Presented at the 6th International Gambling Conference in February 2016. Presented by Glenn Harrison

1. Psychiatric Measures of Gambling in the
General Population: A Reconsideration
Glenn Harrison
Center for the Economic Analysis of Risk
Auckland, February 2016
Joint with Morten Lau and Don Ross

2. Motivating questions
> What do we mean by “problem gambling”?
o People that are “bad for business”
o People that (should) clinically present for treatment
o People that suffer a welfare loss from gambling choices
> What is the prevalence of problem gambling …
o In the general population
o In self-selected populations, such as active gamblers
> Can surveys be used to assess this prevalence?

3. Our overall design
Surveys
Lab
Experiments
Field
Experiments

4. Three issues with surveys
> I. They tend to reflect existing gambling, not the latent
propensity to gamble
o Likely to imply understatement of gambling problems
> II. They use “trigger questions” which lead to the
possibility of sample selection bias in inferences about
general population prevalence
o Likely to imply understatement of gambling problems
> III. They are statistically analyzed in a way that suggests
massive co-morbidities with many other psychiatric
disorders

5. Surveys of general prevalence, I
> Generally “reflective” of a history of gambling
o Does it lead to “disruptions in life,” such as bankruptcy, lying,
divorce, criminal activity? DSM-IV: “persistent and recurrent
maladaptive gambling behavior” (p.615)
o Does it lead people to “clinically present” for treatment?
> Not well designed to detect latent, “formative” propensity
to gamble (whether or not it leads to problem gambling)
o Some surveys take these into account, all in our Danish work
reviewed by Morten and Don in the next session…
 Focal Adult Gambling Screen (FLAGS)
 The Gambling Craving Scale (GACS)
 The Gambling Related Cognition Scale (GRCS)
 The Gambling Urge Screen (GUS)

6. Surveys of general prevalence, II
> The use of trigger questions
o Various forms, but things like “Have you ever lost $100 from
gambling?”
o Only if this is answered affirmatively are the diagnostic questions
asked
> Should be classified as “no detectable risk,” as in FLAGS
o But they can never, by definition, show up as problem gamblers
> To an economist, this is simply sample selection bias
o Some process generates the observed sample in a way that could
lead to biased inferences about the population
o Standard statistical corrections

7. Existing surveys
> Objective has been to mimic the criteria that psychiatrists
would use to diagnose “problem gambling” for clinical
purposes
o People only ever present clinically if they have had a gambling
problem causing them to be concerned, so reflective constructs
are therefore natural
> Dominance of criteria from the Diagnostic and Statistical
Manual of Mental Disorders (DSM)

8. Existing surveys
> Objective has been to mimic the criteria that psychiatrists
would use to diagnose “problem gambling” for clinical
purposes
o People only ever present clinically if they have had a gambling
problem causing them to be concerned, so reflective constructs
are therefore natural
> Dominance of criteria from the Diagnostic and Statistical
Manual of Mental Disorders (DSM) Important changes in
these, which have
generated massive
controversies (see
Allen Frances books)

10. Every
Question
assumes
someone has
done some
gambling

13. Major prevalence surveys
Survey Country Year Sample
NESARC Wave 1 USA 2000‐2001 43,093
NCS‐R USA 2001‐2003 9,282
CCHS Mental Health and Well‐Being Canada 2002 34,770
BGPS U.K. 2010 7,756
Legend:
NESARC – National Epidemiologic Survey on Alcohol and Related Conditions
NCS-R – National Comorbidity Survey Replication
CCHS – Canadian Community Health Survey
BGPS – British Gambling Prevalence Survey

14. By the way…
> Gambling disorders now completed dropped
o from later NESARC waves in the US
o from later CCHS waves in Canada
> Why?
> Extremely low prevalence estimates?

15. NESARC
Response N Fraction
Yes 11,153 26%
No (or invalid) 31,940 74%

16. NESARC, II

20. So 0.45% pathological gambling in the general population.
Less than half of 1 percentage point.

21. Why use trigger questions?
> Saves time on long surveys
o Yes, we get this
o But with popular instruments that only have a few questions?

