In this section we examine selected prevalence studies that emphasise adolescent gambling and the use of relevant screening tools. There is a plethora of prevalence studies - far too many to cover in this limited review - so the researchers have endeavoured to select representative studies from different countries, for different age ranges and to reflect the use of different screens.
3.1 England and Wales
The British Gambling Prevalence Survey involved interviews and self-report questionnaires with some 7,680 respondents to ascertain the current (last 12 months) prevalence rate. For our purposes the point of interest here is that both SOGS and DSMIV were used together in the national prevalence survey. Orford et al (2003) concluded on the basis of the use of the two screens that:
- ‘no single existing screening questionnaire adequately reflects the multidimensional nature of problem gambling’ (p. 53);
- there needs to be agreement on threshold levels as to what constitutes a problem gambler (e.g., witness use of 5+, 10+ in SOGS);
- transferability to other countries and cultures ‘derives from a simple view of problem gambling as a mental disorder’ (p. 63).
It is suggested relatively consistently in regard to SOGS and DSM-IV that they continue to measure two different facets of problem gambling, principally dependence (DSM-IV) and gambling related problems such as financial stress, preoccupation with gambling (SOGS).
Fisher (2000) used and developed the Revised Diagnostic Statistical Manual Adapted for Juveniles (DSM-IV-MR-J). Two pilot studies, amounting to 80 completed questionnaires, were conducted to fine-tune the contents of the questionnaire. The final sample included 9,774 students at high schools in England and Wales, both Year 8 (12-13 years) and Year 10 (14-15 years). The study found that 5.6 per cent scored in the problem gambling range of DSM-IV-MR-J. The particular emphasis of this study was on players of fruit machines and National Lottery Scratch Cards as these games were causing the most public concern.
Griffiths (2000) reports that 6 per cent of adolescents may have patterns of problem gambling on UK lotteries, on scratchcard gambling and between 0.5 and 6.0 per cent probable pathological gambling on slot machines based on the DSM-IV-J. It is also argued that electronic cash, the structure of video games and internet gambling are increasingly risk factors for young people. Because 16 is the legal age for gambling in the UK an additional factor is that young people commence gambling earlier. Different social norms highlight the risk of simply transferring results from one country to another. The high prevalence rates and the younger legal age to commence gambling contests the assertion of some that Australia has a more entrenched gambling culture than other nations.
Wood, Griffiths, Derevensky and Gupta (2002) conducted research with adolescents aged 11 to 15 years using Q-cards to understand rather than measure young people’s behaviour in regard to the UK national lottery and scratchcards. The process involved scaled rating of agreement/disagreement with statements leading to attitudinal positions, viewpoints or perceptions. The strength or utility of Q-sorts is that it can help to understand ‘the views of gamblers and non-gamblers alike’, to test questions/responses, develop new types of hypothesis and may be used in behavioural counselling.
AUTHOR: Wood, R.T.A., Griffiths, M., Derevensky, J., and Gupta, R., (2002).
COUNTRY: UK.
METHODOLOGY: Adolescents 11-15 years (N=62), Q-cards/Q-sorts which are statements on 49 cards taken from screens (e.g., DSM-IV-J), from prevalence studies to test attitudinal dimensions to statements.
SCREEN/MODEL: Matrix of card responses to make choices about statements; potentially useful tool to help frame prevalence studies, test understanding of questions.
STRENGTHS: Designed to also reflect social context in which youth experience gambling issues; and to assess attitudes. Procedural tool for qualitative research.
WEAKNESSES: N/A.
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3.2 Canada11 : Manitoba
Wiebe et al (2000) examined the gambling behaviour of Manitoba youth using SOGS-RA. Study participants were part of a province-wide telephone survey of 1,000 youth between the ages of 12 and 17. Households were randomly selected from a listed sample; controls were used to ensure representation by region, gender and age. One thousand four hundred and forty (1,440) households were identified containing an eligible youth, 214 parents refused to allow their child to participate, 217 youth were unwilling to participate, 9 youth ended the survey during the course of the actual interview, with a thousand youth completing the survey.
