Gambling, suicide and mental health treatment utilisation in Wales: case–control, whole-population-based study
Bibliographic record
Abstract
Background Gambling-related harm is a global public health concern. Suicide mortality is increased among people who experience gambling harm, and people who die by suicide often have contact with mental health treatment services in the months preceding their death. Aims To assess via a case–control study how gambling diagnosis predicts suicidal death and mental healthcare utilisation using linked routinely collected healthcare data. Method We linked the Welsh Longitudinal General Practice Dataset, Annual District Death Extract, Patient Episode Database for Wales, and Outpatient Appointments Dataset Wales using the Secure Anonymised Information Linkage (SAIL) Databank. A sample of individuals with gambling diagnosis who died by suicide and an age- and sex-matched comparator group of all-cause decedents between 1993 and 2023 were extracted. Predictors of suicidal death, including mental health diagnosis and treatment contacts, were analysed using binary logistic regression models and chi-squared tests. Results A matched cohort of 92 individuals diagnosed with a gambling diagnosis (mean age 61.5 years, s.d. 13.1; 71% male) who died by suicide and 2990 comparators were identified. Gambling diagnosis status was a significant predictor of suicide (odds ratio 30.94; 95% CI 3.57–268.28; P = 0.002). Individuals with gambling disorder had significantly more mental health treatment contacts (P < 0.001), particularly in-patient contacts (P < 0.001). No difference in out-patient contacts was found. Conclusions Historical diagnosis of gambling harm is a significant predictor of suicidal death and mental health treatment utilisation. Improved screening and coding practices would facilitate greater data linkage research on gambling-related suicide and suicide prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".