Study design and procedure
The present data are part of the Cohort Study on Substance Use Risk Factors (C-SURF), a longitudinal study designed to assess substance use patterns and their related consequences in young Swiss men. Enrolment in the study occurred between 2010 and 2011 in three Swiss army recruitment centres, which cover 21 of the 26 Swiss cantons. (A canton is a type of administrative division of a country and the Swiss cantons are semi-sovereign states.) Switzerland has a mandatory army recruitment process: virtually all young men are contacted at approximately 19 years of age for determination of their eligibility for military or civil service. Thus, not only individuals who were finally selected to serve in the army were enrolled in the study, but a virtually complete census of the Swiss male population in this age group was eligible. The participants filled in the questionnaire online or via mail and were rewarded with a voucher of 30 Swiss Francs (CHF).
The follow-up assessment was conducted approximately 15 months after the baseline measurement, and the participants were reimbursed with a similar voucher. The participants who filled in both questionnaires received an additional voucher (30 CHF). Between the assessments, the participants were invited twice to update their contact details online. Each of these two updates was rewarded with a voucher (5 CHF) once the second questionnaire was completed.
The Ethics Committee for Clinical Research of Lausanne University Medical School approved the study (Protocol No. 15/07).
Participants
A total of 15,074 young men visited the recruitment centres. Among them, 1,829 (12.1 %) did not meet the research staff because they were sick (but not chronically ill), were randomly selected to participate in another study [22], or were not informed about the study by the military staff. These non-inclusions were random and should not have influenced the findings. More information about sampling and non-response can be found in Studer et al. [23]. Of the 13,245 conscripts informed about the study, 7,563 (57.1 %) provided consent for participation, and 5,990 of those (79.2 %) completed the baseline questionnaire. The follow-up questionnaire was completed by 5,223 participants (87.2 %).
For the model of progression from no or occasional cigarette use at baseline to daily cigarette use at follow-up, we excluded 1,275 of the 5,990 individuals (21.3 %) because of daily cigarette use already at baseline and an additional 485 individuals (8.1 %) because of missing data, resulting in a final sample of 4,230 individuals.
Measures
Outcome variable: onset of daily cigarette use
The participants indicating cigarette use during the previous 12 months were asked how often they usually smoke cigarettes. The possible answers (“every day”, “5–6 days per week”, “3–4 days per week”, “1–2 days per week”, “2–3 days per month”, and “once per month or less”) were dichotomised (daily vs. non-daily use). For the analysis of the onset of daily cigarette smoking, the sample included all the participants who used cigarettes less than daily or not at all at baseline. Among these participants, reporting daily cigarette smoking at follow-up was classified as the onset of cigarette use.
Predictor variables
All the predictor variables were measured at baseline.
Socio-demographics
The socio-demographic predictors included age in years and the highest completed level of education divided into two categories: a lower educational level (compulsory education or vocational school training) and a higher educational level (upper secondary education, college and university degrees). Additional socio-demographic predictors included the housing situation (living alone, living with a parent or parents, living with a partner, living with friends or in an institution), the means of subsistence (own person, own person and other persons or institutions, other persons or institutions), living in a partnership (yes/no), and the number of siblings.
Religion and religiosity
Religious denomination was assessed by the question “What is your religion (even if you do not practice or believe in God)?” with nine response categories, which we merged into four categories: Christian religion, Muslim religion, other religion, and no religion. To measure religiosity, we used the first question of the Religious Background and Behaviour Questionnaire (RBB) [24] with the response categories (1) “I believe in God and practice religion”, (2) “I believe in God but do not practice religion”, (3) “I do not know what to believe about God”, (4) “I believe we cannot really know about God” (agnostic), or (5) “I do not believe in God” (atheist).
Health and health behaviour
Physical and mental health were measured by the Physical Component Summary and the Mental Component Summary of the 12-Item Short-Form Health Survey (SF-12) [25], the Major Depression Inventory (MDI) [26], and the International Physical Activity Questionnaire (IPAQ) [27]. In a study using data from 9 different countries, correlations of both the Mental and the Physical Component Summary measures of the SF-12 and the SF-36 were between .94 and .97 [28]. Various studies have shown that the SF-36 is a valid and reliable measure of population health [28, 29]. A study of the psychometric properties of the MDI indicated adequate internal and external validity (high correlation of 0.86 with the Hamilton Depression Scale) [26].
Social context
The parental situation was assessed by a question derived from the Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV) [30]. The parents’ educational level was assessed and categorised analogous to the educational level of the study participant (see above). The financial situation of the family was measured with a question from the European School Survey Project on Alcohol and Other Drugs (ESPAD) [31]; this question uses a 7-point scale (“very much below average”—“very much above average”) to assess how well off the individual’s family is compared to other Swiss families.
Parenting was assessed by three variables used in the ESPAD. Two questions (“Before you were 18 years old, how satisfied were you usually with your relationship with (a) your mother and (b) your father?”) measured the participants’ retrospective satisfaction with the relationship with their parents on scales ranging from 1 (“very satisfied”) to 5 (“not satisfied at all”). The scores were dichotomised at the median of the mean of the items assessing maternal and paternal relationships. Two questions with response scales ranging from 1 (“almost always”) to 5 (“almost never”) were used to derive parental regulation at the age of 15 years: “My parents set definite rules about what I was allowed to do (a) at home and (b) outside the home”. The scores were averaged and dichotomised at the median. The retrospective assessment of parental knowledge of peers and the whereabouts at age 15 were derived by averaging and dichotomising the scores obtained from the responses to two five-point items: “My parents knew (a) whom I was with and (b) where I was in the evenings.” Parental rule setting and knowledge of peers and the whereabouts was asked at around age 15 because this is the time when peer influences become stronger, and particularly meeting with friends without the participation of parent increases [32]. For example, the Study on Health Behaviour in School-Aged Children (HBSC) across 41 European countries showed that peer influences, such as being four or more days per week out with friends, increase strongly between the ages of 11 and 15 [33].
