INTRODUCTION
The Chief Medical Officer for England recommends that young people remain alcohol
free until 18 years of age. This recommendation was accompanied by advice that young
people under the age of 15 should abstain completely, but if those aged 15 to 17 years
choose to consume alcohol, they should drink no more than once per week under adult
supervision and the weekly quantity consumed should not exceed the daily adult daily
limits of six units (Donaldson, 2009).
In the UK alcohol consumption is on decline among adolescents, although those who
do drink tend to drink more (Emerson et al., 2016). When compared with other Western
European countries, the UK has some of the highest levels of drinking among adolescents
and the North East England has one of the highest levels of adolescent alcohol consumption
in the UK (NHS Digital, 2016a), with 49% of 11 to 15 year olds indicating that they
have consumed alcohol (Fuller, 2015). The proportion of young people who consume alcohol
in the UK increases with age; in 2018, 11% of females and 9% of males aged 11–15 years
reported consuming alcohol in the past week (NHS Digital, 2018). At the age of 11 years,
2% of adolescents report consuming alcohol in the past week and this rises to 23%
by the age of 15 (NHS Digital, 2018). The mean weekly alcohol consumed is lowest among
11- to 13-year-olds at 8.8 UK units, where one unit equates to 8 g of ethanol, and
the highest is among 15-year-olds at 11.1 units. Males consume more alcohol on average
than females, 11.1 versus 9.6 units.
The British Birth Cohort Study followed up 16,000 births born between 5 and 11 April
1970 at ages 5, 10, 16 and 30 years. Data from this study were used to explore the
relationship between alcohol use during adolescence and negative consequences in adulthood
(Viner and Taylor, 2007). More frequent heavy episodic alcohol use was associated
with higher rates of alcohol dependence, homelessness, lower educational attainment
and greater involvement with the criminal justice system. More proximal consequences
of adolescent alcohol use include increased risk of injury, higher prevalence of anxiety
and depression, more regretted and unsafe sexual activity, worse peer and family relationships
and an increased likelihood of being a victim of crime (Newbury-Birch et al., 2009).
Alcohol use in adolescence is also associated with an increased prevalence of smoking,
poorer quality of life and greater levels of emotional dysregulation, conduct disorder
and hyperactivity (Donoghue et al., 2017). Alcohol and substance use are the most
common reason adolescents are excluded from education in the UK (Department for Education,
2017) and the number of alcohol-related exclusions have risen by 57% in the past 5 years.
Alcohol Screening and Brief Intervention (ASBI) is a form of secondary prevention
that targets a population who are already consuming alcohol at a level that may be
risking their current or future health. This approach has become the cornerstone of
alcohol treatment for at-risk alcohol users (Babor and Higgins-Biddle, 2001; Milner
and Rollnick, 2013; NHS Digital, 2016b). They are typically delivered to opportunistically
identified, non-treatment-seeking populations by generalist, rather than addiction
specialist, practitioners in a variety of settings. They largely consist of two differing
approaches. First, simple structured advice following screening that seeks to raise
awareness of alcohol use through the provision of personalized feedback and simple
practical steps that may be employed to reduce drinking. Second, extended brief interventions,
usually involving more intensive behavioural change counselling, whereby individuals
are given the opportunity to explore their alcohol use as well as their motivations
and strategies to effect change (National Institute for Health and Social Care Excellence,
2010). Both approaches to brief intervention share a common goal of helping people
to reduce alcohol consumption, aiming for moderation rather than abstinence and to
promote better physical and psychological health. While there is a wide variation
in the duration and frequency of brief interventions they are usually delivered as
a single session or a series of related sessions, not exceeding five, and last between
5 and 60 min (Kaner et al., 2018).
