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Canadian Journal of Public Health = Revue Canadienne de Santé Publique logoLink to Canadian Journal of Public Health = Revue Canadienne de Santé Publique
. 2010 Nov 1;101(6):464–469. doi: 10.1007/BF03403965

A Comparison of Methods for Forecasting Emergency Department Visits for Respiratory Illness Using Telehealth Ontario Calls

Alexander G Perry 111,211,, Kieran M Moore 111,311, Linda E Levesque 111,411, C William L Pickett 311,411, Michael J Korenberg 211
PMCID: PMC6973844  PMID: 21370782

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

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

References

  • 1.Menec V, Bruce S, MacWilliam L. Exploring reasons for bed pressures in Winnipeg acute care hospitals. Can J Aging. 2005;24(Supplement1):121–31. doi: 10.1353/cja.2005.0051. [DOI] [PubMed] [Google Scholar]
  • 2.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. doi: 10.1007/BF03405054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hanratty B, Robinson M. Coping with winter bed crises. BM. 1999;319:1511–12. doi: 10.1136/bmj.319.7224.1511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Ontario Ministry of HealthLong-Term Care. Ontario Health Plan for an Influenza Pandemic. 2008. [Google Scholar]
  • 5.Ontario Ministry of HealthLong-Term Care. Pandemic H1N1 (pH1N1) Alternate Influenza Assessment, Treatment, and Referral Services. Decision Document. 2009. [Google Scholar]
  • 6.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. [Google Scholar]
  • 7.Campbell A. The SARS Commission Interim Report: SARS and Public Health in Ontario. Ontario Ministry of Health and Long-Term Care. 2004. [DOI] [PubMed] [Google Scholar]
  • 8.Jones S, Joy M. Forecasting demand of emergency care. Health Care Management Sc. 2002;5:297–305. doi: 10.1023/A:1020390425029. [DOI] [PubMed] [Google Scholar]
  • 9.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. doi: 10.1017/s1481803500009969. [DOI] [PubMed] [Google Scholar]
  • 10.Brillman JC, Burr T, Forslund D, Joyce E, Picard R, Umlan E. BMC Med Informatics Decision Making. 2005. Modeling emergency department visit patterns for infectious disease complaints: Results and application to disease surveillance. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Reis B, Mandl K. BMC Med Informatics Decision Making. 2003. Time series modeling for syndromic surveillance. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Gilbert P. State space and ARMA models: An overview of the equivalence. Bank of Canada Working Paper 93–4. 1993. [Google Scholar]
  • 13.Makridakis S, Wheelwright S, Hyndman R. Forecasting: Methods and Application. 3rd. New York, NY: Wiley; 1998. [Google Scholar]
  • 14.Overschee P, DeMoor B. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatic. 1994;30(1):75–93. doi: 10.1016/0005-1098(94)90230-5. [DOI] [Google Scholar]
  • 15.Korenberg M. A robust orthogonal algorithm for system identification and time-series analysis. Biological Cybernetic. 1989;60:267–76. doi: 10.1007/BF00204124. [DOI] [PubMed] [Google Scholar]
  • 16.Korenberg M. Parallel cascade identification and kernel estimation for nonlinear systems. Ann Biomedical Engineering. 1991;19:429–55. doi: 10.1007/BF02584319. [DOI] [PubMed] [Google Scholar]
  • 17.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. doi: 10.3201/eid1302.060557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Statistics Canada. Health Regions: Boundaries and Correspondence with Census Geography. 82–402-XIE. 2007. [Google Scholar]
  • 19.Statistics Canada. Postal Code Conversion File (PCCF), Reference Guide. 2006. [Google Scholar]
  • 20.Ljung L. MATLAB System Identification Toolbox User’s Guid. Natick, MA: The MathWorks; 2003. [Google Scholar]
  • 21.The MathWorks Inc. MATLAB Version 7 Release 14. 2003. [Google Scholar]
  • 22.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) 2009. [Google Scholar]
  • 23.Chan B, Schull MJ, Schultz SE. Atlas Report: Emergency Department Services in Ontario 1993–2000. Toronto, ON: Institute for Clinical Evaluative Sciences; 2001. [Google Scholar]

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