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Journal of Gambling Studies

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13.07.2017 | Original Paper

Disordered Gambling Prevalence: Methodological Innovations in a General Danish Population Survey

verfasst von: Glenn W. Harrison, Lasse J. Jessen, Morten I. Lau, Don Ross

Erschienen in: Journal of Gambling Studies | Ausgabe 1/2018

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Abstract

We study Danish adult gambling behavior with an emphasis on discovering patterns relevant to public health forecasting and economic welfare assessment of policy. Methodological innovations include measurement of formative in addition to reflective constructs, estimation of prospective risk for developing gambling disorder rather than risk of being falsely negatively diagnosed, analysis with attention to sample weights and correction for sample selection bias, estimation of the impact of trigger questions on prevalence estimates and sample characteristics, and distinguishing between total and marginal effects of risk-indicating factors. The most significant novelty in our design is that nobody was excluded on the basis of their response to a ‘trigger’ or ‘gateway’ question about previous gambling history. Our sample consists of 8405 adult Danes. We administered the Focal Adult Gambling Screen to all subjects and estimate prospective risk for disordered gambling. We find that 87.6% of the population is indicated for no detectable risk, 5.4% is indicated for early risk, 1.7% is indicated for intermediate risk, 2.6% is indicated for advanced risk, and 2.6% is indicated for disordered gambling. Correcting for sample weights and controlling for sample selection has a significant effect on prevalence rates. Although these estimates of the ‘at risk’ fraction of the population are significantly higher than conventionally reported, we infer a significant decrease in overall prevalence rates of detectable risk with these corrections, since gambling behavior is positively correlated with the decision to participate in gambling surveys. We also find that imposing a threshold gambling history leads to underestimation of the prevalence of gambling problems.

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These are: (8) have you been forced to go beyond what is strictly legal, in order to finance gambling or to pay gambling debts?; (9) have you risked or lost a significant relationship, job, educational or career opportunity because of gambling?; (10) have you sought help from others to provide money to relieve a desperate financial situation caused by gambling?

The question was, “Have you ever gambled at least 5 times in any one year of your life?”

For example, Regier et al. (1998; p. 110) comment that “Both the scientific and political implications of these high prevalence rates were highlighted by the timing of this release during the national debate on health reform. Major policy questions were raised about the need for mental health services that were implied by these high rates, along with concerns about possible insurance cost-benefit consequences. Some major media commentators identified such high rates as indicating a bottomless pit of possible demand for mental health services.” More recently, from Petry et al. (2014; p. 497): “The American Psychiatric Association requires strong empirical data in support of changes to DSM-5 that would substantially increase the base rate of a disorder.” But the only motivation then mentioned is the circular argument that reducing the number of threshold criteria would make the base rate increase.

On http://www.americangaming.org/social-responsibility/responsible-gaming. Accessed 9/21/2014.

Analyse Danmark have a panel of 25,000 active members, and Userneeds have a panel of 140,000 members. The two internet panels are regularly updated and member are recruited via the internet (banners, newsgroups, etc.), email, and by phone.

The gift cards were issued by www.gavekortet.dk, an internet based portal for gift cards.

The inconclusive findings where the SOGS is concerned might also stem from the absence of a conceptual or historical relationship between the SOGS and DSM criteria for GD.

Personal communication, August 24, 2016.

For instance, evaluating data from the NESARC Petry et al. (2005; Table 3, p. 570; model 3) report lower bounds of 95% confidence intervals of Odds Ratios in excess of 1 for alcohol dependence, any drug abuse, any drug dependence, nicotine dependence, major depressive episodes, dysthymic disorders, manic episodes, panic disorders, social phobia, specific phobia, generalized anxiety, and every personality disorder considered (avoidant, dependent, obsessive–compulsive, paranoid, schizoid, histrionic and antisocial). Kessler et al. (2008; Table 2, p. 1357) report a similar list from the NCS-R.

Major Depression and Anxiety are regarded as Axis-1 mental disorders. Impulsivity, in the psychiatric literature, is constructed as a personality trait.

There is a natural aggregation of these 10 sub-blocks into three groupings, often used in the field implementation of FLAGS (§1: RCB, RCM and POD, §2: ICC, RBE and RBL, and §3: ICB, POO, NGC and PST). We do not randomize across those groupings but do randomize within each grouping.

In the development of FLAGS by Schellink et al. (2015a; p. 149) the original 132 statements were randomized.

Sample weights are often used by economists when they are provided with data from well-designed surveys, but are rarely constructed by economists or gambling researchers in our experience. Harrison et al. (2005; §4.1) provide an exposition of procedures that can be used to construct sample weights for simple surveys. More complex sample designs require more sophisticated methods to construct sample weights: see Levy and Lemeshow (2008; Chapter 16).

As discussed earlier, when more refined categorizations are applied to DSM-based screen assessments, as in Gerstein et al. (1999), Fisher (2000), and Stone et al. (2015), the categories other than the designation as a current probable DG are sometimes interpreted by reference to ‘sub-clinical risk.’ This suggests that what is being measured is relative severity of symptoms. However, as with PGSI assessment, ‘risk’ refers not to risk of subsequently developing a more severe level of GD, but rather to risk that observation of lower symptom severity will lead to current false negative diagnosis.

