations in developing the initiative logic model. The focus of the case study is Implementation Science Centers in Cancer Control (ISC 3 ), a multi-year initiative funded by the National Cancer Institute (NCI). Methods: The case study was collectively constructed by representatives of the seven centers funded under ISC 3 . Members of the Cross-Center Evaluation (CCE) Work Group jointly articulated the process through which the logic model was developed and refined. Individual Work Group members contributed descriptions of how their respective centers reviewed and used the logic model. Cross-cutting themes and lessons emerged through CCE Work Group meetings and the writing process. Results: The initial logic model for ISC 3 changed in significant ways as a result of the input of the funded groups. Authentic participation in the development of the logic model led to strong buy-in among the centers, as evidenced by their utilization. The centers shifted both their evaluation design and their programmatic strategy to better accommodate the expectations reflected in the initiative logic model. Conclusions: The ISC 3 case study provides a positive example of how participatory logic modeling can be mutually beneficial to funders, grantees and evaluators of multi-site initiatives. Funded groups have important insights about what is feasible and what will be required to achieve the initiative's stated objectives. They can also help identify the contextual factors that either inhibit or facilitate success, which can then be incorporated into both the logic model and the evaluation design. In addition, when grantees co-develop the logic model, they have a better understanding and appreciation of the funder's expectations, and thus are better positioned to meet those expectations.

Change in cardiovascular health among adults with current or past major depressive disorder enrolled in intensive smoking cessation treatment.

BACKGROUND: Elevated depressive symptoms and cigarette smoking are independently associated with poorer cardiovascular health (CVH), but it is unknown whether their treatment can synergistically improve CVH. We sought to characterize CVH of adults with comorbid depression and smoking and examine changes in CVH associated with changes in smoking and depression. METHODS: Participants (N = 300, 55 % women) were adult smokers (≥ 1 cigarette/day) with lifetime major depressive disorder enrolled in a 12-week intervention trial targeting depression and smoking. Multiple linear regression examined prospective associations between changes in depression (Beck Depression Inventory-II), smoking (past 24-hour cigarettes or smoking abstinence), and modified CVH score (per American Heart Association, excluding smoking: diet, physical activity, body mass index, blood glucose, cholesterol, blood pressure). RESULTS: Baseline mean CVH score was 5.87/12 points (SD = 2.13). No participants met "ideal" on all CVH components (blood glucose: 48 %, cholesterol: 46 %, physical activity: 38 %, body mass index: 24 %, blood pressure: 22 %, diet: 3 %). CVH scores did not change from baseline to end-of-treatment (M = 0.18 points, SD = 1.36, p = .177), nor did change in depression × smoking predict change in CVH (p = .978). However, greater reductions in depression were significantly associated with greater improvements in CVH (ß = -0.04, SE = 0.01, p = .015). LIMITATIONS: This study was limited by a short follow-up period, missing blood glucose and cholesterol data, and treatment-seeking smokers. CONCLUSIONS: Adults with comorbid depression and smoking had poor CVH. Although integrated treatment for depression and smoking improved both conditions, only reductions in depression were associated with improvements in CVH. These findings have implications for integrating psychosocial treatment into CVH promotion efforts. REGISTRATION: NCT02378714 (clinicaltrials.gov).

Efficacy and safety of combination behavioral activation for smoking cessation and varenicline for treating tobacco dependence among individuals with current or past major depressive disorder: A 2 × 2 factorial, randomized, placebo-controlled trial.