There is a paucity of research exploring the secondary prevention targeting alcohol
users in the school setting; what evidence there is tends to focus on older adolescents
and young adults in college and university settings (National Institute for Clinical
and Health Excellence, 2010). Most of the research addressing younger adolescents
in school settings has employed a primary prevention approach, which aims to prevent
the onset of unhealthy alcohol use by targeting all young people irrespective of whether
they drink or not. This body of research has typically focussed on universal interventions
comprising of classroom curricula, parents and family-based interventions or a combination
of the two. One large Randomised Controlled Trial (RCT) of a universal classroom intervention
delivered in the UK was the Steps Towards Alcohol Misuse Prevention Programme trial
(STAMPP; Sumnall et al., 2017), which investigated the effectiveness of a combined
school and parent intervention. The study found a significant reduction in the frequency
of heavy episodic alcohol use among 12- to 13-year-olds at 33-month follow-up, although
the effects had diminished by 57 months. Notably, the intervention was delivered at
class level and targeted the whole class rather than individuals who exhibited risky
drinking behaviour.
Two pilot studies have been conducted of ASBI in the school setting. One conducted
in Mexico (Martínez Martínez et al., 2008) targeted 40 moderate-to-high-risk drinkers,
mainly male (65%), with an average age of 16 years. The ASBI group received one 90-min
ASBI compared with a waiting list control. At the follow-up points of 3 and 6 months,
the ASBI group showed a significant reduction in the amount of alcohol consumed compared
with the control. The second study conducted in the USA targeted 79 young people who
had a substance use disorder (Winters and Leitten, 2007). Most participants were male
(52%), with an average age of 16 years. The ASBI comprised of two 60-min sessions,
with one group also receiving a parental session. Significant reductions were reported
for the number of days alcohol was consumed compared with the assessment-only control
group. These two school-based studies therefore suggest the potential effectiveness
of using a school setting to deliver ASBI to young people.
A meta-analysis of school-based interventions to reduce risk taking behaviours suggested
that interventions in school settings may be effective in reducing alcohol use (Wilson
et al., 2001), whereas a more recent review exploring school-based interventions to
reduce multiple risk behaviours demonstrated only a small effect on alcohol consumption
(Bonnell et al., 2013). Overall, there is mixed evidence of whether school-based ASBI
can be beneficial (Hale et al., 2014) and very limited evidence of the effect for
high risk drinkers (Gmel et al., 2012). Similarly, there is literature indicating
the potential benefits of family- and school-based interventions in reducing alcohol
use (Toumbourou et al., 2013) but the evidence is from outside the UK education system
and the evidence from the UK does not explore the use of targeted ASBI.
METHODS
Prior to the embarking on the reported study, we conducted a small pilot cluster randomized
controlled trial to explore the acceptability and feasibility of ASBI delivered in
the school setting (O’Neil et al., 2012; Newbury-Birch et al., 2014) and to inform
the design of this trial. Young people, aged 14–15 years, who indicated frequent heavy
episodic alcohol use and consented to take part (n = 229), were allocated to one of
the three arms; a control arm of a simple advice leaflet, a 30-min brief intervention
consisting of structured advice delivered by school pastoral staff and an advice leaflet
(intervention 1); the same brief intervention and advice leaflet augmented with a
60-min intervention including parents and caregivers (intervention 2). A total of
202 (88%) participants were followed up at 12 months. While this pilot study confirmed
the proposed research procedures were feasible and acceptable to young people and
schools, with high rates of engagement for control and intervention 1, there were
poor levels of engagement with intervention 2 from parents and caregivers and so it
was dropped from the main trial.
Design
A multi-centre, prospective, pragmatic, two-arm, individually randomized controlled
trial was conducted in accordance with the declaration of Helsinki, and ethical approval
was granted by the Teesside University Ethics Committee (ref 164/15). The trial was
registered (ref ISRCTN45691494). A full protocol was published in advance of analysis
of the trial data (Giles et al., 2016).
Participants
Adolescents aged 14–15 years, in high schools located in four areas of England (North
East, North West, Kent and London), were eligible for inclusion if they had not been
opted out of the study by parents, screened positive on the Adolescent-Single Alcohol
Question (A-SAQ; Williams and Vinson, 2001; Canagasaby and Vinson, 2005) and were
willing and able to provide informed consent for trial participation. We excluded
participants who were already seeking help for an alcohol use disorder or had a recognized
mental health condition or presented with challenging behaviour as identified by school
staff.