This SNP approach is computationally less intensive than comparable approaches based on the estimation of kernel densities. There is some evidence from Stewart (2005) and De Luca (2008) that this SNP approach has good finite sample performance when compared to conventional parametric alternatives and other SNP estimators. Stewart (2004; §3) provides an excellent discussion of the mild regularity conditions required for the SNP approximation to be valid, and the manner in which it is implemented so as to ensure that a special case is the (ordered) probit specification.

It is common to refer to different levels of severity of psychiatric disorders. For instance, the American Psychiatric Association (2015; p. 586) discusses GD as follows, referring to DSM-5 criteria: “Severity is based on the number of criteria endorsed. Individuals with mild gambling disorder may exhibit only 4–5 of the criteria, with the most frequently endorsed criteria usually related to preoccupation with gambling and ‘chasing’ losses. Individuals with moderately severe gambling disorder exhibit more of the criteria (i.e., 6–7). Individuals with the most severe form will exhibit all or most of the nine criteria (i.e., 8–9).”

This is implied by the fact that the ‘at risk’ levels are jointly complementary to the ‘no detectable risk’ level, which is statistically significantly different from zero.

We do not dismiss results that cross the trip-wire of a 95% confidence interval, and simply avoid referring to them as statistically significant.

The NODS was developed by Gerstein et al. (1999) and builds on the DSM-IV gambling screen. It probes 17 reflective constructs with questions that measure lifetime and past-year risky gambling behaviors. Bonke and Borregaard (2006) compare the NODS instrument with the SOGS in a pre-test with 1232 subjects and find that the NODS detects a lower prevalence of GD.

Bonke and Borregaard (2006, 2009) also estimated prevalence based on a lifetime frame. Given the interpretation of ‘risk’ at work in the NODS, the lifetime frame prevalence risk estimate should be understood as referring to the probability that a respondent would have been falsely negatively diagnosed at some time in his or her life. In light of the FLAGS risk concept being prospective, there is no corresponding interpretation of any FLAGS measure.

The two questions are: “Have you ever lied to people important to you about how much you gambled?” and “Have you ever felt the need to bet more and more money?” The possible answers were: Yes, in the past 12 months; Yes, previously; No, I never gamble.

Ekholm et al. (2014) also estimated lifetime-frame prevalence, which has the same interpretation as in Bonke and Borregard (2006, 2009), and again has no counterpart measure in a FLAGS lifetime frame.

Ekholm et al. (2014; p. 8) discuss whether differences in response rates between 2005 and 2010 may affect the results. They mention that “non-response adjusted prevalence estimates did not indicate that non-response bias affects the conclusion of the present study (data not shown).”

A Pearson χ ² test of the hypothesis of no association has a p-value less than 0.001.

This value of setting FLAGS questions in a lifetime frame is thus conceptually distinct from the value of using lifetime frames for screens that understand ‘risk’ as probability of incorrect false diagnosis.

A Pearson χ ² test of the hypothesis of no association has a p-value less than 0.001. This is a two-sided test, but the direction of the effect is in the opposite of the alternative hypothesis that lifetime risks can be no smaller than recent risks.

Table F1 in Supplementary Appendix F tabulates the detailed results.

As mentioned previously, the DSM-based instrument we used was designed for a binary classification with respect to GD, so no interpretation of ‘risk’ is appropriate for it. Hence we do not display a counterpart to Fig. 7 for the DSM-based screen assessment.

Another example of additional threshold questions being used is the Canadian Community Health Survey of Mental Health and Well-Being of 2002. Of the sample of 36,984, 24.6% said that they had not engaged in any of 13 gambling activities in the past year. Then 46.3% of the total sample was not asked the full set of CPGI questions because they had only gambled between 1 and 5 for each of the 13 activities. And then 24.0% of the sample was not asked the full set of CPGI questions because they said that they were a non-gambler on the first CPGI question. There were 98 subjects that refused to answer the initial questions about gambling activity, resulting in only 1759 being asked the full set of questions and having any chance of being scored as “at risk.” These deviations from the CPGI screen, and the PGSI index derived from it, were “approved by the authors of the scale” (Statistics Canada 2004; p. 19).

There is some evidence from the field of clinical drug trials that persuading patients to participate in randomized studies is much harder than persuading them to participate in non-randomized studies (e.g., Kramer and Shapiro 1984; p. 2742ff). The same problem applies to social experiments, as evidenced by the difficulties that can be encountered when recruiting decentralized bureaucracies to administer random treatments (e.g., Hotz 1992). For example, Heckman and Robb (1985) note that the refusal rate in one randomized job training program was over 90%.

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Titel: Disordered Gambling Prevalence: Methodological Innovations in a General Danish Population Survey
verfasst von: Glenn W. Harrison
Lasse J. Jessen
Morten I. Lau
Don Ross
Publikationsdatum: 13.07.2017
Verlag: Springer US
Erschienen in: Journal of Gambling Studies / Ausgabe 1/2018
Elektronische ISSN: 1573-3602
DOI: https://doi.org/10.1007/s10899-017-9707-1

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Disordered Gambling Prevalence: Methodological Innovations in a General Danish Population Survey

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