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Forecasting Hospital Emergency Department Visits For FORECASTING HOSPITAL EMERGENCY DEPARTMENT VISITS FOR RESPIRATORY ILLNESS USING ONTARIO’S TELEHEALTH SYSTEM An Application of Real-Time Syndromic Surveillance to Forecasting Health Services Demand by ALEXANDER GORDON PERRY A thesis submitted to the Department of Community Health and Epidemiology in conformity with the requirements for the degree of Master of Science Queen’s University Kingston, Ontario, Canada August 2009 Copyright © Alexander Gordon Perry, 2009 Abstract Background: 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 i 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. ii Acknowledgements This project was unique and challenging because it combined elements of Epidemiology and Engineering. The following individuals and organizations deserve recognition for their roles in this project: Dr. Kieran Moore, Adam van Dijk, and the other members of the Queen’s Public Health Informatics (QPHI) team for their advice and for providing the resources necessary to carry out the project Dr. Will Pickett whose open-mindedness and willingness to supervise this cross- disciplinary project made it possible Dr. Michael Korenberg of the Department of Electrical and Computer Engineering for his insightful suggestions and for agreeing to supervise a project outside his home department in addition to the many other projects with which he is involved Dr. Miu Lam for his advice on statistical aspects of the project The Kingston General Hospital for its financial support through the KGH Scholarship Don McGuinness for his advice and help with ICD code translation Dr. Linda Levesque for her advice and support Finally, I would like to thank my grandfather, Dr. V. R. Perry, for his enthusiasm in my return to school to study Epidemiology iii Table of Contents Abstract ..................................................................................................................................................... i Acknowledgements .................................................................................................................................. iii Table of Contents ..................................................................................................................................... iv List of Acronyms and Abbreviations ........................................................................................................ vi List of Symbols....................................................................................................................................... vii List of Tables ......................................................................................................................................... viii List of Figures .......................................................................................................................................... x Chapter 1 Introduction ....................................................................................................................... 1 1.1 Background ............................................................................................................................ 1 1.1.1 Real-Time Syndromic Surveillance ................................................................................ 1 1.1.2 Applications of Syndromic Surveillance......................................................................... 2 1.2 Study Objectives..................................................................................................................... 3 Chapter 2 Literature Review and Study Rationale ............................................................................... 5 2.1 Previous Research on the Telehealth Ontario Call-Emergency Department Visit Relationship for Respiratory Illness .......................................................................................................................... 5 2.2 Time Series Forecasting .......................................................................................................... 7 2.3 Previous Research on Health Service Demand Forecasting ...................................................... 8 2.4 Gaps in Existing Knowledge ..................................................................................................13 2.5 Study Rationale .....................................................................................................................15 2.5.1 Conceptual Framework .................................................................................................15 2.5.2 Addressing Gaps in Knowledge ....................................................................................16 Chapter 3 Study Design and Methods ................................................................................................19 3.1 Study Population, Setting, and Design ....................................................................................19 3.2 Data Sources and Ethics Approval .........................................................................................19 3.3 Definitions.............................................................................................................................20 3.4 Emergency Department Visits: the NACRS Database .............................................................25 3.4.1 Coverage and Data Quality ...........................................................................................25 3.4.2 Inclusion/Exclusion ......................................................................................................26 3.5 Telehealth Ontario Calls ........................................................................................................27 3.5.1 Coverage and Data Quality ...........................................................................................27 3.5.2 Inclusion/Exclusion ......................................................................................................27 3.6 Confounders ..........................................................................................................................28 3.7 Geographic Grouping of Telehealth Calls and Emergency Visits ............................................29 3.8 Analytic Techniques for Establishing the Relationship between Calls and Visits .....................30 3.8.1 Background ..................................................................................................................30 3.8.2 Numerical Algorithms for Subspace State Space System Identification ..........................33 iv 3.8.3 Fast Orthogonal Search .................................................................................................35 3.8.4 Parallel Cascade Identification ......................................................................................38 3.8.5 Model Implementation ..................................................................................................40 3.9 Measures ...............................................................................................................................47 Chapter 4 Results ..............................................................................................................................62 4.1 Summary Statistics of Telehealth Ontario Calls and Emergency Department Visits by
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