Background: Hospitalization presents a window of opportunity to treat smoking, and hospital-initiated smoking treatment has demonstrated effectiveness. Despite effective interventions, not all smokers will discontinue use, highlighting the need to better understand which patients achieve cessation. Traditional regression methods may not capture the complexity of inpatient smoker subgroups.
Methods: Latent class analysis (LCA) was conducted with data from 397 hospitalized adult cigarette smokers enrolled in a randomized trial. Six categorical indicator variables known to impact cessation were selected to estimate subgroups: health conditions (smoking-related disease [SRD], depressive symptoms, positive screen for alcohol problems) and smoking-related variables (time to first cigarette, cigarettes/day, smoking indoors). The probability of achieving biologically verified 7-day tobacco cessation 6 months after discharged was estimated.
Results: A 3-class model best fit the trial data: a Light Smokers subgroup had lower probability for most indicators; a High Health Burden subgroup had high smoking behavior probabilities and similar health problems to the Light Smokers subgroup; and a Heavy Smoking Drinking Depressed subgroup had high nicotine dependence, depressive symptoms, and alcohol misuse probabilities. Probability of biologically verified cessation conditional on class membership was significantly higher (P < .001) for the High Health Burden and the Light Smokers subgroups compared with the Heavy Smoking Drinking Depressed subgroup.
Conclusion: Results suggest that subgroups with lower probabilities of alcohol misuse and depression and higher probability of SRD had higher probability of successful cessation after hospital discharge. Hospitalized patients with nicotine dependence combined with behavioral and mental health problems have additional cessation barriers that may require intervention focus.
Keywords: Hospitalized smokers; latent class analysis; smoking cessation.