Abstract
OBJECTIVE:
The goal of this study was to investigate whether the association between exposure to images of alcohol use in movies and binge drinking among adolescents is independent of cultural context.
METHODS:
A cross-sectional survey study in 6 European countries (Germany, Iceland, Italy, Netherlands, Poland, and Scotland) was conducted. A total of 16 551 pupils from 114 public schools with a mean (± SD) age of 13.4 (± 1.18) years participated. By using previously validated methods, exposure to alcohol use in movies was estimated from the 250 top-grossing movies of each country (years 2004−2009). Lifetime binge drinking was the main outcome measure.
RESULTS:
Overall, 27% of the sample had consumed >5 drinks on at least 1 occasion in their life. After controlling for age, gender, family affluence, school performance, television screen time, sensation seeking and rebelliousness, and frequency of drinking of peers, parents, and siblings, the adjusted β-coefficient for lifetime binge drinking in the entire sample was 0.12 (95% confidence interval: 0.10−0.14; P < .001). The crude relationship between movie alcohol use exposure and lifetime binge drinking was significant in all countries; after covariate adjustment, the relationship was still significant in 5 of 6 countries. A sensitivity analysis revealed that the association is content specific, as there was no significant association between lifetime binge drinking and exposure to smoking in movies.
CONCLUSIONS:
The link between alcohol use in movies and adolescent binge drinking was robust and seems relatively unaffected by cultural contexts.
KEY WORDS: alcohol, Europe, exposure, media, youth
What’s Known on This Subject:
Some studies reveal an association between exposure to alcohol consumption in movies and youth drinking, but the evidence is sparse.
What This Study Adds:
Exposure to alcohol consumption in movies is associated with youth binge drinking, is little influenced by cultural differences between countries (Germany, Iceland, Italy, Netherlands, Poland, and Scotland), and is specific to movie alcohol, not movie smoking, depictions.
Alcohol-related health and social problems are prevalent in almost all societies that consume alcohol.1 For young people, injury remains the leading cause of death in every region of the world, with most deaths associated with motor vehicles; more than half of these are associated with driving under the influence of alcohol.2 There is consistent evidence that higher alcohol consumption in adolescence continues into adulthood and is also associated with later alcohol problems and alcohol dependence.3 Furthermore, heavy alcohol use in adolescence has been found to be related to suicide,4,5 neurocognitive impairment,6 and impaired brain development.7 Thus, preventing underage drinking is an important public health goal.
Previous work has emphasized parental and peer risk factors for alcohol use.8–11 Recently, attention has switched to alcohol exposure in the media as another set of risk factors. Alcohol advertising such as direct advertising on television, at movie theaters, and product placement in movies has been linked to adolescent drinking.12–15 Favorable images of alcohol use are common in movies, and many such images are delivered to children and adolescents through entertainment media (box office, DVD, television, and Internet).16 Exposure to such imagery has been associated with youth drinking patterns,17–19 but the evidence in this area is sparse, with only a few studies providing evidence for the association between exposure to movie alcohol use and binge drinking of adolescents.20–24 However, recent experimental studies using a randomized design have shown that alcohol portrayals in movies directly influence actual alcohol intake, presumably by imitation and cue-reactivity processes.25–27 In addition, exposure to alcohol use in movies might increase social acceptability of alcohol use and change cognitions, resulting in initiation and escalation of alcohol use in adolescents.12,14,28 Thus, further study of this association seems warranted.
This study reports results of a large-scale cross-sectional survey of young adolescents from 6 European countries. These countries differ substantially in their alcohol control policies. Two countries (Poland and Iceland) are in the top 10 group of a total of 30 countries regarding 5 regulatory domains: physical availability of alcohol, drinking context, alcohol prices, alcohol advertising, and operation of motor vehicles. One country is in the middle group (United Kingdom), and the remaining 3 countries (Netherlands, Italy, and Germany) are in the bottom group.29 These countries also show large differences in the alcohol use prevalence among young people. Based on data collected in 2007, the 30-day binge drinking prevalence among 15- to 16-year-olds ranged from 22% in the country with the lowest prevalence (Iceland) to 54% in the country with the highest prevalence (United Kingdom).30
The goal of this article was to investigate whether the association between exposure to movie alcohol use and adolescent binge drinking occurs independently of cultural contexts, and alcohol control policies, which would support the argument that alcohol use in movies is an independent risk factor for initiation of potentially harmful patterns of drinking in youth.
