Problem gambling patterns among Australian young adults: Associations with prospective risk and protective factors and adult adjustment outcomes
Introduction
There is growing recognition of the varied course of problem or disordered gambling over time (LaPlante, Nelson, LaBrie, et al., 2008). Contrary to previously held beliefs that disordered gambling is a chronic and progressive condition, recent longitudinal studies have shown that there are patterns of change in individual gambling behaviour across time (e.g. LaBrie, R. A., et al., 2003, LaPlante, D. A., et al., 2008), despite overall rates of problem gambling [PG] remaining stable. These findings emphasize the importance of examining the gambling behaviour trajectories (longitudinal patterns) of individuals over time (Bray, Lee, Liu, et al., 2014). While there are a growing number of prospective longitudinal studies investigating predictors of PG, these studies largely investigate PG at an aggregate level (variable centred approach) without regard for the documented variability in individual PG patterns over time (e.g. Billi, R., et al., 2014, Hayatbakhsh, M. R., et al., 2006, Scholes-Balog, K. E., et al., 2014, Scholes-Balog, K. E., et al., 2015, Slutske, W. S., et al., 2005). Further, there is significant diversity in the types of potential predictors investigated, with these studies largely focused on individual level predictors of PG selected based on cross-sectional correlates of PG and other problem behaviours. Accordingly, these studies have yielded diverse findings. Studies such as these that investigate PG trends and predictors at an aggregate level may obscure sub-group differences in PG patterns which are identifiable with person-centred approaches (Welte, J. W., et al., 2004, Winters, K. C., et al., 2005), and which may have potentially important implications for intervention and prevention of relapse (LaPlante et al., 2008).
Different sub-groups of problem gamblers have been identified based on the course of the disorder over time (LaPlante et al., 2008). For example, Winters et al. (2005) studied a non-representative community sample of 305 adolescents in the United States (U.S.) at ages 16, 17 and 23 years. They found that the most prevalent pattern was resistance (no PG at any time-point; 60%), followed by new incidence (developed PG during the study; 21%), desistance (moved away from PG the study; 13%), and persistence (consistent PG throughout the study; 4%). A large representative study of Swedish adolescents and young adults found much smaller proportions for non-resistant pathways: 2.64% were desistors; 2.26% were new incidence; and 0.67% were persistors (LaBrie et al., 2003). Cultural, sample size and composition, and measurement differences may have resulted in discrepancies in the prevalence of the sub-groups in these two studies.
Nonetheless, while there are a growing number of studies investigating these different patterns of PG over time, there remains a lack of research investigating their behavioural, social, and contextual determinants (LaPlante et al., 2008). Understanding the factors that influence PG progression, and in particular, those associated with persistence of, or desistance from PG, will serve as a foundation for the development of efficacious prevention and intervention efforts (LaPlante et al., 2008). For example, understanding what makes an individual more or less likely (i.e., risk and protective factors) to show a pattern of desistance would also allow health care providers to better tailor treatment plans to help bring about positive changes and prevent relapse (LaPlante et al., 2008).
Similarly, to our knowledge there have been no published studies investigating outcomes associated with the different patterns of PG across time. For example, it is unclear whether desistance from PG is associated with lasting poor social and/or behavioural outcomes, such as engagement in other problem behaviours, lack of employment, and mental health problems, despite an absence of gambling problems. Such findings would have important ramifications for prevention of relapse and provision of appropriate support services following recovery from PG.
This exploratory study analysed data from a large prospective study of Australian youth to investigate temporal changes in young adult gambling behaviour and examine: (1) the prevalence of sub-groups with similar patterns of PG behaviour over time (defined as resistance, persistence, desistance, and new incidence; Winters et al., 2005); (2) social developmental and behavioural risk and protective factors for these PG patterns; and (3) young adult behavioural outcomes associated with these PG patterns. The social developmental risk and protective constructs were selected based on their established associations with PG (Scholes-Balog et al., 2014) and other problem behaviours (e.g., Hemphill, Heerde, Herrenkohl, et al., 2011), and were drawn from the Communities That Care (CTC) youth survey. The broad CTC framework is designed to comprehensively assess risk and protective factors across the social environmental domains that are considered to be influential in the development of youth behaviour (community, family, school, peer/individual) (Catalano & Hawkins, 1996) so as to facilitate community needs assessment for prevention and/or intervention (Arthur, Briney, Hawkins, et al., 2007).
It was hypothesised that the new incidence problem gambling sub-group would be the most prevalent problem gambling group identified, and the persistent problem gambling sub-group would be the least prevalent. Further, persistent problem gamblers were predicted to experience the greatest number of poor behavioural outcomes in young adulthood.
Section snippets
Participants
The sample was drawn from the International Youth Development Study (IYDS). The present analyses focus on the Australian arm of the IYDS; this sample was recruited in 2002, with 2884 students from Victoria completing the first survey. Participants were recruited to be state-representative of public and private schools. A two-stage cluster sampling approach was employed (response rate of 73.5%), with sampling designed to yield a state-representative sample of students in grades five, seven and
Descriptive statistics
The aggregate prevalence of PG was 4.70% [95% CI 3.68,5.97] at T1 and 5.68% [4.60,7.00] at T2. The prevalence of each gambling pattern, together with descriptive statistics for measures can be found in Table 2. The majority (91.69%) of the sample were classified as resistors. The new incidence group was the most prevalent PG group (3.62%), followed by the desistance group (2.63%) and the persistence group (2.07%).
Risk and protective factors for patterns of problem gambling
The results from the partially adjusted multinomial logistic regression analyses
Discussion
This prospective study of PG patterns extends previous findings suggesting that PG is a varied, transitory, and episodic condition in Australian young adults. The majority of the sample (91.69%) reported no PG at both T1 and T2 (i.e., resistors). The new incidence group was the most prevalent PG group (3.62%), followed by the desistors (2.63%). Consistent with other studies (LaBrie, R. A., et al., 2003, Winters, K. C., et al., 2005), the persistent PG group was the least prevalent group (2.07%).
Role of funding sources
The authors are grateful for the financial support of the National Institute on Drug Abuse (R01-DA012140) for the International Youth Development Study initial data collection. Continued data collection in Victoria, Australia has been supported by three Australian Research Council Discovery Projects (DPO663371, DPO877359, and DP1095744) and an Australian National Health and Medical Research Council grant (project number, 594793). The authors acknowledge the infrastructure funding received by
Contributors
Authors B and C took part of the design of the original IYDS study and have overseen continued longitudinal data collection. Author A conceived the idea for the data analyses presented, with all other authors providing intellectual input to the proposal. Author A conducted the data analysis with input from all authors. Author D provided topic specific expertise throughout data analysis and manuscript writing. Author A wrote the first draft of the manuscript; all authors contributed to refining
Conflict of interest
All authors declare that they have no conflicts of interest.
Acknowledgements
The authors wish to thank the participants and study staff of the International Youth Development Study for their invaluable contribution.
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