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  • It may be likely that generic prevention programs for problem and pathological gambling, whether they are universal or targeted, will not be effective or cost-effective. This may be because there are fundamental differences in the factors that motivate and sustain gambling behavior between individuals. This study investigates whether there are types of gambling behavior beyond the typical “non-problem,” “problem,” and “pathological” gambler labels.
  • Again, this study investigates whether there are types of gambling behavior beyond the typical “non-problem,” “problem,” and “pathological” gambler labels.
  • It is conceptually possible to conceptualize gambling behavior as lying along a continuum of non-problem to pathological gambling behavior, with a (more or less) linear escalation in gambling related problems between the two ends of the spectrum. In fact, it might be expected given that the most commonly used definition of problem gambling is based on a sum of the individual diagnostic criteria endorsed and it is generally regarded that an increasing number of endorsed criteria signals more severe problems. Almost universally, however, problem gambling is treated as categorical by researchers. Despite the fact that the categories are almost always defined by the number of diagnostic criteria endorsed, the actual number of diagnostic criteria endorsed are almost never used in description or statistical analysis – an individual is almost always labeled a particular type of gambler. (Describe types.) It is appropriate to think of gambling as a latent variable both intuitively and because of the way it is operationalized in the field. Intuitively, it is not possible to follow individuals constantly and continuously observe their gambling behavior. In some sense, their gambling behavior is inherently “unobservable” or latent. In terms of the way problem gambling is operationalized, researchers ask individuals questions about their gambling behavior, which are answered with some degree of error, in order to tap into and gain information about the participant’s unknown gambling behavior.
  • Currently, there are approximately 20 measures for assessing problematic gambling behavior, including the: South Oaks Gambling Screen (SOGS), Lie/Bet Screen, NORC DSM Screen for Gambling Problems, Diagnostic Interview Schedule, Gambling Behavior Inventory, the Canadian Problem Gambling Index, the Adolescent Problem Gambling Index, and Massachusetts Adolescent Gambling Screen. The SOGS is a 20-question measure that operationalizes the diagnostic criteria described by the DSM-III-R. The SOGS was originally developed to screen for pathological gambling primarily in clinical populations. The Lie/Bet Screen is the most popular measure used to assess high-risk gambling behavior. The Lie/Bet Screen consists of only two questions: (1) have you ever felt the need to bet more and more money and, (2) have you ever had to lie to people important to you about how much you gamble? Most of the measures have been a set of ten to fifteen questions that directly ask about each of the ten diagnostic criteria, and have been used in only one or two studies. An example of this is the fifteen-question measure of gambling behavior included as part of the “Pathological Gambling (Betting)” section of the NESARC questionnaire. However, two new measures that are currently being developed in Canada, the Canadian Problem Gambling Index and Adolescent Problem Gambling Index, appear very promising.
  • Developmentalists, however, know that classifying individuals as non-pathological and pathological gamblers is not enough. There is a developmental process underlying pathological gambling; at any given point in time there is a group of individuals who are in the middle of developing into pathological gamblers. Furthermore, it is likely that there are types of problem and pathological gamblers who share similar developmental patterns and individual and contextual characteristics. Valid and reliable measures of pathological gambling should, ideally, distinguish individuals at a variety of stages along a continuum of problem gambling behavior, and distinguish types of problem gamblers with similar characteristics, broadly defined. As discussed above conceptually, researchers have attempted to use the current measures of problem and pathological gambling to do just that, but in a fairly crude way. By far, the most common way of distinguishing individuals is to sum the number of diagnostic criteria endorsed for each individual and label the individual a “non-problem,” “problem,” or “pathological” gambler based on this figure (0-2 = non-problem, 3-4 = problem [or “at-risk”], 5 or more = pathological [or “probable pathological”]). Rarely, if ever, are the specific criteria endorsed investigated for differences that may play an important role in the etiology and prevention of the development of more serious problem or pathological gambling. In addition, rarely, if ever, are additional characteristics about an individual or an individual’s behavior included as indicators of an individual’s gambling label.
  • Recently, researchers have begun to become interested in implementing problem gambling prevention programs. They have, however, been struggling with the best ways to intervene in a cost-effective manner. A few attempts have been made to design and implement problem gambling prevention programs with promising pilot study results. Currently, prevention initiatives for adult problem gambling favor a targeted approach, in which individuals identified as being at high-risk for developing pathological gambling are screened into the program. Existing prevention programs often use operationalizations of the diagnostic criteria to label individuals as “non-problem,” “problem,” and “pathological” gamblers based on the number of diagnostic criteria endorsed, in the usual way. Then… In addition, existing prevention programs provide the same content to all participants identified as being at high-risk, which is often a modified version of a substance use prevention program. There are many factors that are known to be related to problem gambling that are not addressed by the diagnostic criteria. These include impulsivity, gambling frequency, and drinking alcohol while gambling, to name a few. One option for targeted prevention… In addition, problem gambling prevention has met with limited success thus far. This may be the case because… It is likely that researchers interested in implementing prevention need to move beyond the traditional “non-problem,” “problem,” and “pathological” gambler labels. For example…
  • Be sure to talk about the diagnostic cutoffs for pathological gambling on the different measures.
