Predictors of long-term outcome of a smoking cessation programme in primary care.

Abstract

BACKGROUND: It would be helpful for general practitioners to know which smokers are the most or the least likely to achieve long-term cessation, so that efforts in promoting lifestyle changes can be prioritised. AIM: To identify predictors of abstinence and assess effectiveness over a two-year follow-up of a smoking cessation programme in routine general practice. DESIGN OF STUDY: Quasi-experimental non-randomised controlled trial. SETTING: Primary healthcare centres of the Basque Health Service, Spain. METHOD: All smokers attending seven intervention (n = 1203) and three control (n = 565) practices during one year (from September 1995 to October 1996) were included. The associations between attempts to stop smoking, relapses, and sustained biochemically confirmed abstinence between 12 and 24 months' follow-up, with baseline characteristics and patients' preference with regard to three possible therapeutic options, were assessed by means of logistic regression and survival analyses. RESULTS: Sustained abstinence was biochemically confirmed in 7.3% of smokers in the intervention practices (relative probability = 2.8, 95% confidence interval [CI] = 1.6 to 4.7; probability difference = 4.7%, 95% CI = 2.7% to 6.7%); in 5% of smokers who received advice and a handout (adjusted odds ratio [AOR] = 1.9, 95% CI = 1.0 to 3.4), in 16% who received advice, a handout and follow-up (AOR = 6.6, 95% CI = 2.9 to 14.6), and in 22% who received advice, a handout, follow-up and nicotine patches (AOR = 13.1, 95% CI = 6.6 to 25.9). Positive predictors included previous attempts to stop smoking (AOR = 1.8, 95% CI = 1.1 to 2.7), and age (for each 10 years AOR = 1.32, 95% CI = 1.13 to 1.44). The Fagerström nicotine dependence score was negatively associated (for each point AOR = 0.89, 95% CI = 0.82 to 0.97). CONCLUSION: The intensity of the programme can be tailored to the probability of long-term cessation estimated by the statistical model including these predictors.