Data were derived from a follow-up study of 4391 patients who underwent a coronary angiography at the INCCI in 2008/2009 and participated in the research project “Social Determinants and Health Status” . Using the “Monitoring and Dynamics of health status through the Risk Factors for Cardiovascular diseases” project framework, patients were re-contacted by letter in June 2013 and asked to complete and return a self-questionnaire addressing physiological and behavioural CVD risk factors as well as socioeconomic and demographic characteristics . In total, information on 1289 patients was available for use in the follow-up study, whereas at least 547 patients had died during the follow-up period (based on information provided by relatives and/or indicated on the questionnaire). One-to-one data linkage between the initial study and the follow-up study was possible because we used a unique identifier for every patient/participant.
A total of 1001 smokers were part of the initial 2008/2009 group, whereas 250 respondents in the 2013/2014 follow-up study were smokers. Despite the small number of smokers, the characteristics of the respondents who were smokers did not differ significant from the characteristics of non-respondents who were smokers.
The questionnaire used in the follow-up study was similar to the one used in the 2008/2009 study and addressed risk factors for CVD, such as overweight/obesity, lack of physical activity, smoking, high cholesterol levels, hypertension, and diabetes in addition to demographic and socio-economic data (age, gender, nationality, marital status, educational level, and occupation). Added to the questionnaire were items regarding household income and changes in lifestyle during the follow-up period.
Patients were considered to have successfully quit smoking (ex-smokers) if they indicated regular or occasional smoking on the initial 2008/2009 survey and not smoking on the follow-up survey, regardless of when they had quit smoking (i.e. recently or early in the follow-up period). Patients were categorised as current smokers if they indicated regular or occasional smoking on both the initial survey and the follow-up survey. Changes between regular and occasional smoking were not addressed.
Household income level was used as a proxy for the socio-economic status of the patients. Data on the income level of the patients could only be derived from the follow-up survey, where patients could indicate the income category (8 categories) of their household. However, given the relatively high level of missing data for the income indicator (missing data for 180 patients), an imputation process was applied where, based on data for sex, age-group, and professional status, income level was imputed in cases of missing data. For each combination of sex, age-group, and professional status, the mean income based on the patients for which the income was known was imputed.
Patients were then categorised into three income groups: those with an annual household income of less than 36,000 euros (€), those with an income between 36,000 euros and 53,999 euros, and those with an income higher than 54,000 euros. The income cut-off points were chosen based on pragmatic reasons, assuring that each group covered (+/−) one-third of all patients.
Because the majority of CVD patients in this study were older, patient age was divided into four categories: 54 or younger, 55–64 years, 65–75 years, and 75 years or older.
Patients were classified as those with the Luxembourger nationality and those with another nationality.
Married patients/patients in a partnership were distinguished from single patients, widowed, and divorced patients.
Diagnostic risk factors
Information on the diagnostic risk factors was derived from the initial 2008/2009 study. During the follow-up study, no diagnostic information was collected.
Cardiovascular risk factors
Information on cardiovascular (CV) risk factors was derived from the follow-up study and addressed only the status at follow-up. Changes in risk factors during the follow-up period were not assessed. Patients with a body mass index (BMI, based on self-assessed weight and height) ≥25 kg/m2 and <30 kg/m2 were classified as overweight, and those with a BMI ≥30 kg/m2 were considered obese. Physical activity was categorised in four groups: regular practice (30 min of physical exercise at least 3 times per week), occasional practice (30 min, less than 3 times per week), no physical activity due to health problems, and no physical activity for other reasons. Patients were asked if they were aware of the risk of using tobacco on their health. Based on this, two categories were distinguished: patients aware of the risk and patients not to be aware of the risk.
In the first step, the composition of the sample at the initial study was compared with the composition of the sample at the follow-up regarding smoking status, household income, demographic data, diagnostic risk factors, and cardiovascular risk factors. Then, using a chi-square test (threshold p-value <10%), former smokers were compared with current smokers. The distribution of the awareness of tobacco as CV risk factor was analysed by smoking status.
To assess the association between smoking cessation and income, three consecutive logistic regression models without an interaction term were fitted, as well as one logistic regression model with an interaction term. In model 1, sex, age, nationality, marital status, and household income were added as covariates. In model 2, data on the patients’ initial diagnosis as assessed by a coronary specialist—pectoris angina, acute myocardial infarction, an ischemic heart disease, or another coronary disease—were added to model 1. In model 3, other potential CVD risk factors—BMI, physical activity, and awareness of tobacco as a cardiovascular risk factor—were added to model 2. In model 4, an interaction term composed of household income and awareness of tobacco as a CV risk factor was added to model 3. The introduction of the interaction term allowed us to assess the magnitude of the association between household income and smoking cessation by awareness of tobacco as a CV risk factor, as well as enabled us to study the effect modification of this magnitude. Here, we have mainly analysed the coefficients of the interaction term as a modification of the effect of income by awareness of tobacco as a CV risk factor.
All statistical analyses were performed using SAS 9.3 software (SAS Institute).