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.
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.
Article PDF
Similar content being viewed by others
References
Menec V, Bruce S, MacWilliam L. Exploring reasons for bed pressures in Winnipeg acute care hospitals. Can J Aging. 2005;24(Supplement 1):121–31.
Menec V, Black C, MacWilliam L, Aoki F. The impact of influenza-associated respiratory illnesses on hospitalizations, physician visits, emergency room visits, and mortality. Can J Public Health. 2003;94(1):59–63.
Hanratty B, Robinson M. Coping with winter bed crises. BM. 1999;319:1511–12.
Ontario Ministry of Health and Long-Term Care. Ontario Health Plan for an Influenza Pandemic, 2008.
Ontario Ministry of Health and Long-Term Care. Pandemic H1N1 (pH1N1) Alternate Influenza Assessment, Treatment, and Referral Services. Decision Document. August 2009.
Walker D, Keon W, Laupacis A, Low D, Moore K, Kitts J, et al. The Ontario Expert Panel on SARS and Infectious Disease Control: For the Public’s Health: A Plan of Action—Final Report of the Ontario Expert Panel on SARS and Infectious Disease Control, 2004.
Campbell A. The SARS Commission Interim Report: SARS and Public Health in Ontario. Ontario Ministry of Health and Long-Term Care, April 2004.
Jones S, Joy M. Forecasting demand of emergency care. Health Care Management Sc. 2002;5:297–305.
van Dijk A, McGuinness D, Rolland E, Moore KM. Can Telehealth Ontario respiratory call volume be used as a proxy for emergency department respiratory visit surveillance by public health? CJE. 2008;10(1):18–24.
Brillman JC, Burr T, Forslund D, Joyce E, Picard R, Umlan E. Modeling emergency department visit patterns for infectious disease complaints: Results and application to disease surveillance. BMC Med Informatics Decision Making. 2005;5(4).
Reis B, Mandl K. Time series modeling for syndromic surveillance. BMC Med Informatics Decision Making. 2003;3(2).
Gilbert P. State space and ARMA models: An overview of the equivalence. Bank of Canada Working Paper 93–4, 1993.
Makridakis S, Wheelwright S, Hyndman R. Forecasting: Methods and Application., 3rd ed. New York, NY: Wiley, 1998.
Overschee P, DeMoor B. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatic. 1994;30(1):75–93.
Korenberg M. A robust orthogonal algorithm for system identification and time-series analysis. Biological Cybernetic. 1989;60:267–76.
Korenberg M. Parallel cascade identification and kernel estimation for nonlinear systems. Ann Biomedical Engineering. 1991;19:429–55.
Marsden-Haug N, Foster V, Gould P, Elbert E, Wang H, Pavlin J. Code-based syndromic surveillance for influenza-like illness by international classification of diseases, Ninth Revision. Emerg Infect Dis. 2007;13(2):207–16.
Statistics Canada. Health Regions: Boundaries and Correspondence with Census Geography. 82–402-XIE, 2007.
Statistics Canada. Postal Code Conversion File (PCCF), Reference Guide. 92F0153GIE, 2006.
Ljung L. MATLAB System Identification Toolbox User’s Guid., Version 6 ed. Natick, MA: The MathWorks, 2003.
The MathWorks Inc. MATLAB Version 7 Release 14. 2003.
Statistics Canada. Table 109–5315 - Estimates of population (Census and administrative data), by age group and sex, Canada, provinces, territories, health regions and peer groups, annual (number) (table), CANSIM (database), Using E-STAT (distributor). Available at: http://www.estat.statcan.gc.ca/cgi-win/cnsmcgi.exe?Lang=E&EST-Fi=EStat/English/CII_1-eng.htm (Accessed August 10, 2009).
Chan B, Schull MJ, Schultz SE. Atlas Report: Emergency Department Services in Ontario 1993–2000. Toronto, ON: Institute for Clinical Evaluative Sciences, 2001.
Author information
Authors and Affiliations
Corresponding author
Additional information
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
Rights and permissions
About this article
Cite this article
Perry, A.G., Moore, K.M., Levesque, L.E. et al. A Comparison of Methods for Forecasting Emergency Department Visits for Respiratory Illness Using Telehealth Ontario Calls. Can J Public Health 101, 464–469 (2010). https://doi.org/10.1007/BF03403965
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/BF03403965