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Psychosocial correlates of substance use in adolescence: A cross-national study in six European countries
Drug and Alcohol Dependence 86 (2007) 67–74
Psychosocial correlates of substance use in adolescence: A cross-national study in six European countries Anna Kokkevi a,b,∗ , Clive Richardson c,d , Silvia Florescu e , Marina Kuzman f , Eva Stergar g a
b
Department of Psychiatry, Medical School, University of Athens, Greece University Mental Health Research Institute (UMHRI), Soranou tou Efesiou 2, P.O. Box 66 517, 156 01 Papagou, Athens, Greece c Panteion University, Athens, Greece d UMHRI, Greece e National Institute for Research and Development in Health (INCDS), Bucharest, Romania f Croatian National Institute of Public Health, Zagreb, Croatia g Clinical Institute of Occupation, Traffic and Sports Medicine, Ljubljana, Slovenia Received 3 August 2005; received in revised form 4 April 2006; accepted 14 May 2006
Abstract Aims: To examine the psychosocial correlates of substance use among adolescents in six European countries. Design: Cross-sectional school population survey (ESPAD) based on standardized methodological procedures. Setting: High schools in six European countries: Bulgaria, Croatia, Greece, Romania, Slovenia and UK. Participants: Representative samples of a total sample of 16,445 high school students whose 16th birthday fell in the year of data collection. Measurements: Anonymous self-administered questionnaire. Self-reported substance use was measured by core items on tobacco, alcohol, marijuana and any illegal drug use. Psychosocial correlates included scales of self-esteem, depression, anomie and antisocial behavior, and items pertaining to family, school and peers. Findings: Logistic regression analyses for each potential correlate adjusted for country, taking into account the clustered sample, showed statistically significant associations with each substance use variable separately, in almost every case. Particularly strong associations were found between smoking and going out most evenings and having many friends who smoke, while cannabis and illegal drugs were strongly correlated with having friends or older siblings who used these substances. The self-esteem scale score was not correlated with substance use. Anomie and antisocial behavior were more strongly associated than depression with substance use. In the case of depression, anomie and most of the other items examined, associations were stronger for girls than for boys. Conclusion: The present cross-national study identified correlates of legal and illegal substance use which extend outside specific countries, providing grounds to believe that they can be generalized. They provide evidence for the need to address both the use of the gateway drugs and deviant behavior in conjunction with environmental risk factors when designing and implementing preventive interventions in schools. © 2006 Elsevier Ireland Ltd. All rights reserved. Keywords: Tobacco; Alcohol; Illegal drug use; Psychosocial correlates; Adolescents; School survey; Cross-national European study; ESPAD
1. Introduction The multifactorial origin of substance use and abuse is well documented. Various social and psychological factors in adolescence have been identified as significant correlates of substance use. They can be classified into three broad domains: (a) the environmental domain including substance use by peers and significant others (such as older siblings), family structure, relationships with parents and school adaptation (Hays and Ellickson, 1990; Garnefski and Okma, 1996); (b) the behavioral
∗
Corresponding author. Tel.: +30 210 6536902; fax: +30 210 6537273. E-mail address: [email protected] (A. Kokkevi).
0376-8716/$ – see front matter © 2006 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.drugalcdep.2006.05.018
domain in line with Jessor’s “problem behavior theory” or “theory of psychosocial risk”, which places substance use within a wider class of problem behavior containing alcohol abuse, delinquency, early sexual experience and other deviant behavior usually sanctioned by society (Jessor and Jessor, 1977; Hays and Ellickson, 1996); (c) the emotional or mood disorder domain, where the “self-medication theory” emphasizes the importance of negative affect or mood disorders whose relief is sought through substance use (Kandel and Davies, 1982; Paton et al., 1977; Brook et al., 1998; Fergusson et al., 2002; Kandel and Chen, 2000; Rey et al., 2002; Martin et al., 2002; Stefanis and Kokkevi, 1986). Of course, research on the earliest stages of drug involvement during early and mid-adolescence often has provided little evidence to support
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A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
the self-medication hypothesis, as discussed by Chassin et al. (1999). Sociocultural factors related to substance use affect social attitudes towards use and the prevalence of use in a mutually reinforcing way. A high prevalence of drug use in the general population and among peers lends drug use the attributes of a social phenomenon rather than a deviant one: such behavior, although frowned upon or even punished by society, approaches normal behavior, statistically speaking. In contrast, in a social environment where the use of a substance is restricted to a small minority of the population, its use represents a considerable deviation from the norms of society. In this case, it could well be expected that individuals involved in drug use would tend to be on the margins, both psychologically and socially. The large majority of studies of factors associated with adolescent drug use have been carried out in the USA. Crossnational studies in this area are scarce. A notable recent exception is the PACARDO research project in Central America and the Dominican Republic (Dormitzer et al., 2004). Studies carried out in individual countries are not capable of providing insight into the impact on the psychosocial correlates of substance use of a country’s history of substance use, the specific sociocultural milieu and the level reached by the prevalence of drug use. Cross-national comparisons using uniform approaches have been recognized to be invaluable in highlighting worthwhile policy directions (Pirkis et al., 2003; Morgan et al., 1999). The present study aims to examine the psychosocial correlates of substance use among adolescents in six European countries: one Western European (the UK), one Mediterranean (Greece) and four from Central and Eastern Europe (Bulgaria, Croatia, Romania and Slovenia). Data are drawn from the European School Survey Project on Alcohol and Other Drugs (ESPAD) carried out in 1999 (Hibell et al., 2000). ESPAD presents an excellent basis for international comparisons, because all the participating countries follow a standardized common methodology, including sampling, instruments and data collection procedures. Previous analysis based on the ESPAD survey has shown the roles of family structure and societal factors (alcohol availability in the given country) in heavy alcohol use and tobacco smoking by adolescents (Bjarnason et al., 2003a,b). Implications for prevention have also been drawn from other analyses of ESPAD data where the importance of cultural and contextual factors and of the specific substance involved were underlined (Morgan et al., 1999; Plant and Miller, 2001). The six countries included in the present analysis are the ESPAD participants who chose to include in their questionnaire an optional module of questions on psychosocial factors. They have a variety of sociocultural backgrounds, and differ in the development of the drug use epidemic among young people and in the current prevalences of substance use. 2. Methods 2.1. Sampling and data collection The sampling and data collection procedures followed in each country are summarized in this paragraph; full details can be found in the 1999 ESPAD
Report (Hibell et al., 2000, http://www.espad.org/reports.html). The target population comprises high school students whose 16th birthday falls in the year of data collection. Thus in the 1999 round, data were collected from students born in 1983. The total sample size in our study is 16,445 students. In Bulgaria (sample size n = 3229), a two-stage sampling procedure was carried out. A sample of schools (stratified by four school types) was drawn first, followed by the sampling of one class per selected school. Classes were selected from two grades in order to cover the entire age cohort. In Croatia (n = 3602), one grade was sampled and a sample of classes was drawn in a single stage. In Greece (n = 2205), two grades were sampled. A two-stage sample was drawn, first of schools (within four geographical strata) then of classes. In Romania (n = 2393), the sampling procedure was similar to that of Greece. In Slovenia (n = 2375), a sample of classes (stratified by school type) was drawn from one grade. In the UK (n = 2641), a geographically stratified sample of schools was drawn, followed by the sampling of one class per school from the classes with 1983-born students.