The SOGS-RA categorised the adolescents into four categories:
- non-gambling: youths: who had not gambled in the last year;
- non-problem gambling: defined as scores of 0 and 1;
- at-risk gambling; individuals had scores of 2 and 3; and
- problem gambling: based on scores of 4 or greater.
Wiebe et al (2000) reported that 8 per cent of the total sample could be classified as ‘atrisk’ for problems, and 3.2 per cent classified as having severe gambling related problems.
AUTHOR: Wiebe et al (2000).
COUNTRY: Manitoba, Canada.
METHODOLOGY: 1,000 youth, aged between 12 and 17 years, random survey of households seeking parental consent to participate. Telephone survey.
SCREEN/MODEL: SOGS-RA: problem gambling 4+.
STRENGTHS: Invariant across gender, internal consistency.
WEAKNESSES: Author considers that clinical interviews required to test sensitivity of screen. Some items require rewording to reduce over and under endorsement.
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3.3 Canada: Atlantic Provinces
Poulin (2000) undertook a survey to determine the prevalence of gambling among adolescent students in the Atlantic provinces of Canada. In 1998, a total of 13,549 students in grades 7, 9, 10 and 12 in the public school systems of the four Atlantic provinces completed a self-reported anonymous questionnaire that included SOGS-RA. A response rate of approximately 98 per cent of students present on the day of survey, and approximately 13 per cent of students were absent on the day of survey. Poulin gives both prevalence estimates using the standard narrow SOGS-RA score totals (explained under SOGS-RA: Section 2.4) and a broader definition. The broader definition is as follows:
No problem gambling: No gambling activity: or, Gambling less than daily and SOGS-RA score =0
At-risk gambling: Weekly gambling and SOGS-RA score = 1; or, Gambling less than weekly and SOGS-RA score of > or = 1.
Problem Gambling: Daily gambling regardless of the SOGS-RA score; or, Weekly gambling and a SOGS-RA score > or = 2.
Poulin (2000) found that 8.2 percent and 6.4 per cent of adolescent students met the broad definition of at-risk and problem gambling, respectively. Furthermore, approximately, 3.8 per cent and 2.2 per cent of adolescent students met the narrow definition of at-risk and problem gambling, respectively.
3.4 Canada: Quebec
In one of the very few longitudinal studies incorporating gambling issues, Vitaro (2001) used a ‘French version of SOGS-RA to assess gambling behaviour and gambling related problems’ involving 717 French-speaking male Caucasians from disadvantaged neighbourhoods in Montreal, Quebec. They sought to relate results of measures collected (impulsivity, status of friends and parental supervision) when the respondent males were aged 13 and 14 years to measures of gambling frequency and gambling problems with delinquency and drug/alcohol use when the boys were aged 16 and at 17 years. The study sought to establish predictive relationships.
While at age 17 years, the study found gambling behaviour did not explain any increase in delinquency or substance use, it did find a concurrent association between gambling, delinquency and substance use. The study concluded that the three risks factors at age 13/14 years helped to predict behaviours at 16/17 years, suggesting some underlying, common risk-factors.
3.5 North America
Jacob (2000) reviewed 20 juvenile gambling prevalence studies in the USA conducted in the period 1984-1999 in both the USA and Canada and concluded that there is ‘little doubt that juvenile gambling has increased significantly’ over this time frame, with the medium level of participation rising from 45 per cent to 66 per cent. A significant issue with this account is that all forms of gambling are lumped together (illegal and legal based on age). Wagering with peers or a ‘side-bet on the outcome of a game of pool between two players’ is not the same as illegal entry and play in a hotel, club, or casino. Playing the stock market is equated with buying a raffle ticket. While this approach is consistent with gambling viewed as a ‘continuum of activities’ studies rarely inform how much is gambled, the source of income, extent of illegal access/behaviour, etc..
