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The Structure of Gambling Behavior in Adulthood

Bethany Cara BrayThe Methodology Center, The Prevention Research

CenterDepartment of Human Development and Family Studies

The Pennsylvania State University

Society for Prevention ResearchAnnual Meeting

Thursday, June 1, 2006

Acknowledgements

• Co-author : Dr. Linda M. Collins

• Funding : Center for Prevention and Treatment Methodology (NIDA) : P50-DA-10075

Prevention and Methodology Training (PAMT) Program (NIDA) : T32-DA-017629

• Special thanks : S. T. Lanza, M. M. Maldonado-Molina, T. L. Root, K. J. Auerbach, J. L. Schafer

Outline

• The Idea• My Motivation• My Methods• Current Results• Discussion

Outline


The Idea

• Are there different types of gamblers for whom different targeted prevention programs should be designed?

• Important to move beyond the typical classification of gamblers and gambling behavior : “Non-problem” gamblers “Problem” or “At-risk” gamblers “Pathological” gamblers

Research Questions

• Are there identifiable types of gamblers? If so, are these types different for men and women?

• Is latent class analysis (LCA) a more useful method than traditional approaches when classifying gambling behavior?

• What can LCA tell us about the performance of the diagnostic criteria when they are used to identify individuals with non-clinical levels of problem gambling for targeted prevention programs?

Outline


Issues in the Conceptualization of PG

• Continuous vs. Categorical CATEGORICAL

• Manifest vs. Latent LATENT

Operationalizations of PG

• DSM-III-R South Oaks Gambling Screen

• DSM-IV Lie/Bet Screen General DSM-IV Screens Canadian Problem Gambling Index

Operationalization of PG

“…the unidimensional additive scoring of screening instruments is inadequate to represent a multidimensional latent state. The method of summing endorsed characteristics assumes that all dimensions exist on the same additive continuum and that all dimensions equally predict gambling disorders… This equivalence is highly unlikely and misleading.”

(Shaffer et al., 2004)

The Prevention of PG

• Universal Programs• Targeted/Indicated Programs

Screening typically uses diagnostic criteria

Outline


The NESARC

• 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions

• Sponsored by the National Institute on Alcohol Abuse and Alcoholism (NIAAA)

• A source for information and data on the U.S. population for : Alcohol and drug use Alcohol and drug abuse and dependence Associated psychiatric and other medical comorbidities

• Representative sample of the U.S. population, aged 18 years and older

• N = 43,093

Participants

• Screening question Includes all participants who had ever gambled five or more times in any one year

• N = 11,153 Males : n = 6,000 Females : n = 5,153

Measures

• Questionnaire 15 questions operationalize 10 diagnostic criteria for lifetime pathological gambling

• Lifetime pathological gambling indicators 1 indicator created for each of the 10 diagnostic criteria

Measures

• DSM-IV Diagnostic Criteria : Preoccupation Tolerance Loss of control Withdrawal Escape Chasing Lying Illegal acts Risking significant relationship Bailout

Measures

• DSM-IV Diagnostic Criteria : Preoccupation with gambling Needing to gamble with increasing amounts of money Being unsuccessful at controlling/stopping gambling Being restless/irritable when controlling gambling Gambling to escape problems or a dysphoric mood Returning another day to get even (chasing) Lying to conceal extent of gambling involvement Committing illegal acts to finance gambling Risking significant relationship/job/opportunity Relying on others to relieve a financial situation

Traditional Analyses

• Classify participants based on the number of diagnostic criteria endorsed : 0 – 2 criteria = “Non-problem” gamblers 3 – 4 criteria = “Problem” gamblers 5 + criteria = “Pathological” gamblers

• Examine proportion of participants endorsing each individual criterion

Latent Class Analysis

• Statistical method• Identifies exclusive groups of individuals Groups characterized by similar patterns of behavior

• Models underlying group structure of a single, static, categorical latent (unobserved) variable Uses categorical indicators of behavior

LCA Parameters

• : Gamma : marginal probability of latent class membership Probability of membership in the “non-problem gambler” latent class

• : Rho : measurement parameter; describes how individuals within a latent class response to indicators Probability of endorsing the “preoccupation” indicator, conditional on latent class membership

Models

• Different numbers of latent classes 2, 3, 4, 5 class models

• Bayesian Information Criterion (BIC) used to select the most well-fitting model

