Introduction
In high income countries there is concern about the consequences of excessive alcohol consumption [
1], especially in youth when these behaviours are common [
1‐
4] and may track into adulthood [
5,
6]. There is evidence of a "dramatic rise" in alcohol consumption in young people in the west of Scotland and in the UK more broadly [
7]. The reduction of alcohol (mis)use, and binge drinking in particular, are priorities for the British Government [
3]. This reflects concerns about public drunkenness and anti-social behaviour on the one hand, and the longer term health effects of excessive drinking, such as increased mortality in heavy drinkers [
8].
There is also evidence from Scotland of an increase in the lifetime prevalence (ever use) of illicit drugs in recent decades, with ever-use of cannabis by young adulthood being much more common than ever-use of other drugs [
9]. Cannabis use in young people is associated with psychotic symptoms and dependence on other illicit drugs [
10,
11], although there is debate over its health consequences [
12]. Among young adults who have been long-term drug users, there is evidence of poor self-rated health and increased mortality [
13,
14].
This evidence on increasing substance use in adolescents and young adults, together with the lack of effective treatment for substance dependence [
15], raises questions about which factors facilitate the uptake of excessive alcohol and drug use.
Media portrayals are one potential influence shaping young people's views of various behaviours. However, it has been demonstrated that portrayals of substance use in films are often unrealistic, as has been well documented for smoking [
16‐
18]. They often glamourise smoking and make smoking appear to be more prevalent than contemporary figures support. Thus, despite the dramatic fall in adult smoking in the UK and USA since the 1950s, it has been suggested that smoking in films was as common in 2002 as in 1950 [
19]. Smoking imagery declined in top US box office hits between 1996 and 2004, but not within films intended for youth audiences [
20]. Similar findings have been reported for the most popular films in the UK, showing that despite a substantial fall between 1989 and 2008 overall, tobacco imagery appeared in 70% of all films, and predominantly in films categorised as suitable for children and young people [
21]. This has alerted health professionals and policy-makers to the potential of media images to shape substance use in young people [
22]. Evidence is now building to suggest a causal link between viewing images of smoking in films and young people's initiation of smoking [
22‐
26]. To date, little attention has been paid to the influence of film images of other behaviours, such as alcohol and illicit drug use, on young people's own use of these substances.
Alcohol consumption is also very commonly portrayed in films, including in (US) G-rated (General Audience) [
27] and animated [
28] films. A content analysis of 100 of the top grossing US films between 1986 and 1994 reported that 96% had references that supported alcohol use, and 79% included at least one character who used alcohol. Whilst incidents of alcohol use were common, portrayals of the hazards of drinking were not reflected [
29]. Similarly, a study of the most popular US film rentals from 1996-7 found 93% included alcohol use and 22% illicit drug use; in 12% of films one or more of the major characters used drugs and 65% of adult characters used alcohol; and in 43% of films alcohol use was portrayed as a positive experience [
30]. A content analysis of the top grossing US films from 1999-2001 found 15% of teen characters used illicit drugs and again were unlikely to be shown as suffering any consequences (positive or negative, short or long-term) of their drug use [
31].
In very recent years a few studies have reported an association between exposure to alcohol images in films and young people's own alcohol consumption [
32‐
35]. These studies followed earlier ones which had demonstrated an effect of exposure to alcohol advertising, marketing and portrayals on young people's subsequent drinking behaviours [
36]. Thus, in the USA, a strong relationship was seen between film alcohol exposure and onset of drinking in 3577 10-14 year olds who were never drinkers at baseline [
32]. Cross-sectional associations between film alcohol exposure and drinking were observed in 5581 13-year olds from 27 schools in Germany. After adjustment (for socio-demographic, parenting and personal characteristics and friends' drinking), the odds ratios were 1.47 (95% confidence interval [CI] 1.19-1.82), 2.12 (95% CI 1.75-2.57) and 2.95 (2.35-3.70) for drinking without parental knowledge (comparing the higher three quartiles of exposure to the lowest) and 1.42 (0.93-2.28), 1.84 (1.27-2.67) and 2.59 (1.70-3.95) for binge drinking [
33]. To our knowledge, no studies have reported on exposure to images of illicit drug use and own drug use.
