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Understanding the adverse consequences of gambling
This report presents secondary analysis of Year 2 (2024) GSGB data
Published: 2 October 2025
Last updated: 2 October 2025
This version was printed or saved on: 2 October 2025
Online version: https://www.gamblingcommission.gov.uk/report/understanding-the-adverse-consequences-of-gambling
Executive summary
Understanding gambling-related harm is one of our key evidence gaps and priorities. The Problem Gambling Severity Index (PGSI)1 was designed to measure gambling behaviours in the general population, and is often used as a proxy measure of harm. However the PGSI does not capture the range of adverse consequences that people can experience from gambling, as it was not specifically developed for this purpose. We therefore recently developed a new set of survey questions to assess negative impacts from gambling. The questions align with the Framework for Action by Wardle and others (2018)(opens in new tab), which categorises harms across 3 domains:
- Resources (for example, financial strain).
- Relationships (for example, conflict or isolation).
- Health (for example, psychological distress).
Survey questions differentiate between severe consequences, which are clearly and unequivocally harmful (that is, relationship breakdown, experiences of violence, losing significant financial assets, and crime), and potential adverse consequences, which can vary in severity and often have more cumulative effects (for example, reduced spending on everyday items). These questions are included in the Gambling Survey for Great Britain (GSGB), and reported on an annual basis.
This report presents secondary analysis of Year 2 (2024) GSGB data. We aimed to address the following research questions:
- To what extent are demographic characteristics (age, sex, ethnicity, and household income) associated with experiencing potential adverse consequences (affecting resources, health, and relationships) and severe consequences (such as relationship breakdown, violence, significant financial loss, and crime)?
- Among those who reported negative impacts from gambling, what proportion had experienced these impacts across multiple areas of their life (that is, affecting financial resources, relationships, and health)?
Key findings
Both potential and severe adverse consequences from gambling were most prevalent amongst males, younger individuals, those identifying as Mixed, Asian, or Black (compared with White), and people with lower financial income.
The negative impacts of gambling were rarely confined to a single area of life. Instead, people tended to report negative impacts across multiple domains:
Among the 15 percent of participants who had gambled in the past 4 weeks and reported at least one potential adverse consequence, over a quarter (29.5 percent) experienced consequences across all 3 domains (resources, relationships, and health), while half (49.9 percent) reported consequences in at least 2 domains.
Of the 2.1 percent of participants who had gambled in the past 4 weeks and reported at least 1 severe consequence, nearly half experienced 2 or more types (44.5 percent), and 8.1 percent reported all 4 severe consequences (crime, significant financial loss, relationship breakdown, and violence).
An important caveat to these findings is that they do not account for differences in the type of gambling activities that people play. It is therefore possible that differences in activity type may partly account for the observed associations between demographics and adverse consequences. We plan to conduct further analysis to test this hypothesis.
Results from this secondary analysis of Year 2 (2024) GSGB data provide insight into who may be most at risk of experiencing negative consequences from gambling, and how different types of consequences co-occur. Our findings have important implications for harm reduction strategies, such as the need to ensure that interventions and safer-gambling messaging engage a diverse range of consumers. The new GSGB survey questions capture adverse consequences that other tools, such as the PGSI, often miss. The inclusion of these questions, alongside the PGSI, enables us to monitor both behavioural risk and the tangible impacts of gambling on people’s lives. This broader understanding of harm is essential for ensuring that regulatory decisions are guided by robust and comprehensive data.
1 Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index: Final report. Ottawa: Canadian Centre on Substance Abuse. The PGSI is a 9-item validated scale that measures gambling behaviours and experiences, and categorises individuals into ‘risk categories’ based on their scores. Further details about the PGSI scale can be found in our Problem gambling screens report.
Introduction
Understanding gambling-related harm is one of our key evidence gaps and priorities. In particular, we aim to gather insight into how gambling harms are experienced, and who may be most at risk. The PGSI was designed to measure gambling behaviours in the general population. The Gambling Commission uses the PGSI to estimate the prevalence of ‘at risk’ patterns of gambling and inform regulatory decisions. While the PGSI is often used as a proxy measure of harm, it was not specifically developed for this purpose. As a result, the PGSI does not fully capture the range of adverse consequences that people can experience from gambling. Furthermore, GambleAware (2023) suggest that the PGSI does not align with established frameworks of gambling harm and that it overlooks the broader impacts on friends, family and the wider community. The PGSI also conflates gambling behaviours (for example, uncontrolled gambling) with potential consequences2. These limitations highlight the need to develop new methods of assessing gambling-related harm that are rooted in established theoretical frameworks.