Methods
Study Sample and Procedure
The study was conducted by 6 research centers, in Germany (Kiel), Iceland (Reykjavik), Italy (Turin and Novara), Poland (Poznan), Netherlands (Nijmegen), and Scotland (Glasgow). The 6 study samples were all recruited from state-funded schools (Appendix 1 provides sample details). Overall, a total of 19 268 students from 114 schools and 865 classes were examined for eligibility. A total of 1059 students (5.5%) could not be included in the study due to missing parental consent, 1559 students (8.1%) were absent on the day of assessment and could not be reached by mail, and 99 students (0.5%) refused to participate, resulting in a final overall sample of 16 551 students (85.9% response rate); 51% were male. The mean age was 13.4 ± 1.18 years, with an age range of 10 to 19 years. Within this final sample, the number of participating students per school ranged from 14 to 603, and the number of participating students per class ranged from 1 to 45.
Survey
In each country, data were collected through self-report questionnaires, administered by trained research staff. Students were assured that their individual data would not be seen by parents or school staff. Each completed questionnaire was placed in an envelope and sealed in front of the participant. Study implementation was approved in all 6 research centers by the respective ethical boards and data protection agencies.
Measures
Exposure to Movie Alcohol Use
Exposure to alcohol use in movies was assessed by using a method developed by researchers at Dartmouth Medical School, which relies on the recall of seeing movies presented to respondents as a list of titles.31 For this procedure, each participating research center provided a list of 250 box-office hits based on publicly available data on movie revenues in each respective country. Each of the 6 lists of 250 movies contained the 50 most successful movies of the years 2005−2008 and the 25 most successful movies of the years 2004 and 2009. Students in each country received a random selection of 50 movies (20%) from their country-specific list of 250, creating a unique individual movie list for each student. To minimize subject-to-subject disparities in movie composition, selection of movies was stratified according to year of release and by country-specific age rating so that each randomly generated list of 50 titles had the same distribution with regard to year and country-specific age ratings. Students were asked to indicate how often they had seen each movie (from 0 = “never” to 3 = “>2 times”). For the present analysis, answers were dichotomized into “seen” and “not seen.”
In a parallel procedure, all included movies were content coded with regard to alcohol use. Due to a high overlap of box-office hits between countries, the complete sample of 1500 movies (6 countries × 250 movies) contained 655 unique movies. Fifty-six percent of these movies (n = 368) were included within the top 100 box-office hits in the United States and had already been content-coded at the Dartmouth Media Research Laboratory.16 The remaining 44% (n = 287) were content-coded in the 6 European research centers. In this coding process, trained coders review each movie and count the number of occurrences of on-screen alcohol use. An alcohol occurrence is counted whenever a major or minor character handles or uses alcohol in a scene or when alcohol use is shown in the background (eg, extras drinking alcohol in a bar scene). Occurrences are counted each time the alcohol use appears on the screen. Interrater reliability was studied via 2 types of correlations: (1) between the coding results of the European coders and the European trainer on a selected number of training movies; and (2) between the European trainer and the Dartmouth coders, based on a blinded European recoding of a random sample of 40 Dartmouth-coded movies. European coder-trainer correlations ranged between r = 0.93 (Iceland) and r = 0.99 (Italy); the European re-counts of alcohol occurrences in the random movie selection correlated (r = 0.87) with the Dartmouth counts.