  • Alternatively, this study uses latent class analysis (LCA), a statistical method that identifies groups of individuals characterized by similar patterns of behavior. There are conceptual similarities between LCA and factor analysis and LCA and cluster analysis but there are important differences between the methods.
  • Class membership probabilities. Symptom endorsement probabilities.
  • Models including from two to five latent classes were fit and compared. In addition to the BIC there were additional considerations used when assessing model fit like content, interpretation, substantive differences between classes, etc.
  • This is a table of the proportion of participants endorsing each diagnostic criterion.
  • Results suggest that a 3-class model best describes the gambling behavior of participants.
  • Results also suggest that it is important to take into account the nature of the diagnostic criteria endorsed, in conjunction with the total number of criteria met. In addition, results suggest that the current diagnostic criteria do not perform well when distinguishing types of individuals who are in the process of developing pathological gambling. These findings are particularly relevant to prevention research.
  • Note that men and women had similar class solutions, so we will not examine them separately here. The results presented here use the full sample.
  • Question #1: 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? By classifying individuals based on the nature of the diagnostic criteria endorsed, LCA provides an alternative approach to classifying simply based on the total number of diagnostic criteria met.
  • 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? ρ parameters for latent class 2 suggest that there is a lot of heterogeneity in behavior among members of that class. That is, the indicators are not distinguishing types of individuals very well at non-clinical levels of problem gambling. This suggests a possible need for other types of indicators or criteria in order to understand behavior at this level of development.
  • Gambling as a public health concern: traditional method confirms previous findings epidemiologically Etiology of gambling and problem gambling: not much here but did examine gender differences Implications for problem gambling prevention: screening!
  • .ppt

    1. 1. The Structure of Gambling Behavior in Adulthood Bethany Cara Bray The Methodology Center, The Prevention Research Center Department of Human Development and Family Studies The Pennsylvania State University Society for Prevention Research Annual Meeting Thursday, June 1, 2006
    2. 2. Acknowledgements <ul><li>Co-author : Dr. Linda M. Collins </li></ul><ul><li>Funding : </li></ul><ul><ul><li>Center for Prevention and Treatment Methodology (NIDA) : P50-DA-10075 </li></ul></ul><ul><ul><li>Prevention and Methodology Training (PAMT) Program (NIDA) : T32-DA-017629 </li></ul></ul><ul><li>Special thanks : S. T. Lanza, M. M. Maldonado-Molina, T. L. Root, K. J. Auerbach, J. L. Schafer </li></ul>
    3. 3. Outline <ul><li>The Idea </li></ul><ul><li>My Motivation </li></ul><ul><li>My Methods </li></ul><ul><li>Current Results </li></ul><ul><li>Discussion </li></ul>
    4. 4. Outline <ul><li>The Idea </li></ul><ul><li>My Motivation </li></ul><ul><li>My Methods </li></ul><ul><li>Current Results </li></ul><ul><li>Discussion </li></ul>
    5. 5. The Idea <ul><li>Are there different types of gamblers for whom different targeted prevention programs should be designed? </li></ul><ul><li>Important to move beyond the typical classification of gamblers and gambling behavior : </li></ul><ul><ul><li>“ Non-problem” gamblers </li></ul></ul><ul><ul><li>“ Problem” or “At-risk” gamblers </li></ul></ul><ul><ul><li>“ Pathological” gamblers </li></ul></ul>
    6. 6. Research Questions <ul><li>Are there identifiable types of gamblers? </li></ul><ul><ul><li>If so, are these types different for men and women? </li></ul></ul><ul><li>Is latent class analysis (LCA) a more useful method than traditional approaches when classifying gambling behavior? </li></ul><ul><li>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? </li></ul>
    7. 7. Outline <ul><li>The Idea </li></ul><ul><li>My Motivation </li></ul><ul><li>My Methods </li></ul><ul><li>Current Results </li></ul><ul><li>Discussion </li></ul>
    8. 8. Issues in the Conceptualization of PG <ul><li>Continuous vs. Categorical </li></ul><ul><ul><li>CATEGORICAL </li></ul></ul><ul><li>Manifest vs. Latent </li></ul><ul><ul><li>LATENT </li></ul></ul>