Given the limitations of the PGSI, we recently developed a new set of survey questions that aim to provide a comprehensive understanding of the potential negative impacts of gambling. This involved a multi-stage process involving stakeholder consultation, piloting, and cognitive testing. Items were selected from a broader 72-item harms checklist3, and aligned with the Framework for Action by Wardle and others, which categorises harms across 3 domains:
- Resources (for example, financial strain).
- Relationships (for example, conflict or isolation).
- Health (for example, psychological distress).
The final set of questions differentiate between severe consequences, which are clearly and unequivocally harmful (for example, relationship breakdown, experiences of violence, losing significant financial assets, and crime), and potential adverse consequences, which can vary in severity and often have more cumulative effects (for example, reduced spending on everyday items). Severe consequences are assessed using binary (Yes or No) questions, while potential adverse consequences are measured on a 4-point frequency scale, ranging from 'Never' to 'Very often'.
Initial analysis found that the new survey questions demonstrated good internal reliability and construct validity. Notably, participants’ responses to the questions predicted additional variance in mental wellbeing scores (assessed using the Short Warwick-Edinburgh Mental Wellbeing Scale, SWEMWBS), above and beyond the variance predicted by PGSI scores alone. This finding highlights the added value of the new items in capturing the negative impacts of gambling that may be missed by the PGSI. The finalised survey questions are now included in the Gambling Survey for Great Britain (GSGB) and reported on an annual basis.
In collaboration with NatCen, we recently conducted qualitative research to gain a deeper understanding of individuals’ negative experiences with gambling. The project involved follow-up interviews with participants who had completed the GSGB and reported experiencing adverse or severe consequences from their own gambling. Our findings showed that different types of consequences, such as those affecting one’s finances, relationships, and health, are often experienced simultaneously or in reinforcing cycles. For example, participants talked about how financial strain from gambling could lead them to spend less on essential items, trigger arguments with family members, and contribute to heightened levels of stress or anxiety. In some cases, relatively infrequent harms were described as having substantial and lasting effects.
The aim of this report was to provide further insight into who is most at risk of experiencing adverse consequences from gambling, and the extent to which different types of consequences overlap. Specifically, we conducted secondary analysis of Year 2 (2024) GSGB data to address the following research questions:
- To what extent are demographic characteristics (age, sex, ethnicity, and household income) associated with experiencing potential adverse consequences (affecting resources, health, and relationships) and severe consequences (that is relationship breakdown, violence, significant financial loss, and crime)?
- Among those who reported negative impacts from gambling, what proportion had experienced these impacts across multiple areas of their life (that is, affecting financial resources, relationships, and health)?
To examine associations between demographics and consequences, we controlled for the number of gambling activities participants engaged with. However, our analysis does not account for differences in the types of activities played. This is an important caveat given that certain activities (for example, betting on non-sport events, online slots, and casino games) tend to be associated with higher PGSI scores4. We therefore plan to conduct a follow-up analysis to explore the extent to which associations between demographic profiles and adverse consequences can be explained by differences in gambling activity.
2 National Centre for Social Research, University of Plymouth. Frameworks and Measurement of Gambling Related Harm: A Scoping Study (2023).
3 Li, E., Browne, M., Rawat, V., Langham, E., & Rockloff, M. (2017). Breaking Bad: Comparing Gambling Harms Among Gamblers and Affected Others. Journal of gambling studies, 33(1), 223–248.
4 Wardle H and Tipping S. (2025) Exploring the relationship between gambling activities and Problem Gambling Severity Index (PGSI) scores. Gambling Commission: Birmingham.
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).
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).
Results
Potential adverse consequences
The following tables (Table 1a to Table 1e) show the percentages of participants who reported adverse consequences, within each socioeconomic category. Base includes participants who had gambled in the past four weeks. Percentages are weighted, and base size values are unweighted.
