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Exploring demographic differences in adverse consequences from gambling

Examining whether associations between 'at-risk' gambling behaviour (measured using the PGSI) and adverse consequences vary across demographic groups.

Published
16 July 2026

Last updated
16 July 2026 - Changes

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  1. Contents
  2. Method

Method

Measuring adverse consequences from gambling

The 2025 Gambling Survey for Great Britain (GSGB) collected data from adults aged 18 years and older living in Great Britain (N=20,775). Fieldwork was carried out between January 2025 and January 2026. Further details, including the strengths and weaknesses of the methodology, can be found in the GSGB technical report (Gambling Commission, 2024b).

The following questions were used to assess potential adverse consequences from gambling (that is, consequences which vary in severity and can have more cumulative effects)1:

How often, in the last 12 months, has gambling led you to:

  1. reduce or cut back spending on everyday items such as food, bills, and clothing (potential adverse consequence to resources)
  2. use savings or increase use of credit, such as credit cards, overdrafts, and loans (potential adverse consequence to resources)
  3. experience conflict or arguments with friends, family, or work colleagues (potential adverse consequence to relationships)
  4. feel isolated from other people, left out, or feel completely alone (potential adverse consequence to relationships)
  5. lie to family, or others, to hide the extent of gambling (potential adverse consequence to relationships)
  6. be absent or perform poorly at work or study (potential adverse consequence to resources).

Response options were 'Never', 'Occasionally', 'Fairly often', and 'Very often'. Item endorsement was defined as responding 'Occasionally', 'Fairly often', or 'Very often'. A binary indicator was then derived to identify participants who reported one or more potential adverse consequences. Findings from our recent analysis support the use of 'one or more' potential adverse consequences as a valid population-level indicator of gambling-related harm (Gambling Commission, 2026).

To assess severe consequences from gambling (that is, those that are unequivocally harmful), participants were asked the following:

In the last 12 months:

  1. Have you lost something of significant financial value such as your home, business, car, or been declared bankrupt because of your own gambling? (severe consequence to resources).
  2. Has your relationship with someone close to you, such as a spouse, partner, family member or friend broken down because of your own gambling? (severe consequence to relationships).
  3. Have you experienced violence or abuse because of your own gambling? (severe consequence to relationships).
  4. Have you committed a crime in order to finance gambling or to pay gambling debts? (severe consequence to resources).

Response options to these questions were 'Yes' or 'No'.

Statistical analysis

Regression analyses were used to examine whether some demographic groups are more likely than others to report potential and severe adverse consequences, and whether demographic differences varied across Problem Gambling Severity Index (PGSI) scores. The following variables were included in regression models as predictors: age, sex, ethnicity, household income, educational attainment, and PGSI score2. Results from regression models were used to estimate predicted probabilities of potential and severe adverse consequences, by demographic group and PGSI score3.

References

1 Potential adverse consequences were also captured using 3 PGSI items:

  • borrowing money or selling items to gamble (potential adverse consequence to resources)
  • feeling that gambling has caused health problems including stress or anxiety (potential adverse consequence to health)
  • feeling guilty about gambling (potential adverse consequence to health).

To avoid confounding the outcome and predictor variables in subsequent analyses, responses to these items were used only in the calculation of PGSI scores and were not included as indicators of adverse consequences.

2 Logistic regression models were conducted separately for 'one or more' potential adverse consequences and severe adverse consequences. Predictor variables included PGSI score, ethnicity, household income quintile, educational attainment, age, and sex. Age was included as a binary variable (under 35 versus 35 and over) to allow comparisons with the other variables. Models also included interaction terms between PGSI score and each demographic characteristic to test whether associations between PGSI score and adverse consequences differed across groups. Regression analyses included participants who had gambled in the past 12 months, and who provided valid responses to PGSI and demographic questions.
Models also included interaction terms between PGSI score and each demographic characteristic to test whether associations between PGSI score and adverse consequences differed across groups. Regression analyses included participants who had gambled in the past 12 months, and who provided valid responses to PGSI and demographic questions.

3 Predicted probabilities were calculated by combining the model's estimated coefficients on the log-odds scale, holding all other characteristics at their reference levels (White ethnicity, household income in the third quintile or above, holds an educational qualification, female, aged 35 and over). The total was converted to a probability using the inverse-logit function. The following PGSI anchor points were used to summarise findings: PGSI = 1, PGSI = 3 and PGSI = 8 for potential adverse consequences, and PGSI = 3 and PGSI = 8 for severe consequences. Predicted probabilities were calculated for variables showing main effects and/or interactions in regression models.

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