Extreme ambient temperatures and cardiorespiratory emergency room visits: assessing risk by comorbid health conditions in a time series study
Bibliographic record
Abstract
BACKGROUND: Extreme ambient temperatures are an increasing public health concern. The aim of this study was to assess if persons with comorbid health conditions were at increased risk of adverse cardiorespiratory morbidity during temperature extremes. METHODS: A time series study design was applied to 292,666 and 562,738 emergency room (ER) visits for cardiovascular and respiratory diseases, respectively, that occurred in Toronto area hospitals between April 1st 2002 and March 31st 2010. Subgroups of persons with comorbid health conditions were identified. Relative risks (RRs) and their corresponding 95% confidence intervals (CIs) were estimated using a Poisson regression model with distributed lag non-linear model, and were adjusted for the confounding influence of seasonality, relative humidity, day-of-the-week, outdoor air pollutants and daily influenza ER visits. Effect modification by comorbid health conditions was tested using the relative effect modification (REM) index. RESULTS: Stronger associations of cardiovascular disease ER visits were observed for persons with diabetes compared to persons without diabetes (REM = 1.12; 95% CI: 1.01 - 1.27) with exposure to the cumulative short term effect of extreme hot temperatures (i.e. 99th percentile of temperature distribution vs. 75th percentile). Effect modification was also found for comorbid respiratory disease (REM = 1.17; 95% CI: 1.02 - 1.44) and cancer (REM = 1.20; 95% CI: 1.02 - 1.49) on respiratory disease ER visits during short term hot temperature episodes. The effect of extreme cold temperatures (i.e. 1st percentile of temperature distribution vs. 25th percentile) on cardiovascular disease ER visits were stronger for individuals with comorbid cardiac diseases (REM = 1.47; 95% CI: 1.06 - 2.23) and kidney diseases (REM = 2.43; 95% CI: 1.59 - 8.83) compared to those without these conditions when cumulated over a two-week period. CONCLUSIONS: The identification of those most susceptible to temperature extremes is important for public health officials to implement adaptation measures to manage the impact of extreme temperatures on population health.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".