Table 1a: PGSI
| PGSI 0 (percentage) | PGSI 1 to 2 (percentage) | PGSI 3 to 7 (percentage) | PGSI 8 or more (percentage) | |
|---|---|---|---|---|
| Relationship | 2.2% | 9.7% | 34.6% | 91.6% |
| Health | 0.1% | 24.4% | 71.9% | 95.9% |
| Resources | 2.0% | 11.2% | 42.2% | 91.3% |
| Base size | 7,065 | 1,322 | 468 | 326 |
Table 1b: Equivalised household income quintile
| Lowest quintile (percentage) | Second quintile (percentage) | Third quintile (percentage) | Fourth quintile (percentage) | Highest quintile (percentage) | |
|---|---|---|---|---|---|
| Relationship | 17.3% | 10.9% | 8.0% | 5.5% | 6.4% |
| Health | 20.0% | 13.3% | 11.9% | 9.3% | 10.5% |
| Resources | 18.7% | 12.2% | 8.5% | 5.3% | 5.4% |
| Base size | 1,872 | 1,930 | 1,423 | 1,827 | 1,747 |
Table 1c: Ethnicity
| White (percentage) | Mixed (percentage) | Asian (percentage) | Black (percentage) | |
|---|---|---|---|---|
| Relationship | 7.9% | 22.7% | 27.9% | 22.8% |
| Health | 11.5% | 23.6% | 28.3% | 20.5% |
| Resources | 8.2% | 24.0% | 28.5% | 27.3% |
| Base size | 8,467 | 175 | 336 | 120 |
Table 1d: Sex
| Male (percentage) | Female (percentage) | |
|---|---|---|
| Relationship | 7.3% | 12.2% |
| Health | 9.8% | 16.2% |
| Resources | 8.1% | 12.4% |
| Base size | 4,770 | 4,417 |
Table 1e: Age group
| 18 to 24 (percentage) | 25 to 34 (percentage) | 35 to 44 (percentage) | 45 to 54 (percentage) | 55 to 64 (percentage) | 65 to 74 (percentage) | 75 and over (percentage) | |
|---|---|---|---|---|---|---|---|
| Relationship | 26.1% | 14.3% | 12.2% | 8.9% | 6.3% | 2.8% | 3.4% |
| Health | 26.8% | 20.1% | 17.6% | 12.8% | 7.6% | 5.0% | 3.5% |
| Resources | 25.4% | 15.6% | 14.6% | 8.3% | 5.8% | 3.4% | 3.5% |
| Base size | 438 | 1,359 | 1,592 | 1,513 | 1,799 | 1,592 | 906 |
Results from regression models are described for each type of potential adverse consequence (resources, relationships, health). For brevity, we describe findings from step 2 of each model (that is, after controlling for the number of gambling activities played), however odds ratios9 and 95 percent confidence intervals from steps 1 and 2 are provided in Table 2 to Table 4.
Adverse Resource Consequences
Females and older participants had lower odds of experiencing potential adverse consequences to resources (that is financial stability or employment) as shown in Table 2. The odds of experiencing adverse consequences to resources were significantly higher amongst Mixed race, Asian, and Black participants, compared with White participants, and amongst those in the lowest income quintile, relative to people in higher income quintiles (that is, quintiles 2 to 5).
Table 2: Odds ratios from logistic regression model predicting potential adverse consequences to resources.
95 percent confidence intervals are given in parentheses. Demographic variables were entered into step 1 of the model, and the ‘number of gambling activities played in the past 4 weeks’ was included in step 2. Analysis includes participants who had gambled in the past 4 weeks.
| Odds Ratio (Step 1) | Odds Ratio (Step 2) | Base size | |
|---|---|---|---|
| Age | 0.96* (0.96–0.97) | 0.97* (0.97–0.98) | 9,199 |
| Equivalised income | |||
| Lowest quintile | n/a | n/a | 1,872 |
| Second quintile | 0.73* (0.6–0.88) | 0.76* (0.63–0.93) | 1,930 |
| Third quintile | 0.44* (0.35–0.56) | 0.50* (0.39–0.63) | 1,423 |
| Fourth quintile | 0.25* (0.2–0.32) | 0.30* (0.23–0.39) | 1,827 |
| Highest quintile | 0.23* (0.18–0.3) | 0.27* (0.21–0.36) | 1,747 |
| Ethnicity | |||
| White | n/a | n/a | 8,467 |
| Mixed | 2.32* (1.65–3.25) | 2.19* (1.54–3.13) | 175 |
| Asian | 2.74* (2.18–3.45) | 2.9* (2.27–3.71) | 336 |
| Black | 3.03* (2.12–4.32) | 2.97* (2.03–4.34) | 120 |
| Sex | |||
| Male | n/a | n/a | 4,417 |
| Female | 0.62* (0.53–0.72) | 0.69* (0.59–0.81) | 4,470 |
Adverse Relationship Consequences
Older age and being female were significantly associated with reduced odds of experiencing potential adverse consequences to relationships. Black, Mixed race and Asian participants had significantly higher odds of experiencing adverse consequences to relationships compared with White participants. Participants in the lowest income quintile (that is, quintile 1) had significantly higher odds of experiencing adverse consequences to relationships compared to those in higher income quintiles (that is, quintiles 2 to 5).
Table 3: Odds ratios from logistic regression model predicting potential adverse consequences to relationships.
