Patterns of nicotine withdrawal symptoms in a population-based study

A smoker's level of nicotine withdrawal symptoms during a quit attempt is a significant predictor of smoking relapse. Clustering approaches, such as latent class analysis, have been used to identify subtypes of other substance use disorders, but rarely in the study of nicotine withdrawal. Data were from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a probability sample aged 18+ in the United States (2001-2002). We selected a subset of smokers (n=12,179) that had consumed at least 100+ cigarettes in their lifetime, and had smoked in the past 10 years. A six-class solution exhibited the best model fit. The class comprising those with a low probability of having all 8 withdrawal symptoms was the largest group (44%). In contrast, a high symptom class accounted for 6% of the sample. A fairly low prevalent symptom class (<1%) was characterized by primarily sleep disturbances. The 3 additional classes were defined by increases in the probability of symptom endorsement (mild~24%; moderate~14%; high-moderate~11%) rather than unique combinations of symptom profiles. However, the moderate and high-moderate groups were similar on the symptoms of weight, depression, and anxiety, but differed such that the high-moderate group had a higher probability of symptoms related to sleep and concentration. Interestingly, the probability of persistence was not highest among the high-symptom class, but rather the sleep disturbance class. Public health and clinical efforts may consider focusing on patterns of nicotine withdrawal symptoms as an intervention channel to increase the likelihood of long-term cessation among those trying to quit.