Extreme temperature and mood disorders: A systematic review of literature
Bibliographic record
Abstract
Introduction The prevalence of extreme temperature is increasing largely due to the progression of climate change globally (LaSorte et al. Climate Change 2021; 166 1-2). Existing research indicates extreme temperatures have an impact on mental health, including its effect on mood disorders (Rony & Alamgir. Health Sci Rep 2023; 6 12). While there is evidence to suggest that mood disorders can be influenced by various environmental, biological, and social factors (Zhang et al. Environmental International 2020; 143), no study has synthesized findings on the relationship between extreme temperature and mood disorders in existing literature. Objectives The study aims to: investigate the linkage between extreme temperature and mood disorders in terms of symptom severity, hospital admissions and adverse events; describe factors moderating the relationship between extreme temperature and mood disorders; outline study-defined interventions and make policy recommendations. Methods This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Major databases (Medline/PubMed, PsychINFO, Scopus, Web of Science) were searched for eligible reports using a search strategy developed for the study. This was supplemented by snowball searching for references in relevant studies. Title and abstract screening and data extraction were completed by at least two independent investigators and conflicts were resolved by discussion amongst investigators or consulting the senior author. All included studies will be assessed with the National Institutes of Health Study Quality Assessment Tools. Results As seen in Image 1, 468 articles were identified from searching databases. Following screening and full-text review, 22 articles were selected for data extraction. Preliminary findings showed that the included studies were conducted in North America, Europe, Asia, and Oceania-Australia among others. The included studies were of different designs, including case-crossover, cohort and cross-sectional studies. Findings across studies indicate that extreme temperatures have a complex and significant impact on mood disorders. High temperatures were associated with increased hospital admissions, with adolescents, women, and the elderly especially vulnerable. Individuals with bipolar disorder and depression showed increased sensitivity to heat exposure. While some studies found increased emergency department visits for mood disorders during periods of extreme heat, others revealed insignificant correlations. Moreover, short-term exposure to humidity was also linked to elevated risk for mood disorders. Image 1: Conclusions This study underscores the impact of extreme temperatures on mood disorders and highlights the need for real-world solutions, like policy implementation, to reduce exposure to such conditions due to climate change. Disclosure of Interest None Declared
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".