95 percent confidence intervals are provided in parentheses. Demographic variables were entered into step 1 of the model, and the ‘number of gambling activities played in the past 4 weeks’ was included in step 2. Analysis includes participants who had gambled in the past 4 weeks.
| Odds Ratio (Step 1) | Odds Ratio (Step 2) | Base size | |
|---|---|---|---|
| Age | 0.96* (0.96–0.97) | 0.97* (0.97–0.98) | 9,199 |
| Equivalised income | |||
| Lowest quintile | n/a | n/a | 1,872 |
| Second quintile | 0.70* (0.57–0.85) | 0.74* (0.6–0.91) | 1,930 |
| Third quintile | 0.45* (0.36–0.57) | 0.51* (0.4–0.65) | 1,423 |
| Fourth quintile | 0.29* (0.23–0.37) | 0.35* (0.27–0.45) | 1,827 |
| Highest quintile | 0.30* (0.24–0.39) | 0.37* (0.29–0.48) | 1,747 |
| Ethnicity | |||
| White | n/a | n/a | 8,467 |
| Mixed | 2.24* (1.6–3.16) | 2.14* (1.5–3.06) | 175 |
| Asian | 2.87* (2.28–3.61) | 3.04* (2.38–3.89) | 336 |
| Black | 2.33* (1.6–3.39) | 2.15* (1.43–3.24) | 120 |
| Sex | |||
| Male | n/a | n/a | 4,417 |
| Female | 0.56* (0.48–0.65) | 0.63* (0.53–0.73) | 4,470 |
Adverse Health Consequences
Older age and being female were associated with reduced odds of reporting adverse consequences to health as shown in Table 4. Asian and Mixed race participants had significantly higher odds of experiencing adverse consequences to health compared to White participants. There was no significant difference amongst Black participants. Participants in the lowest income quintile (that is, quintile 1) had significantly higher odds of experiencing adverse health consequences relative to those in higher income quintiles (that is, quintiles 2 to 5).
Table 4: Odds ratios from logistic regression model predicting potential adverse consequences to health.
95 percent confidence intervals are provided in parentheses. Demographic variables were entered into step 1 of the model, and the ‘number of gambling activities played in the past 4 weeks’ was included in step 2. Analysis includes participants who had gambled in the past 4 weeks.
| Odds Ratio (Step 1) | Odds Ratio (Step 2) | Base size | |
|---|---|---|---|
| Age | 0.96* (0.96–0.97) | 0.97* (0.97–0.98) | 9,199 |
| Equivalised income | |||
| Lowest quintile | n/a | n/a | 1,872 |
| Second quintile | 0.72* (0.6–0.87) | 0.76* (0.63–0.93) | 1,930 |
| Third quintile | 0.56* (0.46–0.69) | 0.64* (0.52–0.8) | 1,423 |
| Fourth quintile | 0.4* (0.33–0.5) | 0.5* (0.4–0.62) | 1,827 |
| Highest quintile | 0.42* (0.34–0.52) | 0.53* (0.43–0.66) | 1,747 |
| Ethnicity | |||
| White | n/a | n/a | 8,467 |
| Mixed | 1.62* (1.17–2.26) | 1.5* (1.06–2.13) | 175 |
| Asian | 1.99* (1.59–2.48) | 2.07* (1.63–2.64) | 336 |
| Black | 1.28 (0.87–1.89) | 1.08 (0.7–1.67) | 120 |
| Sex | |||
| Male | n/a | n/a | 4,417 |
| Female | 0.57* (0.5–0.66) | 0.64* (0.56–0.74) | 4,470 |
9 Odds ratios compare the likelihood of an outcome between two groups. Each demographic group is compared to a baseline category. For example, individuals identified as Mixed race, Asian, or Black, are compared with those who identify as White. Odds ratios represent the relative likelihood of experiencing potential adverse consequences for each subgroup compared to the ‘White’ baseline category. An odds ratio of 1.0 means that there is no difference between groups. Odds ratios greater than 1.0 indicate that the group has higher odds of adverse consequences compared with the baseline category. Odds ratios less than 1.0 mean that the group has lower odds of adverse consequences compared with the baseline category.
Overlap between potential adverse consequences
Table 5 show the degree of overlap between different types of potential adverse consequences. Overall, 15 percent of participants who had gambled in the past 4 weeks reported at least one potential adverse consequence from their own gambling. Health-related adverse consequences were more frequently experienced in isolation (26.2 percent) compared to those related to resources (12.6 percent) and relationships (10.0 percent). Dual-domain overlaps were observed, with 7.3 percent reporting consequences to both health and resources, 7.3 percent reporting consequences to health and relationships, and 5.8 percent reporting both consequences to resources and relationships. Notably, over a quarter of individuals reported experiencing potential adverse consequences across all 3 domains, indicating a substantial degree of co-occurrence.
Table 5: Participants reporting each combination of potential adverse consequences from their own gambling.
