Report
Gambling Survey for Great Britain - Annual report (2023): Official statistics
Gambling Survey for Great Britain - annual report (2023): Official statistics
Consequences from gambling
This section cross refers to information that can be found in an accompanying set of data tables, specifically Tables D.1 to D.16.
Background
Gambling can lead to a range of adverse consequences. This includes the experience of gambling disorder (a recognised health condition1) but can also include wide ranging adverse consequences2 experienced either by the person who gambles or by their family, friends, and wider social networks. These consequences range in severity and include negative effects on physical and mental health, relationship discord and breakdown, and financial difficulties3.
In this chapter, data is first presented on the adverse consequences of gambling as measured using the Problem Gambling Severity Index (PGSI) (opens new tab) (PDF). This is followed by information on a wider range of adverse consequences from gambling which are not included within the PGSI (for example, conflict with family, social isolation, relationship breakdown, experience of violence and abuse).
Prior surveys, including the British Gambling Prevalence Survey (BGPS) series (opens new tab) (PDF) and the Health Surveys (HS) for England (opens in new tab) and Health Surveys (HS) for Scotland (opens new tab) included the PGSI but did not include questions on the wider adverse consequences of gambling. Specifically, they did not measure the effects of gambling on people other than the person gambling. The Gambling Survey for Great Britain (GSGB) is the first survey to include questions about adverse consequences arising from participants’ own gambling and someone else’s gambling.
Measuring adverse consequences from gambling in surveys
The data reported in this chapter relies on direct reporting from participants on a range of experiences. When it comes to the measurement of gambling and its associated impacts and consequences, this is a challenging task.
“Given the widespread negative social norms around gambling, particularly harmful gambling, obtaining representative samples and accurate response data is at the more difficult end of what survey researchers seek to measure in general populations.”
Professor Patrick Sturgis, Assessment of the Gambling Survey for Great Britain (GSGB) - 2024 (opens new tab)
The GSGB uses a push-to-web survey methodology. Push-to-web surveys are increasingly becoming the norm, replacing face-to-face methods which are experiencing rising costs and declining response rates. The new push-to-web methodology means that estimates presented in this chapter are not directly comparable with results from prior gambling or health surveys and such comparisons should not be used to assess trends over time. The GSGB data reported here represents the first year of a new baseline, against which future changes can be compared.
That said, some limited comparisons are useful to assess differences between study methodologies. All surveys are subject to a range of potential biases which may affect results. The GSGB, the prior HS and the BGPS series are no different. These prior studies and the GSGB have all used the PGSI to measure problem gambling, defined by Ferris and Wynne (opens new tab) (PDF) (who created the PGSI) as “gambling behaviour that creates negative consequences for the [person who gambles], others in his or her social network, or for the community”.
The GSGB appears to produce higher estimates of problem gambling than these prior studies. There are several potential reasons for this. The first relates to the lower response rates that the push-to-web design achieves. People who gamble, and those who gamble more heavily, may be more likely to complete the GSGB than those who do not gamble. As PSGI scores are higher for those with more gambling engagement, a lower response rate would serve to increase reported PGSI scores. There is plausible evidence suggesting that this may be the case4.
Second, prior surveys may have underestimated PGSI scores and/or underestimated online gambling behaviours as a result of socially desirable responding. Sturgis noted that “there [were] good grounds to suggest the presence of an interviewer (as used by the BGPS and HS series) induces a downward bias on estimates of the prevalence of gambling harm”.
Third, it may be that PGSI scores have actually increased in the population over time. Online gambling is strongly associated with elevated PGSI scores and gross gambling yield from online gambling has increased substantially since 2018. These changes in the gambling market could affect the PSGI scores estimated in the survey. All these things could be true, either alone or in combination.
Whatever the true cause, the PGSI scores presented in this report are substantially higher than estimates from previous studies. There has been some research undertaken on this matter but, as Sturgis notes, the 2 studies which investigated this were unable to come to a definitive conclusion about the magnitude of the errors. Uncertainty around which estimates (the GSGB or prior studies) are closer to the truth therefore remain. Further investigation of the reasons for this difference is needed to better understand the scale and direction of impact upon the GSGB estimates. Until more and better evidence is available on this question, uncertainty will remain over which methodological approach produces estimates which are closest to the truth.
Nonetheless, the first year of GSGB data provides a new baseline against which future trends can be judged and it will allow more detailed patterns between different groups of people who gamble to be investigated, providing insight in the distribution of adverse consequences across communities. To do it, this chapter presents information on the prevalence of PGSI scores, as future GSGB surveys will seek to compare changes to this baseline. Patterns in PGSI scores between different groups of people and people who engage in different types of gambling activity is also included.
References
1International Classification of Diseases 11th Revision (ICD-11). Geneva: World Health Organisation; 2018. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders - 5th edition. 5th ed. ed. Arlington, VA: American Psychiatric Association; 2013.
2The term adverse consequences is used by Langham et al, to describe the potential range of harms experienced from gambling and it is used by Ferris and Wynne in the Problem Gambling Severity Index’s (PGSI) description of what low risk, moderate risk and problem gambling means. The term is often used interchangeably with negative consequences (this term being used in the ICD-11 definition of gambling disorder) but because this chapter focuses on measurement according to the PGSI and other consequences mapped against various frameworks for measuring harms, including that of Langham et al, it is our preferred term for this chapter.
3 Langham et al. (2015)(opens new tab), Wardle et al. (2018)(PDF)(opens new tab), Marionneau, V., Egerer, M., & Raisamo, S. (2022)(opens new tab)
4Sturgis (2024)(opens new tab), Williams & Volberg (2009)(opens new tab), Gambling Commission
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GSGB Annual report - Problem Gambling Severity Index
Last updated: 25 July 2024
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