Here we report a cross-sectional analysis which investigates the association between exposure to images of a) alcohol and b) drugs in films and a) current drinking and b) ever use of drugs in young adults (aged 19) living in the UK. We have previously reported a lack of association between exposure to smoking in films and smoking in these young adults [
37]. As a number of factors may confound any relationship between film exposure and substance use [
36], we adjust for gender, background characteristics, personal characteristics, friends' substance use and time spent watching television, videos or dvds.
Results
Basic descriptive characteristics of the sample are shown in Table
1. Substance use was common. A third (33%) of the young adults were classed as 'heavy drinkers' and half (47%) as 'binge drinkers'. Over half (56%) reported ever use of cannabis, but many fewer (13%) reported ever use of one or more of the 'hard' drugs listed. Almost all (93%) reported that half or more of their friends drank alcohol (equivalent figure for ever use of cannabis, 21%).
Table 1
Descriptive data: frequency of last week heavy and binge drinking, ever cannabis and 'hard' drugs, and of potential confounders.
Outcome variables | | | |
Heavy drinking previous week (age 19) a
| Yes | 326 | (32.9) |
| No | 663 | (67.1) |
Binge drinking previous week (age 19) b
| Yes | 466 | (46.7) |
| No | 532 | (53.3) |
Ever use of cannabis (age 19) | Ever | 564 | (56.3) |
| Never | 438 | (43.7) |
Ever use of 'hard' drug use (age 19) | Ever | 131 | (13.0) |
| Never | 871 | (87.0) |
Potential confounders
| | | |
Gender | Male | 502 | (50.1) |
| Female | 500 | (49.9) |
Parental social class (age 11) | Non-manual | 413 | (41.2) |
| Skilled manual | 299 | (29.9) |
| Semi/unskilled manual | 225 | (22.4) |
| Missing | 65 | (6.5) |
Parental structure (age 15) | Both birth parents | 696 | (70.6) |
| Birth mother/father and new partner | 115 | (11.7) |
| Birth mother/father alone or with other relatives | 174 | (17.7) |
Parental care (age 15) | Low | 254 | (25.4) |
| Medium | 426 | (42.6) |
| High | 319 | (32.0) |
Parental control (age 15) | Low | 364 | (36.7) |
| Medium | 256 | (25.8) |
| High | 373 | (37.5) |
'I take risks' (age 15) | Very untrue | 50 | (5.0) |
| Untrue | 304 | (30.5) |
| True | 539 | (54.0) |
| Very true | 105 | (10.5) |
'I am a rule-breaker' | Very untrue | 250 | (25.1) |
| Untrue | 487 | (49.1) |
| True | 215 | (21.6) |
| Very true | 41 | (4.2) |
'Highers' (age 19) | None | 434 | (43.3) |
| Any | 567 | (56.7) |
Friends' drinking status (age 19) | None - a few | 67 | (6.8) |
| Half or more | 923 | (93.2) |
Friends' use of cannabis (age 19) | None - a few | 777 | (78.9) |
| Half or more | 208 | (21.1) |
Hours per week tv, videos, dvds (age 19) | 0-9 | 166 | (16.8) |
| 10-19 | 349 | (35.5) |
| 20-29 | 277 | (28.1) |
| 30-39 | 98 | (10.0) |
| 40 or more | 94 | (9.5) |
Respondents had seen a mean of 19.0 (SD = 7.3, range 1-44) of the 50 films presented to them; mean film alcohol and drug exposures were 726 minutes (12.1 hours) and 45 minutes respectively. The mean number of films was higher for males (20.8) than females (17.3, F = 60.5, p = .000) and males' film alcohol and drug exposures were higher (770 vs. 682 minutes, F = 16.5, p = .000 and 51 vs. 38 minutes, F = 23.1, p = .000 respectively). There were no social class differences for films seen or film alcohol exposure, but film drug exposure was higher in those from higher social class backgrounds (non-manual = 51, skilled manual = 40, semi/unskilled manual = 41 minutes, F = 6.6, p = .001). There was a positive correlation between the film alcohol and drug exposure measures (r = .510).
