Introduction: Some studies show that truck drivers use tobacco and other stimulants to stay awake as they drive. Despite their increased risks for many of tobacco-related health disparities, there is limited engagement of truck drivers in smoking cessation programs. The objective of this study was to describe smoking characteristics and identify their preferred smoking cessation methods among truck drivers. Methods: This was a cross-sectional mixed methods study. Participants were truck drivers recruited at trucking companies in Utah in 2019. Participants were either individually interviewed (n = 4), or filled out a survey (n = 33). We conducted qualitative data analysis of the interviews followed by descriptive statistics of smoking and cessation characteristics from the survey. Results: Reasons for smoking included, staying awake, stress reduction, or something to do while driving. Of the drivers surveyed, 68.8% were daily smokers while 97% had smoked at least 100 cigarettes in their life time. The mean number of cigarettes per day (cpd) was 15.7, and 25 among those who had 10 or more cpd. Sixty-one percent had made at least a quit attempt. In addition to counseling or brief advice, 68% reported interest in using Nicotine Replacement Therapy (NRT) either as gum or patch to help them quit. 21% reported interest in telephone text messaging to engage them in treatment. Conclusion: Cigarette smoking is a public health problem among truck drivers. Our findings suggest that truck drivers are interested in quitting smoking. Evidence based interventions tailored to this population are needed to help them quit and reduce their smoking-related morbidity.
Introduction
Smoking rates are up to 2–4 times higher among individuals with mental illness. Hospital readmissions for patients with psychiatric illness within a year of discharge are also high, and there is limited evidence of associations between smoking and these readmissions.
Methods
This study was a secondary data analysis using clinical data of psychiatric inpatients with initial admissions between the years 2000 and 2015. Following a descriptive analysis, logistic regression models were fitted to explore relationships between smoking and psychiatric hospital readmission within 30 days and a year of discharge.
Results
A total of 5439 patients with average age of 30.18 ± 15.97 were identified. Of this number, 47.0% were current smokers and 53.0% were never smokers. Within 30 days of discharge, 11% of the current smokers were readmitted compared to 9% of never smokers. The primary diagnoses with highest proportion of smokers were, opioid or substance use disorders (80.0%), schizophrenia (70.7%), alcohol dependence (68.2%), and bipolar disorders (50.8%). About 31% of current smokers were readmitted within one year of discharge compared to 26% of never smokers. Adjusted odds ratios for readmission within 1-year of discharge were, bipolar disorders (1.41, p = 0.01), schizophrenia (2.33, p < 0.001), and opioid/substance dependence (1.55, p = 0.01).
Conclusion
Significant relationships exist between smoking and readmission for patients with psychiatric illness. Smokers are more likely to be readmitted within 30 days or one year after discharge. Interaction of smoking and certain specific diagnoses significantly increases readmission.
Objectives:
To explore the relationships between tobacco, social support, job satisfaction, and depression among truck drivers.
Methods:
Cross-sectional data were collected from 797 truck drivers in six US states. Data collected included self-reported medical history and biological samples. Modified Zung depression scale and Work Apgar scores were used to measure depression and social support. Adjusted logistic regression models were used to calculate odds ratios (OR).
Results:
24.0% of tobacco users were in the least depressed category and 18.2% were most depressed. 22.8% of the tobacco users had the most social support compared with 27.9% of the non-users. Drivers in the two most depressed categories were significantly less likely to use tobacco (OR = 0.62, 95% confidence interval [CI] = 0.39–0.96, and OR = 0.64, 95% CI = 0.41–0.99).
Conclusions:
Drivers with low social support or low levels of depression are more likely to be tobacco users.
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