Can a local low-budget intervention make a difference to suicide rates? Evaluating the effectiveness of the Barnet (London) suicide prevention campaign using real-time suspected suicide data
Bibliographic record
Abstract
Abstract Background Three quarters of suicides in the UK are by men, of whom only a quarter had contact with mental health services at the time of their death. Community-based interventions are therefore likely to be crucial to reduce (male) suicides, but there is limited evidence to support their effectiveness. The aim of this study was to evaluate the impact of a multi-strategy campaign to increase uptake of mental health services and peer support amongst working-aged men in Barnet, London, via: (1) targeted promotion of the ‘Stay Alive’ app, (2) a large scale digital and outdoor media campaign, (3) community outreach targeting male-dominated industries, (4) the first face-to-face “Andy’s Man Club” peer-to-peer support group in the borough. Methods We used data on suspected suicides in London between 1st March 2021 to 31st November 2023 (N = 1,408) to calculate monthly age-standardised rates in (a) Barnet, (b) surrounding boroughs and (c) the rest of London, for ‘naïve’ and ‘placebo’ comparisons during and outside the campaign period, and then before, during and after the campaign. We also estimated maximum exposure to the campaign beyond its duration, and repeated the analysis using a more conservative (February to December 2020) baseline period for Barnet. Results There was a sizeable drop in suicides in Barnet for the duration of the campaign and the following six months, with 6 to 9 deaths possibly averted thanks to the campaign, which represents a decline of around 20% of the yearly incidence, at a cost of under £6,400 per averted suicide. Conclusions Our analysis suggests that a local, relatively inexpensive community-based campaign can be effective in reducing (suspected) suicides. However, further research is needed to confidently link this decrease in suicides to the campaign, or specific elements of it.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".