2.2. Instruments and measures 2.2.1. The questionnaire. The full English questionnaire can be found in Hibell et al. (2000). It consists of core questions to be used in all countries plus a number of optional modules and additional questions. Back translation procedures for the translation of the questionnaire into the local language were followed where necessary. The English wording of the questions given below may be adjusted to the cultural context, for example by using the local street names of drugs. The optional psychosocial module consisted of four batteries of questions that were used to construct scale scores to measure self-esteem, depressive mood, anomie and antisocial behavior. These batteries can be found numbered C1 to C4, respectively, on the English questionnaire. Each score was expressed as the mean score of the items answered, allowing one missing value. 2.2.1.1. Self-esteem. Rosenberg’s (1989) scale was used to measure selfesteem. This is one of the most widely used self-esteem measures in social science research. It is composed of ten items, each answered on a four-point scale running from “strongly agree” to “strongly disagree”. An example is “I am able to do things as well as most other people”. In the present study, the internal validity (Cronbach’s alpha) ranged from 0.732 in Romania to 0.904 in UK for boys and 0.693 in Croatia to 0.888 in the UK for girls. 2.2.1.2. Depressive mood. The Center of Epidemiological Studies DepressionScale (CES-D) was used to assess depressive mood (Radloff, 1977). The frequency of occurrence of symptoms in the last 7 days is rated on a four-point scale running from “rarely or not at all” to “most or all of the time”, for example, “How often have you had difficulty in concentrating on what you want to do?”. Cronbach’s alpha in the present study ranged from 0.714 in Romania to 0.837 in Croatia for boys and 0.727 in Romania to 0.832 in Croatia for girls. 2.2.1.3. Anomie. The Anomie Scale of Exteriority and Constraint (Bjarnason, 1998) draws on theoretical developments of Durkheim’s theory of anomie. The scale has been validated cross-culturally. It is composed of six items, each answered on a five-point scale running from “totally agree” to “totally disagree”. An example is “I follow whatever rules I want to follow”. Values of Cronbach’s alpha in the present study were from 0.620 in Greece to 0.874 in Croatia for boys and 0.629 in Greece to 0.826 in the UK for girls. 2.2.1.4. Antisocial behavior. The scale used for measuring antisocial behavior was taken from the Monitoring the Future Survey conducted in the U.S.A. (Bachman et al., 1982). It is composed of 10 items concerning behavior in the last 12 months, each answered on a five-point frequency scale running from “never” to “five times or more”. An example is “How often have you taken something from a shop without paying for it?”. Cronbach’s alpha ranged from 0.773 in Romania to 0.877 in Bulgaria for boys and 0.673 in Slovenia to 0.809 in the UK for girls in the present study. 2.2.2. Substance use variables. Four substance use variables were examined: current smoking, current heavy drinking, any lifetime use of cannabis and any
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74 lifetime use of illegal drugs (including cannabis). Current smoking was defined as smoking more than five cigarettes per day in the last 30 days. (The question was “How frequently have you smoked cigarettes during the last 30 days?” with possible responses: not at all, less than one cigarette per week, less than one per day, 1–5 per day, 6–10 per day, 11–20 per day, more than 20 per day.) Current heavy drinking was defined as drinking alcohol at least 10 times in the last 30 days, as indicated by the combined responses to “Think back over the last 30 days. On how many occasions (if any) have you had any of the following to drink?” followed first by “beer (do not include low alcohol beer)”, then by “wine” and lastly by “spirits (whisky, cognac, shot drinks etc. – also include spirits mixed with soft drinks)”, each one with response categories: never, once or twice, 3–5 times, 6–9, 10–19, 20–39 and 40 times or more. The use of cannabis was recorded by the response to the question “On how many occasions (if any) have you used marijuana (grass, pot) or hashish (hash, hash oil) in your lifetime?”, which had the same response categories as the question on alcohol. Any use of illegal drugs was established from the above question on cannabis and the series of similar questions that asked, for each substance separately, how many times the respondent had ever used amphetamines, LSD or other hallucinogenics, crack, cocaine, heroin (by smoking), heroin (by any other route), ecstasy, or magic mushrooms (the response categories were as for the cannabis and alcohol questions). 2.2.3. Family and environmental variables. Several family and environmental items from the questionnaire were also included in the statistical analysis. Family structure was obtained by recoding the responses to the question “Which of the following people live in the same household as you?” to indicate living with both parents (yes/no). Personal relationships were assessed by responses on five-point scales from “very satisfied” to “not at all satisfied” to the three questions “How satisfied are you usually with your relationship (a) to your mother (b) to your father (c) to your friends”? Absence from school was assessed by the responses on six-point scales (0, 1, 2, 3–4, 5–6, 7+) to three questions, “During the last 30 days how many whole days of school have you missed (a) because of illness (b) because you skipped or “cut” (c) for other reasons?” Older siblings’ use of substances was recorded by the answers to the six questions “Does any of your older siblings (a) smoke cigarettes (b) drink alcoholic beverages (c) ever get drunk (d) smoke marijuana or hashish (e) take tranquilizers or sedatives without a doctor’s prescription (f) take ecstasy?” (yes/no responses). Substance use by peers was obtained from the sequence of questions “How many of your friends would you estimate (a) smoke cigarettes (b) drink alcoholic beverages (c) get drunk at least once a week (d) smoke marijuana or hashish” with responses on five-point scales from none to all. Finally, “How often do you go out in the evening (to a disco, caf´e, party etc.)?” and “Do your parents know where you spend Saturday nights?” were used after recoding as “almost every day” versus less frequently and “always” versus not always, respectively. Some other items that were considered for inclusion had to be omitted because the question had not been asked in all countries or had been asked in different ways.
2.3. Statistical analysis Given the large differences observed in substance use between boys and girls, data were analysed separately by gender with the aim of accounting for gender specific factors. First, the prevalences of use of the four substances with their corresponding 95% confidence intervals were computed using the SURVEYMEANS procedure from SAS System software (Version 9.1, from the SAS Institute, Cary, NC, USA) taking into account the complex sampling design. This procedure uses the Taylor expansion method to estimate sampling errors of estimators based on complex samples. The main analyses consisted of a set of separate logistic regression analyses. Every analysis included one of the substance use variables as dependent variable. Dummy variables representing the country were included each time. The interaction between countries and the independent variable was tested by fitting a further logistic regression including the interaction terms. Models were fitted by maximum likelihood using the procedure SURVEYLOGISTIC from SAS System software to allow for the clustering of the sample at the school level. This procedure carries out maximum likelihood estimation using a Fisher scoring algorithm and uses the Taylor expansion approximation to estimate variances of the regression parameters and odds ratios.
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3. Results Table 1 shows the prevalence of each of the four substance use variables by country and gender, together with their 95% confidence limits. Table 2 shows the odds ratios for substances use in each country, compared to Croatia (the country with the largest sample), from logistic regressions which included only the countries as independent variables. Regular tobacco smoking is more widespread among adolescents in Bulgaria, Croatia and Greece than in the UK and Slovenia. In contrast, the UK and, to a lesser extent, Slovenia are the countries with the highest prevalence of illegal drug use. Greece and the UK show the highest rates of frequent alcohol consumption. Generally boys show higher prevalences than girls of legal and illegal substance use. Gender differences tend to be less marked for tobacco use than for alcohol and illegal drug use. Results for the logistic regressions in which the potential correlates of substance use were examined are shown in Table 3. Odds ratios, adjusted for country, and their 95% confidence intervals are presented. Associations with substance use were found for most of the independent variables examined. Regular use of tobacco and alcohol and life time illegal substance use are all associated with concomitant use of different substances, substance use by peer and older sibling, with parental absence and lack of monitoring of children’s going out, dissatisfaction with parents, absences from school, depressive mood, antisocial behavior and anomie. Satisfaction with the relationship with ones friends and the self-esteem scale were the only variables that showed little or no association with substance use. Particularly high odds ratios were found for going out most evenings (especially in association with smoking) and for having an older sibling who used cannabis (especially in association with the use of cannabis and any illegal drug). Having many friends who smoke was strongly associated with regular smoking and having many friends who use cannabis was strongly associated with lifetime use of any illegal drug and of cannabis. In many cases, odds ratios were higher for girls than for boys. In particular this was true for depression and anomie. Although many of the country-covariate product terms (‘interactions’) were statistically significant (p < 0.05) in these initial models, this was not the case when the modeling process was extended to include multiple covariates. The exceptions are too complex and varied to be presented and discussed in this report; they will be reported in a subsequent paper. 4. Discussion The focus of the present study was on the relationship between substance use and psychosocial factors. The ESPAD study provided the possibility of examining correlates of substance use by adolescents in different countries and cultures. To place in context the findings on psychosocial correlates identified in this study we briefly present the epidemiological situation of substance use in the six participating countries. Considerable variation in the prevalence of legal and illegal substance use exists between these countries. Furthermore,
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Table 1 Prevalence of substance use (%) with 95% confidence intervals by country and sex Alcohol 10+ times in last 30 days
Any lifetime use of cannabis
Any lifetime use of illegal drugs
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
22.2 (19.5–24.8) 22.2 (19.2–25.2) 17.9 (14.9–20.9) 7.8 (5.8–9.9) 13.6 (11.3–16.0) 13.0 (10.8–15.2)
23.3 (20.6–26.0) 16.0 (13.6–18.3) 15.4 (12.6–18.2) 3.6 (2.4–4.8) 13.4 (10.6–16.3) 14.2 (11.9–16.5)
5.4 (4.1–6.7) 8.9 (7.4–10.3) 18.9 (16.0–21.9) 6.5 (4.2–8.7) 10.1 (8.0–12.1) 12.7 (10.7–14.8)
4.3 (3.2–5.3) 3.3 (2.2–4.4) 10.1 (8.0–12.1) 1.6 (0.9–2.3) 4.2 (3.2–5.3) 9.6 (7.8–11.4)
13.9 (11.9–15.9) 19.0 (16.4–21.6) 11.1 (8.7–13.5) 2.2 (1.1–3.4) 26.7 (24.1–29.2) 40.0 (36.6–43.4)
10.7 (8.8–12.6) 13.0 (10.7–15.3) 6.6 (5.1–8.1) 0.7 (0.3–1.1) 22.8 (19.8–25.9) 33.1 (30.2–36.0)
15.5 (13.3–17.6) 20.3 (17.7–22.9) 13.0 (10.4–15.6) 10.2 (8.0–12.4) 27.6 (25.2–30.1) 40.8 (37.4–44.2)
12.4 (10.5–14.4) 14.8 (12.5–17.1) 7.2 (5.5–8.8) 9.1 (7.3–10.9) 23.4 (20.2–26.5) 34.2 (31.2–37.1)
Table 2 Odds ratios (with 95% confidence intervals) for substance use in each country Country
Bulgaria (boys: 1472; girls: 1757) Croatiaa (boys: 1965; girls: 1637) Greece (boys: 924; girls: 1281) Romania (boys: 960; girls: 1433) Slovenia (boys: 1296; girls: 1079) UK (boys: 1280; girls: 1361) a
Reference country.
