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dc.contributor.authorPerry, Alexanderen
dc.date2009-08-11 16:20:44.553
dc.date.accessioned2009-08-12T22:01:11Z
dc.date.available2009-08-12T22:01:11Z
dc.date.issued2009-08-12T22:01:11Z
dc.identifier.urihttp://hdl.handle.net/1974/2585
dc.descriptionThesis (Master, Community Health & Epidemiology) -- Queen's University, 2009-08-11 16:20:44.553en
dc.description.abstractBackground: Respiratory illnesses can have a substantial impact on population health and burden hospitals in terms of patient load. Advance warnings of the spread of such illness could inform public health interventions and help hospitals manage patient services. Previous research showed that calls for respiratory complaints to Telehealth Ontario are correlated up to two weeks in advance with emergency department visits for respiratory illness at the provincial level. Objectives: This thesis examined whether Telehealth Ontario calls for respiratory complaints could be used to accurately forecast the daily and weekly number of emergency department visits for respiratory illness at the health unit level for each of the 36 health units in Ontario up to 14 days in advance in the context of a real-time syndromic surveillance system. The forecasting abilities of three different time series modeling techniques were compared. Methods: The thesis used hospital emergency department visit data from the National Ambulatory Care Reporting System database and Telehealth Ontario call data and from June 1, 2004 to March 31, 2006. Parallel Cascade Identification (PCI), Fast Orthogonal Search (FOS), and Numerical Methods for Subspace State Space System Identification (N4SID) algorithms were used to create prediction models for the daily number of emergency department visits using Telehealth call counts and holiday/weekends as predictors. Prediction models were constructed using the first year of the study data and their accuracy was measured over the second year of data. Factors associated with prediction accuracy were examined. Results: Forecast error varied widely across health units. Prediction error increased with lead time and lower call-to-visits ratio. Compared with N4SID, PCI and FOS had significantly lower forecast error. Forecasts of the weekly aggregate number of visits showed little evidence of ability to accurately flag corresponding actual increases. However, when visits were aggregated over a four day period, increases could be flagged more accurately than chance in six of the 36 health units accounting for approximately half of the Ontario population. Conclusions: This thesis suggests that Telehealth Ontario data collected by a real-time syndromic surveillance system could play a role in forecasting health services demand for respiratory illness.en
dc.format.extent8189225 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectForecastingen
dc.subjectHealth Servicesen
dc.subjectRespiratory Illnessen
dc.subjectSurveillanceen
dc.subjectTelehealthen
dc.subjectSyndromic Surveillanceen
dc.subjectHealth Services Demanden
dc.subjectInfluenzaen
dc.subjectParallel Cascade Identificationen
dc.subjectFast Orthgonal Searchen
dc.subjectSubspace Identificationen
dc.subjectN4SIDen
dc.subjectState Space Modelsen
dc.subjectSystem Identificationen
dc.titleForecasting Hospital Emergency Department Visits for Respiratory Illness Using Ontario's Telehealth System: An Application of Real-Time Syndromic Surveillance to Forecasting Health Services Demanden
dc.typethesisen
dc.description.degreeM.Sc.en
dc.contributor.supervisorPickett, Williamen
dc.contributor.supervisorMoore, Kieranen
dc.contributor.supervisorKorenberg, Michael J.en
dc.contributor.departmentCommunity Health and Epidemiologyen
dc.degree.grantorQueen's University at Kingstonen


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