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      Tobacco Use Cessation Among Quitline Callers Who Implemented Complete Home Smoking Bans During the Quitting Process

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          Abstract

          Introduction

          The implementation of a home smoking ban (HSB) is associated with tobacco use cessation. We identified which quitline callers were most likely to report 30-day cessation among those who implemented complete HSBs after enrollment.

          Methods

          Our sample consisted of callers to the Arizona Smokers’ Helpline who enrolled from January 1, 2011, through July 26, 2015, and who reported no HSB at enrollment and a complete HSB by 7-month follow-up. We used logistic regression to estimate associations between no use of tobacco in the previous 30 days (30-day quit) at 7-month follow-up and demographic characteristics, health conditions, tobacco use, and cessation strategies.

          Results

          At 7-month follow-up, 65.4% of 399 callers who implemented a complete HSB reported 30-day quit. Lower odds of tobacco use cessation were associated with having a chronic health condition (odds ratio [OR], 0.31; 95% confidence interval [CI], 0.18–0.56) and living with other smokers (OR, 0.46; 95% CI, 0.29–0.73). Higher odds of tobacco cessation were associated with completing 5 or more telephone coaching sessions (OR, 2.48; 95% CI, 1.54–3.98) and having confidence to quit (OR, 2.05; 95% CI, 1.05–3.99). However, confidence to quit was not significant in the sensitivity analysis.

          Conclusion

          Implementing an HSB after enrolling in quitline services increases the likelihood of cessation among some tobacco users. Individuals with complete HSBs were more likely to quit if they did not have a chronic health condition, did not live with another smoker, and were actively engaged in coaching services. These findings may be used by quitlines to develop HSB intervention protocols primarily targeting tobacco users most likely to benefit from them.

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          Most cited references20

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          Multiple imputation by chained equations: what is it and how does it work?

          Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided.
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            The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire.

            We examine and refine the Fagerström Tolerance Questionnaire (FTQ: Fagerström, 1978). The relation between each FTQ item and biochemical measures of heaviness of smoking was examined in 254 smokers. We found that the nicotine rating item and the inhalation item were unrelated to any of our biochemical measures and these two items were primary contributors to psychometric deficiencies in the FTQ. We also found that a revised scoring of time to the first cigarette of the day (TTF) and number of cigarettes smoked per day (CPD) improved the scale. We present a revision of the FTQ: the Fagerström Test for Nicotine Dependence (FTND).
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              Dose-response analyses using restricted cubic spline functions in public health research.

              Taking into account a continuous exposure in regression models by using categorization, when non-linear dose-response associations are expected, have been widely criticized. As one alternative, restricted cubic spline (RCS) functions are powerful tools (i) to characterize a dose-response association between a continuous exposure and an outcome, (ii) to visually and/or statistically check the assumption of linearity of the association, and (iii) to minimize residual confounding when adjusting for a continuous exposure. Because their implementation with SAS® software is limited, we developed and present here an SAS macro that (i) creates an RCS function of continuous exposures, (ii) displays graphs showing the dose-response association with 95 per cent confidence interval between one main continuous exposure and an outcome when performing linear, logistic, or Cox models, as well as linear and logistic-generalized estimating equations, and (iii) provides statistical tests for overall and non-linear associations. We illustrate the SAS macro using the third National Health and Nutrition Examination Survey data to investigate adjusted dose-response associations (with different models) between calcium intake and bone mineral density (linear regression), folate intake and hyperhomocysteinemia (logistic regression), and serum high-density lipoprotein cholesterol and cardiovascular mortality (Cox model). 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Prev Chronic Dis
                Prev Chronic Dis
                PCD
                Preventing Chronic Disease
                Centers for Disease Control and Prevention
                1545-1151
                2017
                26 October 2017
                : 14
                : E105
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona
                [2 ]School of Psychology, University of Sydney, Sydney, Australia
                [3 ]Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona
                Author notes
                Corresponding Author: Alesia M. Jung, MS, Mel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N Martin Ave, PO Box 245211, Tucson, AZ 85724. Telephone: 408-368-0763. Email: ajung1@ 123456email.arizona.edu .
                Article
                17_0139
                10.5888/pcd14.170139
                5662293
                29072983
                ffaea992-5c71-4bc1-ae4d-21744ff57f53
                History
                Categories
                Original Research
                Peer Reviewed

                Health & Social care
                Health & Social care

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