Dependent variable Smoking >5 cigarettes per day in last 30 days
Alcohol 10+ times in last 30 days
Any lifetime use of cannabis
Any lifetime use of illegal drugs
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
1.00 (0.80–1.26) 1 0.77 (0.59–0.99) 0.30 (0.22–0.42) 0.55 (0.43–0.72) 0.53 (0.41–0.68)
1.60 (1.27–2.01) 1 0.96 (0.73–1.26) 0.20 (0.13–0.29) 0.82 (0.61–1.10) 0.87 (0.68–1.13)
0.58 (0.43–0.79) 1 2.40 (1.85–3.10) 0.71 (0.47–1.06) 1.15 (0.87–1.52) 1.50 (1.16–1.93)
1.30 (0.84–2.02) 1 3.28 (2.17–4.97) 0.48 (0.28–0.85) 1.30 (0.84–2.00) 3.12 (2.08–4.67)
0.69 (0.54–0.87) 1 0.53 (0.40–0.71) 0.10 (0.06–0.17) 1.55 (1.26–1.92) 2.85 (2.29–3.55)
0.80 (0.61–1.06) 1 0.47 (0.34–0.64) 0.05 (0.03–0.09) 1.98 (1.52–2.57) 3.31 (2.61–4.20)
0.72 (0.57–0.90) 1 0.59 (0.45–0.77) 0.45 (0.34–0.59) 1.50 (1.23–1.83) 2.70 (2.19–3.34)
0.81 (0.63–1.05) 1 0.44 (0.33–0.60) 0.57 (0.44–0.76) 1.75 (1.36–2.24) 2.98 (2.38–3.72)
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
Bulgaria (boys: 1472; girls: 1757) Croatia (boys: 1965; girls: 1637) Greece (boys: 924; girls: 1281) Romania (boys: 960; girls: 1433) Slovenia (boys: 1296; girls: 1079) UK (boys: 1280; girls: 1361)
Smoking >5 cigarettes per day in last 30 days
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
although boys generally have higher prevalences than girls, gender differences in substance use are more marked in some countries than in others. Daily smoking is most prevalent in Bulgaria and Croatia where nearly a quarter of students smoke more than five cigarettes per day. In contrast, Romania, Slovenia and the UK have the lowest rates of smoking (from 8% to 13% for boys), with no differences between the sexes except in Romania. The prevalence of frequent alcohol consumption was highest in Greece and the UK, where 19% and 17%, respectively, of male students reported drinking alcohol on more than ten occasions in the last 30 days. The lifetime use of illegal drugs has the highest prevalence in the UK and Slovenia, from 23% (girls in Slovenia) to 39% (boys in the UK), Romania and Greece show the lowest prevalences. Cannabis seems to account for most illegal drug use except in Romania, where other drugs (mainly heroin) are more important. Finally, the largest gender difference in illegal drug use is found in Greece. Cultural factors may explain some of the above variations in legal and illegal substance use, for example the high alcohol consumption in Greece. In this wine producing country, alcohol is traditionally linked to socializing and feasting habits of the population. Furthermore, there is no age limit on the sale of alcohol. In contrast, the use of illegal drugs including cannabis is met with severe sanctions. On the other hand, the UK has one of the oldest illegal drug use epidemics among young people in Europe (Kokkevi, 1990). In the ex-communist countries, widespread illegal drug use is a relatively recent phenomenon. It has however grown so rapidly in the last decade that in some cases it has attained even higher levels than in the west European countries where the epidemic preceded it by many years (EMCDDA, 2003; Hibell et al., 2003). The present study examines a range of potential psychosocial correlates of legal and illegal drug use pertaining to the individual, to the family and to the broader social environment. Self-esteem, antisocial behavior, anomie and depressive mood, which several other studies have suggested to be important correlates of drug use, were assessed in our study with sensitive validated scales. The logistic regression analysis shows a stable relationship across countries between substance use and a series of psychosocial variables. Family factors such as living with both parents, parental interest in the child’s going out and perceived quality of relationships with parents seem to play an important role in substance use. Other environmental factors strongly associated with substance use include older siblings’ and friends’ use of legal and illegal substances. Among the scales, the antisocial behavior scale showed the highest association with substance use for both legal and illegal substance use dependent variables. For girls, it was a stronger predictive variable for life-time cannabis and any illegal drug use than for boys. Anomie in general had weaker associations with substance use than did the antisocial behavior scale. It was significantly associated with regular smoking and any lifetime illegal drug use for both genders, and with frequent alcohol and lifetime cannabis use for girls only. However, both antisocial behavior and anomie showed weaker association with substance use than several other variables.
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Our findings show a clear association between deviant behavior and the use of legal and especially of illegal drugs, in agreement with the findings from recent studies (Hays and Ellickson, 1996). The generally larger odds ratios found for girls than for boys in our study indicate that girls using drugs tend to be more deviant than boys. Peer modeling and peer group bonding in our study seem to be associated with problem behavior (i.e. drug use) as also shown by others (Jessor and Jessor, 1977; Windle and Mason, 2004). Family environment and support and school attendance are strongly negatively correlated with substance use in our sample, in line with findings from other studies (Windle and Mason, 2004) and more specifically with the “behavioral autarcesis” hypothesis deriving from the PACARDO school population survey carried out among adolescents in Central America and the Dominican Republic along similar lines to ESPAD. In the PACARDO survey, socializing behaviors were found to have strong positive relationships with drug use experiences in 16-year-old adolescents whereas, in contrast, connectedness to social institutions (home, religion) and to conventional roles had a shielding effect (Chen et al., 2004a,b). Depressive mood was relatively weakly associated with substance use while self-esteem was not found to be associated significantly with substance use in our analysis. These findings are in line with recent studies that do not support the self-medication model especially regarding infrequent cannabis use (Patton et al., 2002; Degenhardt et al., 2003). King et al. (2004) did not find internalizing disorders to be associated with significantly increased odds of any substance use, except for major depression. Further studies have found that the internalizing pathway for a substance abuse problem may not be in operation until late adolescence (Chassin et al., 1999). Thus, externalizing disorders (behavioral disinhibition) seem to be a stronger correlate of substance use as indicated in the association found in our study between antisocial behavior and substance use. This is consistent with the literature documenting an association between childhood externalizing disorders and the emergence and persistence of a constellation of deviant problem behaviors (Elkins et al., 1997; Iacono et al., 1999). Among the strengths of our study are: the examination of a broader spectrum of legal and illegal substance use by adolescents; the large representative samples from the participating European countries; the concurrent examination of a broader range of relevant psychosocial variables; and the cross-national character of the study. It should be noted that the study also has its limitations. The most important is its cross-sectional character which does not allow for resolving questions of causality. We can only put forward the hypothesis that the young age and frequency of use of the adolescent students in our study indicates that they may tend to be in the phase of experimenting with drugs. The correlates identified here could therefore plausibly be considered as pre-existing or concomitant to substance use rather than being among the effects of it. This is to a large extent confirmed by recent literature providing findings from longitudinal studies on adolescents (Rey et al., 2002). Other limitations include those common to all questionnaire surveys based on self-reports (such as the possibility of under-reporting
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Table 3 Odds ratio (with 95% confidence intervals) for correlates of substance use, adjusted for country Independent variable
a
Dependent variable Smoking >5 cigarettes per day in last 30 days
Alcohol 10+ times in last 30 days
Any lifetime use of cannabis
Any lifetime use of illegal drugs
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
0/1 1–5 0/1 0/1
6.33 (5.42–7.40) 4.13 (3.67–4.65) 2.45 (2.17–2.77) 3.44 (2.74–4.32)
6.19 (5.34–7.16) 4.73 (4.20–5.32) 2.36 (2.07–2.69) 3.46 (2.78–4.30)
4.28 (3.54–5.18) 1.92 (1.73–2.13) 1.58 (1.35–1.86) 2.62 (2.01–3.41)
3.31 (2.60–4.21) 2.12 (1.84–2.45) 1.83 (1.50–2.23) 2.45 (1.80–3.33)
4.30 (3.63–5.09) 2.76 (2.52–3.03) 2.26 (1.99–2.57) 8.74 (6.94–11.01)
4.79 (4.03–5.68) 3.14 (2.79–3.53) 2.27 (1.99–2.60) 8.18 (6.53–10.25)
4.05 (3.47–4.73) 2.55 (2.33–2.78) 2.11 (1.87–2.38) 8.21 (6.54–10.31)
4.10 (3.50–4.80) 2.82 (2.54–3.13) 1.96 (1.73–2.22) 7.47 (5.98–9.32)
1–5 1–5
1.91 (1.79–2.04) 1.58 (1.48–1.69)
2.18 (2.03–2.35) 1.93 (1.80–2.06)
1.60 (1.48–1.72) 1.54 (1.43–1.66)
1.95 (1.78–2.13) 1.90 (1.73–2.10)
3.29 (3.06–3.54) 2.06 (1.93–2.21)
3.92 (3.62–4.24) 2.61 (2.43–2.81)
3.06 (2.85–3.28) 2.05 (1.93–2.19)
3.42 (3.16–3.69) 2.55 (2.37–2.74)
1–5 1–5 0/1 1–4 1–6
1.72 (1.61–1.85) 1.67 (1.58–1.77) 1.55 (1.36–1.76) 1.67 (1.57–1.77) 1.70 (1.61–1.78)
1.92 (1.78–2.07) 1.79 (1.68–1.91) 1.59 (1.38–1.81) 1.94 (1.82–2.07) 1.94 (1.82–2.06)
