David Casey*, Annik Mossière, Rob Williams, Nady el-Guebaly, David Hodgins, Garry Smith, Rob Williams, Don

Schopflocher, & Rob Wood

*Research Coordinator, Leisure, Lifestyle, Lifecycle Project (LLLP), Psychology Department, University of Calgary

Background on the Leisure, Lifestyle, Lifecycle Project (LLLP) Explain the biopsychosocial model

Describe: Adolescent Sample Measures

Present the results of logistic regression analysis for adolescents

Discuss the conclusions

Plans for the future: Examining patterns of relationship over three more collection

points Changes in gambling behavior over time

Cohort longitudinal study of gambling behavior Over 5 years, with 4 data collections Initial sample

Most recruited through Random Digit Dialing (RDD) Stratified by region of the province (urban & rural) 5 age groups (13-15, 18-20, 23-25, 43-45, 63-65) Divided into at-risk gamblers & general population

Data collection at Wave 1: Telephone, computer-based, & face-to-face interviews

Data collection at Wave 2: Web-based survey

Data collection at Wave 3: Just wrapped-up this month using web-based survey

Data collection at Wave 4: (in 12-16 months) Testing a biopsychosocial model of gambling

FAMILY HISTORY- Social & problem gambling- Substance use disorders- Psychiatric disorders- Deviance

COGNITIVE - Intelligence - Attentional Ability- Gambling fallacies- Coping Skills

FAMILY ENVIRONMENT- Parental behavior- Marital Status/conflict- Abuse experiences

EXTRA FAMILIAL ENVIRONMENT- Social Support- Friendships/peers- Religion/Spirituality- Ethnicity/Culture- Social organization

TEMPERAMENT/PERSONALITY- Impulsivity- Trait anxiety- Moral disengagement- Self-esteem

GAMBLING INVOLVEMENT- Frequency & Duration- Type & Range- Context

DEMOGRAPHICS- Religion- Age- SES- Family background- Ethnicity

EXTERNALIZING PROBLEMS- Alcohol use- Substance use- Tobacco use- Delinquent activity- Sexual activity

INTERNALIZING PROBLEMS- Depression- Anxiety

PREVENTION & TREATMENTBROADER SOCIO-CULTURAL FACTORS

- Availability of gambling; public attitudes; prevention programs; legislative changes; gambling knowledge

GAMBLING DISORDERS- Frequency & Duration- Type & Range- Context

BIOLOGICAL RISK- Neuropsychological functioning

- Frontal lobe- Neurotransmitter - DA (blood & saliva DNA)

- MAOI activity- Gender

STRESSORS- Physical health/disability- School/work- Familial/peer- Legal

Total Population Completes (N=1808)

N %

Age 13-15 Year Olds18-20 23-25 43-45 63-65

436315341402314

24.117.418.922.217.4

GenderMaleFemale

837971

46.353.7

LocationCalgaryEdmontonGrande PrairieLethbridge

754536224294

41.729.612.416.3

Marital Status (Adults Only)Single, Never Married Married/Common-lawDivorced /Separated/Widowed

570643156

41.647.0

11.4

Level of EducationLess than High SchoolCompleted High SchoolSome Technical/CollegeCompleted Tech/CollegeSome UniversityUniversity Degree

549279203225236315

30.415.411.212.513.117.5

Current Employment StatusNot Currently EmployedEmployed Part-TimeEmployed Full-Time

746430631

41.323.834.9

This talk will present findings from Wave 1 data only

Focus on adolescent sample

Examining relationship between gambling, family environment, religiosity, externalizing and internalizing problems

Non-Gambler Population (N = 196)

Gambler Population (N = 240)

Total Population(N =436)

n % n % n %

Age: 13 yrs 77 39.3 84 35.0 161 36.9

14yrs 71 36.2 76 31.7 147 33.7

15-16yrs 48 24.5 80 33.3 128 29.4

Gender: Male 91 46.4 144 60.0 235 53.9

Female 105 53.6 96 40.0 201 46.1

Location: Calgary 75 38.3 102 42.5 177 40.6

Edmonton 56 28.6 75 31.3 131 30.0

Grande Prairie

24 12.2 30 12.5 54 12.4

Lethbridge 41 20.9 33 13.8 74 17.0

Employment: Not Employed

158 80.6 176 73.3 334 76.6

Part OR Full-Time

38 19.4 64 26.7 102 23.4

Household Income: $0 TO $29,999

13 6.5 7 2.9 20 4.5

$30,000 TO $49,999

17 8.7 22 9.2 39 9.0

$50,000 TO $79,999

59 30.1 49 20.4 108 24.8

$80,000 OR Greater

107 54.6 162 67.5 269 61.7

Constructs from Figure 1 Construct Measure

Biological Risk Demographics Gender

Internalizing and Externalizing Problems

Psychopathology/Delinquent Activity/

Temperament/Personality

Child Behavior Checklist (CBC)

Alcohol, Substance & Tobacco Use

Canadian Community Health Survey (CCHS)

Cognitive Intelligence Wechsler Abbreviated Scale of Intelligence (WASI)