Table
2 reports the percentage of heavy and binge drinkers by quartile of film alcohol exposure, and the percentage of ever users of cannabis and ever users of 'hard' drugs by film drug exposure. The p values reported in the table relate to heterogeneity within the groups, but we also tested for linear trends. In the cross-tabulations, the tests for linear trends in the percentage of heavy drinkers (p = .018) and binge drinkers (p = 0.012) by film alcohol exposure quartiles (see Table
2) were statistically significant. Similarly, there was an increase in the percent who had ever used cannabis with each quartile of film drugs exposure (linear trend p = .000). The percentage who had used 'hard' drugs was also highest in the highest quartile of film drug exposure (16%), but with less evidence of a stepwise increase (linear trend p = .033).
Table 2
Alcohol and drug use by predictor variables - percentages (significance of chi-square).
Alcohol use in movies seen
| | | | | | | | |
Quartile 1 | 29.9 | | 41.4 | | | | | |
Quartile 2 | 29.8 | | 44.5 | | | | | |
Quartile 3 | 31.7 | | 46.3 | | | | | |
Quartile 4 | 40.2 |
(.054)
| 53.0 |
(.079)
| | | | |
Drug use in movies seen
| | | | | | | | |
Quartile 1 | | | | | 49.8 | | 11.0 | |
Quartile 2 | | | | | 47.8 | | 8.2 | |
Quartile 3 | | | | | 59.3 | | 13.9 | |
Quartile 4 | | | | | 64.2 |
(.001)
| 16.0 |
(.059)
|
Gender
| | | | | | | | |
Male | 40.2 | | 52.7 | | 63.4 | | 16.0 | |
Female | 25.7 |
(.000)
| 40.1 |
(.000)
| 47.3 |
(.000)
| 8.5 |
(.000)
|
Parental social class (age 11)
| | | | | | | | |
Non-manual | 32.2 | | 43.4 | | 53.1 | | 9.2 | |
Skilled manual | 34.2 | | 48.4 | | 57.4 | | 14.1 | |
Semi/unskilled manual | 31.3 | | 47.0 | | 58.2 | | 16.5 | |
Missing | 37.3 |
(.809)
| 53.8 |
(.387)
| 48.0 |
(.382)
| 8.2 |
(.037)
|
Parental structure (age 15)
| | | | | | | | |
Both birth parents | 32.7 | | 45.5 | | 54.3 | | 12.1 | |
Birth mother/father and new partner | 40.2 | | 54.9 | | 62.7 | | 12.7 | |
Birth mother/father alone or with other relatives | 28.7 |
(.154)
| 44.1 |
(.172)
| 54.4 |
(.270)
| 12.1 |
(.984)
|
Parental Bonding Inventory - care (age 15)
| | | | | | | | |
Low | 35.0 | | 49.5 | | 62.2 | | 18.0 | |
Medium | 33.8 | | 47.9 | | 56.8 | | 10.2 | |
High | 30.0 |
(.418)
| 41.5 |
(.128)
| 47.8 |
(.003)
| 10.7 |
(.012)
|
Parental Bonding Inventory - control (age 15)
| | | | | | | | |
Low | 34.5 | | 49.1 | | 54.1 | | 13.6 | |
Medium | 30.1 | | 43.3 | | 53.8 | | 10.4 | |
High | 33.1 |
(.537)
| 45.5 |
(.365)
| 57.3 |
(.605)
| 12.1 |
(.502)
|
'I take risks' (age 15)
| | | | | | | | |
Very untrue | 13.0 | | 21.7 | | 40.4 | | 6.5 | |
Untrue | 22.8 | | 37.2 | | 39.2 | | 5.7 | |
True | 37.3 | | 51.8 | | 64.0 | | 12.9 | |
Very true | 49.5 |
(.000)
| 55.4 |
(.000)
| 64.4 |
(.000)
| 29.7 |
(.000)
|
'I am a rule breaker' (age 15)
| | | | | | | | |
Very untrue | 22.4 | | 34.2 | | 37.0 | | 6.8 | |
Untrue | 31.0 | | 46.2 | | 53.6 | | 7.7 | |
True | 47.9 | | 61.3 | | 74.2 | | 23.2 | |
Very true | 42.5 |
(.000)
| 47.5 |
(.000)
| 87.5 |
(.000)
| 41.0 |
(.000)
|
'Highers' (age 19)
| | | | | | | | |
None | 38.2 | | 49.9 | | 64.3 | | 21.1 | |
Any | 29.2 |
(.004)
