Effect of area-level factors on heat-associated morbidity and mortality in the Eastern Townships
Bibliographic record
Abstract
Abstract Background Multiple individual risk factors for morbidity and mortality during heat waves have previously been identified. Based on these, five categories of area-level indicators are used in Quebec, Canada, to identify geographical regions with presumed increased vulnerability: economic, social and biological vulnerability, housing characteristics and urban environment. However, we do not know which indicators correlate with an increase in morbidity and mortality during heat waves. This study evaluated, among 13 different area-level vulnerability factors from these five categories, which ones predict an effect modification of hot temperatures on morbidity and mortality. Methods For 2008 to 2018, we first explored the association between average daily temperature and four daily health indicators for the Eastern Townships region in Quebec: all-cause ambulance transports, all-cause emergency department (ED) visits, all-cause mortality and heat-related hospitalizations. We then used Poisson regression linear mixed models to investigate effect modification from the 13 area-level risk factors. Results Three vulnerability factors significantly caused positive effect modification between temperature and daily all-cause ambulance counts: the chronic diseases index, the percentage of people aged 65 years or older and the percentage of people aged 65 or older and living alone. For heat-related hospitalizations, four factors caused positive effect modification: the same three as for ambulance counts, plus the proportion of people that spend 30% or more household income on rent. For the ED-temperature association, only the percentage of people with low income caused positive effect modification. There was no significant association between temperature and daily mortality. Conclusions This study will help public health officials target preventive interventions to areas with the vulnerability factors that most strongly predict heat-related morbidity. Key messages Some, but not all, individual risk factors are useful at the area-level to predict areas with the strongest increases in morbidity associated with hot temperatures. The chronic diseases index and the percentage of people 65 and older living alone both accentuated the association between hot temperatures and ambulance transports or hospitalizations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".