Psychiatric Measures of Gambling in theGeneral Population: A Reconsideration

Glenn Harrison

Center for the Economic Analysis of Risk

Auckland, February 2016

Joint with Morten Lau and Don Ross

Motivating questions

> What do we mean by “problem gambling”?o People that are “bad for business”o People that (should) clinically present for treatmento People that suffer a welfare loss from gambling choices

> What is the prevalence of problem gambling …o In the general populationo In self-selected populations, such as active gamblers

> Can surveys be used to assess this prevalence?

Our overall design

Surveys

Lab Experiments

Field Experiments

Three issues with surveys

> I. They tend to reflect existing gambling, not the latent propensity to gambleo 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 prevalenceo 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

Surveys of general prevalence, I

> Generally “reflective” of a history of gamblingo 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)

Surveys of general prevalence, II

> The use of trigger questionso 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

Existing surveys

> Objective has been to mimic the criteria that psychiatrists would use to diagnose “problem gambling” for clinical purposeso 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)

Existing surveys

> Objective has been to mimic the criteria that psychiatrists would use to diagnose “problem gambling” for clinical purposeso 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)

EveryQuestionassumes

someone has done some gambling

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 ConditionsNCS-R – National Comorbidity Survey ReplicationCCHS – Canadian Community Health SurveyBGPS – British Gambling Prevalence Survey

By the way…

> Gambling disorders now completed droppedo from later NESARC waves in the USo from later CCHS waves in Canada

> Why?

> Extremely low prevalence estimates?

NESARC

Response N Fraction

Yes 11,153 26%

No (or invalid) 31,940 74%

NESARC, II

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

Why use trigger questions?

> Saves time on long surveyso Yes, we get thiso But with popular instruments that only have a few questions?

Why use trigger questions?

> Saves time on long surveyso Yes, we get thiso 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 moneyo Yes, but then also use questions getting at formative constructs

Why use trigger questions?

> Saves time on long surveyso Yes, we get thiso 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 moneyo Yes, but then also use questions getting at formative constructs

> Everybody else does it

> Didn’t think of it before today

Why use trigger questions?

> Saves time on long surveyso Yes, we get thiso 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 moneyo 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 prevalenceo Very convenient for gambling industry

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

Solutions

> Ask the threshold gambling question after asking the prevalence questionso 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 2000o 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

Surveys of general prevalence, III

> Gambling as a psychiatric disorder seems highly correlated with lots of other psychiatric disorderso Implications for treatment and therapy

Surveys of general prevalence, III

> Gambling as a psychiatric disorder seems highly correlated with lots of other psychiatric disorderso Implications for treatment and therapy

Surveys of general prevalence, III

> Gambling as a psychiatric disorder seems highly correlated with lots of other psychiatric disorderso Implications for treatment and therapy

Surveys of general prevalence, III

> Gambling as a psychiatric disorder seems highly correlated with lots of other psychiatric disorderso Implications for treatment and therapy

> But this is statistically detected by estimating the unconditional correlation of gambling with other disorders – the “total effect”

Surveys of general prevalence, III

> Gambling as a psychiatric disorder seems highly correlated with lots of other psychiatric disorderso 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 conditionalcorrelation of gambling with other disorders – the “marginal effect”

> Both questions are interesting, but only the first is ever answered

Solution

> Model the correlation and also control for other psychiatric disorderso Ordered probit rather than binary probito Infer the marginal effect of each psychiatric disorder on gambling

disorder, to measure conditional correlation

> Again, measuring unconditional correlation is not an erroro It is just not the only type of correlation we are interested ino I would argue that unconditional comorbidity is not that interesting

What we do

> Evaluate comorbidity of gambling disorders and other disorders using major national epidemiological surveyso 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

Conclusions and Limitations

> Trigger questions can generate massive sample selection bias in gamblers at risko 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

> Limitationso Data on the unwashed and unsampled?o Statistical assumptions are needed

Our overall design

Surveys

Lab Experiments

Field Experiments

1. Interpreting existing surveys2. Designing better surveys

Our overall design

Surveys

Lab Experiments

Field Experiments