The effect modification of extreme temperatures on mental and behavior disorders by environmental factors and individual-level characteristics in Canada
Bibliographic record
Abstract
Ambient extreme temperatures have been associated with mental and behavior disorders (MBDs). However, few studies have assesed whether vulnerability factors such as ambient air pollution, pre-existing mental health conditions and residential environmental factors increase susceptibility. This study aims to evaluate the associations between short-term variations in outdoor ambient extreme temperatures and MBD-related emergency department (ED) visits and how these associations are modified by vulnerability factors. We conducted a case-crossover study of 9,958,759 MBD ED visits in Alberta and Ontario, Canada made between March 1st, 2004 and December 31st, 2020. Daily average temperature was assigned to individual cases with ED visits for MBD using gridded data at a 1 km × 1 km spatial resolution. Conditional logistic regression was used to estimate associations between extreme temperatures (i.e., risk of ED visit at the 2.5th percentile temperature for cold and 97.5th percentile temperature for heat for each health region compared to the minimal temperature risk) and MBD ED visits. Age, sex, pre-existing mental health conditions, ambient air pollution (i.e. PM2.5, NO2 and O3) and residential environmental factors (neighborhood deprivation, residential green space exposure and urbanization) were evaluated as potential effect modifiers. Cumulative exposure to extreme heat over 0–5 days (odds ratio [OR] = 1.145; 95% CI: 1.121–1.171) was associated with ED visits for any MBD. However, cumulative exposure to extreme cold was associated with lower risk of ED visits for any MBD (OR = 0.981; 95% CI: 0.976–0.987). We also found heat to be associated with ED visits for specific MBDs such as substance use disorders, dementia, neurotic disorders, schizophrenia and personality behavior disorder. Individuals with pre-existing mental health conditions, those exposed to higher daily concentrations of NO2 and O3 and those residing in neighborhoods with greater material and social deprivation were at higher risk of heat-related MBD ED visits. Increasing tree canopy coverage appeared to mitigate risks of the effect of heat on MBD ED visits. Findings provide evidence that the impacts of heat on MBD ED visits may vary across different vulnerability factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".