2.17 (1.98–2.39) 1.84 (1.71–1.99) 1.78 (1.51–2.10) 1.56 (1.45–1.68) 1.53 (1.45–1.62)
2.47 (2.15–2.83) 2.04 (1.86–2.25) 1.88 (1.54–2.29) 1.65 (1.51–1.81) 1.49 (1.40–1.60)
2.04 (1.88–2.20) 1.81 (1.71–1.92) 1.83 (1.61–2.07) 1.86 (1.75–1.97) 1.58 (1.50–1.66)
2.21 (2.02–2.43) 1.97 (1.84–2.10) 2.05 (1.78–2.36) 2.09 (1.96–2.24) 1.82 (1.70–1.94)
1.93 (1.79–2.08) 1.74 (1.65–1.84) 1.78 (1.58–1.99) 1.80 (1.70–1.91) 1.52 (1.45–1.60)
2.01 (1.85–2.19) 1.87 (1.75–1.98) 1.80 (1.58–2.05) 1.98 (1.87–2.11) 1.69 (1.59–1.79)
0/1
1.59 (1.40–1.81)
1.64 (1.41–1.90)
1.20 (0.99–1.46)
1.33 (1.04–1.69)
1.59 (1.40–1.82)
1.83 (1.58–2.13)
1.50 (1.32–1.70)
1.72 (1.50–1.97)
1–5
1.32 (1.24–1.40)
1.35 (1.28–1.42)
1.24 (1.15–1.35)
1.27 (1.17–1.37)
1.45 (1.37–1.55)
1.39 (1.31–1.46)
1.41 (1.33–1.49)
1.37 (1.30–1.44)
1–5
1.29 (1.23–1.37)
1.32 (1.26–1.39)
1.25 (1.18–1.34)
1.21 (1.11–1.31)
1.39 (1.32–1.47)
1.37 (1.30–1.44)
1.37 (1.30–1.43)
1.34 (1.28–1.40)
1–5 1–5 1–4 1–4 1–5
1.20 (1.16–1.24) 1.19 (1.15–1.23) 1.09 (1.05–1.13) 1.07 (1.03–1.12) 0.92 (0.85–0.99)
1.16 (1.11–1.22) 1.24 (1.18–1.29) 1.14 (1.10–1.19) 0.99 (0.92–1.05) 0.92 (0.85–1.00)
1.13 (1.10–1.17) 1.12 (1.08–1.17) 1.06 (1.02–1.10) 1.02 (0.96–1.09) 0.95 (0.86–1.05)
1.17 (1.10–1.24) 1.21 (1.14–1.28) 1.18 (1.11–1.25) 1.00 (0.90–1.12) 0.90 (0.79–1.02)
1.18 (1.14–1.22) 1.14 (1.10–1.18) 1.08 (1.04–1.11) 1.02 (0.97–1.06) 1.02 (0.95–1.10)
1.21 (1.15–1.27) 1.21 (1.15–1.27) 1.19 (1.14–1.24) 1.01 (0.95–1.08) 0.89 (0.82–0.98)
1.17 (1.13–1.20) 1.14 (1.10–1.18) 1.07 (1.04–1.11) 1.01 (0.97–1.06) 1.01 (0.94–1.09)
1.19 (1.14–1.25) 1.18 (1.13–1.23) 1.17 (1.13–1.22) 1.00 (0.95–1.06) 0.92 (0.85–0.99)
Parents do not know where you spend Saturday evenings.
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
Go out most evenings Friends smoke Older sibling smokes Older sibling uses cannabis Friends use cannabis Friends take other drugs Friends drink alcohol Friends get drunk Older sibling drinks Parentala monitoring Absent from school (truant) Do not live with both parents Dissatisfied with mother Dissatisfied with father Antisocial scale Anomie scale Depression scale Self-esteem scale Dissatisfied with friends
Response scale
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
illegal behavior) and to surveys limited to school students (such as the possibility that heavier drug use at this age may well be found among the adolescents who do not attend school). The correlates of substance use identified in the present study should be taken account of in preventive interventions. Professionals in the education and health sectors should be sensitized and aware in considering the composition of the family (living with both or one parent), the quality of parent–child relationships, parents’ monitoring of their children’s activities, substance use by older sibling and friends, school attendance, deviance from the social norms and aggressive and delinquent characteristics. Preventive interventions in schools addressing the use of the most popular legal and illegal drugs, environmental risk factors and deviant behavior might be a successful approach to curbing adolescents’ further involvement in a lifestyle of problem behavior. Acknowledgements Funding agencies of the ESPAD survey in the participating countries were as follows: National Centre for Public Health, Bulgarian Lions Quest Foundation, Ministry of Education (Bulgaria); Croatian National Institute of Public Health, The Government of the City of Zagreb, the Governmental Commission for Drug Prevention (Croatia); University Mental Health Research Institute (Greece); Institute of Health Services Management, Compartments of Health Education from Public Health Authority from each district of the country, Ministry of Education (Romania); The Institute of Public Health (Slovenia); The Alcohol Education and Research Council (AERC), The Department of Health and Social Services, Belfast, The Health Education Authority, London, Allied Domecq plc, The North Distillery Company Ltd., the PF Charitable Trust (United Kingdom). Thanks are also due to Anina Chileva and to Patrick Miller (main investigators) for providing data from Bulgaria and the UK, respectively, and to Angeliki Arapaki for her contribution to the statistical analysis. References Bachman, J.G., Johnston, L.D., O’Malley, P.M., 1982. Monitoring the Future: Questionnaire Responses from the Nation’s High School Seniors. University of Michigan, Ann Arbor, Michigan. Bjarnason, T., 1998. Parents, religion and perceived social coherence: a Durkheimian framework of adolescent anomie. J. Sci. Study Religion 37, 742–754. Bjarnason, T., Andersson, B., Choquet, M., Elekes, Z., Morgan, M., Rapinett, G., 2003a. Alcohol culture, family structure and adolescent alcohol use: multilevel modelling of frequency of heavy drinking among 15–16 year old students in 11 European countries. J. Stud. Alcohol 64, 200–208. Bjarnason, T., Davidaviciene, A.G., Miller, P., Nociar, A., Pavlakis, A., Stergar, E., 2003b. Family structure and adolescent cigarette smoking in eleven European countries. Addiction 98, 815–824. Brook, J.S., Cohen, P., Brook, D.W., 1998. Longitudinal study of co-occuring psychiatric disorder and substance use. J. Am. Acad. Child Adolesc. Psychiatr. 37, 322–330. Chassin, L., Pitts, S.C., Delucia, C., Todd, M., 1999. A longitudinal study of children of alcoholics: predicting young adult substance use disorders, anxiety and depression. J. Abnorm. Psychol. 108, 106–119.
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Chen, C., Dormitzer, C.M., Bejarano, J., Anthony, J.C., 2004a. Religiosity and the earliest stages of adolescent involvement in seven countries of Latin America. Am. J. Epidemiol. 159, 1180–1188. Chen, C., Dormitzer, C.M., Gutierrez, U., Vittetoe, K., Gonzalez, G.B., Anthony, J.C., 2004b. The adolescent behavioral repertoire as a context for drug exposure: behavioral autarcesis at play. Addiction 99, 897– 906. Degenhardt, L., Wayne, H., Lynskey, M., 2003. Exploring the association between cannabis use and depression. Addiction 98, 1493–1504. Dormitzer, C.M., Gonzalez, G.B., Penna, M., Bejarano, J., Obando, P., Sanchez, M., Vittetoe, K., Gutierrez, U., Alfaro, J., Meneses, G., Bolivar Diaz, J., Herrena, M., Hasbun, J., Chisman, A.M., Caris, L., Chen, C.Y., Anthony, J.C., 2004. The PACARDO research project: youthful drug involvement in Central America and the Dominican Republic. Pan Am. J. Public Health 15, 400–416. Elkins, I.J., Iacono, W.G., Doyle, A.E., McGue, M., 1997. Characteristics associated with the persistence of antisocial behavior: results from recent longitudinal research. Aggress. Violent Behav. 2, 101–124. EMCDDA (European Monitoring Centre for Drugs and Drug Addiction), 2003. The State of the Drugs Problem in the Acceding and Candidate Countries to the European Union. Annual Report 2003. Office of the Official Publications of the European Communities, Luxembourg. Fergusson, D.M., Horwood, J., Swain-Campbell, N., 2002. Cannabis use and psychosocial adjustment in adolescence and young adulthood. Addiction 97, 1123–1135. Garnefski, N., Okma, S., 1996. Addiction-risk and aggressive/criminal behaviour in adolescence: influence of family, school and peers. J Adolesc. 19, 503–512. Hays, R.D., Ellickson, P.L., 1990. How generalizable are adolescents’ beliefs about pro-drug pressures and resistance self-efficacy? J. Appl. Soc. Psychol. 20, 321–340. Hays, R.D., Ellickson, P.L., 1996. Association between drug use and deviant behavior in teenagers. Addict. Behav. 21, 291–302. Hibell, B., Andersson, B., Ahlstrom, S., Balakireva, O., Bjarnason, T., Kokkevi, A., Morgan, M., 2000. The 1999 ESPAD Report: Alcohol and Other Drugs Among Students in 30 European countries. Swedish Council for Information on Alcohol and Other Drugs (CAN). Hibell, B., Andersson, B., Ahlstrom, S., Balakireva, O., Bjarnason, T., Kokkevi, A., Morgan, M., 2003. The ESPAD Report 2003: Alcohol and Other Drug Use Among Students in 35 European Countries. Swedish Council for Information on Alcohol and Other Drugs (CAN). Iacono, W.G., Carlson, S.R., Taylor, J., Elkins, I.J., McGue, M., 1999. Behavioral disinhibition and the development of substance use disorders: findings from the Minnesota Twin Family Study. Dev. Psychopathol. 11, 869– 900. Jessor, R., Jessor, S.L., 1977. Problem behavior and psychosocial development. In: A Longitudinal Study of Youth. Academic Press, New York. Kandel, D.B., Davies, M., 1982. Epidemiology of depressive mood. Arch. Gen. Psychiatr. 39, 1205–1212. Kandel, D., Chen, K., 2000. Types of marijuana users by longitudinal course. J. Stud. Alcohol 61, 367–378. King, S.M., Iacono, W.G., McGue, M., 2004. Childhood externalizing and internalizing psychopathology in the prediction of early substance use. Addiction 99, 1548–1559. Kokkevi, A., 1990. Illicit drug use among adolescents in Western European countries. In: Stefanis, C., Rabavilas, A., Soldatos, C. (Eds.), Proceedings of the VIII World Congress of Psychiatry on Psychiatry: A World Perspective, vol. 1. Excepta Medica, Austria, Oxford, pp. 1156– 1162. Martin, M., Ledent, C., Parmentier, M., Maldonado, R., Valverde, O., 2002. Involvement of CB1 cannabinoid receptors in emotional behaviour. Psychopharmacology 159, pp. 379–378. Morgan, M., Hibell, B., Anderson, B., Bjarnason, T., Kokkevi, A., Narusk, A., 1999. The ESPAD study: implications for prevention. Drug Educ. Prev. Polic. 6, 243–256. Paton, S., Kessler, R., Kandel, D., 1977. Depressive mood and adolescent illegal drug use: a longitudinal analysis. J. Genet. Psychol. 131, 267– 289.