    9. 9. Operationalizations of PG <ul><li>DSM-III-R </li></ul><ul><ul><li>South Oaks Gambling Screen </li></ul></ul><ul><li>DSM-IV </li></ul><ul><ul><li>Lie/Bet Screen </li></ul></ul><ul><ul><li>General DSM-IV Screens </li></ul></ul><ul><ul><li>Canadian Problem Gambling Index </li></ul></ul>
    10. 10. Operationalization of PG <ul><li>“… 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.” </li></ul><ul><li>(Shaffer et al., 2004) </li></ul>
    11. 11. The Prevention of PG <ul><li>Universal Programs </li></ul><ul><li>Targeted/Indicated Programs </li></ul><ul><ul><li>Screening typically uses diagnostic criteria </li></ul></ul>
    12. 12. Outline <ul><li>The Idea </li></ul><ul><li>My Motivation </li></ul><ul><li>My Methods </li></ul><ul><li>Current Results </li></ul><ul><li>Discussion </li></ul>
    13. 13. The NESARC <ul><li>2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions </li></ul><ul><li>Sponsored by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) </li></ul><ul><li>A source for information and data on the U.S. population for : </li></ul><ul><ul><li>Alcohol and drug use </li></ul></ul><ul><ul><li>Alcohol and drug abuse and dependence </li></ul></ul><ul><ul><li>Associated psychiatric and other medical comorbidities </li></ul></ul><ul><li>Representative sample of the U.S. population, aged 18 years and older </li></ul><ul><li>N = 43,093 </li></ul>
    14. 14. Participants <ul><li>Screening question </li></ul><ul><ul><li>Includes all participants who had ever gambled five or more times in any one year </li></ul></ul><ul><li>N = 11,153 </li></ul><ul><ul><li>Males : n = 6,000 </li></ul></ul><ul><ul><li>Females : n = 5,153 </li></ul></ul>
    15. 15. Measures <ul><li>Questionnaire </li></ul><ul><ul><li>15 questions operationalize 10 diagnostic criteria for lifetime pathological gambling </li></ul></ul><ul><li>Lifetime pathological gambling indicators </li></ul><ul><ul><li>1 indicator created for each of the 10 diagnostic criteria </li></ul></ul>
    16. 16. Measures <ul><li>DSM-IV Diagnostic Criteria : </li></ul><ul><ul><li>Preoccupation </li></ul></ul><ul><ul><li>Tolerance </li></ul></ul><ul><ul><li>Loss of control </li></ul></ul><ul><ul><li>Withdrawal </li></ul></ul><ul><ul><li>Escape </li></ul></ul><ul><ul><li>Chasing </li></ul></ul><ul><ul><li>Lying </li></ul></ul><ul><ul><li>Illegal acts </li></ul></ul><ul><ul><li>Risking significant relationship </li></ul></ul><ul><ul><li>Bailout </li></ul></ul>
    17. 17. Measures <ul><li>DSM-IV Diagnostic Criteria : </li></ul><ul><ul><li>Preoccupation with gambling </li></ul></ul><ul><ul><li>Needing to gamble with increasing amounts of money </li></ul></ul><ul><ul><li>Being unsuccessful at controlling/stopping gambling </li></ul></ul><ul><ul><li>Being restless/irritable when controlling gambling </li></ul></ul><ul><ul><li>Gambling to escape problems or a dysphoric mood </li></ul></ul><ul><ul><li>Returning another day to get even (chasing) </li></ul></ul><ul><ul><li>Lying to conceal extent of gambling involvement </li></ul></ul><ul><ul><li>Committing illegal acts to finance gambling </li></ul></ul><ul><ul><li>Risking significant relationship/job/opportunity </li></ul></ul><ul><ul><li>Relying on others to relieve a financial situation </li></ul></ul>
    18. 18. Traditional Analyses <ul><li>Classify participants based on the number of diagnostic criteria endorsed : </li></ul><ul><ul><li>0 – 2 criteria = “Non-problem” gamblers </li></ul></ul><ul><ul><li>3 – 4 criteria = “Problem” gamblers </li></ul></ul><ul><ul><li>5 + criteria = “Pathological” gamblers </li></ul></ul><ul><li>Examine proportion of participants endorsing each individual criterion </li></ul>
    19. 19. Latent Class Analysis <ul><li>Statistical method </li></ul><ul><li>Identifies exclusive groups of individuals </li></ul><ul><ul><li>Groups characterized by similar patterns of behavior </li></ul></ul><ul><li>Models underlying group structure of a single, static, categorical latent (unobserved) variable </li></ul><ul><ul><li>Uses categorical indicators of behavior </li></ul></ul>
    20. 20. LCA Parameters <ul><li> : Gamma : marginal probability of latent class membership </li></ul><ul><ul><li>Probability of membership in the “non-problem gambler” latent class </li></ul></ul><ul><li> : Rho : measurement parameter; describes how individuals within a latent class response to indicators </li></ul><ul><ul><li>Probability of endorsing the “preoccupation” indicator, conditional on latent class membership </li></ul></ul>
