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Enregistrement W2947760904 · doi:10.5210/ojphi.v11i1.9676

Lessons Learned from an Extreme Heat Event using ACES for Situational Awareness, Ontario, Canada

2019· article· en· W2947760904 sur OpenAlexfundaboutno aff
Nancy VanStone, Adam van Dijk, Paul Bélanger, Kieran Moore

Notice bibliographique

RevueOnline Journal of Public Health Informatics · 2019
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueClimate Change and Health Impacts
Établissements canadiensnon disponible
Organismes subventionnairesOntario Ministry of Health and Long-Term CareGovernment of Ontario
Mots-clésMedicinePublic healthPublic health surveillanceEmergency departmentHealth careEnvironmental healthAcute careMedical emergencyNursing

Résumé

récupéré en direct d'OpenAlex

ObjectiveTo describe the lessons learned for public health decision-makers from an analysis of Acute Care Enhanced Surveillance (ACES) data for the heatwaves experienced in Ontario, Canada in the summer of 2018.IntroductionThe Acute Care Enhanced Surveillance (ACES) system provides syndromic surveillance for Ontario’s acute care hospitals. ACES receives over 99% of acute care records for emergency department (ED) visits; mean daily volume is 17,500 visits. ACES uses a maximum entropy classifier and generates more than 80 standard syndromes, fifteen of which are actively monitored for aberrational activity and are considered of higher public health relevance, including RESP (respiratory infection, non-croup), ILI (influenza-like illness), TOX (toxicological, chemical/drug exposure), AST (asthma), OPI (opioid exposure), CELL (cellulitis), GASTRO (gastroenteritis), ENVIRO (environmental, heat/cold exposure), MH (mental health), EOH (alcohol intoxication), DERM (rash), and SEP (bacteremia, sepsis).Syndromic surveillance provides a salient source of public health surveillance during extreme heat events; monitoring real-time ED visits can inform local public health authorities of health impacts, provide situation awareness to initiate and/or inform public health response, and help decision-makers allocate resources according to geographic (or demographic) vulnerability. While the use of syndromic surveillance has been well-characterized to monitor infectious disease outbreaks, its use to monitor the heat-health impacts is relatively novel for ACES users, specifically local public health authorities. This report describes the the data collected during an extended extreme heat event in Ontario, Canada, to highlight the value of syndromic surveillance during extreme heat events and make recommendations regarding incorporating ACES data into routine workflows.MethodsTemperature data were retrieved from Environment Canada historical databases for mid-June to mid-July 2018. Aggregate counts per day for total ED visits and and for individual syndromes were retrieved from ACES databases. Descriptive statistics were used to analyze all datasets.ResultsAn extreme heat event occurred in the southern region of Ontario in early summer, 2018. Environment Canada issues heat warnings for regions throughout Canada according to region-specific criteria; for southern Ontario, heat warnings are issued when 2 or more consecutive days of daytime maximum temperatures are expected to reach 31°C or when 2 or more consecutive days of humidex values are expected to reach 40. Extended heat warnings are issued when the event lasts beyond 2 days. An extended heat event occurred June 29 to July 5, 2018. Although the region is large, temperature data from Environment Canada’s climate monitoring station at Toronto’s Pearson Airport are shown (Figure 1) as an example of the temperatures observed for this time period in the region.ConclusionsLessons learned from an analysis of ACES data during an extreme heat event:1. The ENVIRO syndrome provides real-time monitoring of the health impacts during a heat event and may provide proxy for estimating the indirect effects of heat (e.g., impacts on chronic conditions). Public health authorities can monitor local health impacts during an extreme heat event.2. Patients seeking help at the ED do not appear to be skewed in acuity, sex nor age. This does not necessarily reflect the population that experiences the greatest impact from extreme heat, but rather those that are seeking help at the ED for the direct effects of heat. That said, an increase in ENVIRO counts does not indicate whether the increase is due to greater exposure to the heat (or sun), engaging in vigorous outdoor activity during the event (recreational or occupational), or lack of access to air conditioning.3. ED visits for ENVIRO can be geolocated to determine areas experiencing greater health impacts. This may allow allocation of resources to specifically address vulnerabilities. ACES has built-in mapping capabilities that allows a geovisualization of the home addresses for patients. Furthermore, aggregate counts for relevant syndromes are available for registered users on the Public Health Information Management System (PHIMS), a web-accessible GIS tool for situational awareness that gives public health decision-makers access to real time health impacts in concert with demographics, weather, and other emergency management information.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,442
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,348
Tête enseignante GPT0,413
Écart entre enseignants0,065 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2019
Routes d'admission2
Résumé présentoui

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