| 43.7 |
(.065)
| 48.8 |
(.000)
| 5.9 |
(.000)
|
Friends' drinking status (age 19)
| | | | | | | | |
None - a few | 4.8 | | 6.3 | | | | | |
Half or more | 34.9 |
(.000)
| 49.2 |
(.000)
| | | | |
Friends' cannabis use (age 19)
| | | | | | | | |
None - a few | | | | | 47.6 | | 7.1 | |
Half or more | | | | | 85.2 |
(.000)
| 32.3 |
(.000)
|
Hours per week tv, videos, dvds (age 19)
| | | | | | | | |
0-9 | 31.2 | | 43.9 | | 53.5 | | 7.6 | |
10-19 | 33.4 | | 48.6 | | 57.3 | | 12.1 | |
20-29 | 31.3 | | 42.6 | | 53.8 | | 14.4 | |
30-39 | 32.6 | | 46.8 | | 58.9 | | 17.9 | |
40 or more | 39.8 |
(.678)
| 51.8 |
(.475)
| 50.6 |
(.702)
| 8.6 |
(.089)
|
(N)
|
(922)
| |
(928)
| |
(926)
| |
(926)
| |
Male gender and perceiving oneself as a risk-taker and rule-breaker were associated with all four substance use measures (p < 0.001 in all cases), and having no 'Highers' at 19 with all (p = 0.004 for heavy drinking, and p < 0.001 for ever use of cannabis, and ever use of hard drugs) except binge drinking (p = 0.65). Respondents from manual class backgrounds were more likely to have used 'hard' drugs (p = 0.037), and those reporting lower parental care were more likely to have ever used both cannabis (p = 0.003) and 'hard' drugs (p = 0.012). Friends' drinking and cannabis use were strongly associated with own drinking and drug status respectively. There were no associations between the substance use measures and parental structure, parental control, or hours per week watching television, videos or dvds.
Table
3 shows the results of the logistic regression models for each outcome, both before and after adjusting for potential confounding or mediating variables. We consider the alcohol outcomes first. In the unadjusted model, those in the highest quartile of film alcohol exposure were more likely to be classed as both heavy and binge drinkers ((OR = 1.56, 95% CI 1.06-2.29) and 1.59 (1.10-2.30) respectively, compared with the lowest quartile) on the basis of their reported alcohol consumption the previous week. Adjustment for gender reduced the associations, but further adjustment for background characteristics returned the odds ratios for the drinking measures to the unadjusted levels. Adjusting for risk-taking, rule-breaking and qualifications, and particularly for friends' drinking status, reduced the odds ratios, but further adjustment for hours watching television, videos or dvds made no difference to the associations. In this final model only gender (OR for females 0.59 (95% CI 0.43-0.80) for heavy drinking and 0.64 (95% CI 0.48-0.85) for binge drinking) and friends' drinking status (OR for half or more friends drinking 1.44 (95% CI 1.27-1.64) for heavy drinking and 1.54 (95% CI 1.36-1.73) for binge drinking) had odds ratios which did not include unity (1.00), i.e. film alcohol exposure was no longer significantly associated with heavy or binge drinking. (Full tables showing OR and 95% CI for all variables included in all models available on request.)
Table 3
Results of logistic regression models including film exposure quartiles, (a) unadjusted and (b-f) adjusted for potential confounders - ORs (95% CIs).