• Examined three groups of participants All participants Males Females

Outline


Non-problem 10,562 94.8% 5,637 94.0% 4,925 95.8%(0-2 criteria)Problem 379 3.4% 235 3.9% 144 2.8%(3-4 criteria)Pathological 202 1.8% 128 2.1% 74 1.4%(5+ criteria)

FemalesN = 6,000 N = 5,143

All GamblersN = 11,143

Males

Traditional Results

Traditional Results

Criteria Frequency ProportionPreoccupation 1355 12.16%Increasing $ 714 6.41%Unable to Control 326 2.93%Restless/Irritable 135 1.21%Escape Problems 671 6.02%Chasing 790 7.09%Lying to Conceal 373 3.35%Illegal Acts 42 0.38%Risk Relationship 116 1.04%Bailout 145 1.30%

Diagnostic Criteria Endorsement

Latent Class Analysis Results

Classes BIC

2 1524.9623 928.6894 939.0235 990.618

Fit Statistics


Probabilities of Endorsing Diagnostic Critera

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Diagnostic Criteria

Pro

babilit

y o

f En

dors

em

en

t

Non-problemPreoccupiedPathological


C1 C2 C3 C4 C5 C6 C7 C8 C9 C10Non-problem 0.050 0.013 0.006 0.000 0.025 0.012 0.004 0.000 0.001 0.000Preoccupied 0.611 0.408 0.120 0.024 0.275 0.451 0.172 0.006 0.034 0.043Pathological 0.966 0.713 0.707 0.573 0.613 0.894 0.764 0.185 0.367 0.487

: Probability of Endorsing Diagnostic Criteria

Non-problem Gamblers 0.883Preoccupied Gamblers 0.100Pathological Gamblers 0.017

γ : Probability of Latent Class Membership


Outline


Question #1

• Are there identifiable types of gamblers? If so, are these types different for men and women?

Question #1

• 3-class model best describes the gambling behavior of participants – 3 identifiable types of gamblers : Non-problem gamblers

No diagnostic criteria endorsed Preoccupied gamblers

Moderate endorsement of being preoccupied with gambling Pathological gamblers

Endorse :• Being preoccupied with gambling• Needing to gamble with increasing amounts of money • Not being able to control/cut back/stop gambling• Resorting to “chasing” behavior to win back losses• Lying to others to conceal extent of gambling involvement

Question #2

• Is latent class analysis (LCA) a more useful method than traditional approaches when classifying gambling behavior?

Question #2

• An alternative approach to classifying individuals simply based on the total number of diagnostic criteria met

• LCA can help identify types of gambles with similar patterns of behavior May be helpful when designing targeted prevention programs

Question #3

• What can LCA tell us about the performance of the diagnostic criteria when they are used to identify individuals with non-clinical levels of problem gambling for targeted prevention programs?

Question #3

• ρ parameters for latent class 2 (“preoccupied” gamblers) suggest a lot of heterogeneity

• Suggests possible need for other types of indicators or criteria in order to understand behavior at this level of development

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10Non-problem 0.050 0.013 0.006 0.000 0.025 0.012 0.004 0.000 0.001 0.000Preoccupied 0.611 0.408 0.120 0.024 0.275 0.451 0.172 0.006 0.034 0.043Pathological 0.966 0.713 0.707 0.573 0.613 0.894 0.764 0.185 0.367 0.487

: Probability of Endorsing Diagnostic Criteria

Our Themes

• Gambling as a Public Health Concern

• Etiology of Gambling and Problem Gambling

• Implications for Problem Gambling Prevention

“Where Do We Go From Here?”

• Further investigation of the categorical latent structure of gambling behavior Posterior predictive check distribution for model selection

Power of hypothesis tests

• Include other indicators of gambling behavior that move beyond the diagnostic criteria

• Include other important grouping variables Race / ethnicity, Age, Income, Religion

“Where Do We Go From Here?”

• Include predictors of latent class membership Alcohol use Other substance use Psychiatric and psychological disorders

Depression Anxiety

• Extend longitudinally to address change in latent class membership LTA, ALTA

References

• NESARC Website : http://niaaa.census.gov

• Shaffer, H. J., LaBrie, R. A., LaPlante, D. A., Nelson, S. E., and Stanton, M. V. (2004). The road less traveled: Moving from distribution to determinants in the study of gambling epidemiology. Canadian Journal of Psychiatry, 49, 8, 504-516.

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