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Patton, G.C., Goffey, C., Carlin, J.B., Degenhardt, L., Lynskey, M., Hall, W., 2002. Cannabis use and mental health in young people: cohort study. BMJ 325, 1195–1198. Pirkis, J., Irwin Jr., C.E., Brindis, C.D., Patton, G.C., Sawyer, M.G., 2003. Adolescent substance use: beware of international comparisons. J. Adolesc. Health 33, 279–286. Plant, M., Miller, P., 2001. Young people and alcohol: an international insight. Alcohol Alcohol. 36, 513–515. Radloff, S.L., 1977. A self-report depression scale for research in the general population. Appl. Psychiatr. Meas. 1, 385–401.
Rey, J., Sawyer, M., Raphael, B., Patton, G., Lynskey, M., 2002. Mental health of teenagers who use cannabis: results of an Australian survey. Br. J. Psychiatr. 180, 216–221. Rosenberg, M., 1989. Society and the Adolescent Self-Image, Revised ed. Wesleyan University Press, Middletown, CT. Stefanis, C.N., Kokkevi, A., 1986. Depression and drug use. Psychopathology 19, 124–131. Windle, M., Mason, W.A., 2004. General and specific predictors of behavioral and emotional problems among adolescents. J. Emot. Behav. Disord. 12, 49–61.
Psychosocial correlates of substance use in adolescence: A cross-national study in six European countries Anna Kokkevi a,b,∗ , Clive Richardson c,d , Silvia Florescu e , Marina Kuzman f , Eva Stergar g a
b
Department of Psychiatry, Medical School, University of Athens, Greece University Mental Health Research Institute (UMHRI), Soranou tou Efesiou 2, P.O. Box 66 517, 156 01 Papagou, Athens, Greece c Panteion University, Athens, Greece d UMHRI, Greece e National Institute for Research and Development in Health (INCDS), Bucharest, Romania f Croatian National Institute of Public Health, Zagreb, Croatia g Clinical Institute of Occupation, Traffic and Sports Medicine, Ljubljana, Slovenia Received 3 August 2005; received in revised form 4 April 2006; accepted 14 May 2006
Abstract Aims: To examine the psychosocial correlates of substance use among adolescents in six European countries. Design: Cross-sectional school population survey (ESPAD) based on standardized methodological procedures. Setting: High schools in six European countries: Bulgaria, Croatia, Greece, Romania, Slovenia and UK. Participants: Representative samples of a total sample of 16,445 high school students whose 16th birthday fell in the year of data collection. Measurements: Anonymous self-administered questionnaire. Self-reported substance use was measured by core items on tobacco, alcohol, marijuana and any illegal drug use. Psychosocial correlates included scales of self-esteem, depression, anomie and antisocial behavior, and items pertaining to family, school and peers. Findings: Logistic regression analyses for each potential correlate adjusted for country, taking into account the clustered sample, showed statistically significant associations with each substance use variable separately, in almost every case. Particularly strong associations were found between smoking and going out most evenings and having many friends who smoke, while cannabis and illegal drugs were strongly correlated with having friends or older siblings who used these substances. The self-esteem scale score was not correlated with substance use. Anomie and antisocial behavior were more strongly associated than depression with substance use. In the case of depression, anomie and most of the other items examined, associations were stronger for girls than for boys. Conclusion: The present cross-national study identified correlates of legal and illegal substance use which extend outside specific countries, providing grounds to believe that they can be generalized. They provide evidence for the need to address both the use of the gateway drugs and deviant behavior in conjunction with environmental risk factors when designing and implementing preventive interventions in schools. © 2006 Elsevier Ireland Ltd. All rights reserved. Keywords: Tobacco; Alcohol; Illegal drug use; Psychosocial correlates; Adolescents; School survey; Cross-national European study; ESPAD
1. Introduction The multifactorial origin of substance use and abuse is well documented. Various social and psychological factors in adolescence have been identified as significant correlates of substance use. They can be classified into three broad domains: (a) the environmental domain including substance use by peers and significant others (such as older siblings), family structure, relationships with parents and school adaptation (Hays and Ellickson, 1990; Garnefski and Okma, 1996); (b) the behavioral
∗
Corresponding author. Tel.: +30 210 6536902; fax: +30 210 6537273. E-mail address: [email protected] (A. Kokkevi).
0376-8716/$ – see front matter © 2006 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.drugalcdep.2006.05.018
domain in line with Jessor’s “problem behavior theory” or “theory of psychosocial risk”, which places substance use within a wider class of problem behavior containing alcohol abuse, delinquency, early sexual experience and other deviant behavior usually sanctioned by society (Jessor and Jessor, 1977; Hays and Ellickson, 1996); (c) the emotional or mood disorder domain, where the “self-medication theory” emphasizes the importance of negative affect or mood disorders whose relief is sought through substance use (Kandel and Davies, 1982; Paton et al., 1977; Brook et al., 1998; Fergusson et al., 2002; Kandel and Chen, 2000; Rey et al., 2002; Martin et al., 2002; Stefanis and Kokkevi, 1986). Of course, research on the earliest stages of drug involvement during early and mid-adolescence often has provided little evidence to support
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A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
the self-medication hypothesis, as discussed by Chassin et al. (1999). Sociocultural factors related to substance use affect social attitudes towards use and the prevalence of use in a mutually reinforcing way. A high prevalence of drug use in the general population and among peers lends drug use the attributes of a social phenomenon rather than a deviant one: such behavior, although frowned upon or even punished by society, approaches normal behavior, statistically speaking. In contrast, in a social environment where the use of a substance is restricted to a small minority of the population, its use represents a considerable deviation from the norms of society. In this case, it could well be expected that individuals involved in drug use would tend to be on the margins, both psychologically and socially. The large majority of studies of factors associated with adolescent drug use have been carried out in the USA. Crossnational studies in this area are scarce. A notable recent exception is the PACARDO research project in Central America and the Dominican Republic (Dormitzer et al., 2004). Studies carried out in individual countries are not capable of providing insight into the impact on the psychosocial correlates of substance use of a country’s history of substance use, the specific sociocultural milieu and the level reached by the prevalence of drug use. Cross-national comparisons using uniform approaches have been recognized to be invaluable in highlighting worthwhile policy directions (Pirkis et al., 2003; Morgan et al., 1999). The present study aims to examine the psychosocial correlates of substance use among adolescents in six European countries: one Western European (the UK), one Mediterranean (Greece) and four from Central and Eastern Europe (Bulgaria, Croatia, Romania and Slovenia). Data are drawn from the European School Survey Project on Alcohol and Other Drugs (ESPAD) carried out in 1999 (Hibell et al., 2000). ESPAD presents an excellent basis for international comparisons, because all the participating countries follow a standardized common methodology, including sampling, instruments and data collection procedures. Previous analysis based on the ESPAD survey has shown the roles of family structure and societal factors (alcohol availability in the given country) in heavy alcohol use and tobacco smoking by adolescents (Bjarnason et al., 2003a,b). Implications for prevention have also been drawn from other analyses of ESPAD data where the importance of cultural and contextual factors and of the specific substance involved were underlined (Morgan et al., 1999; Plant and Miller, 2001). The six countries included in the present analysis are the ESPAD participants who chose to include in their questionnaire an optional module of questions on psychosocial factors. They have a variety of sociocultural backgrounds, and differ in the development of the drug use epidemic among young people and in the current prevalences of substance use. 2. Methods 2.1. Sampling and data collection The sampling and data collection procedures followed in each country are summarized in this paragraph; full details can be found in the 1999 ESPAD
Report (Hibell et al., 2000, http://www.espad.org/reports.html). The target population comprises high school students whose 16th birthday falls in the year of data collection. Thus in the 1999 round, data were collected from students born in 1983. The total sample size in our study is 16,445 students. In Bulgaria (sample size n = 3229), a two-stage sampling procedure was carried out. A sample of schools (stratified by four school types) was drawn first, followed by the sampling of one class per selected school. Classes were selected from two grades in order to cover the entire age cohort. In Croatia (n = 3602), one grade was sampled and a sample of classes was drawn in a single stage. In Greece (n = 2205), two grades were sampled. A two-stage sample was drawn, first of schools (within four geographical strata) then of classes. In Romania (n = 2393), the sampling procedure was similar to that of Greece. In Slovenia (n = 2375), a sample of classes (stratified by school type) was drawn from one grade. In the UK (n = 2641), a geographically stratified sample of schools was drawn, followed by the sampling of one class per school from the classes with 1983-born students.