    21. 21. Models <ul><li>Different numbers of latent classes </li></ul><ul><ul><li>2, 3, 4, 5 class models </li></ul></ul><ul><li>Bayesian Information Criterion (BIC) used to select the most well-fitting model </li></ul><ul><li>Examined three groups of participants </li></ul><ul><ul><li>All participants </li></ul></ul><ul><ul><li>Males </li></ul></ul><ul><ul><li>Females </li></ul></ul>
    22. 22. Outline <ul><li>The Idea </li></ul><ul><li>My Motivation </li></ul><ul><li>My Methods </li></ul><ul><li>Current Results </li></ul><ul><li>Discussion </li></ul>
    23. 23. Traditional Results
    24. 24. Traditional Results
    25. 25. Latent Class Analysis Results
    26. 26. Latent Class Analysis Results
    27. 27. Latent Class Analysis Results
    28. 28. Latent Class Analysis Results 
    29. 29. Outline <ul><li>The Idea </li></ul><ul><li>My Motivation </li></ul><ul><li>My Methods </li></ul><ul><li>Current Results </li></ul><ul><li>Discussion </li></ul>
    30. 30. Question #1 <ul><li>Are there identifiable types of gamblers? If so, are these types different for men and women? </li></ul>
    31. 31. Question #1 <ul><li>3-class model best describes the gambling behavior of participants – 3 identifiable types of gamblers : </li></ul><ul><ul><li>Non-problem gamblers </li></ul></ul><ul><ul><ul><li>No diagnostic criteria endorsed </li></ul></ul></ul><ul><ul><li>Preoccupied gamblers </li></ul></ul><ul><ul><ul><li>Moderate endorsement of being preoccupied with gambling </li></ul></ul></ul><ul><ul><li>Pathological gamblers </li></ul></ul><ul><ul><ul><li>Endorse : </li></ul></ul></ul><ul><ul><ul><ul><li>Being preoccupied with gambling </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Needing to gamble with increasing amounts of money </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Not being able to control/cut back/stop gambling </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Resorting to “chasing” behavior to win back losses </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Lying to others to conceal extent of gambling involvement </li></ul></ul></ul></ul>
    32. 32. Question #2 <ul><li>Is latent class analysis (LCA) a more useful method than traditional approaches when classifying gambling behavior? </li></ul>
    33. 33. Question #2 <ul><li>An alternative approach to classifying individuals simply based on the total number of diagnostic criteria met </li></ul><ul><li>LCA can help identify types of gambles with similar patterns of behavior </li></ul><ul><ul><li>May be helpful when designing targeted prevention programs </li></ul></ul>
    34. 34. Question #3 <ul><li>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? </li></ul>
    35. 35. Question #3 <ul><li>ρ parameters for latent class 2 (“preoccupied” gamblers) suggest a lot of heterogeneity </li></ul><ul><li>Suggests possible need for other types of indicators or criteria in order to understand behavior at this level of development </li></ul>
    36. 36. Our Themes <ul><li>Gambling as a Public Health Concern </li></ul><ul><li>Etiology of Gambling and Problem Gambling </li></ul><ul><li>Implications for Problem Gambling Prevention </li></ul>
    37. 37. “Where Do We Go From Here?” <ul><li>Further investigation of the categorical latent structure of gambling behavior </li></ul><ul><ul><li>Posterior predictive check distribution for model selection </li></ul></ul><ul><ul><li>Power of hypothesis tests </li></ul></ul><ul><li>Include other indicators of gambling behavior that move beyond the diagnostic criteria </li></ul><ul><li>Include other important grouping variables </li></ul><ul><ul><li>Race / ethnicity, Age, Income, Religion </li></ul></ul>
    38. 38. “Where Do We Go From Here?” <ul><li>Include predictors of latent class membership </li></ul><ul><ul><li>Alcohol use </li></ul></ul><ul><ul><li>Other substance use </li></ul></ul><ul><ul><li>Psychiatric and psychological disorders </li></ul></ul><ul><ul><ul><li>Depression </li></ul></ul></ul><ul><ul><ul><li>Anxiety </li></ul></ul></ul><ul><li>Extend longitudinally to address change in latent class membership </li></ul><ul><ul><li>LTA, ALTA </li></ul></ul>
    39. 39. References <ul><li>NESARC Website : http://niaaa.census.gov </li></ul><ul><li>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. </li></ul>

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