Sample size calculation
We used estimates from our pilot study (Newbury-Birch et al., 2014) to estimate likely
school size, eligibility and consent rates and aimed to detect a small standardized
effect size difference in alcohol consumed in the previous 28-days at 12 months of
0.3, equating to a ratio of 1.5 in geometric means. With power at 80%, an alpha of
0.05, a two-sided test required follow-up data from 176 students in each arm at 12 months,
a total of 352. Our evidence suggested loss to follow-up at 12 month was unlikely
to exceed 20%, so the numbers needed to recruit in each arm were inflated to 220,
giving a total sample required of 440.
Randomization
Eligible and consenting participants were randomized with equal probability of being
allocated to intervention or control. Allocation was operationalized using opaque,
sequentially numbered sealed envelopes with the allocation only being revealed after
consent had been obtained and the baseline assessment conducted. The allocation schedule
was designed independent of the research team and employed random permuted blocks
of variable length stratified by school.
PROCEDURE
Prior to conducting screening and eligibility assessments, parents or caregivers of
all potentially eligible participants were able to opt out their children from the
trial. By not opting out it was assumed parents or caregivers were happy for their
child to engage in the screening and if they were screened positive, and provided
assent, participate in the trial. All the young people in the year group, who were
not opted out by their parents, viewed a bespoke video animation containing information
on the trial and expectations for participants. Screening and baseline assessment
were conducted on paper during a scheduled Personal, Health and Social Education (PHSE)
or registration class. Young people were given options to not complete the assessment,
complete the assessment anonymously or complete the assessment and provide their name
and class. Those young people who completed the assessment, screened positive on the
A-SAQ, and left their name were eligible for inclusion in the trial.
Delivery of the intervention
Intervention arm
This comprised a 30-min face-to-face intervention delivered by the learning mentor
or equivalent staff member with pastoral care responsibilities within the school.
The essential components were developed in the feasibility trial and the format was
developed in collaboration with young people. The result was an A3 sheet detailing
a six-step intervention detailed in full, using TIDieR criteria in Table 1.
Table 1
Summary of trial arm components
Component
Control condition
Brief alcohol intervention condition
Rationale, theory or goal
Comparison condition
Motivational interview to reduce alcohol use
Materials
Healthy lifestyle leaflet
Alcohol advice leaflet
Procedure
Provision of healthy lifestyle leaflet by learning mentor in school.
Feedback on alcohol screening results, advice on recommended alcohol consumption levels
and comparison with participants alcohol consumption. Raising awareness of risks associated
with excessive alcohol consumption and delivery of behavioural change counselling.
Intervention provider
Learning mentor
Learning mentor
Delivery mode
Information leaflet
Face-to-face discussion and information leaflet
Location
School
School
Session duration and frequency
1 min
Up to 30 min
Tailoring
None
Yes
Fidelity assessment
All sessions audio recorded and a random 20% checked by an experienced alcohol counsellor
to explore differentiation from the intervention condition in terms of the advice
provided.
All sessions audio recorded and assessed for fidelity using the Behaviour Change Counselling
Index (BECCI) by an experienced alcohol counsellor.
Fidelity outcome
All sessions assessed were considered appropriately differentiated.
Mean BECCI score was 1.6 indicating behaviour change counselling was being delivered.
In brief, the intervention consisted of feedback of screening results and raising
awareness of how many units of alcohol were contained in commonly consumed drinks,
an exploration of a typical drinking day to identify behaviours that may be the focus
of change, exploring personally relevant risks of alcohol consumption, identifying
motivational factors, exploring confidence to change, barriers and facilitators of
behaviour change, developing an action and coping plan for changing drinking behaviour.
In addition, pupils received PHSE as usually provided by the school.
Interventionist training
Learning mentors, or equivalents, were trained on school premises by an experienced
ASBI trainer. Training sessions lasted 3 h and involved both theoretical and practical
aspects of intervention delivery, practice and role play. Training was accompanied
by a detailed intervention manual. Prior to engaging in the trial, interventionists
practiced and recorded the intervention and were assessed as being competent by the
lead trainer. Weekly supervision was provided to the interventionists by the research
team.
Control arm
The control arm is detailed in Table 1. Participants in the control arm of the study
received a healthy lifestyle leaflet addressing diet and exercise. No specific feedback
on the alcohol screening results was provided. In addition, pupils received PHSE as
usually provided by their school.
