Ambient heat and emergency department visits for mental health in Canada: assessing temporal variations by greenspace, urbanicity and socioeconomic status
Bibliographic record
Abstract
BACKGROUND AND AIM Although previous research has suggested that greenspace might attenuate health risks associated with ambient heat, there is limited research on mental health disorders. Thus, we evaluated the effect modification of greenspace on heat-related mental health emergency department (ED) visits and whether these effects changed over time. METHODS We conducted a case-crossover study for mental health-related ED visits in Canada between 2004 and 2021 during warm-season months (May to August). ED visits for all Canadians living in 111 health regions were included. Daily average temperature as well as greenspace exposure, estimated by the Normalized Difference Vegetation Index (NDVI), were assigned to individuals at their residential location. Extreme heat was defined as the 95th percentile of the health region-specific warm-season temperature distribution. Conditional logistic regression was used to estimate associations between heat and mental health-related ED visits. RESULTS A total of 10,638,372 ED visits for any mental health condition were included. Days of extreme heat were associated with an increased risk of ED visits for any mental health condition (OR = 1.07; 95% CI: 1.05-1.09). In the earlier part of the study period (2004–2009), the associations between ambient heat and mental health ED visits were stronger among individuals exposed to the lowest quartile of NDVI exposure (OR = 1.20; 95% CI: 1.15-1.25), compared to individuals in the highest quartile of NDVI (OR = 1.01; 95% CI: 0.96-1.07). This difference, however, accentuated in the later part of the study (2016-2021) with the ORs for regions in the lowest quartile of NDVI increasing to 1.29 (95% CI: 1.25-1.34) and remaining stable for regions in the highest quartile. Effects appeared strongest in the most deprived socioeconomic areas. CONCLUSIONS Potential health benefits of greenspaces should be considered in mitigation strategies regarding the impacts of climate change on mental health ED visits.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".