Includes responses from participants who reported at least one potential adverse consequence (Unweighted base size = 1406).
Includes responses from participants who reported at least one adverse consequence (Unweighted base size = 1406).
| Potential adverse consequences | Participants reporting at least one adverse consequence (percentage) |
|---|---|
| Health | 26.2% |
| Resources | 12.6% |
| Relationships | 10.0% |
| Health, Resources | 7.3% |
| Health, Relationships | 7.3% |
| Relationships, Resources | 5.8% |
| Health, Resources, Relationships | 29.5% |
Severe adverse consequences
The following Tables 6a to 6e show the percentages of participants who reported severe consequences, within each socioeconomic category. Base includes participants who had gambled in the past 4 weeks. Percentages are weighted, and base size values are unweighted.
Table 6a: Participants reporting severe consequences, within the PGSI category.
| PGSI 0 (percentage) | PGSI 1 to 2 (percentage) | PGSI 3 to 7 (percentage) | PGSI 8 or more (percentage) | |
|---|---|---|---|---|
| Crime | 0.1% | 0.2% | 0.7% | 18.1% |
| Financial | 0.2% | 0.5% | 1.1% | 20.2% |
| Relationship | 0.2% | 0.4% | 3.0% | 30.4% |
| Violence | 0.1% | 0.6% | 1.0% | 18.0% |
| Base size | 7,065 | 1,322 | 468 | 326 |
Table 6b: Participants reporting severe consequences, within the equivalised household income quintile category.
| Lowest quintile (percentage) | Second quintile (percentage) | Third quintile (percentage) | Fourth quintile (percentage) | Highest quintile (percentage) | |
|---|---|---|---|---|---|
| Crime | 2.6% | 1.0% | 0.5% | 0.3% | 0.3% |
| Financial | 2.9% | 1.7% | 0.5% | 0.4% | 0.6% |
| Relationship | 4.4% | 2.3% | 1.4% | 0.7% | 0.4% |
| Violence | 2.3% | 1.4% | 0.8% | 0.4% | 0.7% |
| Base size | 1,872 | 1,930 | 1,423 | 1,827 | 1,747 |
Table 6c: Participants reporting severe consequences, within the ethnicity category.
| White (percentage) | Mixed (percentage) | Asian (percentage) | Black (percentage) | |
|---|---|---|---|---|
| Crime | 0.6% | 3.2% | 2.5% | 4.5% |
| Financial | 0.7% | 5.5% | 5.7% | 5.8% |
| Relationship | 1.3% | 7.3% | 7.1% | 3.9% |
| Violence | 0.8% | 1.8% | 3.9% | 3.1% |
| Base size | 8,467 | 175 | 336 | 120 |
Table 6d: Participants reporting severe consequences, within the sex category.
| Male (percentage) | Female (percentage) | |
|---|---|---|
| Crime | 1.4% | 0.6% |
| Financial | 1.8% | 0.7% |
| Relationship | 2.6% | 1.2% |
| Violence | 1.4% | 0.8% |
| Base size | 4,770 | 4,417 |
Table 6e: Participants reporting severe consequences, within the age group category.
| 18 to 24 (percentage) | 25 to 34 (percentage) | 35 to 44 (percentage) | 45 to 54 (percentage) | 55 to 64 (percentage) | 65 to 74 (percentage) | 75 and over (percentage) | |
|---|---|---|---|---|---|---|---|
| Crime | 3.7% | 1.8% | 1.5% | 0.5% | 0.1% | 0.4% | 0.0% |
| Financial | 4.1% | 2.7% | 1.4% | 0.8% | 0.6% | 0.0% | 0.2% |
| Relationship | 6.4% | 3.5% | 2.1% | 1.5% | 0.9% | 0.2% | 0.2% |
| Violence | 3.9% | 2.4% | 1.3% | 0.7% | 0.4% | 0.1% | 0.2% |
| Base size | 438 | 1,359 | 1,592 | 1,513 | 1,799 | 1,592 | 906 |
Regression model results, for each type of severe consequence, are described below. Due to the low number of ethnic minority participants reporting each severe consequence, we combined Black, Asian, Mixed race and Other ethnic groups within a single ‘BAME’ category to maximise statistical power and ensure stable model estimates. For brevity, we describe findings from step 2 of each model (that is, after controlling for the number of gambling activities played), however odds ratios and 95 percent confidence intervals from steps 1 and 2 are provided in Tables 7 to 10.
Financial consequences
Females and older participants had significantly lower odds of experiencing severe consequences to finances. The odds of experiencing severe financial consequences were 4.6 times higher amongst BAME participants, relative to White participants, and were significantly higher amongst those in the lowest income quintile (that is, quintile 1), compared with those in quintiles 3 to 5 as shown in Table 7.
