Suicide place of death and associated demographic characteristics: novel analysis to inform suicide prevention strategies
Bibliographic record
Abstract
Purpose Suicide is a leading cause of preventable death in England and a priority for government and health systems. To reduce suicides, it is crucial to understand where suicides occur and who is most at risk. This study aims, for the first time, to investigate suicide place of death in England and the demographic that may be associated with this. Design/methodology/approach A cross-sectional analysis of routinely collected Office for National Statistics mortality data of 22,314 registered suicide deaths aged 10 years+ in England between 2018 and 2022 was performed. Measures included place of death (home or elsewhere), sex, age, rurality, deprivation and region of residence at time of death. Findings In total, 61.29% (CI = 60.65–61.93) of suicide deaths occurred at home. This was significantly higher (P < 0.05) than suicide deaths that occurred elsewhere (38.71%, CI = 38.07–39.35). Logistic regression analysis revealed that individuals who died from suicide at home were older (OR = 0.983, CI = 0.982–0.985, P < 0.0001) and female (OR = 1.70, CI = 1.59–1.81, P < 0.0001). A significant non-linear association was found between deaths at home, deprivation and rurality but not region of residence. Practical implications It is recommended that future research investigates the granularity of place of suicide death and vulnerable populations and that local practitioners use these findings to inform and tailor suicide prevention programmes, partnership working and resources to better prevent suicides in at-risk populations. Originality/value To the best of the authors’ knowledge, this study is the first to use a representative, generalisable ecologically valid national sample of over 22,000 deaths to investigate suicide place of death and associated demographics in England.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".