Ambient temperature and mental health: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Increasing evidence indicates that ambient outdoor temperature could affect mental health, which is especially concerning in the context of climate change. We aimed to comprehensively analyse the current evidence regarding the associations between ambient temperature and mental health outcomes. METHODS: We did a systematic review and meta-analysis of the evidence regarding associations between ambient outdoor temperature and changes in mental health outcomes. We searched WebOfScience, Embase, PsychINFO, and PubMed for articles published from database origin up to April 7, 2022. Eligible articles were epidemiological, observational studies in humans of all ages, which evaluated real-world responses to ambient outdoor temperature, and had mental health as a documented outcome; studies of manipulated or controlled temperature or those with only physical health outcomes were excluded. All eligible studies were synthesised qualitatively. If three or more studies reported the same or equivalent effect statistics and if they had equivalent exposure, outcome, and metrics, the studies were pooled in a random-effects meta-analysis. The risk of bias for individual studies was assessed using the Newcastle-Ottawa Scale. The quality of evidence across studies was assessed using the Office of Health Assessment and Translation (OHAT) approach. FINDINGS: 114 studies were included in the systematic review, of which 19 were suitable for meta-analysis. Three meta-analyses were conducted for suicide outcomes: a 1°C increase in mean monthly temperature was associated with an increase in incidence of 1·5% (95% CI 0·8-2·2, p<0·001; n=1 563 109, seven effects pooled from three studies); a 1°C increase in mean daily temperature was associated with an increase in incidence of 1·7% (0·3-3·0, p=0·014; n=113 523, five effects pooled from five studies); and a 1°C increase in mean monthly temperature was associated with a risk ratio of 1·01 (95% CI 1·00-1·01, p<0·001; n=111 794, six effects pooled from three studies). Three meta-analyses were conducted for hospital attendance or admission for mental illness: heatwaves versus non-heatwave periods were associated with an increase in incidence of 9·7% (95% CI 7·6-11·9, p<0·001; n=362 086, three studies); the risk ratio at the 99th percentile of daily mean temperature compared with the 50th percentile was 1·02 (95% CI 1·01-1·03, p=0·006; n=532 296, three studies); and no significant association was found between a 10°C increase in daily mean temperature and hospital attendance. In a qualitative narrative synthesis, we found that ambient outdoor temperature (including absolute temperatures, temperature variability, and heatwaves) was positively associated with attempted and completed suicides (86 studies), hospital attendance or admission for mental illness (43 studies), and worse outcomes for community mental health and wellbeing (19 studies), but much of the evidence was of low certainty with high heterogeneity. INTERPRETATION: Increased temperature and temperature variability could be associated with increased cases of suicide and suicidal behaviour, hospital attendance or admission for mental illness, and poor community health and wellbeing. Climate change is likely to increase temperature anomalies, variability, and heatwaves as well as average temperatures; as such, health system leaders and policy makers must be adequately prepared and should develop adaptation strategies. More high-quality, standardised research is required to improve our understanding of these effects. FUNDING: None.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.047 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".