Exposure to alcohol use in movies was calculated for all students by summing the number of alcohol occurrences in each movie they had seen. The measure was adjusted for possible variation in the movie lists by expressing individual exposure to movie alcohol use as a proportion of the total number of possible alcohol occurrences each student could have seen on the basis of the movies included in his or her questionnaire. The final exposure estimate was the proportion of alcohol occurrences the adolescent had seen in his or her particular sample multiplied by the number of alcohol occurrences in the 250 movies of that country.
Binge Drinking
Students were asked the following question about their alcohol use: “How often have you had 5 or more drinks of alcohol on one occasion?” Response categories were 0 = “never,” 1 = “once,” 2 = “2 to 5 times,” or 3 = “>5 times.” Students who reported never were classified as “never binge drinkers,” and all others as “ever binge drinkers.”
Covariates
We included a number of covariates that could confound the relation between exposure to alcohol consumption in movies and binge drinking, including sociodemographic circumstances, behavioral and personality characteristics, television viewing, and drinking of peers, parents, and siblings (Appendix 2). The list of covariates mirrored that of previous studies on movie alcohol use.20,23,32
Statistical Analysis
All data analyses were conducted with Stata version 12.0 (Stata Corp, College Station, TX). Bivariate associations between all study variables were analyzed with Spearman rank correlation coefficients and multiple mean comparisons with Tukey’s test. Locally weighted scatterplot smoothings were used to graphically represent the crude relationship between movie alcohol use exposure and adolescent binge drinking for each country. To compare the dose−response curves, we standardized movie alcohol use exposure for each country so that the lowest value was 0 and the highest was 1. For that purpose, we recoded low outliers to the fifth percentile and high outliers to the 95th percentile, subtracted the difference between fifth and 0, so that the distribution started at 0. We divided by the maximum number and had a rescaled variable from 0 to 1 representing going from the fifth to the 95th percentile.
Because the data were clustered at the country, school, and classroom level, the adjusted associations between amounts of movie alcohol use and lifetime binge drinking were analyzed with multilevel mixed-effects linear regressions with random intercepts for country, school, and class. In a first step, unadjusted models were specified, with movie alcohol use exposure as the only fixed effect. In the adjusted models, all covariates were included as fixed effects. These analyses were restricted to students who had complete data on all model variables.
A sensitivity analysis was undertaken to assess the specificity of the association between exposure to alcohol consumption in the movies and adolescent binge drinking. Specificity of the association was examined by adding a variable assessing exposure to smoking in movies to the regression model. The methods for measurement of onscreen smoking have been described in detail elsewhere.33
Results
Descriptive Statistics
Descriptive statistics for binge drinking and all covariates are presented in Table 1. Overall, 27% had consumed >5 drinks on at least 1 occasion in their life, but this finding varied substantially between countries. For example, 6% of the Icelandic students were classified as ever binge drinking, compared with 38% in the Dutch sample (χ2(5) = 854; P < .001). Differences in binge drinking rates remained after controlling for age, with age-adjusted prevalences for ever binge drinking of 7%, 20%, 23%, 30%, 32%, and 40% for Iceland, Italy, Poland, Netherlands, Germany, and Scotland, respectively.
TABLE 1.