BACKGROUND AND AIMS: Treatment of depression-related psychological factors related to smoking behavior may improve rates of cessation among adults with major depressive disorder (MDD). This study measured the efficacy and safety of 12 weeks of behavioral activation for smoking cessation (BASC), varenicline and their combination. DESIGN, SETTING, PARTICIPANTS: This study used a randomized, placebo-controlled, 2 × 2 factorial design comparing BASC versus standard behavioral treatment (ST) and varenicline versus placebo, taking place in research clinics at two urban universities in the United States. Participants comprised 300 hundred adult smokers with current or past MDD. INTERVENTIONS: BASC integrated behavioral activation therapy and ST to increase engagement in rewarding activities by reducing avoidance, withdrawal and inactivity associated with depression. ST was based on the 2008 PHS Clinical Practice Guideline. Both treatments consisted of eight 45-min sessions delivered between weeks 1 and 12. Varenicline and placebo were administered for 12 weeks between weeks 2 and 14. MEASUREMENTS: Primary outcomes were bioverified intent-to-treat (ITT) 7-day point-prevalence abstinence at 27 weeks and adverse events (AEs). FINDINGS: No significant interaction was detected between behavioral treatment and pharmacotherapy at 27 weeks (χ2 (1) = 0.19, P = 0.67). BASC and ST did not differ (χ2 (1) = 0.43, P = 0.51). Significant differences in ITT abstinence rates (χ2 (1) = 4.84, P = 0.03) emerged among pharmacotherapy arms (16.2% for varenicline, 7.5% for placebo), with results favoring varenicline over placebo (rate ratio = 2.16, 95% confidence interval = 1.08, 4.30). All significant differences in AE rates after start of medication were higher for placebo than varenicline. CONCLUSION: A randomized trial in smokers with major depressive disorder found that varenicline improved smoking abstinence versus placebo at 27 weeks without elevating rates of adverse events. Behavioral activation for smoking cessation did not outperform standard behavioral treatment, with or without adjunctive varenicline therapy.

A randomized clinical trial testing two implementation strategies to promote the treatment of tobacco dependence in community mental healthcare.

INTRODUCTION: People with serious mental illness (SMI) are more likely to smoke and less likely to receive tobacco treatment. Implementation strategies may address clinician and organizational barriers to treating tobacco in mental healthcare. METHODS: A cluster-randomized trial (Clinic N=13, Client N=610, Staff N=222) tested two models to promote tobacco treatment in community mental healthcare: standard didactic training vs. Addressing Tobacco Through Organizational Change (ATTOC), an organizational model that provides clinician and leadership training and addresses system barriers to tobacco treatment. Primary outcomes were changes in tobacco treatment from clients, staff, and medical records. Secondary outcomes were changes in smoking, mental health, and quality of life (QOL), and staff skills and barriers to treat tobacco. RESULTS: Clients at ATTOC sites reported a significant increase in receiving tobacco treatment from clinician at weeks 12 and 24 (ps<0.05) and tobacco treatments and policies from clinics at weeks 12, 24, 36, and 52 (ps<0.05), vs. standard sites. ATTOC staff reported a significant increase in skills to treat tobacco at week 36 (p=0.05), vs. standard sites. For both models, tobacco use medications, from clients (week 52) and medical records (week 36), increased (ps<0.05), while perceived barriers decreased at weeks 24 and 52 (ps<0.05); 4.3% of clients quit smoking which was not associated with model. QOL and mental health improved over 24 weeks for both models (ps<0.05). CONCLUSIONS: Standard training and ATTOC improve use of evidence-based tobacco treatments in community mental healthcare without worsening mental health, but ATTOC may more effectively address this practice gap.

The use of biomarkers to guide precision treatment for tobacco use.

This review summarizes the evidence to date on the development of biomarkers for personalizing the pharmacological treatment of combustible tobacco use. First, the latest evidence on FDA-approved medications is considered, demonstrating that, while these medications offer real benefits, they do not contribute to smoking cessation in approximately two-thirds of cases. Second, the case for using biomarkers to guide tobacco treatment is made based on the potential to increase medication effectiveness and uptake and reduce side effects. Next, the FDA framework of biomarker development is presented along with the state of science on biomarkers for tobacco treatment, including a review of the nicotine metabolite ratio, electroencephalographic event-related potentials, and other biomarkers utilized for risk feedback. We conclude with a discussion of the challenges and opportunities for the translation of biomarkers to guide tobacco treatment and propose priorities for future research.

Treatment adherence in a smoking cessation clinical trial for individuals with current or past major depressive disorder: Predictors and association with cessation.