Table 7: Odds ratios from logistic regression model predicting severe financial consequences due to gambling.
95 percent confidence intervals are provided in parentheses. Demographic variables were entered into step 1 of the model, and the ‘number of gambling activities played in the past 4 weeks’ was included in step 2. Analysis includes participants who had gambled in the past 4 weeks.
| Odds Ratio (Step 1) | Odds Ratio (Step 2) | Base size | |
|---|---|---|---|
| Age | 0.95* (0.94-0.97) | 0.96* (0.95-0.98) | 9,199 |
| Equivalised income | |||
| Lowest quintile | n/a | n/a | 1,872 |
| Second quintile | 0.79 (0.51-1.24) | 0.94 (0.59-1.49) | 1,930 |
| Third quintile | 0.25* (0.11-0.53) | 0.31* (0.14-0.67) | 1,423 |
| Fourth quintile | 0.21* (0.10-0.45) | 0.29* (0.13-0.62) | 1,827 |
| Highest quintile | 0.23* (0.12-0.47) | 0.33* (0.16-0.67) | 1,747 |
| Ethnicity | |||
| White | n/a | n/a | 8,467 |
| BAME | 5.29* (3.60-7.76) | 4.64* (3.12-6.92) | 175 |
| Sex | |||
| Male | n/a | n/a | 4,770 |
| Female | 0.44* (0.29-0.66) | 0.48 * (0.31-0.74) | 4,417 |
Relationship Breakdown
Females and older participants had reduced odds of experiencing relationship breakdown due to gambling. Compared to White participants, the odds of experiencing relationship breakdown were 2.9 times higher amongst BAME participants. Participants in higher income quintiles (that is, quintiles 2 to 5) had significantly lower odds of experiencing relationship breakdown compared to those in the lowest quintile (quintile 1) as shown in Table 8.
Table 8: Odds ratios from logistic regression model predicting relationship breakdown due to gambling.
95 percent confidence intervals are provided in parentheses. Demographic variables were entered into step 1 of the model, and the ‘number of gambling activities played in the past 4 weeks’ was included in step 2. Analysis includes participants who had gambled in the past 4 weeks.
| Odds Ratio (Step 1) | Odds Ratio (Step 2) | Base size | |
|---|---|---|---|
| Age | 0.96* (0.94-0.97) | 0.97* (0.95-0.98) | 9,199 |
| Equivalised income | |||
| Lowest quintile | n/a | n/a | 1,872 |
| Second quintile | 0.69 (0.47-1.00) | 0.79 (0.53-1.18) | 1,930 |
| Third quintile | 0.39* (0.24-0.64) | 0.50* (0.30-0.84) | 1,423 |
| Fourth quintile | 0.18* (0.10-0.34) | 0.25* (0.13-0.48) | 1,827 |
| Highest quintile | 0.10 * (0.05-0.22) | 0.15* (0.07-0.32) | 1,747 |
| Ethnicity | |||
| White | n/a | n/a | 8,467 |
| BAME | 3.36* (2.42-4.65) | 2.86* (2.02-4.06) | 175 |
| Sex | |||
| Male | n/a | n/a | 4,770 |
| Female | 0.46* (0.33-0.65) | 0.52* (0.37-0.74) | 4,417 |
Violence
Older age was significantly associated with reduced odds of experiencing violence. BAME participants had 2.2 times higher odds of experiencing violence due to their gambling compared with White participants. Participants in the fourth income quintile (60 to 80 percent of the income distribution) had odds of experiencing violence that were almost two-thirds lower than those in the lowest quintile. No significant differences were observed for participants in quintiles 2, 3, and 5. There was no significant association between sex and experiencing violence due to gambling as shown in Table 9.
Table 9: Odds ratios from logistic regression model predicting violence due to gambling.
95 percent confidence intervals are provided in parentheses. Demographic variables were entered into step 1 of the model, and the ‘number of gambling activities played in the past 4 weeks’ was included in step 2. Analysis includes participants who had gambled in the past 4 weeks.