Hypotheses
Our primary null hypothesis was that adding ASBI in addition to PHSE for adolescents
in school was no more effective than PHSE alone in reducing the quantity of alcohol
consumed 12 months after randomization.
Our secondary null hypothesis was that adding ASBI in addition to PHSE for adolescents
in school was no more cost-effective than PHSE alone.
Outcome measures
Screening
Potential participants were screened using the A-SAQ (Williams and Vinson, 2001; Canagasaby
and Vinson, 2005). This single question assesses the frequency of heavy episodic alcohol
consumption, defined as six or more standard drinks in a single occasion where one
standard drink equates to 8 g of ethanol, over the previous 6 months. Responses include
‘never’, ‘less than four times’, ‘four or more times but not every month’, ‘more than
once a month but not every week’, ‘every week but not every day’ and ‘every day’.
Endorsing ‘four or more times but not every month’ or more frequent is a positive
screen.
Demographic
At baseline participants were asked to provide information on their sex, ethnicity
and whether they had smoked tobacco in the past 30 days.
Primary outcome measure
The primary outcome measure, assessed at 12 months post randomization, was total alcohol
consumed, in units of alcohol, in the 28-days prior to the assessment. This was assessed
using the Time Line Follow Back method (TLFB; Sobell and Sobell, 1995).
Secondary outcome measures
Percent days abstinent in the previous 28 days at 12 months post randomization was
derived from the TLFB. We also assessed participants scores on the Alcohol Use Disorders
Identification Test (AUDIT; Saunders et al., 1993) and the three AUDIT consumption
items (AUDIT-C) at baseline and 12-months.
Alcohol-related problems were assessed using the Rutgers Alcohol Problems Inventory
(RAPI; (Shono et al., 2018)). Motives for drinking were assessed using the revised
Drinking Motives Questionnaire. This measures motives for drinking over four domains:
social, coping, enhancement and conformity (DMQ-R; Harbke et al., 2019). General psychological
health was assessed using the Warwick–Edinburgh Mental Wellbeing Scale (WEMWBS; Clarke
et al., 2011).
The primary outcome for the economic evaluation was health utility, estimated using
the EQ-5D-3L questionnaire (EQ-5D-3L; EuroQol Research, 1990). This questionnaire
considers five dimensions of health: mobility, self-care, usual activities, pain and
discomfort and anxiety and depression, and is validated for those aged 12 years or
older. The costs of delivery were based on the actual cost each item of resource,
including staff time and materials, used in the training and intervention. Differences
in public sector resource use between the intervention and control arms were assessed
at 12 months using data collection form designed for this population derived from
the client service receipt inventory tool.
Statistical analysis
We used Stata 15 to analyse the trial by treatment allocated; this is analysing participants
as members of their allocated group irrespective of the intervention received. The
analytical team remained blind to participant allocation until they had completed
the primary analysis.
Descriptive statistics were used to report the demographic and outcome data by trial
arm at baseline and follow-up. It was planned that multiple linear regressions would
be used to compare the primary outcome between trial arms at follow-up, either on
the original scale or after a logarithmic transformation if the outcome data were
skewed. However, the degree of zero-inflation in the primary outcome was much higher
than expected, and the planned analysis was not appropriate. We explored the use of
hurdle models, but convergence could not be achieved. As an alternative we employed
quantile regression modelling the median number of units consumed in each group and
adjusting by known baseline covariates: region, gender, level of deprivation and baseline
AUDIT score. Secondary outcomes were analysed in a similar manner with the exception
of the proportion who consumed changed from a higher to lower risk of alcohol consumption
between baseline and month 12, from AUDIT score >3 to 3 or less (Coulton et al., 2019),
this was analysed using a logistic regression model adjusting for the same covariates
as used in the primary analysis. We planned to conduct a secondary analysis of the
primary outcome including only those who had received the treatment as allocated,
a per-protocol analysis, but as all participants received the allocated treatment
this analysis was not necessary. We conducted post hoc analysis to generate Bayes
factors to aid the interpretation of the findings. Bayes factors allow us to interpret
the strength of support for the alternative hypothesis (Dienes et al., 2018).