2.2. Instruments and measures 2.2.1. The questionnaire. The full English questionnaire can be found in Hibell et al. (2000). It consists of core questions to be used in all countries plus a number of optional modules and additional questions. Back translation procedures for the translation of the questionnaire into the local language were followed where necessary. The English wording of the questions given below may be adjusted to the cultural context, for example by using the local street names of drugs. The optional psychosocial module consisted of four batteries of questions that were used to construct scale scores to measure self-esteem, depressive mood, anomie and antisocial behavior. These batteries can be found numbered C1 to C4, respectively, on the English questionnaire. Each score was expressed as the mean score of the items answered, allowing one missing value. 2.2.1.1. Self-esteem. Rosenberg’s (1989) scale was used to measure selfesteem. This is one of the most widely used self-esteem measures in social science research. It is composed of ten items, each answered on a four-point scale running from “strongly agree” to “strongly disagree”. An example is “I am able to do things as well as most other people”. In the present study, the internal validity (Cronbach’s alpha) ranged from 0.732 in Romania to 0.904 in UK for boys and 0.693 in Croatia to 0.888 in the UK for girls. 2.2.1.2. Depressive mood. The Center of Epidemiological Studies DepressionScale (CES-D) was used to assess depressive mood (Radloff, 1977). The frequency of occurrence of symptoms in the last 7 days is rated on a four-point scale running from “rarely or not at all” to “most or all of the time”, for example, “How often have you had difficulty in concentrating on what you want to do?”. Cronbach’s alpha in the present study ranged from 0.714 in Romania to 0.837 in Croatia for boys and 0.727 in Romania to 0.832 in Croatia for girls. 2.2.1.3. Anomie. The Anomie Scale of Exteriority and Constraint (Bjarnason, 1998) draws on theoretical developments of Durkheim’s theory of anomie. The scale has been validated cross-culturally. It is composed of six items, each answered on a five-point scale running from “totally agree” to “totally disagree”. An example is “I follow whatever rules I want to follow”. Values of Cronbach’s alpha in the present study were from 0.620 in Greece to 0.874 in Croatia for boys and 0.629 in Greece to 0.826 in the UK for girls. 2.2.1.4. Antisocial behavior. The scale used for measuring antisocial behavior was taken from the Monitoring the Future Survey conducted in the U.S.A. (Bachman et al., 1982). It is composed of 10 items concerning behavior in the last 12 months, each answered on a five-point frequency scale running from “never” to “five times or more”. An example is “How often have you taken something from a shop without paying for it?”. Cronbach’s alpha ranged from 0.773 in Romania to 0.877 in Bulgaria for boys and 0.673 in Slovenia to 0.809 in the UK for girls in the present study. 2.2.2. Substance use variables. Four substance use variables were examined: current smoking, current heavy drinking, any lifetime use of cannabis and any
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74 lifetime use of illegal drugs (including cannabis). Current smoking was defined as smoking more than five cigarettes per day in the last 30 days. (The question was “How frequently have you smoked cigarettes during the last 30 days?” with possible responses: not at all, less than one cigarette per week, less than one per day, 1–5 per day, 6–10 per day, 11–20 per day, more than 20 per day.) Current heavy drinking was defined as drinking alcohol at least 10 times in the last 30 days, as indicated by the combined responses to “Think back over the last 30 days. On how many occasions (if any) have you had any of the following to drink?” followed first by “beer (do not include low alcohol beer)”, then by “wine” and lastly by “spirits (whisky, cognac, shot drinks etc. – also include spirits mixed with soft drinks)”, each one with response categories: never, once or twice, 3–5 times, 6–9, 10–19, 20–39 and 40 times or more. The use of cannabis was recorded by the response to the question “On how many occasions (if any) have you used marijuana (grass, pot) or hashish (hash, hash oil) in your lifetime?”, which had the same response categories as the question on alcohol. Any use of illegal drugs was established from the above question on cannabis and the series of similar questions that asked, for each substance separately, how many times the respondent had ever used amphetamines, LSD or other hallucinogenics, crack, cocaine, heroin (by smoking), heroin (by any other route), ecstasy, or magic mushrooms (the response categories were as for the cannabis and alcohol questions). 2.2.3. Family and environmental variables. Several family and environmental items from the questionnaire were also included in the statistical analysis. Family structure was obtained by recoding the responses to the question “Which of the following people live in the same household as you?” to indicate living with both parents (yes/no). Personal relationships were assessed by responses on five-point scales from “very satisfied” to “not at all satisfied” to the three questions “How satisfied are you usually with your relationship (a) to your mother (b) to your father (c) to your friends”? Absence from school was assessed by the responses on six-point scales (0, 1, 2, 3–4, 5–6, 7+) to three questions, “During the last 30 days how many whole days of school have you missed (a) because of illness (b) because you skipped or “cut” (c) for other reasons?” Older siblings’ use of substances was recorded by the answers to the six questions “Does any of your older siblings (a) smoke cigarettes (b) drink alcoholic beverages (c) ever get drunk (d) smoke marijuana or hashish (e) take tranquilizers or sedatives without a doctor’s prescription (f) take ecstasy?” (yes/no responses). Substance use by peers was obtained from the sequence of questions “How many of your friends would you estimate (a) smoke cigarettes (b) drink alcoholic beverages (c) get drunk at least once a week (d) smoke marijuana or hashish” with responses on five-point scales from none to all. Finally, “How often do you go out in the evening (to a disco, caf´e, party etc.)?” and “Do your parents know where you spend Saturday nights?” were used after recoding as “almost every day” versus less frequently and “always” versus not always, respectively. Some other items that were considered for inclusion had to be omitted because the question had not been asked in all countries or had been asked in different ways.
2.3. Statistical analysis Given the large differences observed in substance use between boys and girls, data were analysed separately by gender with the aim of accounting for gender specific factors. First, the prevalences of use of the four substances with their corresponding 95% confidence intervals were computed using the SURVEYMEANS procedure from SAS System software (Version 9.1, from the SAS Institute, Cary, NC, USA) taking into account the complex sampling design. This procedure uses the Taylor expansion method to estimate sampling errors of estimators based on complex samples. The main analyses consisted of a set of separate logistic regression analyses. Every analysis included one of the substance use variables as dependent variable. Dummy variables representing the country were included each time. The interaction between countries and the independent variable was tested by fitting a further logistic regression including the interaction terms. Models were fitted by maximum likelihood using the procedure SURVEYLOGISTIC from SAS System software to allow for the clustering of the sample at the school level. This procedure carries out maximum likelihood estimation using a Fisher scoring algorithm and uses the Taylor expansion approximation to estimate variances of the regression parameters and odds ratios.