Descriptive Statistics for Lifetime Binge Drinking and Covariates, Overall and According to Country
| Overall (N = 16 551) | Germany (n = 2754) | Iceland (n = 2664) | Italy (n = 2668) | Poland (n = 4105) | Netherlands (n = 1423) | Scotland (n = 2937) | |
|---|---|---|---|---|---|---|---|
| Adolescents | |||||||
| Lifetime binge drinking | |||||||
| Never | 73 | 76 | 94 | 75 | 67 | 62 | 65 |
| Once | 13 | 12 | 3 | 13 | 18 | 10 | 14 |
| 2–5 times | 9 | 8 | 2 | 8 | 10 | 12 | 13 |
| >5 times | 5 | 4 | 1 | 4 | 5 | 16 | 8 |
| Sociodemographics | |||||||
| Gender | |||||||
| Female | 49 | 49 | 48 | 44 | 53 | 51 | 49 |
| Male | 51 | 51 | 52 | 56 | 47 | 49 | 51 |
| Age, mean (SD), y | 13.4 (1.18) | 12.7 (1.06) | 13.1 (0.89) | 13.6 (1.37) | 14.2 (0.79) | 13.8 (1.36) | 13.0 (0.89) |
| Family affluence | |||||||
| Low | 10 | 8 | 2 | 14 | 17 | 2 | 10 |
| Medium | 36 | 37 | 21 | 45 | 42 | 27 | 39 |
| High | 54 | 55 | 77 | 41 | 41 | 71 | 51 |
| Personal characteristics | |||||||
| School performance | |||||||
| Below average | 8 | 6 | 4 | 15 | 9 | 9 | 3 |
| Average | 33 | 44 | 25 | 39 | 39 | 29 | 21 |
| Good | 42 | 40 | 43 | 39 | 35 | 49 | 51 |
| Excellent | 17 | 10 | 28 | 7 | 17 | 13 | 25 |
| TV screen time | |||||||
| ≤0.5 h | 23 | 25 | 29 | 20 | 19 | 24 | 22 |
| 1–2 h | 51 | 52 | 55 | 48 | 49 | 57 | 50 |
| 3–4 h | 19 | 17 | 13 | 23 | 24 | 17 | 20 |
| >4 h | 7 | 6 | 3 | 9 | 8 | 2 | 8 |
| Sensation seeking and rebelliousness, mean (SD), range 0–4 | 1.31 (0.74) | 1.21 (0.72) | 1.0 (0.69) | 1.43 (0.75) | 1.53 (0.74) | 1.01 (0.59) | 1.40 (0.73) |
| Social environment | |||||||
| Peer drinking | |||||||
| None | 32 | 48 | 69 | 21 | 10 | 22 | 30 |
| A few | 25 | 25 | 19 | 25 | 25 | 18 | 33 |
| Some | 21 | 17 | 7 | 29 | 32 | 21 | 16 |
| Most | 17 | 8 | 3 | 19 | 27 | 27 | 17 |
| All | 4 | 2 | 1 | 6 | 6 | 12 | 4 |
| Mother figure drinking | |||||||
| Never | 23 | 16 | 18 | 39 | 28 | 12 | 14 |
| Seldom | 59 | 64 | 68 | 50 | 63 | 51 | 46 |
| Often but not every day | 17 | 16 | 13 | 8 | 8 | 30 | 37 |
| Every day | 2 | 2 | 1 | 3 | 1 | 7 | 3 |
| Father figure drinking | |||||||
| Never | 13 | 12 | 15 | 18 | 14 | 6 | 12 |
| Seldom | 52 | 58 | 60 | 47 | 61 | 38 | 41 |
| Often but not every day | 29 | 25 | 23 | 24 | 22 | 45 | 42 |
| Every day | 6 | 5 | 2 | 11 | 4 | 11 | 5 |
| Any sibling drinking | |||||||
| No | 65 | 68 | 57 | 75 | 69 | 56 | 57 |
| Yes | 35 | 32 | 43 | 25 | 31 | 44 | 43 |
Data are presented as % or mean ± SD.
Exposure to Alcohol Consumption in Movies
Overall, 86% of the total 655 movies included at least 1 alcohol scene. Figure 1 shows the distributions for the estimated exposure to alcohol use in movies. Almost all histograms were positively skewed, with some differences between the countries with regard to the average amount of exposure (all pairwise comparisons P < .05, with the exception of Germany versus Netherlands, Iceland versus Italy, Iceland versus Poland, and Italy versus Poland). The lowest exposures occurred among Dutch and German adolescents and the highest among those from Italy and Iceland. In each of the countries, it was estimated that the most highly exposed adolescents had seen in excess of 10 000 alcohol depictions from his or her country-specific sample of popular movies.
FIGURE 1.
Exposure to alcohol use in movies among adolescents from 6 European countries (N = 16 551). Estimation based on alcohol use in each country’s 250 most successful movies of the years 2004–2009. Number of seen alcohol occurrences displayed in the x-axes. Mdn = median.