We conducted a cost-effectiveness analysis, assessing both resources used and any
resulting change in health utility. We used data provided by individual participants
to estimate mean differences in mean costs between intervention and control, and converted
their EQ-5D-3L scores at baseline and 12 months to Quality Adjusted Life Years (QALY)
using the area under the curve approach. We adopted a distinct perspective that encompassed
societal, health and personal social services.
As health economic data are usually subject to sampling error, we employed stochastic
sensitivity analysis in the form of 1500 non-parametric bootstrapped replications
of costs and effects stratified by gender, allocated group and geographical location
to derive 95% confidence intervals of the incremental cost-effectiveness ratio and
the cost-effectiveness acceptability curves (CEACs) showing the probability that interventions
were cost-effective over a range of willingness to pay thresholds ranging between
£20,000 and £30,000 per QALY in the UK.
For cost services we used local costs where available and supplemented with published
national costs (Personal Social Services Research, 2015; Department of Health and
Social Care, 2016) and information from previous alcohol studies (Coulton et al.,
2006; Coulton et al., 2008). As all costs occurred within 12 months no discounting
was applied. We estimated the cost of screening and delivering the intervention and
control by estimating the actual costs of activities including the cost of training,
trainers and materials.
RESULTS
Sample and follow-up
The recruitment of schools took place between November 2015 and June 2016. To maximize
generalizability, we only excluded private schools. We approached all government-funded
schools in the research areas. Over the period 154 schools were approached to participate,
of which 33 agreed. The most common reason for non-participation was lacking staff
or time to participate or having a specific school policy not to participate in research.
Figure 1 presents the trial CONSORT diagram indicating trial recruitment, allocation
and follow-up at 12 months.
Figure 1
CONSORT flow diagram.
Of those identified as potentially eligible, 99% (4523) completed the screening tool
and 24% (1064) responded with a positive screen, 443 (42%) assented to participate
in the study meeting our sample size requirements and all of those allocated to the
intervention received it. At 12 months we exceeded our target of 80% follow-up. Half
of the sample were male (50.3%) and 90.2% were identified as white ethnicity; the
mean AUDIT score at baseline was 7.6 (SD 5.8). Table 2 presents the baseline demographic
and outcome variables by allocated group and confirms that these were similar.
Table 2
Baseline demographic and outcome variables by allocated group
Intervention n = 210
Control n = 233
Demographic variables
Male n (%)
104 (49.5)
118 (50.6)
White ethnicity n (%)
189 (90.0)
213 (91.4)
Smoked in the past 30 days n (%)
59 (28.1)
70 (30.3)
Regretted sexual intercourse n (%)
22 (10.5)
16 (6.9)
Unsafe sexual intercourse n (%)
22 (10.5)
23 (9.9)
Outcome variables
Mean AUDIT score (SD)
7.6 (5.6)
7.6 (6.4)
Median AUDIT score (IQR)
6 (3; 11)
6.5 (3; 10)
Mean AUDIT-C score (SD)
3.8 (2.1)
4.0 (2.4)
Median AUDIT-C score (IQR)
3 (2; 5)
4 (2; 5)
Mean RAPI score (SD)
8.1 (9.9)
6.5 (8.7)
Median RAPI score (IQR)
5 (1; 12)
3 (1; 9)
Mean WEMWBS score (SD)
45.4 (12.0)
46.4 (11.4)
Mean DMQ-R – coping score (SD)
1.8 (1.0)
1.7 (0.9)
Mean DMQ-R – social score (SD)
2.7 (1.1)
2.5 (1.0)
Mean DMQ-R – conforming score (SD)
1.3 (0.7)
1.3 (0.7)
Mean DMQ-R – enhancement score (SD)
2.1 (1.0)
2.0 (1.0)
Primary outcome analysis
About a quarter of young people indicated that they had consumed no alcohol in the
previous 28-days at the 12-month follow-up, 21% in the intervention group and 28%
in the control group. Table 3 presents the unadjusted and adjusted median differences
and 95% confidence intervals for the primary outcome, and total units of alcohol consumed
in the previous 28 days at 12 months. This indicates no significant differences between
the groups, and the Bayes factor comparing the intervention versus control was 0.30
and reinforces the null findings of the primary outcome.