Jacob (2000) provides a composite profile of juvenile ‘serious gambling related problem (SGRP) groups’:
- early age onset (before age 12);
- boys more likely to experience problems;
- parents gamble, or family gambling pattern;
- more likely to live in metropolitan rather than regional/rural areas;
- few studies on ethnic group membership, although Native American youth identified;12
- games played are continuous and interactive (as for adults) such as poker, games of personal skill, sports betting and EGMs;
- sources of money: from lunch money through to stealing (but rarely are amounts provided by activity);
- frequent gamblers ‘more likely’ to be involved with heavy use of alcohol and drugs, report more truancy, and poorer school performance; and
- high level of dissociative reactions while gambling and varied motives and psychological states reported for gambling.
Young people over the age of eighteen have been usually surveyed with all adults except for the case of college students that have a number of studies dedicated to them. An example of this is Neighbors et al (2002) that undertook a study on US undergraduate college students. Approximately 560 college students were surveyed using a number of different screening tools. It was found, using SOGS that 83.9 per cent of participants gambled non-problematically (SOGS score less than three), 9.8 per cent of participants were sub-clinical problem gamblers (SOGS score three or four) and 6.3 per cent were probable pathological gamblers (SOGS scores of five and higher).
Neighbors et al also developed a new screening tool for measuring problem gambling prevalence called The Gambling Problem Index (GPI). It is a 20-item questionnaire and for each item respondents are asked to indicate on a five point scale (never, one to two, three to five, six to ten, and more than ten times), how many times during the previous six months they experienced a negative consequence while gambling or as a result of gambling. The GPI score is calculated as the sum of items in which respondents indicated experiencing the gambling related consequence, at least once, during the previous six months. They found that it correlated moderately well with SOGS (0.42) and GA20 (0.52). However, the authors give no indication of what constitutes problem gambling with this measure. This means that it is very difficult to evaluate its use as a screening tool. The GPI is perhaps best understood as an ‘outcome measure’ to be used to inform the participant of the consequences of gambling and through raising awareness, assist with intervention and treatment.
Winters et al (1993) initially trialled the SOGS-RA to assess the gambling experience of adolescents in the 15-18 age group. The authors reported a problem gambling rate of 8.7 per cent, although the sample was not representative of American youth. The study was one of the first studies to discuss the correlates of problem gambling which included problems with academic performance, drug use, parental gambling and exposure to gambling. The survey was inconclusive on rates of gambling by females. The authors also place in context, the experimental nature of most adolescent gambling: ‘ infrequent pattern, a low amount of money spent, and absence of problem signs and symptoms’, while adolescent gamblers prefer skill based gambling’ (sporting events), and low impact forms such as cards, bingo and scratch cards.
AUTHOR: Winters et al (1993).
COUNTRY: Minnesota, USA.
METHODOLOGY: 1,101 adolescents aged 15-18 years, with two-thirds interviewed by telephone, one third from high school (grades 10-12).
SCREEN/MODEL: SOGS-RA Modified SOGS for adolescents, tested alongside statewide adult survey using SOGS.
STRENGTHS: Screen showed internal consistency, reliability and factor analysis of individual items showed a common dimension.
WEAKNESSES: Preliminary study, limited sampling with focus on ‘white male adolescents’, concern that reliability and validity for females was inadequate.
In a large study of Minnesota public school students, Stinchfield (2000) examined the prevalence of gambling using a self-administered questionnaire (see Box). Using a very large sample, gambling questions were included with an Education Department administered alcohol and drug risk survey. The primary findings included that males gambled more frequently than females (8 times greater), that most students did not gamble on a weekly/daily basis and that the gambling activity undertaken changes with age as access patterns change. Consistent with other research findings, young people report that it is very easy to purchase lottery tickets, scratchcards either directly themselves or by family members.
AUTHOR: Stinchfield, R., (2000).
COUNTRY: Minnesota, USA.
METHODOLOGY: Included 5 gambling frequency questions and two problem gambling items in a selfadministered 121 item paper and pencil questionnaire which dealt with alcohol and drug use. Sample was 78,582 9th to 12th grade students aged 14-20 years at high school.