Associations Between Study Variables
Zero-order correlations between the study variables demonstrated significant crude associations between the central constructs. Exposure to movie alcohol use was significantly associated with all study variables, particularly with sensation seeking/rebelliousness (r = 0.25), peer drinking (r = 0.21), and lifetime binge drinking (r = 0.21) (Appendix 3).
Association Between Exposure to Alcohol Consumption in Movies and Adolescent Binge Drinking
The smoothed lowest curves in Fig 2 show the unadjusted association between exposure to movie alcohol use and adolescent binge drinking for each country. The curves illustrate that the dose−response was linear through the exposure range for each country. Icelandic youth had the lowest rates of binge drinking, whereas youths in the Netherlands, Scotland, and Germany had the highest. Across all countries, there was a crude dose–response association between higher exposure to movie alcohol depictions and lifetime binge drinking.
FIGURE 2.
Crude association between exposure to movie alcohol use and adolescents’ lifetime binge drinking, by country. Adjusted β coefficients are adjusted for age, gender, family affluence, school performance, television screen time, sensation seeking and rebelliousness, and frequency of drinking among peers, parents, and siblings. Figures in parentheses are presented as 95% confidence intervals.
Multivariate Analysis
Figure 2 also reports adjusted β-coefficients for the relationship between movie alcohol use exposure and adolescent binge drinking, overall and by country. In the overall (all countries) adjusted model, adolescents with higher exposure to alcohol use in movies were significantly more likely to have engaged in binge drinking, even after controlling for age, gender, family affluence, school performance, television screen time, sensation seeking and rebelliousness, and frequency of drinking of peers, parents, and siblings. We found few between-country differences in the strength of the crude and the adjusted relationship. An exception was the Icelandic model, which revealed a smaller crude relationship and a nonsignificant adjusted association.
Sensitivity Analysis
Table 2 presents the adjusted multilevel regression results with the additional inclusion of exposure to movie smoking. The correlation between exposure to movie smoking and movie alcohol use was r = 0.83. Despite the high correlation, exposure to movie alcohol use remained significantly associated with lifetime binge drinking, whereas exposure to movie smoking was not (Table 2). Other factors that were associated with binge drinking included peer drinking, sensation seeking and rebelliousness, school performance, age, and sibling drinking.
TABLE 2.
Sensitivity Analysis: Adjusted Regression Coefficients for Lifetime Binge Drinking: Results of the Multilevel Mixed-Effects Linear Regression
| Predictor | β | 95% Confidence interval | P |
|---|---|---|---|
| Exposure to movie alcohol use | 0.09 | 0.06 to 0.13 | <.001 |
| Exposure to onscreen smoking | 0.03 | −0.00 to 0.07 | NS |
| Age | 0.03 | 0.02 to 0.04 | <.001 |
| Gender (0 = male, 1 = female) | 0.01 | −0.02 to 0.01 | NS |
| Family affluence | −0.00 | −0.01 to 0.01 | NS |
| School performance | −0.05 | −0.05 to –0.04 | <.001 |
| TV screen time | −0.00 | −0.00 to 0.01 | NS |
| Sensation seeking/ rebelliousness | 0.10 | 0.10 to 0.11 | <.001 |
| Peer drinking | 0.13 | 0.12 to 0.14 | <.001 |
| Mother drinking | 0.01 | −0.00 to 0.02 | NS |
| Father drinking | 0.01 | −0.00 to 0.02 | NS |
| Sibling drinking | 0.05 | 0.04 to 0.06 | <.001 |
Adjusted for all predictor variables named in the table. n = 15 997 students, n = 865 classes, n = 114 schools, n = 6 countries. Country, school, and class as random effects. Analyses were restricted to students who had complete data on all model variables. NS, not significant.
Discussion
To the best of our knowledge this article represents the largest cross-cultural study to date examining the association between exposure to alcohol use in movies and youth drinking. The results reveal that European adolescents are exposed to many images of alcohol use through popular movies. This exposure is substantially associated with lifetime binge drinking, a problematic drinking behavior. Results indicate that exposure to alcohol use in movies is robust in the context of different European cultures, with only a few between-country differences in the strength of the relationship.
