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Syndromic Surveillance in Montreal: An Overview of Practice and Research

Syndromic Surveillance in Montreal: An Overview of Practice and Research. David Buckeridge, MD PhD Epidemiology and Biostatistics, McGill University Surveillance Team, Montreal Public Health QPHI Surveillance Meeting KFL&A Public Health, Kingston, ON June 13 th , 2008.

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Syndromic Surveillance in Montreal: An Overview of Practice and Research

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  1. Syndromic Surveillance in Montreal: An Overview of Practice and Research David Buckeridge, MD PhD Epidemiology and Biostatistics, McGill University Surveillance Team, Montreal Public Health QPHI Surveillance Meeting KFL&A Public Health, Kingston, ON June 13th, 2008

  2. Syndromic Surveillance in Montreal (ou, Vigie Multirisque) Counts, Native coding schemes, ISDS consensus syndromes Routine SaTScan, alerts for shared addresses Daily review of analysis results, not clear protocol 1. Identifying individual cases 2. Detecting population patterns 3. Conveying information for action Individual Event Definitions Population Pattern Definitions Intervention Guidelines Public Health Action Event Reports Pattern Report Event Detection Algorithm Pattern Detection Algorithm Intervention Decision Telehealth 911 Calls Hospital Reportable Data Describing Population Decision Algorithm Population Under Surveillance Knowledge

  3. Vigie Multirisque: Data Sources • Emergency Departments • Currently: All 22 ED in Montreal via web form, total counts, no diagnosis or chief complaint • Future: Automated feeds under development, triage code and level, chief complaint, postal code • EMS Dispatch and Billing • Long-Term Care • Tele Health • Reportable Diseases

  4. Vigie Multirisque: Dashboard

  5. Vigie Multirisque: Analysis

  6. Vigie Multirisque: Analysis

  7. Vigie Multirisque: Descriptive

  8. Surveillance Research

  9. Syndromic Surveillance Research 1. Identifying individual cases 2. Detecting population patterns 3. Conveying information for action Individual Event Definitions Population Pattern Definitions Intervention Guidelines Public Health Action Event Reports Pattern Report Event Detection Algorithm Pattern Detection Algorithm Intervention Decision Subsets of admin data for ILI surveillance Data Describing Population Decision Algorithm Population Under Surveillance Knowledge

  10. Looking for the Leading ILI Indicator in Billing Data

  11. Syndromic Surveillance Research Accuracy of ICD codes and syndromes in ambulatory practice 1. Identifying individual cases 2. Detecting population patterns 3. Conveying information for action Individual Event Definitions Population Pattern Definitions Intervention Guidelines Public Health Action Event Reports Pattern Report Event Detection Algorithm Pattern Detection Algorithm Intervention Decision Subsets of admin data for ILI surveillance Data Describing Population Decision Algorithm Population Under Surveillance Knowledge

  12. Syndromic Surveillance Research 1. Selecting the best algorithm 2. 3. Accuracy of ICD codes and syndromes in ambulatory practice 1. Identifying individual cases 2. Detecting population patterns 3. Conveying information for action Individual Event Definitions Population Pattern Definitions Intervention Guidelines Public Health Action Event Reports Pattern Report Event Detection Algorithm Pattern Detection Algorithm Intervention Decision Subsets of admin data for ILI surveillance Data Describing Population Decision Algorithm Population Under Surveillance Knowledge

  13. Building the Knowledge-Base for Algorithm Selection 2. Evaluate modeled algorithms using high throughput software 1. Model the aberrancy detection process 3. Use machine learning to identify and model the determinants of detection

  14. Syndromic Surveillance Research 1. Selecting the best algorithm 2. Looking for connected cases 3. Accuracy of ICD codes and syndromes in ambulatory practice 1. Identifying individual cases 2. Detecting population patterns 3. Conveying information for action Individual Event Definitions Population Pattern Definitions Intervention Guidelines Public Health Action Event Reports Pattern Report Event Detection Algorithm Pattern Detection Algorithm Intervention Decision Subsets of admin data for ILI surveillance Data Describing Population Decision Algorithm Population Under Surveillance Knowledge

