Background
There is growing interest in addressing adolescent multiple risk behaviours (MRB) [
1]. MRB is broadly defined as engagement in two or more risk behaviours [
2]. Many modifiable MRB (smoking, excessive alcohol intake, poor diet) originate in adolescence, but may become habitual in adulthood, thereby increasing risk of comorbidities and premature mortality. Studies have shown that adolescents involved in one risk behaviour are more likely to be involved in others [
3‐
5]. This can apply both between substances (tobacco, alcohol and illicit drugs) and between substance use and other behaviours such as sexual risk, self-harm and antisocial behaviour [
5].
It has been hypothesised that interventions targeting one behaviour are less successful because they do not address co-occurring MRB. Evidence indicates that universal school-based interventions targeting MRB are most efficacious in preventing tobacco smoking, alcohol consumption, illicit drug use, antisocial behaviour and increasing physical activity among young people. Evidence was less conclusive for cannabis use, sexual risk and unhealthy diet [
2].
Research considering a wider range of behaviours from multiple domains to inform the scope of interventions is complex, often with strongly related behaviours. Hence data-reduction techniques are adopted, to render the information more manageable. Approaches can be classified as either person-centred or variable-centred. There is often a mathematical equivalence between opposing methods, e.g. Confirmatory Factor Analysis (CFA) and Latent Class Analysis (LCA), the choice of model therefore cannot be motivated solely by data. In public health research, LCA brings the potential to extract and study individuals with differing profiles of behaviour who might respond differently to a targeted intervention [
6]. Variable-centred methods include Principle Component Analysis (PCA) and Confirmatory/Exploratory Factor Analysis (CFA/EFA) which simplify observed (co) variation into behavioural traits [
7], whilst alternative approaches cluster the behaviours into smaller subsets.
Beginning with variable-centred studies, multiple group CFA was used by de Looze et al. to examine clustering of smoking, drunkenness and cannabis use and early sexual activity among adolescents aged ~ 15 years, across 27 European and North American countries. They found that substance use and early sexual activity loaded on a single underlying cluster consistently across countries [
1]. Unfortunately, because they have not considered a wider range of MRB, there may be other co-occurring MRB which we are not aware of. A Dutch study used EFA and CFA to investigate whether a wide range of health and antisocial behaviours clustered. Several separate but interrelated clusters were found. At age 12–15 years one broad cluster and a second cluster comprising alcohol, tobacco smoking and drug use. At age 16–18 years alcohol, unsafe sex, unlawful traffic behaviour and vigorous physical activity; and a second cluster of aggressive behaviour, tobacco smoking, drug use, little sleep and delinquency [
8]. It seems that these age groupings have been imposed on the data analysis (rather than them naturally clustering in these groups), so it is difficult to know whether and how age would have impacted the kinds of clusters identified. Hierarchical Agglomerative Cluster Analysis (HACA), was used to explore MRB in Saudi Arabian males, aged ~ 13–19 years. There was evidence for a non-adherence to prevention group (low fruit consumption, less frequent tooth brushing and low physical activity) and a risk behaviour group (high sweets’ consumption, smoking and physical fighting), regardless of age [
7]. This analysis is limited by both the small number and type of risk considered as well as the focus on only male adolescents. HACA was also adopted in a study exploring clustering of 17 risk behaviours among Brazilian adolescents aged 13–15 years [
9]. This also generated a lack of adherence to preventive behaviours (less frequent hygiene practices, unprotected sex, skipping breakfast, no dental visits), and undertaking risky conduct (current smoking, illegal drug use, no helmet and seatbelt use, high sugar intake, physical fighting and current drinking) and a second unhealthy lifestyle group (sedentary habits, such as insufficient physical activity and eating while watching TV or studying, and diet poor in fruit).
