Abstract
Objectives: Anticipating increases in hospital emergency department (ED) visits for respiratory illness could help time interventions such as opening flu clinics to reduce surges in ED visits. Five different methods for estimating ED visits for respiratory illness from Telehealth Ontario calls are compared, including two non-linear modeling methods. Daily visit estimates up to 14 days in advance were made at the health unit level for all 36 Ontario health units.
Methods: Telehealth calls from June 1, 2004 to March 14, 2006 were included. Estimates generated by regression, Exponentially Weighted Moving Average (EWMA), Numerical Methods for Subspace State Space Identification (N4SID), Fast Orthogonal Search (FOS), and Parallel Cascade Identification (PCI) were compared to the actual number of ED visits for respiratory illness identified from the National Ambulatory Care Reporting System (NACRS) database. Model predictor variables included Telehealth Ontario calls and upcoming holidays/weekends. Models were fit using the first 304 days of data and prediction accuracy was measured over the remaining 348 days.
Results: Forecast accuracy was significantly better (p<0.0001) for the 12 Ontario health units with a population over 400,000 (75% of the Ontario population) than for smaller health units. Compared to regression, FOS produced better estimates (p=0.03) while there was no significant improvement for PCI-based estimates. FOS, PCI, EWMA and N4SID performed worse than regression over the remaining smaller health units.
Conclusion: Telehealth can be used to estimate ED visits for respiratory illness at the health unit level. Non-linear modeling methods produced better estimates than regression in larger health units.
Key words: Forecasting, surveillance, respiratory infections, mathematical model, hospital planning
Résumé
Objectifs: Si l’on pouvait prévoir les augmentations des visites à l’urgence associées aux maladies respiratoires, on réduirait leur impact sur les hôpitaux en ciblant mieux, par exemple, les ouvertures de cliniques secondaires. Nous avons comparé cinq méthodes d’estimation du nombre de visites à l’urgence associées aux maladies respiratoires d’après les appels à Télésanté Ontario, y compris deux méthodes de modélisation non linéaires. Nous avons estimé le nombre de visites quotidiennes jusqu’à 14 jours à l’avance pour chacune des 36 circonscriptions sanitaires de l’Ontario.
Méthode: Nous avons inclus les appels reçus par Télésanté entre le 1er juin 2004 et le 14 mars 2006. Les estimations produites par la régression multivariée, la moyenne mobile à pondération exponentielle (MMPE), les méthodes numériques d’identification des sous-espaces (N4SID), la recherche orthogonale rapide (ROR) et l’identification parallèle en cascade (IPC) ont été comparées au nombre réel de visites à l’urgence associées aux maladies respiratoires enregistré dans la banque de données du Système national d’information sur les soins ambulatoires (SNISA). Les variables prédictives des modèles étaient les appels à Télésanté, les jours fériés à venir et les fins de semaine. Les modèles ont été ajustés selon les 304 premiers jours, et la précision des prédictions a été mesurée au cours des 348 jours suivants.
Résultats: La précision des prévisions était significativement supérieure (p<0,0001) dans les 12 circonscriptions sanitaires de plus de 400 000 habitants (75 % de la population de l’Ontario) que dans les circonscriptions plus petites. La ROR a produit les meilleures estimations (p=0,03), tandis que l’IPC n’apportait aucune amélioration significative. Les méthodes ROR, IPC, MMPE et N4SID ont produit de moins bons résultats que la régression dans les petites circonscriptions sanitaires.
Conclusion: Télésanté Ontario peut être utilisée pour estimer les visites à l’urgence associées aux maladies respiratoires dans les circonscriptions sanitaires. Les méthodes de modélisation non linéaires produisent de meilleures estimations que la régression dans les circonscriptions qui englobent la majorité de la population.
Mots clés: prévision, surveillance, infections de l’appareil respiratoire, modélisation mathématique, planification hôpitaux
Footnotes
Acknowledgements: This work was funded in part by an Ontario Graduate Scholarship (OGS) and Natural Sciences and Engineering Research Council of Canada (NSERC) scholarship held by A.G. Perry.
Conflict of Interest: None to declare
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