Greenness and hospital admissions for cause specific mental disorders: multicountry time series study
Notice bibliographique
Résumé
OBJECTIVES: To examine the association between exposure to greenness and hospital admissions for mental disorders, and to estimate greenness related hospital admissions under various greenness intervention scenarios. DESIGN: Multicountry time series study. SETTING: 6842 locations in seven countries (Australia, Brazil, Canada, Chile, New Zealand, South Korea, and Thailand). PARTICIPANTS: 11.4 million hospital admissions for mental disorders, 2000-19. MAIN OUTCOME MEASURES: Hospital admissions for all cause mental disorders and for six categories in relation to greenness (measured by the normalised difference vegetation index (NDVI)): psychotic disorders, substance use disorders, mood disorders, behavioural disorders, dementia, and anxiety. Associations were estimated using quasi-Poisson regression models, controlled for weather conditions, air pollutants, socioeconomic indicators, seasonality, and long term trends. Models were stratified by sex, age, urbanisation, and season. Hospital admissions were estimated under different greenness intervention scenarios. RESULTS: During 2000-19, of hospital admissions related to mental health disorders, 30.8% (3 522 749 patients) were for psychotic disorders, 24.7% (2 821 860) for substance use disorders, 11.6% (1 325 305) for mood disorders, 7.4% (845 561) for behavioural disorders, 3.0% (348 149) for dementia, and 2.5% (283 914) for anxiety. A 0.1 increase in NDVI was associated with a 7% reduction in the risk of hospital admissions for all cause mental disorders (relative risk 0.93, 95% confidence interval (CI) 0.89 to 0.98) in pooled analyses. However, associations varied across countries and disorder types. Brazil, Chile, and Thailand showed consistent protective associations across most disorder categories, while modest adverse (ie, harmful) associations were observed in Australia and Canada for hospital admissions for all cause mental disorders and for several specific disorder categories. Exposure-response analyses showed a generally monotonic and approximately linear relation without clear thresholds. When limited to urban settings where associations were generally more consistent, an estimated 7712 (95% CI 6701 to 8726) hospital admissions for mental health disorders annually in urban areas were statistically attributable to observed greenness levels. Analysis by greenness intervention scenarios in urban areas suggested that a 10% increase in greenness was associated with reductions in hospital admissions for mental disorders ranging from ~1 per 100 000 in South Korea to ~1000 per 100 000 in New Zealand. CONCLUSIONS: Greenness was statistically associated with lower risks of hospital admissions for mental disorders in several countries, particularly in urban settings. Some adverse associations were, however, observed, and findings were heterogeneous across contexts.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».