Table 3
Twelve-month outcomes and difference in medians favouring intervention by allocated
group
Intervention (n = 178)
Control (n = 196)
Difference in medians Intervention versus control (95% CI)
Mean (SD)
Median (IQR)
Mean (SD)
Median (IQR)
Unadjusted
Adjusted
1
Units consumed in past 28 days
2
16.2 (27.9)
7.3 (1.8; 18.5)
13.2 (17.5)
7.7 (0; 18)
−0.5 (−4.2; 3.1)
3
0.8 (−2.4; 4.0)
3
Percent days abstinent in past 28 days
92.1 (9.1)
92.9 (89.3; 96.4)
93 (7.4)
96.4 (89.3; 100)
−3.6 (−4.9; −2.2)
3
−0.4 (−2.2; 1.5)
3
Drinks per drinking day in past 28 days
2
5.3 (5.2)
4.2 (1.5; 7.8)
4.9 (5.2)
3.9 (0; 7.6)
0 (−1.3; 1.3)
3
−0.5 (−1.6; 0.6)
3
AUDIT score
5.7 (4.2)
5 (3;8)
5.5 (4.3)
5 (2; 8)
0 (−1.1; 1.1)
−0.1 (−1.0; 0.8)
AUDIT-C score
3.7 (2.1)
3 (2; 5)
3.4 (2.2)
3 (2; 5)
0 (−0.6; 0.6)
0.1 (−0.4; 0.7)
RAPI
4.5 (5.3)
3 (0; 7)
4.0 (4.8)
3 (0; 6)
0 (−1.3; 1.3)
0.2 (−0.8; 1.2)
WEMWBS
48.9 (9.0)
50 (43; 55)
48.6 (9.4)
49 (41; 55)
1.0 (−1.6; 3.6)
1.7 (−0.7; 4.1)
DMQ-R – Coping
1.5 (0.6)
1.4 (1; 1.8)
1.6 (0.7)
1.4 (1; 2)
0 (−0.2; 0.2)
−0.1 (−0.3; 0.1)
DMQ-R – Social
2.7 (1.0)
2.6 (2; 3.6)
2.6 (1.0)
2.4 (1.8; 3.2)
0.2 (−0.1; 0.5)
0.1 (−0.2; 0.5)
DMQ-R – Conforming
1.1 (0.4)
1 (1; 1.2)
1.1 (0.3)
1 (1; 1.2)
0 (−0.2; 0.2)
0 (−0.04; 0.04)
DMQ-R – Enhancement
1.9 (0.9)
1.6 (1.2; 2.4)
1.9 (0.8)
1.8 (1.2; 1.8)
−0.2 (−0.4; 0.03)
−0.1 (−0.3; 0.2)
1Adjusted for covariates in the model; baseline value where available, gender, index
of deprivation and baseline AUDIT score.
2UK standard unit; 8 g or 10 ml of ethanol.
3Difference in medians derived using quantile regression.
Secondary outcome analysis
Adjusted mean differences for secondary outcomes are presented in Table 3. At 12-months
60% of those in the intervention arm and 59% of those in the control arm stated that
they had reduced the amount of alcohol they consumed. No evidence of differences was
found between the intervention and control groups on any secondary outcomes. Logistic
regression analysis of those who reduced consumption, from higher to lower risk between
baseline and 12 months showed no evidence of association with trial arm with an adjusted
odds ratio of 1.04 (95% CI 0.53 to 1.56) with the control group as the referent category.
Economic analysis
The marginal additional mean cost of delivering the intervention versus the control
was £31.30 (95% CI 30.9 to 31.7) per intervention participants. The intervention group
had higher mean costs on average over the 12-month follow-up than the control group,
£79, although the confidence interval included zero (95% CI -£104 to £260). The difference
in mean QALY’s was −0.004 (95% CI -0.019 to 0.011) but again the confidence interval
included zero.
