Recession, recovery and suicide in mental health patients in England: time trend analysis
Bibliographic record
Abstract
BACKGROUND: The 2008 economic recession was associated with an increase in suicide internationally. Studies have focused on the impact in the general population with little consideration of the effect on people with a mental illness. AIMS: To investigate suicide trends related to the recession in mental health patients in England. METHOD: Using regression models, we studied suicide trends in mental health patients in England before, during and after the recession and examined the demographic and clinical characteristics of the patients. We used data from the National Confidential Inquiry into Suicide and Safety in Mental Health, a national data-set of all suicide deaths in the UK that includes detailed clinical information on those seen by services in the last 12 months before death. RESULTS: Between 2000 and 2016, there were 21 224 suicide deaths by patients aged 16 or over. For male patients, following a steady fall of 0.5% per quarter before the recession (quarterly percent change (QPC) 2000-2009 -0.46%, 95% CI -0.66 to -0.27), suicide rates showed an upward trend during the recession (QPC 2009-2011 2.37%, 95% CI -0.22 to 5.04). Recession-related rises in suicide were found in men aged 45-54 years, those who were unemployed or had a diagnosis of substance dependence/misuse. Between 2012 and 2016 there was a decrease in suicide in male patients despite an increasing number of patients treated. No significant recession-related trends were found in women. CONCLUSIONS: Recession-associated increases in suicide were seen in male mental health patients as well as the male general population, with those in mid-life at particular risk. Support and targeted interventions for patients with financial difficulties may help reduce the risk at times of economic hardship. Factors such as drug and alcohol misuse also need to be considered. Recent decreases in suicide may be related to an improved economic context or better mental healthcare. DECLARATION OF INTEREST: N.K. is supported by Greater Manchester Mental Health NHS Foundation Trust. L.A. chairs the National Suicide Prevention Strategy Advisory Group at the Department of Health (of which N.K. is also a member) and is a non-executive Director for the Care Quality Commission. N.K. chairs the National Institute for Health and Care Excellence (NICE) depression in adults guideline and was a topic expert member for the NICE suicide prevention guideline.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".