  15. Current Case Management System Web-based Cartography Software Statistical Analysis Server Mapping and Web Server Web Client Python, R-Server, SaTScan Firefox, Explorer Apache + PHP, MapServer + MapScript DCIMI Client Oracle Forms DCIMI Database Spatial Database Oracle PostGreSQL / PostGIS DB System Architecture

  16. Organizing Data by Person, Place and Time Spatial Database PostGreSQL / PostGIS DB Episode Onset Date Disease Type … Contact Person MADO Name Birthdate … Situation Role (Home, Work, School, …) Active Date … Place Address X, Y Place Type (Residence, Workplace) …

  17. Address Validation and Correction in a Public Health System

  18. Dracones – Query Form Person Time Place

  19. Dracones – SaTScan Results

  20. Syndromic Surveillance Research 1. Selecting the best algorithm 2. Looking for connected cases 3. Spatial TB clusters Accuracy of ICD codes and syndromes in ambulatory practice Optimal decision making after an alarm 1. Identifying individual cases 2. Detecting population patterns 3. Conveying information for action Individual Event Definitions Population Pattern Definitions Intervention Guidelines Public Health Action Event Reports Pattern Report Event Detection Algorithm Pattern Detection Algorithm Intervention Decision Subsets of admin data for ILI surveillance Data Describing Population Decision Algorithm Population Under Surveillance Knowledge

  21. Using Surveillance Information to Manage Outbreaks Effectively • Much research on the statistical accuracy of aberrancy detection algorithms • Little attention to what happens next • Some attempts to describe response protocols (e.g., flow chart, wait a day) • No quantitative modeling of response • Rational response is important • Small window to obtain benefit • Surveillance information uncertain

  22. The Traditional Surveillance Alert Response Model Environmental Data Knowledge Detection Method No Intervention Intervention No Alert Alert No Outbreak No Wait Yes No Review Records Yes Investigate No Yes Confirm

  23. Identifying an Optimal Policy • The goal is to identify a policy, or a mapping from a belief state (probability distribution over states) to actions • The belief state, provides the same information as maintaining the complete history • Value iteration is used to solve POMDP

  24. Applying a POMDP to Surveillance S - True outbreak state {No Outbreak, D1, ….} O - Output from detection algorithm {0,1} A - Possible public health actions T(s,a,s’) - Impact of actions given the state R(s,a) - Costs of actions and outbreak states Action Transition Do nothing Review records Investigate cases Declare outbreak (Izadi M & Buckeridge DL, 2007)

  25. POMDP Policy Dominates Ad Hoc Policy

  26. Syndromic Surveillance Research 1. Selecting the best algorithm 2. Looking for connected cases 3. Spatial TB clusters Accuracy of ICD codes and syndromes in ambulatory practice Optimal decision making after an alarm 1. Identifying individual cases 2. Detecting population patterns 3. Conveying information for action Individual Event Definitions Population Pattern Definitions Intervention Guidelines Public Health Action Event Reports Pattern Report Event Detection Algorithm Pattern Detection Algorithm Intervention Decision Subsets of admin data for ILI surveillance Evaluating Syndromic Surveillance in Public Health Practice: Detecting Waterborne Outbreaks Data Describing Population Decision Algorithm Population Under Surveillance Knowledge

  27. Automated and ‘Traditional’ Surveillance for Waterborne Outbreaks Syndromic Surveillance S Historical Tele- health and ED Data Tele-health Infectious (Asymptomatic) S Analysis by Public Health O S,R O O S Latent Infected Infectious (Symptomatic) ED Outbreak Detection R Stool Test Analysis by Public Health R R R R Out-patient Historical Case Reports Exposure Disease Health Care Utilization Reportable Disease Surveillance Dispersion

  28. Modeling Dispersion of Microorganisms Dispersion

  29. Modeling Infection: Mobility

  30. Mobility-Weighted Infection Probability by Home Address

  31. Modeling Disease, Visits, Testing, Reporting to Public Health

  32. Evaluating the Effect of Surveillance Enhancements

  33. For more information…

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