Evaluating cumulative temperature indicators for heat alert and response systems : a case study in Vancouver, British Columbia
Notice bibliographique
Résumé
Extreme heat events (EHEs) are escalating in frequency, intensity, and duration due to climate change, posing significant health risks. To mitigate their impacts, many countries, including Canada, have established heat-health warning systems (HHWSs). However, these systems often rely on absolute temperature thresholds that require all conditions to be met simultaneously. This rigidity can overlook different sequences of day-night heat that accumulate over multiple days, potentially leading to missed warnings. In this study, seven cumulative temperature metrics were created by summing daily high and overnight low temperatures over one to three days. To assess each metric’s relationship with all-cause mortality, heat-related ED visits, and ambulance dispatches in Western Metro Vancouver (WMV) from 2008-2023, generalized additive models with negative binomial distributions were fitted. The statistical performance of the metrics was evaluated using significance tests, the Akaike Information Criterion (AIC), and forecasting accuracy. All cumulative metrics demonstrated stable and comparable statistical performance. Given its applicability, the High+Low+High (HLH) metric—summing two daily highs and one overnight low—was selected as an example for further analyses. A split value analysis was performed by dividing HLH into one hundred equally spaced intervals and creating a binary variable for each interval, refitting a model to determine the odds ratio (OR) at each split. Points at which the OR for the three outcomes became significantly greater than 1.0 were designated as baseline thresholds. 58°C (all-cause mortality), 47°C (ED visits), and 42°C (ambulance dispatches) were identified as the baseline points. Using the mortality-based threshold as a template, further “separate baseline thresholds” of 51°C and 15°C were identified for two daily highs (HH) and overnight low (L) respectively. Accordingly, an HLH threshold algorithm was formulated such that the OR of mortality exceeds 1.0 when HLH ≥ 58°C and either L ≥ 15°C or HH ≥ 51°C. Subgroup analyses demonstrated elevated risks among individuals with cardiovascular, renal, or respiratory conditions, mental illnesses, multiple comorbidities, or lower socioeconomic status. Overall, this study developed methods for a more dynamic and flexible HHWS framework to issue heat alerts, offering a multi-trigger approach that can better account for cumulative day-night heat sequences. [An errata to this thesis was added on 2025-10-20.]
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».