Discussion

The aim of this study was to identify the correlates of former smoking in smokers with reported stroke symptoms at multilevels. Income and older age were predictive of former smoking while higher education predicted former smoking in this cohort. These results are in line with previous investigations of gender effects,11–13 older age11 ,12 and level of income 11 ,12 ,14 vis a vis smoking and cessation. Our results indicate there are fewer women who are former smokers than men. Reynaso et al13 outline that there may be several working hypotheses that may explain this result. First, women may respond poorly to NRT. Second, women are more vulnerable to depression and anxiety symptomatology following cessation. Third, there may be concerns regarding postcessation weight gain and body-shape concerns for women compared to men. Fourth, women's menstrual cycle effects enhance nicotine withdrawal symptoms following cessation. Fifth, women do not receive or do not respond to the beneficial effects of social support during cessation.13 They suggest that a varied approach to smoking cessation be taken in light of these hypotheses. For example it is imperative to consider an approach with lengthier treatments for women following NRT termination.13 Health professionals should also consider adjunct programmes during the cessation attempt for women who have concerns about weight gain.13 There should be concurrent treatment programmes for women who have a history of anxiety/depression.13 Finally, the timing of smoking cessation interventions early in the follicular phase of the menstrual cycle should be considered.13

In regards to income, there seems to be an inverse effect of those who are at the lower levels of SES and former smoking. A recent review by Hiscock et al14 suggested that the higher smoking prevalence in lower SES groups and lower smoking cessation rates might be a result of the clustering of disadvantages. These disadvantages include: a reduced social support for quitting, low motivation to quit, increased addiction to tobacco, increased likelihood of not completing courses of pharmacotherapy or behavioural support sessions, psychological differences such as lack of self-efficacy, and susceptibility tobacco industry marketing.14 As a result, quit attempts in this population are significantly less likely to be successful.14

Koning et al15 found that each additional year of education reduced the risk of continued smoking.15 Their data suggested that people with higher education may be able to better understand the consequences of long-term smoking and may have more resources available for them to quit smoking.

Our findings suggest that comorbid conditions such as alcohol consumption and depression significantly decreased the likelihood of former smoking. These findings are supported by evidence suggesting that cerebrovascular patients experience higher rates of comorbidity particularly depression.16–18 Compared to cardiac patients, patients with a recent stroke suffered a three to fivefold increased risk of depressive disorders16 as well as higher proportions of alcohol consumption and hypertension.19

These findings may have clinical implications particularly for this population, as comorbid conditions such as depression and increased alcohol consumption are significantly more common in patients who smoke. Considering their association with increased smoking behaviour, comorbidities may be hindering the success of quitting smoking. The hindering effect of comorbidity on former smoking is especially problematic as smoking increases blood coagulability, platelet aggregation, thrombus formation and endothelial damage,20 thus increasing the chance of a stroke twofold21 and of stroke recurrence.21 ,22 Quitting smoking has been shown to reduce the risk of stroke to that of a non-smoker after 5 years23 and reduce the risk HR of stroke recurrence from 1.71 to 1.39 (p<0.05).24 It is imperative that smoking cessation be incorporated in secondary prevention practice while taking these significant comorbidities into account. Depression and excessive alcohol consumption might impede cessation in people with cerebrovascular disease. However due to the limitation of cross-sectional studies, we do not know if these comorbidities existed before or after the reported stroke. Further study regarding the effects of these comorbidities on cessation using other study designs might be warranted.

