Extreme temperature and mood disorders: A systematic review of literature
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,009 |
| Bibliométrie | 0,017 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».