Alcohol/drug use in movies seen
| | | | | | | | |
(a) unadjusted
| | | | | | | | |
Quartile 1 | 1.00 | | 1.00 | | 1.00 | | 1.00 | |
Quartile 2 | 0.99 | (0.67-1.47) | 1.12 | (0.78-1.62) | 0.92 | (0.64-1.32) | 0.73 | (0.39-1.36) |
Quartile 3 | 1.08 | (0.73-1.61) | 1.21 | (0.84-1.75) | 1.46 | (1.01-2.10) | 1.30 | (0.75-2.26) |
Quartile 4 | 1.56 | (1.06-2.29) | 1.59 | (1.10-2.30) | 1.80 | (1.24-2.62) | 1.57 | (0.91-2.69) |
(b) adjusted for gender
| | | | | | | | |
Quartile 1 | 1.00 | | 1.00 | | 1.00 | | 1.00 | |
Quartile 2 | 1.00 | (0.67-1.50) | 1.14 | (0.79-1.64) | 0.92 | (0.64-1.33) | 0.73 | (0.39-1.40) |
Quartile 3 | 1.07 | (0.71-1.59) | 1.20 | (0.83-1.74) | 1.38 | (0.95-1.99) | 1.21 | (0.69-2.12) |
Quartile 4 | 1.47 | (0.99-2.17) | 1.51 | (1.04-2.19) | 1.66 | (1.14-2.43) | 1.42 | (0.82-2.46) |
(c) adjusted for gender and background
(parental social class, parental
structure, PBI care and control)
| | | | | | | | |
Quartile 1 | 1.00 | | 1.00 | | 1.00 | | 1.00 | |
Quartile 2 | 1.05 | (0.70-1.58) | 1.20 | (0.83-1.75) | 0.92 | (0.64-1.34) | 0.71 | (0.38-1.34) |
Quartile 3 | 1.12 | (0.74-1.68) | 1.26 | (0.86-1.84) | 1.39 | (0.95-2.02) | 1.24 | (0.70-2.18) |
Quartile 4 | 1.55 | (1.04-2.30) | 1.59 | (1.09-2.33) | 1.67 | (1.14-2.46) | 1.50 | (0.86-2.62) |
(d) adjusted for gender, background
and personal characteristics
(take risks, rule-breaker, 'Highers')
| | | | | | | | |
Quartile 1 | 1.00 | | 1.00 | | 1.00 | | 1.00 | |
Quartile 2 | 0.93 | (0.61-1.41) | 1.11 | (0.76-1.63) | 0.78 | (0.52-1.15) | 0.61 | (0.31-1.19) |
Quartile 3 | 1.04 | (0.68-1.57) | 1.19 | (0.81-1.74) | 1.27 | (0.86-1.89) | 1.16 | (0.63-2.13) |
Quartile 4 | 1.42 | (0.95-2.13) | 1.49 | (1.01-2.19) | 1.49 | (0.99-2.24) | 1.40 | (0.76-2.56) |
(e) adjusted for gender, background,
personal characteristics and friends'
drinking status/cannabis use (as appropriate)
| | | | | | | | |
Quartile 1 | 1.00 | | 1.00 | | 1.00 | | 1.00 | |
Quartile 2 | 0.80 | (0.52-1.23) | 0.94 | (0.63-1.41) | 0.74 | (0.48-1.13) | 0.54 | (0.26-1.12) |
Quartile 3 | 0.94 | (0.61-1.44) | 1.06 | (0.71-1.58) | 1.21 | (0.78-1.86) | 1.03 | (0.53-1.99) |
Quartile 4 | 1.25 | (0.82-1.89) | 1.28 | (0.86-1.91) | 1.34 | (0.85-2.09) | 1.24 | (0.64-2.39) |
(f) adjusted for gender, background,
personal characteristics, friends'
drinking/cannabis and own tv, dvd
or video hours. | | | | | | | | |
Quartile 1 | 1.00 | | 1.00 | | 1.00 | | 1.00 | |
Quartile 2 | 0.80 | (0.52-1.23) | 0.95 | (0.63-1.41) | 0.76 | (0.49-1.17) | 0.55 | (0.26-1.14) |
Quartile 3 | 0.94 | (0.61-1.45) | 1.07 | (0.71-1.60) | 1.26 | (0.82-1.96) | 1.07 | (0.55-2.09) |
Quartile 4 | 1.25 | (0.82-1.90) | 1.29 | (0.86-1.92) | 1.41 | (0.90-2.22) | 1.28 | (0.66-2.47) |
(N in each analysis)
|
(922)
| |
(928)
| |
(926)
| |
(926)
| |
We turn now to consider the relationship between exposure to film images of illicit drug use and ever-use of cannabis and 'hard' drugs. In the unadjusted model, ever use of cannabis showed a stepped association with film drug exposure (OR for third and highest, compared with the lowest quartile of film drug exposure = 1.46 (95% CI 1.01-2.10) and 1.80 (95% CI 1.24-2.62)). Adjustment for gender attenuated the association, whereas adjusting additionally for family background made little difference. The OR was further attenuated (with 95% confidence which included unity) after adjusting for personal characteristics, friends' reported cannabis use, and then tv/dvd/video watching (see table
