The Relationship Between Temperature and 911 Medical Dispatch Data for Heat-Related Illness in Toronto, 2002-2005: An Application of Syndromic Surveillance
Notice bibliographique
Résumé
Heat-related illness (HRI) is of growing public health importance, particularly with climate change and an anticipated increased frequency of heat waves. A syndromic surveillance system for HRI could provide new information on the population impact of excessive heat and thus be of value for public health planning. This study describes the association between 911 medical dispatch calls for HRI and temperature in Toronto, Ontario during the summers of 2002-2005. A combination of methodological approaches was used to understand both the temporal trend and spatial pattern in the relationship between 911 medical dispatch data and temperature. A case definition for HRI was developed using clinical and empirical assessments. Generalized Additive Models (GAM) and Zero inflated Poisson regression (ZIP) were used to determine the association between 911 calls and mean and maximum temperature. The validity of the HRI case definition was investigated by making comparisons with emergency department visits for HRI. Descriptive, aberration detection, and cross-correlation methods were applied to explore the timing and volume of HRI calls in relation to these visits, and the declaration of heat alerts. Finally, the existence of neighbourhood level spatial variation in 911 calls for HRI was analyzed using geospatial methods. This is the first study to demonstrate an association between daily 911 medical dispatch calls specifically for HRI and temperature. On average, 911 calls for HRI increased up to a maximum of 36% (p<.0001) (median 29%) for each 1°C increase in temperature. The temporal trend of 911 calls for HRI was similar to emergency department visits for HRI and heat alerts, improving confidence in the validity of this data source. Heterogeneity in the spatial pattern of calls across neighbourhoods was also apparent, with recreational areas near the waterfront demonstrating the highest percentage increase in calls. Monitoring 911 medical dispatch data for HRI could assist public health units carrying out both temporal and geospatial surveillance, particularly in areas where synoptic based mortality prediction algorithms are not being utilized. This previously untapped data source should be further explored for its applications in understanding the relationship between heat and human health and more appropriately targeting public health interventions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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 ».