Suicide and Ambient Temperature: A Multi-City Multi-Country Study
Bibliographic record
Abstract
Previous literature suggests that higher temperature may play a role in increasing the risk of suicide, but little is known about the nonlinear temperature-suicide association. We examined a nonlinear exposure-response curve of the short-term association using a daily time-series data covering 294 locations in 10 countries (Brazil, Canada, Japan, South Korea, Philippines, Spain, Taiwan, UK, USA, and Vietnam) ranging from 4 to 40 years.We conducted a two-stage meta-analysis. In the first stage, we conducted a location-specific time-stratified case-crossover analysis to examine the short-term association between suicide and temperature (daily mean) using conditional Poisson regression. A distributed lag nonlinear structure for temperature was incorporated with the maximum lag of 6 days. In the second stage, we used a multivariate meta-regression to combine the location-specific lag-cumulative nonlinear associations by country and identify a range of temperature with the highest risk of suicide.In general, higher temperature was associated with the increased risk of suicide. However, suicide risk decreased rather than increased during extremely high temperatures (inverted J-shaped curve) in some locations, particularly for northeast Asian countries. The temperature with the highest risk of suicide for each country ranged from 91st to 99th percentile except the Philippines and the USA. The country mean cumulative relative risks at the temperature with the highest risk relative to that at the lowest were fairly consistent across countries, ranging from 1.27 (95% CI= 1.15–1.40) in the UK to 1.70 (95% CI= 1.35–2.15) in Taiwan, except for Vietnam at 2.69 (95% CI= 1.10–6.56).We found nonlinearity of the short-term temperature-suicide association. Our findings suggest that there may be a critical range of temperature that maximizes the risk of suicide, with the risk less high at extremely high temperature such as heat waves.On behalf of the MCC collaborative research network.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".