3). In the final model having any 'Highers' (OR 0.56, 95% CI 0.39-0.80), seeing oneself as a rule-breaker (OR 10.70, 95% CI 3.43-3.38, comparing those saying 'very true' as compared with those saying 'very untrue'), and reporting that half or more of one's friends used cannabis (OR 2.14, 95% CI 1.87-2.45) were the only ORs in the model with 95% confidence intervals that did not include unity.
For 'hard' drug use the confidence intervals for the unadjusted ORs in third (OR 1.30, 95% CI 0.75-2.26) and highest (OR 1.57, 95% CI 0.91-2.69) quartiles overlapped with unity. Although the odds were greatest in the highest film drug exposure quartile in each of the models for ever use of 'hard' drugs, none of the associations reached conventional levels of significance, even in the unadjusted model. In the final model hard drug use was significantly inversely associated with having any 'Highers' (OR 0.26, 95% CI 0.15-0.45), and positively associated with seeing oneself as a rule-breaker (OR 3.02, 95% CI 1.05-8.69, comparing those saying 'very true' as compared with those saying 'very untrue') and reporting that half or more of one's friends used cannabis (OR 2.10, 95% CI 1.76-2.51).
Discussion
In this cross-sectional analysis, we have demonstrated an association between film exposure to alcohol and both binge and heavy drinking in young adults, and, to our knowledge for the first time, an association between film exposure to illicit drugs and ever use of cannabis. These associations persisted after adjusting for gender, social class, family structure and levels of parental control, but not after adjusting for other variables, including personal characteristics such as risk-taking, rule-breaking and achievement of school qualifications, and in particular friends' substance use. It is somewhat difficult to know how to interpret these attenuations in the associations, particularly in this cross-sectional analysis. It is likely, for example, that young people who drink heavily or take drugs are not only more inclined to do this in the company of like-minded friends, but they may also share, or develop similar tastes in cultural representations of substance use with them, which may in turn determine the kinds of films they choose to watch. On the other hand, portrayals of substance use could directly influence an individual's uptake of drinking and drug use which could itself influence the friendship groups that they choose to maintain or develop.
The cross-sectional nature of the analysis thus means that it is not possible to establish the direction of causality. Even before concerning ourselves with the impact of potential mediating or confounding factors, we cannot distinguish here between two plausible but competing explanations, either that film images of substance use may influence behaviours or that people who have already adopted particular patterns of substance use may choose to watch films that reflect similar lifestyles and values. Furthermore, it is important to acknowledge that images of substance use in films occur within a wider media context in which a vast array of different images are portrayed over time from a variety of sources (including magazines, TV, newsprint, websites and social messaging sites). A few other studies (e.g. [
32,
33]), one including prospective data [
32], have reported an association between film alcohol exposure and drinking in younger adolescents, using similar methods. A German study (mean age 13) obtained much stronger associations between film alcohol exposure and measures of drinking, before and after adjustment for a comparable set of potential confounders [
33].
