Report
Understanding the adverse consequences of gambling
This report presents secondary analysis of Year 2 (2024) GSGB data
Methods
The Year 2 (2024) GSGB collected data from 19,714 adults aged 18 years and older living in Great Britain. Fieldwork was carried out between January 2024 and January 2025 (further details of the GSGB methodology can be found in our technical report). Participants who had gambled in the past 12 months were asked whether they had experienced any potential adverse or severe consequences from gambling, described as follows.
Potential adverse consequences
To assess potential adverse consequences, participants were asked how often, in the past 12 months, gambling had caused them to:
- reduce or cut back your spending on everyday items such as food, bills and clothing? (resources)
- use savings or increase your use of credit, such as credit cards, overdrafts and loans? (resources)
- experience conflict or arguments with friends, family and/or work colleagues? (relationships)
- feel isolated from other people, left out or feel completely alone? (relationships)
- lie to family, or others, to hide the extent of your gambling? (relationships)
- be absent or perform poorly at work or study? (resources).
Response options were ‘Never’, ‘Occasionally’, ‘Fairly often’ and ‘Very often’.
Potential adverse consequences were also captured using the following 3 PGSI items:
- have you borrowed money or sold anything to get money to gamble? (resources)
- have you felt that gambling has caused you any health problems, including stress or anxiety? (health)
- have you felt guilty about the way you gamble or what happens when you gamble? (health)
Response options were 'Almost always', 'Most of the time', 'Sometimes' and 'Never'.
Each item related to one of the 3 domains specified in Wardle’s ‘Framework for Action’ (that is, resources, relationships, health).
For the purposes of analysis, 3 binary indicators were derived to examine whether participants had experienced adverse consequences to resources, relationships, or health. These indicators were coded as ‘Yes’ if participants responded “Occasionally/Sometimes”, “Fairly often/Most of the time”, or “Very often/Almost always” to at least one item within each of the 3 domains.
Severe consequences
To assess severe consequences, participants were asked whether they had experienced any of the following outcomes due to their own gambling:
- losing something of significant financial value (that is, home, job, business and so on)
- relationship with spouse or partner or family member breaking down
- experiencing violence or abuse
- committing a crime to fund gambling or pay gambling debts.
Response options were 'Yes' and 'No'.
Statistical analyses
We conducted secondary analysis of data from the Year 2 (2024) GSGB. To minimise variation in gambling engagement (which could obscure associations between demographics and adverse consequences), we decided to restrict our analysis to participants who had gambled in the past 4 weeks (n=9200). Initial descriptive analyses were conducted to examine the proportion of people who reported severe consequences (that is, relationship breakdown, significant financial loss, violence, or crime), and potential adverse consequences (that is, health, resources, or relationships) from gambling, within the following sociodemographic variables: age group, sex, ethnicity, equivalised household income quintile5, and PGSI risk level.
A series of hierarchical logistic regression models6 were conducted to examine the extent to which demographic variables predicted each severe consequence. In each model, demographic variables were entered into step 1, and the ‘number of gambling activities played in the past 4 weeks’ was included in step 2. This enabled us to establish associations between demographic variables and consequences, while controlling for differences in the number of gambling activities participants played. In all models, comparisons for ethnicity, income and sex were made against the following reference categories, respectively: White ethnicity, equivalised income quintile 1 (that is, the lowest income quintile), and male sex. Age was included as a continuous variable. Regression models were conducted using weighted data7, and factor levels8 with fewer than 50 observations were not included due to low power.
To explore how different consequences overlap, we calculated the percentage of participants who reported each type of consequence in isolation, and across 2 or more domains. Percentages were calculated amongst participants who reported at least one type of potential adverse consequence (n=1406), and at least one type of severe consequence (n=195). Analyses were conducted separately for potential adverse consequences (that is, health, resources, and relationships), and for severe consequences (that is, relationship breakdown, violence, significant financial loss, and crime).
References
5 Equivalised income refers to income that has been adjusted to account for differences in household size and composition. It provides a direct, household-level indicator of financial resources and affordability, and allows fair comparisons of living standards across different household tyles. Participants are grouped into equal sized quintiles: Quintile 1 represents the lowest 20 percent of the income distribution, and quintile 5 represents the top 20 percent.
6 Logistic regression model is a statistical method used to estimate the likelihood of an outcome occurring (for example, experiencing adverse consequences from gambling) based on one or more ‘predictor’ variables (such as gender, age, and so on).
7 Data was weighted so that the sample reflects the demographic profile of the Great Britain population, ensuring results are nationally representative.
8 Factor levels refer to the categories within a variable (for example, different income quintiles or ethnicities).
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Understanding the adverse consequences of gambling - Results
Last updated: 2 October 2025
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