Recent commentators have questioned whether the movie smoking effect is specific to the behavior depicted.34 Because risk behaviors cluster in movies, exposure assessments of these individual behaviors are correlated, and it requires studies of substantial sample size to disentangle the respective effects. From a theoretical standpoint, the most simple explanation for an association between seeing movies and drinking is the direct modeling effect. If that were the case, one would expect exposure to movie alcohol use and not exposure to movie smoking to predict drinking behavior, and that is what we report in this study. Thus, the association is present after controlling for confounding characteristics of the adolescent (characteristics that would be expected to draw the adolescents to other elements in movies, such as excitement) and is specific to the depiction of movie alcohol. The results suggest that if steps were taken to decrease exposure of adolescents to movie depictions of alcohol, then fewer young people would take up binge drinking.
The current study yielded important insights on the link between alcohol movie exposure and alcohol use in Europe. Nevertheless, several questions remain. Most importantly, we do not know to what extent the associations are moderated, for example, by individual factors (eg, personality factors), parental alcohol use, and parenting styles in relation to regulating the amount and types of movies seen (eg, whether age-appropriate or older) movie exposure. Second, an important question may be whether adolescents are differentially affected by movies that differ in country of production (United States versus indigenous) or content.
There are several limitations to the current study, the most important one being the cross-sectional design. Cross-sectional data cannot provide information on the temporal sequence of events; that is, in this case, whether exposure to alcohol consumption in movies preceded the binge drinking episode(s) or whether adolescents who had consumed alcohol in this way were more inclined to watch movies with more alcohol exposure. Temporal antecedence is one important determinant of a causal relationship. However, a major advantage of the cross-cultural design of the study is the fact that unmeasured confounding is accounted for by the country-level random effect. In the present context, exposure to alcohol consumption in movies is the constant on a background of many other between-country differences such as the age at which someone can legally purchase alcohol or the prevalence of drinking during meals with family at home, which is relatively rare in Iceland but more common in Italy. The consistent finding of a movie alcohol use effect after controlling for variance at the country level seems to be an important contributor to the causal argument. Other limitations relate to the assessment of movie alcohol use exposure, which was based on student recall of films seen and hence open to error and biases. However, there is no obvious reason for memory distortions to be systematically related to the amount of movie alcohol use exposure. A final issue is the potential bias due to the 14% of students who provided no data because of absence from school on the day of the survey or lack of parental consent.
Conclusions
The consistency of the association between movie alcohol use exposure and adolescent binge drinking across cultures, specifically 6 European countries with different norms regarding teen and adult alcohol use and different prevalences of youth alcohol use, argues in favor of movie alcohol exposure as an independent social risk factor. Although these cross-sectional findings need to be confirmed through studies with a longitudinal design, our findings raise concern about the role popular movies may play in Europe and beyond in the early experimentation with patterns of alcohol consumption in adolescents. These patterns have the potential to have a detrimental influence on individual health and future drinking trajectories and to be costly at a societal level.
APPENDIX 1 Study Sample Details
APPENDIX 2 Covariates and Their Assessment
APPENDIX 3 Zero-Order Correlation Matrix
Acknowledgments
We thank Abita Bhaskar, Daria Buscemi, Lars Grabbe, Roberto Gullino, Leonie Hendriksen, Maksymilian Kulza, Martin Law, Dan Nassau, Balvinder Rakhra, Monika Senczuk-Przybylowska, and Tiziano Soldani for coding the movies. We are also thankful to all pupils and staff in participating schools and the survey field forces in each country. The Italian center acknowledges the work of Piera Arata, Silvia Caristia, Diego Concina, and Silvia Randino who substantially helped in conducting the study. The Scottish center acknowledges the work of Catherine Ferrell, Elaine Hindle, and Abita Bhaksar and colleagues who substantially helped with data collection. The Icelandic center acknowledges the work of Professor Thorolfur Thorlindsson, Viðar Jensson, and Jón Óskar Guðlaugsson for their help in conducting the study.