Family Environment Abuse Experiences Childhood Trauma Questionnaire (CTQ)

General Functioning/Family Support

Family Environment Scale

Extra-Familial Environment Social Support Lubben Social Network Scale (LSNS)

Religiosity Rohrbaugh Jessor Religiosity Scale (RJRS)

Culture York Ethnicity Scale

Social Organization Buckner Neighborhood Cohesion Scale (2 questions )

Stressors/Life Events Life Events Life Events Questionnaire

Physical Health SF-10 Health Survey

Gambling Involvement Frequency, Expenditure, Type, Range, Context, Motivation, & Knowledge

Canadian Problem Gambling Index (CPGI)

Attitude Gambling Attitude Questionnaire

Gambling Disorders Problem Gambling Fisher DSM-IV-MR-J

n Mean $ Spent

Lottery & Raffle Tickets 120 6.25

Instant Win Tickets 25 4.64

VLTs & Casinos 6 6.06

Private Games 94 10.84

Sport Betting 41 7.71

Other [Bingo, Horse Racing, High Risk Stocks]

18 62.22

n %

Never Gambled or Not in Last 12 Months

237 54.4

Gambled in Last 12 Months 199 45.6

Non-Gambler or Non Problem Gambler 333 76.4

Low Risk Gambler 72 16.6

Moderate Risk / Problem Gambler 31 7.0

First Step in analysis of adolescent gambling:

Univariate analysis were used to compare: Non-gamblers vs. Gamblers

Those significant at the univariate level were used in logistic regression analysis

Why Logistic Regression? Allows categorization into groups based on predictors Can use continuous and categorical variables

Data was weighted based on gender, age, and demographic location for Alberta

Bootstrap weights were used in the present analysis Refine the confidence interval in logistic regression

Male Adolescent Correlations

Female Adolescent Correlations

Total Correlations

Drug Use .26** .11 .17**

Alcohol Consumption .26** .18** .22**

CBC: Somatic .16* .14* .12**

Thought .10 .20** .15*

Attention .02 .19** .11*

Rule Breaking .24** .15* .21**

Aggressive .14* .07 .11*

Contact Friends

.18** .00 .10*

Anxious .09 .14* .08

Religiosity -.03 -.05 -.05

FES: Conflict .29** .10 .19**

Active/Recreational

.23** .08 .14**

Moral Religious

-.14* -.14* -.15**

Age .28** .02 .15**

Gender -.11*

Location .16* .18** .16**

Household Income .15* .07 .12**

Employment .06 .08 .08

Peer Gambling .32** .30** .31**

Sibling Gambling -.09 .09 .01

WASI IQ Score .00 .17** .09*

** . Correlation is significant at the 0.01 level (2-tailed) *. Correlation is significant at the 0.05 level (2-tailed)

Male Adolescents

Female Adolescents

Adjusted Odds Ratio (OR)(95% Confidence Intervals)

Non-Gambler (N = 95)

Gambler (N = 108)

Non-Gambler (N = 143)

Gambler (N = 91)

Male OR

p-value

Female OR

p-value

Religiosity 11.99 11.34 13.68 13.05 1.08(0.98,1.18)

.055 1.09 (0.97,1.21)

.013

FES - Conflict 2.11 3.47 2.51 3.11 1.27 (1.01,1.61)

.003

Active 5.75 6.71 6.33 6.64 1.52 (1.15,2.02)

.000 1.22(0.90,1.57)

.043

Moral 4.78 3.96 4.86 4.24 .78 (0.61,1.00)

.020 .78 (0.57,1.04)

.005

Peer Gambling 2.24 9.93 1.19 4.18 1.05 (1.01,1.10)

.010

Age 13.74 14.21 13.89 13.97 1.53 (0.89,2.62)

.051

Drug Use .02% .15% .09% .16& .18 (0.03,1.19)

.044

Male Adolescents

Female Adolescents

Adjusted Odds Ratio (OR)(95% Confidence Intervals)

Non-Gambler (N = 95)

Gambler (N = 108)

Non-Gambler (N = 143)

Gambler (N = 91)

Male OR

p-value

Female OR

p-value

CBC –Attention 4.20 4.32 3.82 4.79 1.26 (0.94,1.64)

.012

Thought

3.54 4.27 3.61 5.49 1.22 (0.98,1.41)

.006

Rule Break

3.09 4.53 2.81 3.95 1.20 (0.93,1.49)

.036

Aggressive

6.22 7.98 6.28 7.79 0.87 (0.75,1.03)

.040

Religiosity 11.99 11.34 13.68 13.05 1.08 (0.98,1.18)

.055 1.09 (0.97,1.21)

.013

FES – Active 5.75 6.71 6.33 6.64 1.52 (1.15,2.02)

.000 1.22 (0.90,1.57)

.043

Moral 4.78 3.96 4.86 4.24 .78 (0.61,1.00) .020 .78 (0.57,1.04) .005

Location 89.9% 95.9% 93.2% 99.7% 0.04 (0.01, 0.15)