Among the person-centred studies, a New Zealand study used LCA to examine clustering of MRB (alcohol use problems, smoking cigarettes, marijuana use, motor vehicle risk, violence, unsafe sexual health, delinquency, depression and attempted suicide) in a national sample of secondary school students, aged 12–18 years (80% of the sample were aged 14/15 years). The analysis identified a four-class model: the ‘healthy’ group which constituted the majority of students (79.6%), all of whom presented with ≤1 health concerns; the ‘distressed’ group (5.9% of the sample) the majority of whom had depressive symptoms, 48% of whom had attempted suicide in the past year and 52% of whom presented ≥3 health concerns. The ‘risky’ group (10.8% of the sample) with higher rates of risky behaviours, but low rates of emotional concerns. The ‘multiple’ group (3.5% of the sample), reported high levels of both risky behaviours and emotional problems [
10]. A similar LCA investigating clustering of MRB among Australians aged ~ 18 years [
11], found three classes: moderate risk (52%): moderately likely to binge drink and not eat enough fruit, high probability of insufficient vegetable intake; inactive, non-smokers (24%): high probabilities of not meeting guidelines for physical activity, sitting time and fruit/vegetable consumption, very low probability of smoking; and smokers and binge drinkers (24%): high rates of smoking and binge drinking, poor fruit/vegetable intake. The classes were differentially associated with psychological distress, depression and anxiety. Using LCA, Laxer et al. [
12] examined the associations of 15 MRB and overweight/obesity among Canadian adolescents in grades 9 to 12 (age ~ 14–18 years). All groups were more likely to be overweight/obese when compared to the health conscious group: traditional school athletes odds ratio (OR) = 1.15(95%CI:1.03–1.29), inactive screenagers OR = 1.33(95%CI:1.19–1.48) and moderately active substance users OR = 1.27(95%CI:1.14–1.43).
Evidence regarding the clustering of MRB is mixed, with some research finding distinct risk profiles, while others find only broad clusters. This is further obscured when age and sex/gender are considered. Although most studies presented here cover a range of ages across adolescence, two studies de Looze et al. [
1] and Champion et al. [
11] focus on age 15 and 18 years, respectively, which may be considered limitations to their analysis. While some studies include wide-ranging behaviours, others continue to use only a limited number or type of risk behaviour. Reducing the analysis to similar MRB that are hypothesised to co-occur, runs the risk of missing important relationships that have not been included. It is therefore imperative to test the validity of these profile-specific approaches, incorporating a larger number of divergent risk behaviours for a UK adolescent population.
We aimed to explore the utility of a latent class approach to investigate patterns of MRB using the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort with a view to informing future public health interventions. We chose to examine MRB at age ~ 16 years because adolescent brain development, is second only to infancy as a dynamic period, making it a crucial period of study [
13]. In addition, the General Certificate of Secondary Education (GCSE) examinations are completed at age 16 in the UK, determining entrance to post-16 education and university, making it a time of great importance. Further, evidence using ALSPAC data shows that, while not at their highest prevalence, age 16 is when both tobacco and cannabis see their most dramatic increase in use [
14]. Similarly, alcohol use is rapidly increasing and antisocial behaviour is at its peak at this age [
15].
Conclusion
Our research calls into question the utility of the clustering approach as a useful way to describe patterns of MRB. The three classes identified were mainly distinguished by the number of MRB engaged in. A better strategy, therefore, is to sum the behaviours to create an overall score. We have shown in a previous analysis that despite individual risk behaviours patterning differently according to sex, females and males engaged in a similar number of MRB [
26]. Further, while the associations between individual MRB and socioeconomic status were highly variable, a more consistent relationship was established with MRB score [
18]. The evidence points to the volume of behaviours being the critical factor, rather than the types of behaviours engaged in. This has implications for the design of public health interventions aimed at reducing MRB, providing further evidence that MRB co-occur among adolescents, and therefore prevention strategies should focus on multiple rather than single risk behaviours. This is already being encouraged in national policy and while there is some evidence this is being implemented by local authorities among adults [
27], more work needs to be done regarding adolescents. Prevention strategies should focus on the quantity, rather than the type of MRB and evidence has shown that interventions targeting multiple-substance use can also be effective for other MRBs, providing an excellent basis for MRB prevention. A recent Cochrane Systematic Review showed that universal school-based interventions are most effective in preventing alcohol consumption, tobacco use, illicit drug use and antisocial behaviour, and increasing physical activity among young people but did not find strong evidence of benefit for family or individual-level interventions for the MRB studied [
2]. Therefore, efforts for MRB prevention should focus on developing appropriate school-based interventions.
Acknowledgements
We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. This publication is the work of the authors who will serve as guarantors for the contents of this paper. The UK Medical Research Council and Wellcome Trust (Grant Ref: 092731) and the University of Bristol provide core support for ALSPAC.
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