SCREEN/MODEL: Not a gambling screen but gambling questions included in broader survey. Administered in class room setting.
STRENGTHS: Can be undertaken across a school system, very large sample and provides for more accurate measurement, does not require sample to population inference.
WEAKNESSES: Restricted to those attending school, may contain self-report bias and not concerned only with gambling behaviour.
One of the benefits of this type of approach is that it is known that gambling, drug and alcohol use are associated at least in the stage of adolescent experimentation. Whether this overlap continues into adulthood is still strongly debated.
One extension of the Stinchfield study compared rates of gambling among the same Minnesota student cohort in 1992, 1995 and again in 1998. While the survey was not intended as a comprehensive review of gambling behaviour it found that fewer students gambled in 1998 than in 1992. This finding goes against the reported rise in patterns of youth gambling over this time period. The benefit of this longitudinal study compared with the myriad ‘of point in time studies’, using different methodologies and across different age groups (which does restrict the ability to compare gambling rates) is that changes in youth gambling can be compared over time. Stinchfield (2001) refers to four studies that specifically examined the question of changes in youth gambling over time and reported ‘the predominant findings were stability and some slight declines in the number of frequent gamblers’ (p. 276). As youths get older, gambler preferences change from informal gaming activities to legalised activities.
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3.6 Australia
As part of an Australian study on a model for predicting adolescent gambling frequency and problem gambling, Moore and Ohtsuka (1997) examined problem gambling prevalence among young people aged between 14 and 25 years. Participants were volunteers from Years 10, 11 and 12 of six secondary schools and first year undergraduates from four geographically separate campuses of a university in Melbourne. The university and the schools were all situated in the western suburbs of Melbourne, a predominantly working class area. Usable responses were obtained from 757 participants in the school sample and 250 participants in the university sample. A modified version of the SOGS was used, with changes made for Australian idiom and the age of the population. The major change was that a 5-point Likert scale was applied to the problem gambling statements to maintain consistency in response requirements across the questionnaire. Moore and Ohtsuka (1997) found 3 per cent of the young people surveyed classified themselves as problem gamblers, lower than expected given previous research.
AUTHOR: Moore and Ohtsuka (1997).
COUNTRY: Australia.
METHODOLOGY: Objective to examine potential predictors of gambling behaviour and problem gambling, 1,017 young people, age 14-25 years.
SCREEN/MODEL: Predictive model based on Theory of Reasoned Action (TRA), with survey; sections to measure gambling intentions and behaviour. Authors modified SOGS.
STRENGTHS: Attitudes to gambling study.
WEAKNESSES: Not a screen but a model of ‘predictive behaviour’, and not able to predict for problem gambling.
A recent study of South Australian high school attending students (Delfabbro et al, 2003) sampled surveyed year 10, 11 and 12 students. Interestingly in this study, the authors stated that ‘most adolescents did not experience gambling related problems. Problem gambling was classified as a score of 4 or higher on the DSM-IV-J. Based on this classification, 3.5 per cent of participants could be categorised as problem gamblers.’13 This is at the low end of rates for youth problem gambling reported in North America, Canada and the UK which are said to range from 3.5 per cent up to 8 per cent.
AUTHOR: Delfabbro, P and Thrupp, L., (2003).
COUNTRY: South Australia, Australia.
METHODOLOGY: Survey in 6 schools, sample of 505 year 10, 11 and 12 students, use of 5 point LIKERT scale to assess gambling habits, attitudes towards gambling, problem gambling measure and other factors.
SCREEN/MODEL: DSM-IV-J Fisher 1999 version to assess problem gambling, 9 questions, yes/no response, 4+ indicate problem gambling.
STRENGTHS: N/A.
WEAKNESSES: Not an assessment of DSM-IV-J. 13 Delfabbro, P., et al (2003), ‘The social determinants of youth gambling in South Australian adolescents’, Journal of Adolescents, Vol. 26. p. 323.