APPENDIX 1 Study Sample Details
| Germany | Iceland | Italy | Poland | Netherlands | Scotland | |
|---|---|---|---|---|---|---|
| Setting | Public schools, 4 school types: Gymnasium, Gemeinschaftsschule, Regionalschule, Hauptschule | Public schools | Public schools, 2nd class of secondary school and first class of high school | Public schools, 1 school type (Gymnasium) | Public schools, 4 different school types | Mainstream (state-funded) schools |
| Locations | Schleswig-Holstein, Germany; District of Kiel, Flensburg, Schleswig-Flensburg, and Rendsburg-Eckernförde | Schools from each region (north, south, east, west) of Iceland in addition to the capital area (Reykjavík and surrounding municipalities) | Piedmont region, Italy; Schools with head office in Turin or Novara provinces | Wielkopolska region | Gelderland, Limburg, Brabant | Central belt of Scotland |
| Time of data assessment | Nov–Dec 2009 | Jan–Feb 2010 | March–June 2010 | April-June 2010 | Dec 2009–June 2010 | Jan–Mar 2010 |
| Eligibility criteria for schools | Location; Number of classes > 8; No special pedagogic education center; No participation in other studies of IFT-Nord | Number of participating students > 100 | Location in Turin or Novara provinces | Location in Wielkopolska region; No special pedagogic education center | No special pedagogic education center; No current participation in other studies of the Behavioural Science Institute, Radboud University | Location in either Midlothian or East Dumbartonshire; Not providing special education; Not providing private (non state-funded) education |
| No. of schools potentially eligible | N = 104 | Not known | N = 578 | N = 253 | Not known | N = 14 |
| No. of schools invited | n = 60 | n = 23 | n = 31 | n = 253 | n = 43 | n = 7 |
| Invitation criteria for schools | Random | Convenience sampling | Convenience sampling | All eligible schools | Random | Selected on the basis of deprivation, based on the most recent (2007−2008) nationally available data relating to the proportion of free school meals |
| No. of schools that agreed to participate | n = 21 | n = 20 | n = 26 | n = 35 | n = 5 | n = 7 |
| Eligibility criteria for students | Active (“opt-in”) parental consent; Presence on the day of assessment or, if absent, willing to complete a questionnaire and return by post; Willingness to participate | Passive (“opt-out”) parental consent; Students presence on the day of assessment; Willingness to participate | Active or passive parental consent; Willingness to participate or, if absent, willing to complete a questionnaire and return by post | Active (“opt-in”) parental consent; Presence on the day of assessment; Willingness to participate | Passive parental consent; Presence on the day of assessment; Willingness to participate | Passive (“opt-out”) parental consent; Presence on the day of assessment or, if absent, willing to complete a questionnaire and return by post; Willingness to participate (written informed) consent from pupil |
| No. of students examined for eligibility | n = 3544 | n = 2798 | n = 2953 | n = 5078 | n = 1706 | n = 3189 |
| No. confirmed eligibility | n = 2754 | n = 2664 | n = 2668 | n = 4105 | n = 1423 | n = 2937 |
| Reasons for nonparticipation | No parental consent (n = 515); absence (n = 264); pupil refusal (n = 11) | No parental consent (n = 19); absence (n = 102); pupil refusal (n = 13) | No parental consent (n = 100); absence (n = 175); pupil refusal (n = 10) | No parental consent (n = 396); absence (n = 527); pupil refusal (n = 50) | No parental consent (n = 18); absence (n = 265); pupil refusal (n = 0) | No parental consent (n = 11); absence (n = 226); pupil refusal (n = 15) |
| No. participated in the study | n = 2754 | n = 2664 | n = 2668 | n = 4105 | n = 1423 | n = 2937 |
