Dataset Information

Follow-up loss in smoking cessation consultation: can we predict and prevent it?

ABSTRACT:

Background

Cigarette smoking has a considerable health and economic burden in modern society, with increased risk of morbidity and mortality. Therefore, smoking cessation policies and medical treatments are essential. However, cessation rates are low and the abandonment of the consultation is common. The identification of characteristics that may predict adherence will help defining the best treatment strategy. This study aimed to identify predictors of follow-up loss in smoking cessation consultation.

Methods

We made a retrospective observational study, including a cohort of patients who started smoking cessation consultation (April-December 2018). Clinical data from consultations was collected and analyzed with IBM SPSS Statistics (SPSS, RRID:SCR_002865).

Results

A total of 175 patients was selected (41.1% female), with a mean age of 53±12 years. Eighty-five patients (48.6%) were discharged for abandonment. They had a median pack-year unit 38±36 (P=0.011), Fagerström and Richmond scores of 5±2 and 7±2, respectively. There was an association between women (P<0.001), younger age (P<0.001), depression/anxiety (P=0.023), lower smoking load (P=0.019), starting the treatment in the first appointment (P=0.004) and the abandonment of the consultation. In binary logistic regression, younger age (less than 50 years) (OR =4.39; 95% CI: 1.99-9.70), starting the treatment in the first appointment (OR =3.04; 95% CI: 1.44-6.42) and depression/anxiety (OR =2.30; 95% CI: 1.08-4.88) remained independent predictors of loss in follow-up.

Conclusions

Women, younger age, depression/anxiety, lower smoking load and starting treatment in the first appointment are predictors of follow-up loss, so, these patients may benefit from more frequent evaluations and intensive cognitive approach. This study also raises awareness about the adequate timing to start pharmacological support for smoking cessation.

SUBMITTER: Cabrita BMO

PROVIDER: S-EPMC8107513 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

Follow-up loss in smoking cessation consultation: can we predict and prevent it?

Cabrita Bruno Miguel Oliveira BMO Galego Maria-Antónia MA Fernandes Ana-Luísa AL Dias Sara S Correia Sílvia S Simão Paula P Ferreira Jorge J Amado Joana J

Journal of thoracic disease 20210401 4

<h4>Background</h4>Cigarette smoking has a considerable health and economic burden in modern society, with increased risk of morbidity and mortality. Therefore, smoking cessation policies and medical treatments are essential. However, cessation rates are low and the abandonment of the consultation is common. The identification of characteristics that may predict adherence will help defining the best treatment strategy. This study aimed to identify predictors of follow-up loss in smoking cessatio ...[more]

PMID: 34012582

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Project description:BackgroundMigrant populations usually report higher smoking rates. Among those migrant populations, Turkish- and Kurdish-speaking migrants are often overrepresented. Providing equal access to health services is one of the major challenges of our time. The need for adapted smoking-cessation treatments for Turkish-speaking populations to achieve equity in health led, in 2006, to the development and implementation of the Tiryaki-Kukla smoking-cessation program. The aims of the current study were to evaluate one-year quit rates for smoking-cessation courses held from 2006-2018 and investigate whether certain characteristics predict long-term smoking cessation or reduction.MethodsProgram evaluation included a pre/post questionnaire (session 1/ 3 months after the quit day) and a follow-up telephone call twelve months after the quit day. To elucidate factors associated with long-term smoking cessation and reduction, Cox regression analysis and Weighted Generalized Equation Models were used.ResultsOf the 478 who participated in smoking-cessation courses, 45.4% declared themselves non-smokers at one-year follow-up. This quit rate is higher than that achieved during the preliminary evaluation of the program involving 61 participants (37.7%). Predictors of long-term smoking cessation were course length (eight vs. six sessions) (95% CI = 1.04-1.36, p = .01), adherence to the course (95% CI = 0.98-0.99, p<0.01), use of pharmacotherapy or nicotine replacement therapy products (95% CI = 0.74-0.98, p = .02), and time passed in the morning until the first cigarette is smoked (95% CI5min = 1.17-1.77, p<0.001; 95% CI30min = 1.09-1.65, p<0.01). Predictors of change in cigarettes smoked per day among smokers were-the time passed until the first cigarette in the morning (5min p < .001; 30min p < .001; 60min p < .01)-, gender (p < .001), and level of motivation to quit at baseline (p = .04).ConclusionsOur findings are consistent with existing evidence supporting adapted smoking cessation interventions to reduce health inequity in migrant populations. However, achieving harm reduction in smokers with higher dependence scores remains challenging.

Dataset Information

Follow-up loss in smoking cessation consultation: can we predict and prevent it?

Background

Methods

Results

Conclusions

Publications

Follow-up loss in smoking cessation consultation: can we predict and prevent it?

Similar Datasets

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