BackgroundMental health and/or substance use issues are associated with significant disparities in morbidity and mortality. The aim of this study was to identify the mechanisms underlying poor primary care access for this population.MethodThis was a community-based participatory action qualitative study, in which 85 adults who self-identified as having a serious mental health and/or substance use issue and 17 service providers from various disciplines who worked with this population participated in a semi-structured interview.ResultsClient, service provider and health system barriers to access were identified. Client factors, including socioeconomic and psychological barriers, make it difficult for clients to access primary care, keep appointments, and/or prioritize their own health care. Provider factors, including knowledge and personal values related to mental health and substance use, determine the extent to which clients report their specific needs are met in the primary care setting. Health system factors, such as models of primary care delivery, determine the context within which both client and service provider factors operate.ConclusionsThis study helps elucidate the mechanisms behind poor primary health care access among people with substance use and/or mental health issues. The results suggest that interdisciplinary, collaborative models of primary healthcare may improve accessibility and quality of care for this population, and that more education about mental health and substance use issues may be needed to support service providers in providing adequate care for their clients.

Rationale Varenicline is an α4β2 nicotinic acetylcholine receptor partial agonist that has been found to be effective for treating tobacco dependence. However, the subjective and behavioral mediators of its efficacy are not known. Objectives Using multiple sessions of laboratory-based assessment, this double-blind, placebo-controlled experiment was designed to test if varenicline reduced both tonic and cue-provoked tobacco cravings, and if it attenuated perceived reward from smoking. Methods Participants in the present analysis include 100 smokers who were scheduled for three assessment sessions: at baseline, before receiving medication; at mid-run-in, 5– 7 days after beginning medication; and after full dosage was reached, 12–15 days. Following overnight abstinence, each session included assessment of tonic craving, reactivity (including craving) to smoking cues, expected value of a cigarette, smoking behavior, and self-reported reward following smoking. Results Varenicline, compared to placebo, reduced tonic craving, cue-provoked craving by the final assessment, the expected value of cigarettes, number of puffs and time spent smoking, and self-reported reward (i.e., satisfaction) from smoking. Conclusions Results showing that varenicline reduced tonic craving levels and perceived reward from smoking are consistent with reports from clinical trials, strengthening the evidence in support of these subjective mechanisms of action. This is the first placebo-controlled study to demonstrate that varenicline reduced cue-provoked cravings, thereby offering another potential mediator of its therapeutic effects. Findings may aid in the development of more targeted interventions for tobacco dependence.

BACKGROUND:Cancer patients who continue smoking are at increased risk for adverse outcomes including reduced treatment efficacy and poorer survival rates. Many patients spontaneously quit smoking after diagnosis; however, relapse is understudied. The goal of this study was to evaluate smoking-related, affective, cognitive, and physical variables as predictors of smoking after surgical treatment among patients with lung cancer and head and neck cancer. METHODS: A longitudinal study was conducted with 154 patients (57% male) who recently quit smoking. Predictor variables were measured at baseline (ie, time of surgery); smoking behavior was assessed at 2, 4, 6, and 12 months after surgery. Analyses of 7-day point prevalence were performed using a Generalized Estimating Equations approach. RESULTS: Relapse rates varied significantly depending on presurgery smoking status. At 12 months after surgery, 60% of patients who smoked during the week prior to surgery had resumed smoking versus only 13% who were abstinent prior to surgery. Smoking rates among both groups were relatively stable across the 4 follow-ups. For patients smoking before surgery (N ¼ 101), predictors of smoking relapse included lower quitting self-efficacy, higher depression proneness, and greater fears about cancer recurrence. For patients abstinent before surgery (N ¼ 53), higher perceived difficulty quitting and lower cancer-related risk perceptions predicted smoking relapse. CONCLUSIONS: Efforts to encourage early cessation at diagnosis, and increased smoking relapse-prevention efforts in the acute period following surgery, may promote long-term abstinence. Several modifiable variables are identified to target in future smoking relapse-prevention interventions for cancer patients. Cancer 2013;119:1420-7. V C 2012 American Cancer Society.KEYWORDS: tobacco use, smoking relapse, head and neck cancer, lung cancer. INTRODUCTIONCigarette smoking is responsible for 30% of all cancer-related mortalities. 1 Lung cancers and head and neck cancers are among the malignancies most strongly linked to tobacco use, 1 and a significant proportion of patients with lung cancer and/or head and neck cancer are current smokers at the time of diagnosis. 2,3 Smoking cessation reduces morbidity and mortality in these patients, 4,5 whereas continued smoking after diagnosis increases patients' risk for other smoking-related illnesses (eg, coronary heart disease), second primary tumors, 6,7 and disease recurrence. 5,6 Continued smoking also has more immediate adverse impact, including reduced cancer treatment efficacy, 8,9 higher rates of treatment complications and side effects, 10-13 greater treatment-related weight loss, 14 and poorer quality of life. [15][16][17] The majority of patients smoking at the time of their diagnosis spontaneously quit smoking (eg, 86% among patients with lung cancer 18 ; 84% among patients with head and neck cancer 19 ), with the greatest proportion of quit attempts occurring at diagnosis. 20 Furthermore, smoking cessation interventions for can...

Introduction High rates of tobacco use among people with serious mental illness (SMI), along with their unique needs, suggest the importance of developing tailored smoking cessation interventions for this group. Previous early-phase work empirically validated the design and content of Learn to Quit, a theory-based app designed for this population. Methods In a pilot randomized controlled trial we compared the feasibility, acceptability and preliminary efficacy of Learn to Quit versus QuitGuide, an app designed for the general population. All participants received nicotine replacement therapy and technical assistance. Daily smokers with SMI (N=62) participated in the trial with outcomes assessed at weeks 4, 8, 12, and 16. Results Compared to QuitGuide, Learn to Quit participants had similar number of days of app use (34 vs 32, p=0.754), but larger number of app interactions (847 vs. 205; p<0.001), longer durations of app use (4.24 hrs. vs. 2.14 hrs; p<0.044), and higher usability scores (85 vs 79, p=0.046). At week 16, Learn to Quit led to greater reductions in cigarettes per day (12.3 vs 5.9 for QuitGuide; p<0.01). Thirty-day point prevalence abstinence was verified in 12% of LTQ participants vs. 3% of QuitGuide participants (OR=3.86, CI: 0.41-36, p=0.239). Changes in psychiatric symptoms and adverse events were not clinically significant between conditions. Conclusions This pilot trial provides strong evidence of Learn to Quit’s usability, feasibility, and safety. Preliminary evidence suggests the app may be efficacious. A randomized controlled efficacy trial is needed to test the app in a larger sample of smokers with SMI. Implications This study suggests that the Learn to Quit app is a feasible approach to deliver smoking cessation treatment in patients with co-occurring tobacco use disorder and serious mental illness. This means that, if found efficacious, this technology could be used to deploy smoking cessation treatment to larger segments of this population, hence improving public health. Therefore, a randomized controlled trial should be conducted to examine the efficacy of this digital intervention.