Clare Meernik, MPH 1 ; Anna McCullough, MSW, MSPH, CTTS 1 ; Leah Ranney, PhD 1 ; Barbara Walsh 2 ; Adam O. Goldstein, MD, MPH 1 Predictors of Quit for.

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Clare Meernik, MPH 1 ; Anna McCullough, MSW, MSPH, CTTS 1 ; Leah Ranney, PhD 1 ; Barbara Walsh 2 ; Adam O. Goldstein, MD, MPH 1 Predictors of Quit for Smokers with Mental Illness Enrolled in Connecticut’s Community-Based Cessation Programs Background Results Community-based tobacco use treatment programs were implemented as a key component of the Connecticut Department of Public Health (CT DPH) Tobacco Use Prevention and Control Program in 2009 Five programs funded between 2011 – 2014 served a large number of clients with history of mental illness Services included face-to-face counseling in individual and group settings and up to 12 weeks of free nicotine replacement therapy (NRT) Final evaluation conducted by UNC Tobacco Prevention and Evaluation Program Program Description 1.Tobacco Prevention and Evaluation Program, Department of Family Medicine, UNC School of Medicine 2. Tobacco Use Prevention and Control Program, Connecticut Department of Public Health Methods Conclusions Low response rates at 4 and 7 month follow-up limits ability to assess longer-term impact of programs Self-reported smoking status rather than biochemical validation may limit reliability of quit rates Lack of specific psychiatric diagnoses or information about severity of mental illness limits ability to make recommendations related to targeting services Tobacco use treatment can be effectively delivered to smokers with a history of mental illness in a variety of community-based settings Predictive factors related to social characteristics and program service utilization may inform improved programming for this population Given the effectiveness of higher intensity counseling combined with pharmacotherapy for smokers with mental illness, it is imperative to continue targeting community cessation programs to this high-risk population Multivariable logistic regression was used to examine association between client characteristics and program utilization and being quit at time of clients’ final session Combined behavioral support and pharmacotherapy is the most effective method of tobacco use treatment, but few recommendations are tailored to persons with mental illness The impact of cessation interventions for smokers with mental illness can be maximized by evaluating community-based programs targeted towards this high- risk population Variable Adjusted Odds Ratio (95% Confidence Interval) p-value Enrolled in Medicaid (ref: Private insurance)0.27 (0.12, 0.57) Live with smoker (ref: No)0.40 (0.23, 0.71) Number of sessions attended1.16 (1.08, 1.23)<.0001 Used NRT alone (ref: No)6.59 (2.98, 14.56)<.0001 Used prescription medication alone (ref: No)10.20 (2.94, 35.41) Used nicotine replacement therapy and prescription medication (ref: No) 7.32 (1.24, 43.18) Table 2. Multivariable logistic regression model a of 30-day smoking abstinence at time of final session (n=503) a Model is adjusted for all listed variables, as well as gender, age, race/ethnicity, education, previous quit attempt, cigarettes per day at time of enrollment, and history of substance abuse treatment Clients > 18 with history of mental illness who reported being a current cigarette smoker at time of program enrollment (smoked cigarettes within the past 30 days) and attended > 1 counseling session (n=830) Excluded due to missing data (n=327) Clients with complete data at final session (n=503) Clients who self-reported 30-day abstinence at their final session (n=88) Clients who self-reported current smoking at their final session (n=415) Limitations This work was supported by the Connecticut Department of Public Health Tobacco Cessation Program Evaluation contract. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views and policies of the Connecticut Department of Public Health. The authors have no conflicts of interest to report. Figure day quit rates at time of final session Figure 1. Data sample Characteristic% GenderMen40.4% Women59.6% Race/ethnicityNon-Hispanic white74.4% Nonwhite25.6% Education levelLess than high school20.6% High school/GED37.4% Some college or more42.0% Insurance statusPrivate14.1% Medicaid60.8% Medicare19.1% None6.0% Table 1. Client demographics (n=503)