Population-based interventions such as household, workplace, vehicle and public smoking restrictions have all been found to predict smoking abstinence.25–27 They have also been found to reduce cigarette consumption, and initiation and increase smoking cessation rates.25–27 These authors suggest that population-based interventions are antitobacco socialisation tools that may promote the internalisation of behavioural norms against the initiation or continuation of smoking. Our results are partially in line with this evidence. We found that household and vehicle smoking restrictions predicted smoking cessation but not so with workplace or public smoking restrictions. It is not known why workplace and public smoking restrictions did not predict smoking cessation especially since their implementation under the Smoke Free Ontario Act (SFOA)28–30 in Ontario and similar legislations across Canada. Since their implementation, smoking prevalence in Canada has been dramatically decreased. Perhaps the insignificant effect of public and workplace smoking restrictions may be explained in the decrease of funding in the SFOA in 2007–2008 of 60 million, down 2.5 million from the year before of 62.5 million in 2006–2007.31 ,32 Similar reductions in tobacco control funding can be observed in other provinces.29 ,30 There is a documented association between population interventions effectiveness and sustained funding.33

A similar situation was observed with the California Model in the state of California. The California Model is similar to the SFOA and is a population intervention that used workplace and public place smoking restrictions to de-normalise tobacco use.33 Pierce et al33 found that the initial effect of the California Model to decrease smoking prevalence in the state dissipated as their funding was reduced.

In light of this conundrum, there is evidence that suggests that household and vehicle-smoking restrictions are more effective because they are less regulated.34 ,35 These authors suggest that smoking restrictions such as at home or in a vehicle are effective because those who implement them do so by choice and not through forced legislation33–35 thereby increasing the odds of smoking cessation.

We found that the use of pharmacotherapy such as bupropion and physician counselling increased the odds of former smoking but NRT use did not. According to Fiore et al36, pharmacotherapy along with counselling and follow-up increases the odds of smoking cessation. NRT and bupropion have each been found to be more efficacious than placebo for increasing the odds of smoking cessation. 37

The lack of effect of NRT may be indicative of the well-documented practice gap in healthcare in regards to smoking cessation. Young and Ward38 found that only 32% of physicians provided written materials for their patients and only 28% of physicians set a ‘quit date’ with their patients. Likewise Shaohua et al39 found that many family physicians feel lack of time was their biggest barrier in terms of implementing smoking cessation practices. Their study found that less than half were willing or able to assist their patients to quit with the use of counselling, pharmacotherapy or arrange a follow-up visit to reinforce the benefits of smoking cessation.38 This is consistent with the stroke population as documented by Mouradian et al.6 Perhaps another explanation may be the lack of information regarding the effectiveness of smoking cessation medications and similar interventions in stroke and patients with TIA. Furthermore, physicians may be reluctant to prescribe NRT's due to their availability over the counter. Further research is required to determine if the latter explanations are supported by evidence.

Cross-sectional surveys such as the CCHS are useful for initial exploratory studies. They are far reaching and reflect ‘a snapshot’ of the population. However, there are limitations to our study and they will be explored here. Since both exposure and outcome were measured at the same time, one cannot be certain which is the exposure or the outcome. In other words, the rules for contributory cause cannot be fulfilled. For example, it is possible that respondents stopped smoking years before their stroke diagnosis. Owing to the nature of the cross-sectional design of the CCHS, there is no way to ascertain which of the two (former smoking vs stroke diagnosis) came first. Unfortunately there is no available variable that quantifies the time-point of cessation in relation to respondents’ stroke diagnosis. Another limitation would be the results that found sex and age as significant correlates of former smoking. Unfortunately, these are unmodifiable correlates. Future interventions should take into account modifiable correlates such as the implementation of household and vehicle smoking restrictions and the availability of pharmacotherapy and counselling support. Another limitation is the mode of collection of the data. Social desirability and recall bias for example could play an important role and a source of biases within this study.40 For example, since smoking status, the presence of stroke symptoms and comorbidities such as depression were self-reported, special care should be taken when interpreting our results. An example of social desirability effect would be respondents not accurately reporting their smoking status. Since smoking would be an undesirable image for some depending on age, gender or SES, data obtained might not be representative of the real picture found in the population. Ideally all smoking-related measures should be validated biochemically with breath samples measuring carbon monoxide levels or coitinine levels measuring the amount of nicotine in the blood. Furthermore, without an expert assessment from a healthcare professional of stroke symptoms or depression would also limit the generalisability of the results.