Our findings of some association between exposure to film images of alcohol and illicit drugs and young people's own substance use in this cross-sectional analysis are of interest, particularly because, in contrast to other studies which have reported to date (e.g. [
23‐
26]), we did not see any association in this study population between exposure to smoking in films and young people's own smoking at age 19 [
37]. We speculated that this lack of association with smoking may be attributable to several factors; these factors could also explain the smaller association we observe in this UK study between film alcohol exposure and drinking in comparison with the USA and Germany.
First, there are methodological issues, one of which relates to respondent age. Our study differs from previous research studies which have focussed on (early) adolescent experimentation with smoking and drinking. It is plausible that, by age 19, other influences (e.g. direct observation or substance use amongst peers) could have had such a strong effect that the impact of exposure to these behaviours in films is 'swamped'. Young adults may also have a more sophisticated and critical reading of media images which makes them more resistant to their effects. A second methodological issue relates to the timing of the film exposure. We used coding of substance use in films completed by our American colleagues at the time of our fieldwork (2002-4). At this point coding was only available on films up to and including 1999 (when our sample were aged 15). Hence we missed exposures to more contemporaneously released films.
Our second group of potential explanations for a lack of an association between smoking in films and own smoking in these young people [
37] related to the cultural environment and the prevalence and social prominence of the behaviours in question. Although the mass film industry is increasingly globalised, it is plausible that Scottish viewers empathise less with Hollywood film stars, or are distanced from American culture. Fictional or real-life visual portrayals of substance use in TV programmes (such as soap operas), popular with young people in the UK, may be more salient in the Scottish context.
Another potential difference lies in the prevalence of substance use in the various countries which have been studied. Scotland is commonly described as having an 'alcohol culture'. Compared with most other European countries, where levels have remained static or fallen over the last 10-15 years, alcohol consumption has increased rapidly in the UK, and within the UK, rates are highest in Scotland [Scottish Government Health & Community Care website;
http://www.scotland.gov.uk/Topics/Health/health/Alcohol]. Similarly, rates of drug use in the UK are higher than most other European countries [European Monitoring Centre for Drugs and Drug Addiction, Statistical Bulletin 20008;
http://www.emcdda.europa.eu/stats08]. This widespread recreational drug use, regardless of social background has been described as 'normalisation' [
48]. Against such a background, any impact of the portrayal of substance use in films may be diminished.
Additional caveats that we raised in our previous paper on smoking [
37] are also relevant here: we have no measure of how accurate young adults' recall of the films they had seen was; and we did not record whether films had been viewed once or repeatedly. Also alcohol and drug use were self-reported in the study (as in most similar studies), although our interviewers went to some lengths to ensure confidentiality and privacy whilst reporting on substance use. Furthermore, alcohol use measures were based on reports of consumption in the last week and this may not have been representative of the usual pattern and frequency of drinking in every individual.
Other limitations that we have raised earlier in this paper are important to rehearse. There was considerable and differential attrition between the first wave of the study (when 11 year old pupils were representative of all 11 year olds in the areas in which they lived) and the wave of data collection at age 19 years. Although we selected a weighting system designed to address differential attrition, it is possible that some residual attrition bias remains. For the alcohol variables we were able to use current measures of consumption as our outcome, whilst for the drug use variables we were only able to analyse ever-use. In the latter case we cannot know when this drug use took place or for how long it was a feature of the young person's life.
Our measures of film exposure are comparable to those reported previously. For example, a study of American 10-14 year olds, based on the same parent film sample reported that respondents had seen a median of 16 of the 50 films on their unique list (compared with 19 in our study), which translated into a median exposure to alcohol use of 8.3 hours in the sample of 601 films (compared with a mean exposure of 12.1 hours in our study). The relatively higher alcohol exposure would be expected, given the nature of films likely to have been watched by the older adolescents in our study.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
KH, JS and HS specified the analyses to be undertaken and led on interpretation of the findings. All drafts of the paper were written by KH, with input from all co-authors. HS, PW and RW oversaw the design and data collection for the 11 to 16/16+ Study. HL and HS undertook the statistical analyses. JS oversaw the coding of images of drinking alcohol and drug use in the films. All authors read and approved the final manuscript.