69
3. Results Table 1 shows the prevalence of each of the four substance use variables by country and gender, together with their 95% confidence limits. Table 2 shows the odds ratios for substances use in each country, compared to Croatia (the country with the largest sample), from logistic regressions which included only the countries as independent variables. Regular tobacco smoking is more widespread among adolescents in Bulgaria, Croatia and Greece than in the UK and Slovenia. In contrast, the UK and, to a lesser extent, Slovenia are the countries with the highest prevalence of illegal drug use. Greece and the UK show the highest rates of frequent alcohol consumption. Generally boys show higher prevalences than girls of legal and illegal substance use. Gender differences tend to be less marked for tobacco use than for alcohol and illegal drug use. Results for the logistic regressions in which the potential correlates of substance use were examined are shown in Table 3. Odds ratios, adjusted for country, and their 95% confidence intervals are presented. Associations with substance use were found for most of the independent variables examined. Regular use of tobacco and alcohol and life time illegal substance use are all associated with concomitant use of different substances, substance use by peer and older sibling, with parental absence and lack of monitoring of children’s going out, dissatisfaction with parents, absences from school, depressive mood, antisocial behavior and anomie. Satisfaction with the relationship with ones friends and the self-esteem scale were the only variables that showed little or no association with substance use. Particularly high odds ratios were found for going out most evenings (especially in association with smoking) and for having an older sibling who used cannabis (especially in association with the use of cannabis and any illegal drug). Having many friends who smoke was strongly associated with regular smoking and having many friends who use cannabis was strongly associated with lifetime use of any illegal drug and of cannabis. In many cases, odds ratios were higher for girls than for boys. In particular this was true for depression and anomie. Although many of the country-covariate product terms (‘interactions’) were statistically significant (p < 0.05) in these initial models, this was not the case when the modeling process was extended to include multiple covariates. The exceptions are too complex and varied to be presented and discussed in this report; they will be reported in a subsequent paper. 4. Discussion The focus of the present study was on the relationship between substance use and psychosocial factors. The ESPAD study provided the possibility of examining correlates of substance use by adolescents in different countries and cultures. To place in context the findings on psychosocial correlates identified in this study we briefly present the epidemiological situation of substance use in the six participating countries. Considerable variation in the prevalence of legal and illegal substance use exists between these countries. Furthermore,
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Table 1 Prevalence of substance use (%) with 95% confidence intervals by country and sex Alcohol 10+ times in last 30 days
Any lifetime use of cannabis
Any lifetime use of illegal drugs
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
22.2 (19.5–24.8) 22.2 (19.2–25.2) 17.9 (14.9–20.9) 7.8 (5.8–9.9) 13.6 (11.3–16.0) 13.0 (10.8–15.2)
23.3 (20.6–26.0) 16.0 (13.6–18.3) 15.4 (12.6–18.2) 3.6 (2.4–4.8) 13.4 (10.6–16.3) 14.2 (11.9–16.5)
5.4 (4.1–6.7) 8.9 (7.4–10.3) 18.9 (16.0–21.9) 6.5 (4.2–8.7) 10.1 (8.0–12.1) 12.7 (10.7–14.8)
4.3 (3.2–5.3) 3.3 (2.2–4.4) 10.1 (8.0–12.1) 1.6 (0.9–2.3) 4.2 (3.2–5.3) 9.6 (7.8–11.4)
13.9 (11.9–15.9) 19.0 (16.4–21.6) 11.1 (8.7–13.5) 2.2 (1.1–3.4) 26.7 (24.1–29.2) 40.0 (36.6–43.4)
10.7 (8.8–12.6) 13.0 (10.7–15.3) 6.6 (5.1–8.1) 0.7 (0.3–1.1) 22.8 (19.8–25.9) 33.1 (30.2–36.0)
15.5 (13.3–17.6) 20.3 (17.7–22.9) 13.0 (10.4–15.6) 10.2 (8.0–12.4) 27.6 (25.2–30.1) 40.8 (37.4–44.2)
12.4 (10.5–14.4) 14.8 (12.5–17.1) 7.2 (5.5–8.8) 9.1 (7.3–10.9) 23.4 (20.2–26.5) 34.2 (31.2–37.1)
Table 2 Odds ratios (with 95% confidence intervals) for substance use in each country Country
Bulgaria (boys: 1472; girls: 1757) Croatiaa (boys: 1965; girls: 1637) Greece (boys: 924; girls: 1281) Romania (boys: 960; girls: 1433) Slovenia (boys: 1296; girls: 1079) UK (boys: 1280; girls: 1361) a
Reference country.
Dependent variable Smoking >5 cigarettes per day in last 30 days
Alcohol 10+ times in last 30 days
Any lifetime use of cannabis
Any lifetime use of illegal drugs
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
1.00 (0.80–1.26) 1 0.77 (0.59–0.99) 0.30 (0.22–0.42) 0.55 (0.43–0.72) 0.53 (0.41–0.68)
1.60 (1.27–2.01) 1 0.96 (0.73–1.26) 0.20 (0.13–0.29) 0.82 (0.61–1.10) 0.87 (0.68–1.13)
0.58 (0.43–0.79) 1 2.40 (1.85–3.10) 0.71 (0.47–1.06) 1.15 (0.87–1.52) 1.50 (1.16–1.93)
1.30 (0.84–2.02) 1 3.28 (2.17–4.97) 0.48 (0.28–0.85) 1.30 (0.84–2.00) 3.12 (2.08–4.67)
0.69 (0.54–0.87) 1 0.53 (0.40–0.71) 0.10 (0.06–0.17) 1.55 (1.26–1.92) 2.85 (2.29–3.55)
0.80 (0.61–1.06) 1 0.47 (0.34–0.64) 0.05 (0.03–0.09) 1.98 (1.52–2.57) 3.31 (2.61–4.20)
0.72 (0.57–0.90) 1 0.59 (0.45–0.77) 0.45 (0.34–0.59) 1.50 (1.23–1.83) 2.70 (2.19–3.34)
0.81 (0.63–1.05) 1 0.44 (0.33–0.60) 0.57 (0.44–0.76) 1.75 (1.36–2.24) 2.98 (2.38–3.72)
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
Bulgaria (boys: 1472; girls: 1757) Croatia (boys: 1965; girls: 1637) Greece (boys: 924; girls: 1281) Romania (boys: 960; girls: 1433) Slovenia (boys: 1296; girls: 1079) UK (boys: 1280; girls: 1361)
Smoking >5 cigarettes per day in last 30 days
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
although boys generally have higher prevalences than girls, gender differences in substance use are more marked in some countries than in others. Daily smoking is most prevalent in Bulgaria and Croatia where nearly a quarter of students smoke more than five cigarettes per day. In contrast, Romania, Slovenia and the UK have the lowest rates of smoking (from 8% to 13% for boys), with no differences between the sexes except in Romania. The prevalence of frequent alcohol consumption was highest in Greece and the UK, where 19% and 17%, respectively, of male students reported drinking alcohol on more than ten occasions in the last 30 days. The lifetime use of illegal drugs has the highest prevalence in the UK and Slovenia, from 23% (girls in Slovenia) to 39% (boys in the UK), Romania and Greece show the lowest prevalences. Cannabis seems to account for most illegal drug use except in Romania, where other drugs (mainly heroin) are more important. Finally, the largest gender difference in illegal drug use is found in Greece. Cultural factors may explain some of the above variations in legal and illegal substance use, for example the high alcohol consumption in Greece. In this wine producing country, alcohol is traditionally linked to socializing and feasting habits of the population. Furthermore, there is no age limit on the sale of alcohol. In contrast, the use of illegal drugs including cannabis is met with severe sanctions. On the other hand, the UK has one of the oldest illegal drug use epidemics among young people in Europe (Kokkevi, 1990). In the ex-communist countries, widespread illegal drug use is a relatively recent phenomenon. It has however grown so rapidly in the last decade that in some cases it has attained even higher levels than in the west European countries where the epidemic preceded it by many years (EMCDDA, 2003; Hibell et al., 2003). The present study examines a range of potential psychosocial correlates of legal and illegal drug use pertaining to the individual, to the family and to the broader social environment. Self-esteem, antisocial behavior, anomie and depressive mood, which several other studies have suggested to be important correlates of drug use, were assessed in our study with sensitive validated scales. The logistic regression analysis shows a stable relationship across countries between substance use and a series of psychosocial variables. Family factors such as living with both parents, parental interest in the child’s going out and perceived quality of relationships with parents seem to play an important role in substance use. Other environmental factors strongly associated with substance use include older siblings’ and friends’ use of legal and illegal substances. Among the scales, the antisocial behavior scale showed the highest association with substance use for both legal and illegal substance use dependent variables. For girls, it was a stronger predictive variable for life-time cannabis and any illegal drug use than for boys. Anomie in general had weaker associations with substance use than did the antisocial behavior scale. It was significantly associated with regular smoking and any lifetime illegal drug use for both genders, and with frequent alcohol and lifetime cannabis use for girls only. However, both antisocial behavior and anomie showed weaker association with substance use than several other variables.