.088

Household Income *

100341 115099 93035 118341 1.00 (1.00,1.00)

.029

WASI IQ Score 107.70 107.74 104.48 107.82 1.04 (0.99,1.08)

.014

* Household Income in Thousands, rounded to the nearest dollar

Compared to non-gamblers, male gamblers were: Older Identified more conflict in their family Involved in more activity and recreation with their family More likely to have used drugs in the past 12 months More likely to have peers who also gambled

Compared to non-gamblers, female gamblers: Scored higher on attention problems, thought problems, rule-

breaking, and aggression Were more involved with activity and recreation with their

family Came from households with a higher annual income Scored higher on the measure of intelligence

Moral and religious beliefs were protective factors for both adolescent males and females

Both males and females – less likely to gamble if they identified having strong moral and religious beliefs, either themselves or their families

Adolescents identified as having strong moral and religious beliefs associate gambling with immoral behavior, and thus it would be seen negatively, by their families and communities, for them to partake in the activity

Compare findings with data collected at Waves 2, 3, & 4:

Do the results remain consistent or change?

Are there still gender differences?

Availability to consider other constructs:

Do they help distinguish between non-gamblers and gamblers?

Examining change in gambling behavior over time:

How does the pattern change over 5 years? LLLP Waves 2-4: provide opportunity to examine changes in

behaviors associated with : Gambling Changes as they mature into young adulthood Changes in family environment Changes in moral and religious beliefs Other lifestyle altercations

What changes occur once they are of legal age to gamble? Important to examine changes in intensity of gambling over the

years, and expenditure in relation to their psychological health

The influence of other risky behaviors, such as the use of drugs and alcohol, will be important to consider as these adolescents mature into adulthood

Findings highlight interesting factors related to gambling behavior among a sample of adolescent males and females

Identifying the relationship between adolescent gambling, their peers gambling behavior, family, religion, and alcohol and substance abuse

can offer insight into guiding treatment approaches adolescents with gambling problems

Agencies could use these findings to: educate the public about the dangers of gambling creating awareness of the potential harm it can have on youth the role that religiosity, family, peers, and substance use can play

Legislators could develop more effective laws and policies regarding age restrictions associated with gambling, advertisement regulations, and access to gambling

David Casey, PhDdcasey@ucalgary.ca

University of CalgaryPsychology Department

We Would Like to Acknowledge Funding for this Study from the Alberta Gambling Research

Institute (AGRI)

Gambling in Alberta 82% of adults gambled in previous year

Few studies of determinants of gambling & disordered gambling

Interested in better understanding: Factors that promote responsible gambling Factors that make some susceptible to problem gambling

Low prevalence of problem gambling requires over-sampling of at-risk groups

Longitudinal study as optimal methodology Over 5 years, with 4 data collections

Recruited through Random Digit Dialing (RDD) at 4 locations: Calgary Edmonton Grande Prairie (and surrounding communities) Lethbridge (and surrounding communities)

Start and end for data collection was staggered between sites Start: Feb 8, 2006 to Mar 20, 2006 End: Aug 26, 2006 to Oct 21, 2006

Recruited the following: Participants from the general population Participants at-risk of developing gambling problems

Based on frequency & amount of gambling

For all participants who met the criteria for age, residence, etc., there was the following at Wave 1:

Telephone interview by subcontract Adult interviews (~ 45 minutes) Adolescent interviews (~ 30 minutes) Majority of demographic & gambling questions

Face-to-face interview by RA’s Adult interviews (~ 3 hrs) Adolescent interviews (~ 2 hrs) Parent interviews (~ 40 minutes)

Response rate <10%

Differences for males and females

Pattern of relationship with predictor variables was different

Logistic regressions were separate for males and females

Constructs from Figure 1

Construct Measure

Internalizing and Externalizing

Problems

Individual Risk Taking Risk Taking

Family Environment Parental Monitoring Parental Monitoring (Adolescent & Parent)

Extra-Familial Environment

Social Support Loneliness and Social Dissatisfaction Scale (16 items)

Stressors/Life Events Coping Coping Inventory for Stressful Situations (CISS)

Physical Health – Eating Disorders

Eating Disorder Examination Questionnaire (EDE-Q 6.0)

Physical and Mental Health Wellness Index

Difficulty to recruit using Random Digit Dialing: Used Computer-Assisted Telephone Interview (CATI) Call display; Blocking; “Do not call” lists Saturation of the saturation

Difficulty to recruit at-risk or high-risk gamblers Supplemental recruitment techniques N=30 only!

Media release; Ads in local papers; Posters in casinos; “Snowball” e-mail

Telephone to face-to-face interview loss: Some did not feel $75 was enough incentive Booming economy vs. recession

Ability to look at changes in patterns of gambling behavior over time

3 more data collections:

Wave 2 completed from Nov. 2007 to Jun. 2008

Wave 3 started in Jul. 2009 to April 2010

Wave 4 will begin in the Winter of 2010

Wave 2 to 4 participants will complete web-based surveys

Gambling behavior will be tracked over all 5 years

Constructs associated with biological, psychological, & social factors will also be tracked