The lifetime prevalence of psychiatric, alcohol or drug problems in the parents was assessed separately for both parents. The participants indicated whether a significant problem had ever been present in one or several domains (i.e., psychiatric problem, alcohol problem, or drug problem). A similar question addressed previous significant alcohol, drug, or psychiatric problems in peers. Peer pressure was assessed by a shortened version of the Peer Pressure Inventory (PPI) [34].
Substance use
Lifetime use of alcohol was assessed by the question “Did you have at least 12 alcoholic standard drinks in your entire life?” Examples for alcoholic standard drinks were pictured. Furthermore, the age of the first use of at least one standard alcoholic drink, the 12-month prevalence, and at-risk drinking were assessed. The possible answers (“every day”, “5–6 days per week”, “3–4 days per week”, “1–2 days per week”, “2–3 days per month”, “once per month or less”) were dichotomised (daily vs. non-daily use). Participants who indicated ‘yes’ were classified by the Alcohol Use Disorders Identification Test (AUDIT-C) [35] as not at risk (score < 4) or at risk drinkers (score ≥ 4) [36]; participants who indicated ‘no’ were classified into the category ‘no alcohol use in the previous 12 months’. In studies, which compared the AUDIT-C to other, more comprehensive screening instruments for alcohol use disorders, the AUDIT-C showed good sensitivity, specificity and positive predictive validity [36, 37].
To assess the lifetime use of cigarettes, participants indicated whether they consumed at least 50 cigarettes in their life. Furthermore, the age of first cigarette smoking and the 12-month prevalence of cigarette smoking were assessed (see above). Additionally, the 12-month prevalence for the use of tobacco products other than cigarettes (i.e., water pipes (shisha, smoked only with tobacco), snus, snuff, chewing tobacco, cigars/cigarillos, tobacco pipes) was measured.
The lifetime use of cannabis was assessed by asking “Have you ever consumed cannabis (grass, hashish, marihuana), more than just to try?” Subsequent questions measured the age of first cannabis use and problematic cannabis use, which was assessed with the Cannabis Use Disorders Identification Test (CUDIT) [38]. Although the internal consistency of the CUDIT seems appropriate (.72–.78), the predictive power of the instrument, tested in different studies, is mixed [39]. A cut-off value of 8 was used to discriminate problematic from non-problematic cannabis use.
The lifetime use of illicit drugs other than cannabis at baseline was assessed by a series of questions measuring the frequency of use of 15 illicit drugs within the course of the individual’s life (e.g., hallucinogens, speed, amphetamines, crystal meth, poppers, ecstasy, cocaine/crack/freebase, and heroin). The lifetime use of illicit drugs was defined as having used at least one of these substances at least once.
Personality
Screening for adult attention deficit syndrome was performed with the Attention Deficit Syndrome Self Report Scale (ASRS-v1.1) [40]. Sensation seeking was measured by the Brief Sensation Seeking Scale (BSSS-8) [41]. In two studies, the BSSS-8 showed good internal consistencies (α = .76 and α = .74) and was predictive of other risk and protective factors [42]. Aggression/hostility, sociability and neuroticism/anxiety were assessed by the corresponding subscales of the Zuckerman-Kuhlman Personality Scale (ZKPQ-50-cc) [43]. In a validation sturdy, this instrument showed good psychometric and structural properties in four different languages with alpha coefficients above .70 [44]. Peer pressure was assessed by a shortened version of the Peer Pressure Inventory (PPI), which showed acceptable test-retest and inter-rater reliability in a study examining the perception of peer pressure [34]. The presence of an anti-social personality disorder (ASPD) was assessed by questions of the Mini International Neuropsychiatric Interview [45]. It involves two sections with six childhood criteria. If two of these criteria were positive, then the subjects were asked about six behaviours since age 15. Three affirmative answers qualified for ASPD.
Analyses
Starting with separate logistic regression analyses (subsequently termed ‘univariate analyses’), we evaluated the potential of each baseline variable to predict the onset of daily cigarette use. To reduce multicollinearity within the final multivariate model, we developed separate multivariate prediction models for each of the following categories of predictor variables: (1) socio-demographics, (2) religion and spirituality, (3) health and health behaviour, (4) social context, (5) substance use, and (6) personality. Variable selection comprised the following steps: (1) Significant predictors from the univariate analyses were entered into the separate models. (2) Variables that were not significant were removed manually one by one; variables with the highest p-values were removed first (backward selection). (3) To account for suppressor effects, the resulting models were verified by tentatively adding the excluded variables separately. Only significant variables were retained in the category-specific multivariate models (forward selection). Based on the results of these models, we developed one final model for the onset of daily cigarette use. Variable selection was conducted in an analogous way as described above, with the exception of including all significant predictors from the category-specific models at step (1). Nagelkerke’s R2 was calculated as a goodness-of-fit measure for all multivariate models. All the analyses were performed using SPSS version 20 [46], and p < 0.05 was set as the significance level.