INTRODUCTION: Individuals with major depressive disorder (MDD) exhibit high rates of tobacco use and lower responsiveness to tobacco cessation treatments. Treatment adherence is a strong predictor of treatment outcomes in the general population but has not been evaluated in this under-served community of smokers with MDD. METHODS: We used data from a randomized clinical trial on smoking cessation treatment among 300 smokers with MDD to examine the rate of adherence (medication and counseling), the association of adherence with cessation outcomes, and factors associated with adherence, including demographic and smoking characteristics, psychiatric characteristics, smoking cessation processes (e.g., withdrawal, reinforcers), and treatment-related side effects (e.g., nausea). RESULTS: Overall, 43.7% of participants were adherent with medication and 63.0% were adherent with counseling. Medication adherence was significantly associated with cessation, with 32.1% of adherent vs. 13.0% of non-adherent participants quitting smoking at EOT. Counseling adherence was also significantly associated with cessation, with 32.3% of adherent vs. 2.7% of non-adherent participants quitting smoking. Multivariate regression models showed that medication adherence was associated with higher engagement in complementary reinforcers and higher baseline smoking reward, while counseling adherence was associated with identifying as female, lower alcohol use and nicotine dependence, higher baseline smoking reward, and higher engagement in substitute and complementary reinforcers within the first weeks of medication use. CONCLUSIONS: As with the general population of smokers, non-adherence to treatment in smokers experiencing depression is widespread and a significant barrier to cessation. Interventions that target reinforcers may improve rates of treatment adherence.

A Clinical Decision Support System for Motivational Messaging and Tobacco Cessation Treatment for Parents: Pilot Evaluation of Use and Acceptance.

BACKGROUND: Research is needed to identify how clinical decision support (CDS) systems can support communication about and engagement with tobacco use treatment in pediatric settings for parents who smoke. We developed a CDS system that identifies parents who smoke, delivers motivational messages to start treatment, connects parents to treatment, and supports pediatrician-parent discussion. OBJECTIVE: The objective of this study is to assess the performance of this system in clinical practice, including receipt of motivational messages and tobacco use treatment acceptance rates. METHODS: The system was evaluated at one large pediatric practice through a single-arm pilot study from June to November 2021. We collected data on the performance of the CDS system for all parents. Additionally, we surveyed a sample of parents immediately after the clinical encounter who used the system and reported smoking. Measures were: (1) the parent remembered the motivational message, (2) the pediatrician reinforced the message, and (3) treatment acceptance rates. Treatments included nicotine replacement therapy, quitline referral (phone counseling), and/or SmokefreeTXT referral (text message counseling). We described survey response rates overall and with 95% confidence intervals (CIs). RESULTS: During the entire study period, 8,488 parents completed use of the CDS: 9.3% (n = 786) reported smoking and 48.2% (n = 379) accepted at least one treatment. A total of 102 parents who smoke who used the system were approached to survey 100 parents (98% response rate). Most parents self-identified as female (84%), aged 25 to 34 years (56%), and Black/African American (94%), and had children with Medicaid insurance (95%). Of parents surveyed, 54% accepted at least one treatment option. Most parents recalled the motivational message (79%; 95% CI: 71-87%), and 31% (95% CI: 19-44%) reported that the pediatrician reinforced the motivational message. CONCLUSION: A CDS system to support parental tobacco use treatment in pediatric primary care enhanced motivational messaging about smoking cessation and evidence-based treatment initiation.

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.

The Impact of the COVID-19 Pandemic on Stress, Isolation, Smoking Behaviors, and Motivation to Quit in People with HIV Who Smoke.

People with HIV (PWH) smoke at higher rates compared with the general population and have lower cessation rates. The primary aim of this study was to examine the impact of the COVID-19 pandemic on smoking in PWH. A survey was administered to participants in two smoking cessation trials in the United States. Mean cigarettes per day was 13.9 (SD 8.6), and participants reported they had smoked on average for 30.93 years (SD 10.4). More than half (55.7%) of participants (N = 140) reported not changing their smoking during the pandemic, while 15% reported decreasing, and 25% reported increasing their smoking. In bivariate analyses, worrying about food due to lack of money (χ2 = 9.13, df 2, p = 0.01) and greater Covid-related worry (rs = 0.19, p = 0.02) were significantly associated with increased smoking. Qualitative research may be needed to more clearly elucidate factors related to smoking behaviors among PWH.