| Odds Ratio (Step 1) | Odds Ratio (Step 2) | Base size | |
|---|---|---|---|
| Age | 0.95* (0.94-0.96) | 0.96* (0.95-0.98) | 9,199 |
| Equivalised income | |||
| Lowest quintile | n/a | n/a | 1,872 |
| Second quintile | 0.83 (0.51-1.34) | 1.05 (0.63-1.76) | 1,930 |
| Third quintile | 0.43* (0.22-0.83) | 0.63 (0.32-1.26) | 1,423 |
| Fourth quintile | 0.23* (0.10-0.50) | 0.38* (0.17-0.84) | 1,827 |
| Highest quintile | 0.33* (0.17-0.64) | 0.57 (0.28-1.15) | 1,747 |
| Ethnicity | |||
| White | n/a | n/a | 8,467 |
| BAME | 2.94* (1.94-4.45) | 2.16* (1.37-3.39) | 175 |
| Sex | |||
| Male | n/a | n/a | 4,770 |
| Female | 0.60* (0.40-0.90) | 0.74 (0.48-1.14) | 4,417 |
Crime
Older age and being female were significantly associated with lower odds of experiencing gambling-related crime. The odds of experiencing crime due to gambling were 2.7 times higher among BAME participants, than among White participants. Participants in higher income quintiles (quintiles 3 to 5) had significantly lower odds of experiencing crime relative to those in the lowest quintile as shown in Table 10.
Table 10: Odds ratios from logistic regression model predicting crime due to gambling.
95 percent confidence intervals are provided in parentheses. Demographic variables were entered into step 1 of the model, and the ‘number of gambling activities played in the past 4 weeks’ was included in step 2. Analysis includes participants who had gambled in the past 4 weeks.
| Odds Ratio (Step 1) | Odds Ratio (Step 2) | Base size | |
|---|---|---|---|
| Age | 0.95* (0.93-0.96) | 0.96* (0.95-0.98) | 9,199 |
| Equivalised income | |||
| Lowest quintile | n/a | n/a | 1,872 |
| Second quintile | 0.49* (0.29-0.85) | 0.58 (0.33-1.02) | 1,930 |
| Third quintile | 0.23* (0.10-0.51) | 0.32* (0.14-0.73) | 1,423 |
| Fourth quintile | 0.16* (0.07-0.38) | 0.25* (0.10-0.60) | 1,827 |
| Highest quintile | 0.12* (0.05-0.31) | 0.19* (0.07-0.51) | 1,747 |
| Ethnicity | |||
| White | n/a | n/a | 8,467 |
| BAME | 3.59* (2.32-5.55) | 2.70* (1.68-4.33) | 175 |
| Sex | |||
| Male | n/a | n/a | 4,770 |
| Female | 0.44* (0.27-0.70) | 0.49* (0.30-0.81) | 4,417 |
Overlap of severe consequences
Table 11 shows the percentage of participants reporting each type of severe consequence, both on its own and in combination with other types. Overall, 2.1 percent of participants who had gambled in the past 4 weeks reported at least one severe consequence from their own gambling. Relationship breakdown was most commonly experienced in the absence of other severe consequences (31 percent), while fewer participants experienced severe financial consequences, violence, and crime in isolation, accounting for 11.4 percent, 6.6 percent, and 6.2 percent of cases, respectively. Over 8 percent of participants who reported severe consequences from gambling had experienced all 4 types.
Table 11: Participants reporting each combination of severe consequences from their own gambling.
Includes responses from participants who reported at least one severe consequence (Unweighted base size = 195).
| Severe consequences | Participants reporting at least one severe consequence (percentage) |
|---|---|
| Relationship | 31.4% |
| Finance | 11.4% |
| Finance, relationship, violence, crime | 8.1% |
| Violence | 6.6% |
| Crime | 6.2% |
| Finance, relationship | 6.1% |
| Relationship, violence, crime | 5.1% |
| Finance, violence, crime | 5.1% |
| Finance, violence | 4.8% |
| Relationship, crime | 3.5% |
| Relationship, violence | 3.5% |
| Finance, crime | 2.9% |
| Finance, violence | 2.8% |
| Violence, crime | 1.7% |
| Finance, relationship, crime | 0.9% |
Discussion
Findings from this report help identify who may be most at risk of experiencing negative consequences from gambling, and provide insight into how different types of consequences may co-occur. After controlling for the number of gambling activities people played, we found that the risk of experiencing potential or severe adverse consequences was greater for males, younger individuals, those identifying as Mixed race, Asian, or Black, and people with lower financial income. While findings were largely consistent across all different types of consequences, there were a few notable exceptions. For example, the odds of experiencing potential adverse consequences to health did not differ between Black participants and White participants. Amongst severe consequences, experiencing violence due to gambling was not significantly predicted by sex.
Overall, our findings are consistent with previous research which has found a higher prevalence of ‘at risk’ gambling behaviours and harm amongst males, younger adults10, and those with lower financial income11. Furthermore, a recent scoping review found that negative consequences from gambling disproportionately impact minority ethnic groups, and may be exacerbated by stigma and barriers to obtaining support services12. Notably, much of the existing research on demographic differences in gambling behaviour has relied on screening tools, such as the PGSI, as proxy measures of harm. Our analysis extends previous findings by demonstrating similar sociodemographic disparities when examining negative consequences from gambling. In doing so, findings from this analysis provide support for our long-term strategy to move beyond single screening tools to a more comprehensive understanding of gambling harms.