22. Why use trigger questions?
> Saves time on long surveys
o Yes, we get this
o But with popular instruments that only have a few questions?
> Many of the follow-on questions sound contrived or odd
if someone has never gambled or lost money
o Yes, but then also use questions getting at formative constructs

23. Why use trigger questions?
> Saves time on long surveys
o Yes, we get this
o But with popular instruments that only have a few questions?
> Many of the follow-on questions sound contrived or odd
if someone has never gambled or lost money
o Yes, but then also use questions getting at formative constructs
> Everybody else does it
> Didn’t think of it before today

24. Why use trigger questions?
> Saves time on long surveys
o Yes, we get this
o But with popular instruments that only have a few questions?
> Many of the follow-on questions sound contrived or odd
if someone has never gambled or lost money
o Yes, but then also use questions getting at formative constructs
> Everybody else does it
> Didn’t think of it before today
> Want to generate low estimates of gambling prevalence
o Very convenient for gambling industry

25. Not just gambling…
> In NESARC there are trigger questions for every
psychiatric disorder
> My favorite: Specific Social Phobias
> After a trigger question they are then asked if they have
strong fear or avoidance of being interviewed

26. Solutions
> Ask the threshold gambling question after asking the
prevalence questions
o We do this in Denmark with FLAGS, PGSI & DSM – Morten and
Don to discuss in the next session
> Correct statistically using sample selection bias methods
o Due to James Heckman: Nobel Prize in Economics for 2000
o Basic logic is to jointly model the sample-generating process and
the process explaining extent of gambling problems
 Probit model of participation (needs data on non-participants)
 Then see if errors in that participation model are correlated with the
errors of the process of interest, the extent of gambling problems

28. Surveys of general prevalence, III
> Gambling as a psychiatric disorder seems highly
correlated with lots of other psychiatric disorders
o Implications for treatment and therapy

29. Surveys of general prevalence, III
> Gambling as a psychiatric disorder seems highly
correlated with lots of other psychiatric disorders
o Implications for treatment and therapy

30. Surveys of general prevalence, III
> Gambling as a psychiatric disorder seems highly
correlated with lots of other psychiatric disorders
o Implications for treatment and therapy

31. Surveys of general prevalence, III
> Gambling as a psychiatric disorder seems highly
correlated with lots of other psychiatric disorders
o Implications for treatment and therapy
> But this is statistically detected by estimating the
unconditional correlation of gambling with other
disorders – the “total effect”

32. Surveys of general prevalence, III
> Gambling as a psychiatric disorder seems highly
correlated with lots of other psychiatric disorders
o Implications for treatment and therapy
> But this is statistically detected by estimating the
unconditional correlation of gambling with other
disorders – the “total effect”
> A different question is answered by the conditional
correlation of gambling with other disorders – the
“marginal effect”
> Both questions are interesting, but only the first is ever
answered

33. Solution
> Model the correlation and also control for other psychiatric
disorders
o Ordered probit rather than binary probit
o Infer the marginal effect of each psychiatric disorder on gambling
disorder, to measure conditional correlation
> Again, measuring unconditional correlation is not an error
o It is just not the only type of correlation we are interested in
o I would argue that unconditional comorbidity is not that interesting

34. What we do
> Evaluate comorbidity of gambling disorders and other
disorders using major national epidemiological surveys
o US (NESARC and NCS-R), Canada (CCHS) and Britain (BGPS)
o Just show results for NESARC here
 Correct and replicate Petry et al. [2005]
 Same qualitative results for NCS-R, CCHS and BGPS
> Show marginal effect and total effect to compare
> Then correct estimates of comorbidities for sample
selection bias
> Then show predicted gambling hierarchy with sample
selection correction

40. Conclusions and Limitations
> Trigger questions can generate massive sample
selection bias in gamblers at risk
o Have we been significantly underestimating the “at risk” fraction
of the population?
> Comorbidities of gambling should be evaluated
conditionally and unconditionally (total and marginal)
o Dramatic overstatement of comorbidity if unconditional
comorbidity is interpreted as a conditional comorbidity
> Avoid trigger questions and do more econometrics
> Limitations
o Data on the unwashed and unsampled?
o Statistical assumptions are needed

42. Our overall design
Surveys
Lab
Experiments
Field
Experiments
1. Interpreting existing surveys
2. Designing better surveys

43. Our overall design
Surveys
Lab
Experiments
Field
Experiments