Elsevier

Preventive Medicine

Volume 38, Issue 3, March 2004, Pages 330-337
Preventive Medicine

Smoking cessation within the context of family medicine: which smokers take action?

https://doi.org/10.1016/j.ypmed.2003.10.012Get rights and content

Abstract

Background. According to Prochaska's transtheorical model, the stage of action is a milestone in the long-term smoking cessation process. Describing smokers' variables preceding this stage is essential.

Methods. A retrospective case-study design using data from the FAMUS primary care register. The objective was to develop a predictive model for the smoker's action of quitting smoking. The quit-attempt rate (QAR) is defined as the proportion of patients who attempted to quit at least once during the follow-up period. Tree-based and logistic regression models were applied to 7,567 smokers with at least two recorded visits.

Results. The QAR is 25% and is higher for men (26.5%) than for women (24.5%). For men, the groups generated by the trees are light (≤3 cigarettes), moderate (4–15), or heavy smokers (>15). For women, the pattern observed involves four groups: very light (1 cigarette), moderate–low (2–10), moderate–high (11–20), and heavy smokers (>20). In both cases, the QAR decreases from light to heavy smokers, and the number of cigarettes, nicotine replacement therapy, and age remain relevant factors predicting the QAR.

Conclusions. Medical professionals should consider smoking an addiction and take specific approaches to smoking cessation based on age, gender, and cigarettes consumed.

Introduction

The World Health Organization (WHO) has identified smoking as the most important preventable cause of death in Europe [1]. There are over 1.2 billion smokers worldwide and, annually, approximately four million tobacco-related deaths. Smoking prevalence in Western Europe and North America is 25% and the estimated lifetime quit rate is approximately 51%. Each year, only 2% of smokers succeed in quitting although 75% of adult smokers would like to stop and at least 60% have tried. Once addicted, most smokers are unable to give up smoking even when they develop smoking-related diseases [2], [3], [4], [5].

Smoking cessation counseling is widely recommended by national and international health organizations, which underscore the great potential for a beneficial intervention at the primary care level [6], [7], [8]. Based on a comprehensive review of the literature published between 1975 and 1994, the Agency for Health Care Policy and Research (AHCPR) guideline emphasized this potential and set clear institutional as well as professional guidelines (the four A's: Ask, Advise, Assist, Arrange) for smoking cessation counseling in primary care. Almost 90% of all contacts between people and the health care system take place in primary care [9], [10]. A survey of primary care physicians in Massachusetts found that 90% of them routinely asked patients about smoking and 58% were prepared to counsel patients about smoking. According to the same survey, only 3% expressed confidence about the success of their efforts [11]. In Quebec, the FAMUS (Family Medicine/University of Sherbrooke) register revealed that 90% of physicians made it a point to discuss smoking cessation and recognized that reducing cigarette smoking significantly lowers their patients' risk of cardiovascular (CV) disease. However, 59% did not believe in the efficacy of the smoking cessation intervention [12]. Andrews et al. [13] reported that feedback impacts individuals and team performance in adherence with the AHCPR guideline.

Prochaska and DiClemente [14] have proposed a transtheorical model for behavioral change based on different stages: precontemplation, contemplation, preparation, action, and maintenance. This model has been experimented in numerous behavior problems and particularly for smoking cessation [15]. Although its practical utility seems to be limited by concerns about the validity and reliability of stage assessments [16], [17], [18], [19], Littell, in a review of 87 studies, thinks the stage model has considerable heuristic value [20]. Martin also finds that movement through the stages is not always linear but the probability of a movement forward was greater than a movement backward and the probability of moving to adjacent stages was greater than the probability of two-stage progression [21]. Considering that the action of quitting is a milestone in the smoking cessation process, describing smokers' variables preceding the stage of action is essential. As far as we know, there has been no work published to date based on the transtheorical model that specifically studied these factors.

Given the wide scientific recognition of the addictive effect of nicotine [22] and based on results published on the natural history of smoking cessation [14], [23], we expect that duration and consumption level are the most important variables related to smoking cessation. Other variables are reported to be involved in the process. Routine check-ups, having cardiovascular or chronic pulmonary disease, and being pregnant, are all associated with a greater likelihood of cessation advice [24], [25], [26], [27], [28]. Being more educated or motivated or believing in one's chances of success, or being ready to change, are conditions that are positively associated with a higher cessation rate, as well as being male, a light smoker, having tobacco-related symptoms, or using nicotine replacement therapy [29], [30], [31], [32], [33], [34], [35], [36].

Within the context of primary care, the questions addressed by this paper aim to determine factors involved in the shifting of smokers from the pre-action to action stage. More specifically, this study addresses the following questions within the primary care medical context.

  • Which factors characterize and differentiate smokers who attempt to quit from those who do not?

  • With respect to gender, are there any ages or cigarette consumption levels where the quit-attempt rate (QAR) changes significantly and what are the relative weights of patient variables on the QAR?

Section snippets

Methods

To answer these questions, we used a retrospective case-study design with data from the FAMUS register [8], [11], [12], [37], [38], [39]. FAMUS is a multilevel primary care register based on a computerized network of family physicians, proportional to the population size in each of the 16 administrative regions of the Province of Quebec, Canada. The primary goal of the project was to record data on patients' health status regarding CV risk factors and disease. Physicians were recruited from

Smokers' characteristics

The 7,565 patients with at least two recorded visits were followed for almost two years (1.9 ± 1.2) on average. Table 1 presents patient characteristics and compares patients who changed to the action stage with those who did not. Patients with quit attempts were older (average age 48.4 vs. 44.9 years, small effect size), smoked fewer cigarettes per day (average number of daily cigarettes: 16.6 vs. 21.8, small effect size), had a personal history of CV disease (33.7% vs. 24.5%, small effect

Discussion

First, we recognize that using the QAR as a dependent variable falls short of the usual “1-year smoking cessation evaluation”. However, this study specifically focused on a change from the pre-action to action stage, according to Prochaska's transtheorical model. We also recognize that the QAR data were self-reported by the smokers to the physicians and are not as reliable as the urinary cotinine level, the Fagerstrom Tolerance Questionnaire [52], [53] or the Fagerstrom Test for Nicotine

Acknowledgements

Financial support for this work was received from Merck Frosst Canada Ltd., Bristol Myers Squibb, Fonds de la Recherche en Santé du Québec, Clinical Research Center, Sherbrooke University Hospital, and the Family Medicine Department, Université de Sherbrooke. The authors wish to thank all the physicians participating in the FAMUS project as well as the members of the FAMUS team for their commitment to the project, in particular Nathalie Vanasse for her expertise in database processing.

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