An important caveat to these findings is that they do not account for differences in the type of gambling activities that people play. Previous research has found that certain activities, such as betting on non-sport events, online fruit and slots, or casino games are associated with a greater likelihood of scoring 8 or more on the PGSI (indicative of ‘problem gambling’). Notably, Year 2 (2024) GSGB data shows that engagement in these activities is most prevalent amongst men, those aged younger than 45, people of non-white ethnicity, and those in lower income quintiles. It is therefore possible that the observed associations between demographics and adverse consequences may be partly explained by differences in activity type and we plan to conduct further analysis to test this hypothesis.
A secondary aim of our analysis was to examine the co-occurrence and overlap of different types of consequences. We found that among those who reported at least one potential adverse consequence, over a quarter (29.5 percent) experienced consequences across all 3 domains (resources, relationships, and health), while half (49.9 percent) reported consequences in at least 2 domains. A similar pattern emerged for people who experienced severe consequences (n=195); nearly half reported 2 or more severe consequences (44.5 percent), and 8.1 percent reported all 4 severe consequences (crime, significant financial loss, relationship breakdown, and violence).
The observed pattern of overlap between different types of consequences is consistent with findings from our qualitative research, which highlighted that gambling-related harms often occur concurrently or sequentially. Findings are also consistent with recent research examining posts from a UK-based online gambling support forum. Using natural language processing13, it was found that people most frequently referred to emotional and psychological harms from gambling, and these often co-occurred with financial, relationship, and health-related harms. Our findings build on this evidence and provide further support to the idea that gambling-related harms often affect multiple aspects of a person’s life.
Findings from our secondary analysis also highlight the distinction between PGSI scores and reported adverse consequences. For example, 2 percent of individuals with a PGSI score of 0 reported potential adverse consequences to resources, indicating that some individuals may experience adverse consequences even at relatively low levels of gambling engagement. This supports the idea that the PGSI alone does not provide a comprehensive measure of harm. Using the new consequences questions alongside the PGSI enables us to capture both behavioural risk and the real-world impacts of gambling,
The inclusion of additional consequences questions in the GSGB also strengthens our ability to examine the longer-term impacts of gambling. This is identified within our evidence gaps and priorities as an important and under-researched area. While the PGSI primarily reflects current gambling behaviours, it is less suited to capturing harms that persist or emerge after gambling has stopped (known as ‘legacy harms’). In contrast, the new consequences questions capture a range of impacts that may be experienced over a longer duration. These questions therefore provide a basis for future research to explore the factors that contribute to legacy harms, identifying who is most at risk, and which types of harm are most likely to persist.
Findings from this report have important implications for harm reduction strategies. In particular, the association between lower household income and adverse consequences highlights the need to ensure customers are gambling within their means (for example, using financial vulnerability checks and tailored deposit limits). Our findings also indicate that younger individuals and people from minority ethnic backgrounds may be at particular risk of encountering gambling harm. It is therefore important that harm-reduction strategies and safer gambling messages are inclusive, culturally sensitive, and designed to engage a diverse range of consumers.
Our findings directly inform Theme 3 of our evidence gaps and priorities, which focuses on understanding gambling-related harm. By assessing a range of negative impacts, our findings help strengthen the evidence base and provide valuable insight into how gambling harms are experienced, and who is most at risk. Including consequences questions alongside the PGSI allows us to capture both behavioural risk and the tangible impacts of gambling on people’s lives. This broader understanding of harm is essential for ensuring that regulatory decisions are guided by robust and comprehensive data.
10 Allami, Y., Hodgins, D. C., Young, M., Brunelle, N., Currie, S., Dufour, M., Flores-Pajot, M. C., & Nadeau, L. (2021). A meta-analysis of problem gambling risk factors in the general adult population. Addiction (Abingdon, England), 116(11), 2968–2977.
11 Raybould, J. N., Larkin, M., & Tunney, R. J. (2021). Is there a health inequality in gambling related harms? A systematic review. BMC Public Health, 21(1), 305.
12 Wheaton, J., Collard, S., and Nairn, A. (2024). Experience, risk, harm: What social and spatial inequalities exacerbate gambling related harms? Bristol Hub for Gambling Harms Research, University of Bristol.
13 van Baal, S. T., Bogdanski, P., Daryanani, A., Walasek, L., & Newall, P. (2025). The lived experience of gambling-related harm in natural language. Psychology of addictive behaviors: journal of the Society of Psychologists in Addictive Behaviors, 39(4), 397–409.