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Our findings show a clear association between deviant behavior and the use of legal and especially of illegal drugs, in agreement with the findings from recent studies (Hays and Ellickson, 1996). The generally larger odds ratios found for girls than for boys in our study indicate that girls using drugs tend to be more deviant than boys. Peer modeling and peer group bonding in our study seem to be associated with problem behavior (i.e. drug use) as also shown by others (Jessor and Jessor, 1977; Windle and Mason, 2004). Family environment and support and school attendance are strongly negatively correlated with substance use in our sample, in line with findings from other studies (Windle and Mason, 2004) and more specifically with the “behavioral autarcesis” hypothesis deriving from the PACARDO school population survey carried out among adolescents in Central America and the Dominican Republic along similar lines to ESPAD. In the PACARDO survey, socializing behaviors were found to have strong positive relationships with drug use experiences in 16-year-old adolescents whereas, in contrast, connectedness to social institutions (home, religion) and to conventional roles had a shielding effect (Chen et al., 2004a,b). Depressive mood was relatively weakly associated with substance use while self-esteem was not found to be associated significantly with substance use in our analysis. These findings are in line with recent studies that do not support the self-medication model especially regarding infrequent cannabis use (Patton et al., 2002; Degenhardt et al., 2003). King et al. (2004) did not find internalizing disorders to be associated with significantly increased odds of any substance use, except for major depression. Further studies have found that the internalizing pathway for a substance abuse problem may not be in operation until late adolescence (Chassin et al., 1999). Thus, externalizing disorders (behavioral disinhibition) seem to be a stronger correlate of substance use as indicated in the association found in our study between antisocial behavior and substance use. This is consistent with the literature documenting an association between childhood externalizing disorders and the emergence and persistence of a constellation of deviant problem behaviors (Elkins et al., 1997; Iacono et al., 1999). Among the strengths of our study are: the examination of a broader spectrum of legal and illegal substance use by adolescents; the large representative samples from the participating European countries; the concurrent examination of a broader range of relevant psychosocial variables; and the cross-national character of the study. It should be noted that the study also has its limitations. The most important is its cross-sectional character which does not allow for resolving questions of causality. We can only put forward the hypothesis that the young age and frequency of use of the adolescent students in our study indicates that they may tend to be in the phase of experimenting with drugs. The correlates identified here could therefore plausibly be considered as pre-existing or concomitant to substance use rather than being among the effects of it. This is to a large extent confirmed by recent literature providing findings from longitudinal studies on adolescents (Rey et al., 2002). Other limitations include those common to all questionnaire surveys based on self-reports (such as the possibility of under-reporting
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Table 3 Odds ratio (with 95% confidence intervals) for correlates of substance use, adjusted for country Independent variable
a
Dependent variable Smoking >5 cigarettes per day in last 30 days
Alcohol 10+ times in last 30 days
Any lifetime use of cannabis
Any lifetime use of illegal drugs
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
0/1 1–5 0/1 0/1
6.33 (5.42–7.40) 4.13 (3.67–4.65) 2.45 (2.17–2.77) 3.44 (2.74–4.32)
6.19 (5.34–7.16) 4.73 (4.20–5.32) 2.36 (2.07–2.69) 3.46 (2.78–4.30)
4.28 (3.54–5.18) 1.92 (1.73–2.13) 1.58 (1.35–1.86) 2.62 (2.01–3.41)
3.31 (2.60–4.21) 2.12 (1.84–2.45) 1.83 (1.50–2.23) 2.45 (1.80–3.33)
4.30 (3.63–5.09) 2.76 (2.52–3.03) 2.26 (1.99–2.57) 8.74 (6.94–11.01)
4.79 (4.03–5.68) 3.14 (2.79–3.53) 2.27 (1.99–2.60) 8.18 (6.53–10.25)
4.05 (3.47–4.73) 2.55 (2.33–2.78) 2.11 (1.87–2.38) 8.21 (6.54–10.31)
4.10 (3.50–4.80) 2.82 (2.54–3.13) 1.96 (1.73–2.22) 7.47 (5.98–9.32)
1–5 1–5
1.91 (1.79–2.04) 1.58 (1.48–1.69)
2.18 (2.03–2.35) 1.93 (1.80–2.06)
1.60 (1.48–1.72) 1.54 (1.43–1.66)
1.95 (1.78–2.13) 1.90 (1.73–2.10)
3.29 (3.06–3.54) 2.06 (1.93–2.21)
3.92 (3.62–4.24) 2.61 (2.43–2.81)
3.06 (2.85–3.28) 2.05 (1.93–2.19)
3.42 (3.16–3.69) 2.55 (2.37–2.74)
1–5 1–5 0/1 1–4 1–6
1.72 (1.61–1.85) 1.67 (1.58–1.77) 1.55 (1.36–1.76) 1.67 (1.57–1.77) 1.70 (1.61–1.78)
1.92 (1.78–2.07) 1.79 (1.68–1.91) 1.59 (1.38–1.81) 1.94 (1.82–2.07) 1.94 (1.82–2.06)
2.17 (1.98–2.39) 1.84 (1.71–1.99) 1.78 (1.51–2.10) 1.56 (1.45–1.68) 1.53 (1.45–1.62)
2.47 (2.15–2.83) 2.04 (1.86–2.25) 1.88 (1.54–2.29) 1.65 (1.51–1.81) 1.49 (1.40–1.60)
2.04 (1.88–2.20) 1.81 (1.71–1.92) 1.83 (1.61–2.07) 1.86 (1.75–1.97) 1.58 (1.50–1.66)
2.21 (2.02–2.43) 1.97 (1.84–2.10) 2.05 (1.78–2.36) 2.09 (1.96–2.24) 1.82 (1.70–1.94)
1.93 (1.79–2.08) 1.74 (1.65–1.84) 1.78 (1.58–1.99) 1.80 (1.70–1.91) 1.52 (1.45–1.60)
2.01 (1.85–2.19) 1.87 (1.75–1.98) 1.80 (1.58–2.05) 1.98 (1.87–2.11) 1.69 (1.59–1.79)
0/1
1.59 (1.40–1.81)
1.64 (1.41–1.90)
1.20 (0.99–1.46)
1.33 (1.04–1.69)
1.59 (1.40–1.82)
1.83 (1.58–2.13)
1.50 (1.32–1.70)
1.72 (1.50–1.97)
1–5
1.32 (1.24–1.40)
1.35 (1.28–1.42)
1.24 (1.15–1.35)
1.27 (1.17–1.37)
1.45 (1.37–1.55)
1.39 (1.31–1.46)
1.41 (1.33–1.49)
1.37 (1.30–1.44)
1–5
1.29 (1.23–1.37)
1.32 (1.26–1.39)
1.25 (1.18–1.34)
1.21 (1.11–1.31)
1.39 (1.32–1.47)
1.37 (1.30–1.44)
1.37 (1.30–1.43)
1.34 (1.28–1.40)
1–5 1–5 1–4 1–4 1–5
1.20 (1.16–1.24) 1.19 (1.15–1.23) 1.09 (1.05–1.13) 1.07 (1.03–1.12) 0.92 (0.85–0.99)
1.16 (1.11–1.22) 1.24 (1.18–1.29) 1.14 (1.10–1.19) 0.99 (0.92–1.05) 0.92 (0.85–1.00)
1.13 (1.10–1.17) 1.12 (1.08–1.17) 1.06 (1.02–1.10) 1.02 (0.96–1.09) 0.95 (0.86–1.05)
1.17 (1.10–1.24) 1.21 (1.14–1.28) 1.18 (1.11–1.25) 1.00 (0.90–1.12) 0.90 (0.79–1.02)
1.18 (1.14–1.22) 1.14 (1.10–1.18) 1.08 (1.04–1.11) 1.02 (0.97–1.06) 1.02 (0.95–1.10)
1.21 (1.15–1.27) 1.21 (1.15–1.27) 1.19 (1.14–1.24) 1.01 (0.95–1.08) 0.89 (0.82–0.98)
1.17 (1.13–1.20) 1.14 (1.10–1.18) 1.07 (1.04–1.11) 1.01 (0.97–1.06) 1.01 (0.94–1.09)
1.19 (1.14–1.25) 1.18 (1.13–1.23) 1.17 (1.13–1.22) 1.00 (0.95–1.06) 0.92 (0.85–0.99)
Parents do not know where you spend Saturday evenings.
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
Go out most evenings Friends smoke Older sibling smokes Older sibling uses cannabis Friends use cannabis Friends take other drugs Friends drink alcohol Friends get drunk Older sibling drinks Parentala monitoring Absent from school (truant) Do not live with both parents Dissatisfied with mother Dissatisfied with father Antisocial scale Anomie scale Depression scale Self-esteem scale Dissatisfied with friends
Response scale
A. Kokkevi et al. / Drug and Alcohol Dependence 86 (2007) 67–74
illegal behavior) and to surveys limited to school students (such as the possibility that heavier drug use at this age may well be found among the adolescents who do not attend school). The correlates of substance use identified in the present study should be taken account of in preventive interventions. Professionals in the education and health sectors should be sensitized and aware in considering the composition of the family (living with both or one parent), the quality of parent–child relationships, parents’ monitoring of their children’s activities, substance use by older sibling and friends, school attendance, deviance from the social norms and aggressive and delinquent characteristics. Preventive interventions in schools addressing the use of the most popular legal and illegal drugs, environmental risk factors and deviant behavior might be a successful approach to curbing adolescents’ further involvement in a lifestyle of problem behavior. Acknowledgements Funding agencies of the ESPAD survey in the participating countries were as follows: National Centre for Public Health, Bulgarian Lions Quest Foundation, Ministry of Education (Bulgaria); Croatian National Institute of Public Health, The Government of the City of Zagreb, the Governmental Commission for Drug Prevention (Croatia); University Mental Health Research Institute (Greece); Institute of Health Services Management, Compartments of Health Education from Public Health Authority from each district of the country, Ministry of Education (Romania); The Institute of Public Health (Slovenia); The Alcohol Education and Research Council (AERC), The Department of Health and Social Services, Belfast, The Health Education Authority, London, Allied Domecq plc, The North Distillery Company Ltd., the PF Charitable Trust (United Kingdom). Thanks are also due to Anina Chileva and to Patrick Miller (main investigators) for providing data from Bulgaria and the UK, respectively, and to Angeliki Arapaki for her contribution to the statistical analysis. References Bachman, J.G., Johnston, L.D., O’Malley, P.M., 1982. Monitoring the Future: Questionnaire Responses from the Nation’s High School Seniors. University of Michigan, Ann Arbor, Michigan. Bjarnason, T., 1998. Parents, religion and perceived social coherence: a Durkheimian framework of adolescent anomie. J. Sci. Study Religion 37, 742–754. Bjarnason, T., Andersson, B., Choquet, M., Elekes, Z., Morgan, M., Rapinett, G., 2003a. Alcohol culture, family structure and adolescent alcohol use: multilevel modelling of frequency of heavy drinking among 15–16 year old students in 11 European countries. J. Stud. Alcohol 64, 200–208. Bjarnason, T., Davidaviciene, A.G., Miller, P., Nociar, A., Pavlakis, A., Stergar, E., 2003b. Family structure and adolescent cigarette smoking in eleven European countries. Addiction 98, 815–824. Brook, J.S., Cohen, P., Brook, D.W., 1998. Longitudinal study of co-occuring psychiatric disorder and substance use. J. Am. Acad. Child Adolesc. Psychiatr. 37, 322–330. Chassin, L., Pitts, S.C., Delucia, C., Todd, M., 1999. A longitudinal study of children of alcoholics: predicting young adult substance use disorders, anxiety and depression. J. Abnorm. Psychol. 108, 106–119.
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