Association between suicide reporting in the media and suicide: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the association between reporting on suicides, especially deaths of celebrities by suicide, and subsequent suicides in the general population. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed/Medline, PsychInfo, Scopus, Web of Science, Embase, and Google Scholar, searched up to September 2019. REVIEW METHODS: Studies were included if they compared at least one time point before and one time point after media reports on suicide; follow-up was two months or less; the outcome was death by suicide; and the media reports were about non-fictional suicides. Data from studies adopting an interrupted time series design, or single or multiple arm before and after comparisons, were reviewed. RESULTS: 31 studies were identified and analysed, and 20 studies at moderate risk of bias were included in the main analyses. The risk of suicide increased by 13% in the period after the media reported a death of a celebrity by suicide (rate ratio 1.13, 95% confidence interval 1.08 to 1.18; 14 studies; median follow-up 28 days, range 7-60 days). When the suicide method used by the celebrity was reported, there was an associated 30% increase in deaths by the same method (rate ratio 1.30, 95% confidence interval 1.18 to 1.44; 11 studies; median follow-up 28 days, range 14-60 days). For general reporting of suicide, the rate ratio was 1.002 (0.997 to 1.008; five studies; median follow-up 1 day, range 1-8 days) for a one article increase in the number of reports on suicide. Heterogeneity was large and partially explained by celebrity and methodological factors. Enhanced funnel plots suggested some publication bias in the literature. CONCLUSIONS: Reporting of deaths of celebrities by suicide appears to have made a meaningful impact on total suicides in the general population. The effect was larger for increases by the same method as used by the celebrity. General reporting of suicide did not appear to be associated with suicide although associations for certain types of reporting cannot be excluded. The best available intervention at the population level to deal with the harmful effects of media reports is guidelines for responsible reporting. These guidelines should be more widely implemented and promoted, especially when reporting on deaths of celebrities by suicide. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019086559.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".