The CEAC indicates that there is only a 20% probability that the intervention is cost-effective
at a willingness to pay threshold of £20,000 to £30,000. Sensitivity analysis was
conducted where extreme values for use of GP, nurse and social worker values were
truncated. Doing this made no difference to the overall findings. In addition, we
explored the influence of missing data by conducting a sensitivity analysis including
values of costs and QALY derived from multiple imputation. Again, this had no influence
on the findings.
DISCUSSION
The overall aim of the study was to conduct a definitive, appropriately powered pragmatic
randomized controlled trial to evaluate the effectiveness and cost-effectiveness of
ASBI for higher risk adolescent alcohol users in a school setting. We achieved both
our recruitment and retention targets and we found no significant effects of the intervention
when compared with the control. The calculation of posterior Bayes Factors supported
the finding that ASBI was no more effective than screening in addition to PHSE provided
as usual by the school.
Our economic analysis highlighted that there was only a 20% likelihood that the intervention
was cost-effective compared with the control. This finding did not markedly change
in all the sensitivity analyses conducted.
These findings appear to contrast with previous studies of brief interventions delivered
in school settings (Winters and Leitten, 2007; Martínez Martínez et al., 2008), in
part because these studies tended to be single-site, small-scale and underpowered
and consequently are prone to type I error, incorrectly rejecting the null hypothesis.
It is also of note that positive evidence of the efficacy of ASBIs has not translated
into evidence of effectiveness when evaluated using large-scale, multi-centre, pragmatic
trial designs. This finding has important implications because pragmatic trials evaluate
interventions in real-world environments rather than ideal, tightly controlled environments.
Our results concur with a number of more recent studies of ASBI in adolescent populations
(Deluca et al., 2020; Deluca et al., 2021), school settings (Strom et al., 2014) and
adult populations (Kaner et al., 2013; Drummond et al., 2014) that indicate that ASBI
is not any more effective than screening and simple advice alone.
It should also be noted that most young people indicated they had reduced their alcohol
consumption at 12 months compared with baseline, and this occurred equally in both
arms of the study. This is likely to be an artefact of the trial design, regression
to the mean, whereby when participants are selected because they consume alcohol above
a threshold, they tend to fall towards the population mean over time.
Primary prevention approaches delivered in schools take a variety of forms but tend
to focus on education about risks and the development of life skills. Two systematic
reviews have found a paucity of evidence for other forms of primary prevention delivered
in school settings but have highlighted the emerging evidence for the ‘unplugged’
program (Foxcroft and Tsertsvadze, 2011; Agabio et al., 2015). This program involves
teacher-based delivery of a multi-session educational intervention over 12 weeks that
addresses alcohol and other substance use and explores knowledge and attitudes in
addition to both inter- and intra- personal skill development. Participation in the
program is associated with both short- and long-term significant reductions in the
frequency of heavy episodic alcohol use and alcohol-related problems. In a similar
vein, the STAMPP intervention is a universally delivered primary prevention program
targeting both 12–13 years olds in school and their parents; this intervention approach
demonstrated significant differences in heavy episodic alcohol use at 33 months, although
no differences in alcohol-related problems (Sumnall et al., 2017).
Limitations of the study include the fact that only a minority of schools approached
were willing to participate ( 21%). Of those who were not willing to participate,
the most common reasons were a lack of time and resources, although a number cited
that they did not consider alcohol use something that should be addressed within the
school environment. Those that did participate are likely to be representative of
schools who would deliver an alcohol intervention of this sort if available. An additional
limitation relates to our use of self-report drinking as the primary outcome and the
potential accuracy of this approach highlighted in other studies, particularly as
it relates to potential recall bias (Percy et al., 2005; Shillington et al., 2011).
However adolescent self-report of alcohol use is generally considered to be reliable
(Leigh et al., 1998; Lintonen et al., 2004) and as the study was individually randomized
within schools any bias would be equally distributed between the intervention and
control groups.
The reported study is an evaluation of ASBI in real-life environments. It included
a large sample size, appropriate methodology and the use of valid and reliable outcome
measures. While there is some evidence for effectiveness, universally delivered primary
prevention approaches for this population, the combined effectiveness and cost-effectiveness
analysis of this study suggest it is not worthwhile implementing ASBI as a secondary
prevention approach for adolescents in the school setting.
Supplementary Material
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