| No. analyzed | n = 2754 | n = 2664 | n = 2668 | n = 4105 | n = 1423 | n = 2937 |
| Response rate | 78% | 95% | 90% | 81% | 83% | 92% |
APPENDIX 2 Covariates and Their Assessment
| Variable | Survey Question | Response Categories |
|---|---|---|
| Sociodemographic | ||
| Age | How old are you? | Years |
| Gender | Are you a girl or a boy? | Boy/girl |
| Family affluence scale, (Cronbach’s α = 0.44) | Does your family own a car, van or truck? | No/yes, one/yes, ≥2 |
| Do you have your own bedroom for yourself | No/yes | |
| During the past 12 mo, how many times did you travel away on holiday with your family? | Not at all/once/twice/more than twice | |
| How many computers does your family own? | None/1/2/>2 | |
| Personal characteristics | ||
| School performance | How would you describe your grades last year? | Excellent/good/average/below average |
| Television screen time | On a school day, how many hours a day do you usually spend watching television? | None/less than 1 h/1–2 h/3–4 h/>4 h |
| Number of movies seen | Below is a list of film titles. Please mark if, and how often, you have seen each film. | Never/once/twice/more than twice |
| Sensation seeking/rebelliousness (Cronbach’s α = 0.70) | How often do you do dangerous things for fun? | Not at all/once in a while/sometimes/often/very often |
| How often do you do exciting things, even if they are dangerous? | Not at all/once in a while/sometimes/often/very often | |
| I believe in following rules (recoded). | Not at all/a bit/quite well/very well | |
| I get angry when anybody tells me what to do. | Not at all/a bit/quite well/very well | |
| Frequency of drinking in close associates | ||
| Peer drinking | How many of your friends drink alcohol? | None/a few/some/most/all |
| Mother drinking | How often does your mother/female guardian drink alcohol? | Never/seldom/often but not every day/every day/do not have (coded “never”) |
| Father drinking | How often does your father/male guardian drink alcohol? | Never/seldom/often but not every day/every day/do not have (coded “never”) |
| Sibling drinking | Do any of your brothers or sisters drink alcohol? | Yes/no/do not have (coded “no”) |
APPENDIX 3 Zero-Order Correlation Matrix
| Movie Alcohol Use Exposure | Lifetime Binge Drinking | Age | Gender | Family Affluence | School Performance | Television Screen Time | Sensation Seeking/Rebelliousness | Peer Drinking | Mother Drinking | Father Drinking | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Lifetime binge drinking | 0.21 | ||||||||||
| Age | 0.15 | 0.26 | |||||||||
| Gender (0 = male, 1 = female) | −0.04 | –0.06 | –.03 | ||||||||
| Family affluence | 0.10 | –0.03 | –.07 | –0.04 | |||||||
| School performance | –0.08 | –0.21 | –.16 | 0.12 | 0.15 | ||||||
| Television screen time | 0.10 | 0.09 | 0.06 | –0.09 | –0.08 | ||||||
| Sensation seeking/ rebelliousness | 0.25 | 0.37 | 0.18 | –0.22 | –0.03 | –0.23 | 0.13 | ||||
| Peer drinking | 0.21 | 0.49 | 0.50 | –0.08 | –0.19 | 0.04 | 0.42 | ||||
| Mother drinking | 0.05 | 0.10 | –.06 | 0.15 | 0.07 | 0.07 | 0.10 | ||||
| Father drinking | 0.03 | 0.12 | 0.08 | 0.03 | 0.11 | 0.16 | 0.48 | ||||
| Sibling drinking | 0.12 | 0.18 | 0.09 | 0.04 | –0.05 | 0.04 | 0.13 | 0.19 | 0.16 | 0.16 |
All displayed coefficients are significant at P < .001.
Footnotes
FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.
FUNDING: Supported by the European Commission, the Ministry of Health of the Federal Republic of Germany. The coding of the US movies was supported by the National Institutes of Health (AA015591/AA/NIAAA NIH HHS/United States). The Scottish fieldwork was supported by additional funds from the UK Medical Research Council (MC_US_A540_0041). Funded by the National Institutes of Health (NIH).
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