Greenness and hospital admissions for cause specific mental disorders: multicountry time series study
Bibliographic record
Abstract
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".