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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 Syndromic Surveillance in Montreal (ou, Vigie Multirisque) Counts, Native coding schemes, 1. Identifying ISDS consensus individual cases syndromes Individual Event Definitions Event Detection Algorithm Telehealth 911 Calls Hospital Reportable Data Describing Population Routine SaTScan, Daily review of 2. Detecting alertspopulation for shared patterns 3. analysis Conveying information results, for action addresses not clear protocol Population Pattern Definitions Event Reports Intervention Guidelines Pattern Report Pattern Detection Algorithm Population Under Surveillance Intervention Decision Decision Algorithm Knowledge Public Health Action 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 Vigie Multirisque: Dashboard Vigie Multirisque: Analysis Vigie Multirisque: Analysis Vigie Multirisque: Descriptive Surveillance Research Syndromic Surveillance Research 1. Identifying individual cases 2. Detecting population patterns Individual Event Definitions Event Detection Algorithm Subsets of admin data for ILI surveillance Data Describing Population 3. Conveying information for action Population Pattern Definitions Event Reports Intervention Guidelines Pattern Report Pattern Detection Algorithm Population Under Surveillance Intervention Decision Decision Algorithm Knowledge Public Health Action Looking for the Leading ILI Indicator in Billing Data Syndromic Surveillance Research Accuracy of ICD codes and 1. Identifying 2. Detecting population syndromes in individual cases patterns ambulatory practice Individual Event Definitions Event Detection Algorithm Subsets of admin data for ILI surveillance Data Describing Population 3. Conveying information for action Population Pattern Definitions Event Reports Intervention Guidelines Pattern Report Pattern Detection Algorithm Population Under Surveillance Intervention Decision Decision Algorithm Knowledge Public Health Action Syndromic Surveillance Research 1. Selecting the best algorithm Accuracy of ICD 2. population codes and 1. Identifying 2. Detecting syndromes in individual cases patterns 3. ambulatory practice Individual Event Definitions Event Detection Algorithm Subsets of admin data for ILI surveillance Data Describing Population 3. Conveying information for action Population Pattern Definitions Event Reports Intervention Guidelines Pattern Report Pattern Detection Algorithm Population Under Surveillance Intervention Decision Decision Algorithm Knowledge Public Health Action Building the Knowledge-Base for Algorithm Selection 1. Model the aberrancy detection process 3. Use machine learning to identify and model the determinants of detection 2. Evaluate modeled algorithms using high throughput software Syndromic Surveillance Research 1. Selecting the best algorithm Accuracy of ICD 2. Looking for codes and 1. Identifying 2. Detecting population connected cases syndromes in individual cases patterns 3. ambulatory practice Individual Event Definitions Event Detection Algorithm Subsets of admin data for ILI surveillance Data Describing Population 3. Conveying information for action Population Pattern Definitions Event Reports Intervention Guidelines Pattern Report Pattern Detection Algorithm Population Under Surveillance Intervention Decision Decision Algorithm Knowledge Public Health Action System Architecture Current Case Management System Web Client Firefox, Explorer DCIMI Client Web-based Cartography Software Statistical Analysis Server Mapping and Web Server Python, R-Server, SaTScan Apache + PHP, MapServer + MapScript Oracle Forms DCIMI Database Oracle Spatial Database PostGreSQL / PostGIS DB Organizing Data by Person, Place and Time Spatial Database PostGreSQL / PostGIS DB Episode Contact Onset Date Disease Type … Person MADO Name Birthdate … Situation Role (Home, Work, School, …) Active Date … Place Address X, Y Place Type (Residence, Workplace) … Address Validation and Correction in a Public Health System Dracones – Query Form Person Time Place Dracones – SaTScan Results Syndromic Surveillance Research 1. Selecting the best algorithm Accuracy of ICD 2. Looking for codes and 1. Identifying 2. Detecting population connected cases syndromes in individual cases patterns 3. Spatial TB clusters ambulatory practice Individual Event Definitions Event Detection Algorithm Subsets of admin data for ILI surveillance Data Describing Population Optimal 3. decision Conveying information for action making after an alarm Population Pattern Definitions Event Reports Intervention Guidelines Pattern Report Pattern Detection Algorithm Population Under Surveillance Intervention Decision Decision Algorithm Knowledge Public Health Action 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 The Traditional Surveillance Alert Response Model Environmental Data Knowledge Intervention Alert Wait Yes Yes Yes Review Records Investigat e Confirm Detection Method No Alert No No No No Outbreak No Intervention 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 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 (Izadi M & Buckeridge DL, 2007) Action Do nothing Review records Investigate cases Declare outbreak Transition POMDP Policy Dominates Ad Hoc Policy Syndromic Surveillance Research 1. Selecting the best algorithm Accuracy of ICD 2. Looking for codes and 1. Identifying 2. Detecting population connected cases syndromes in individual cases patterns 3. Spatial TB clusters ambulatory practice Individual Event Definitions Event Detection Algorithm Subsets of admin data for ILI surveillance Data Describing Population Optimal 3. decision Conveying information for action making after an alarm Population Pattern Definitions Event Reports Intervention Guidelines Pattern Report Pattern Detection Algorithm Population Under Surveillance Intervention Decision Decision Algorithm Evaluating Syndromic Surveillance in Public Health Practice: Detecting Knowledge Waterborne Outbreaks Public Health Action Automated and ‘Traditional’ Surveillance for Waterborne Outbreaks Syndromic Surveillance S Infectious (Asympto matic) O O Latent Infected O Infectious (Symptom atic) Historical Telehealth and ED Data Telehealth S Analysis by Public Health S ED R R Outpatient Stool Test R R R Analysis by Public Health S,R Outbreak Detection Historical Case Reports Dispersion Exposure Disease Health Care Utilization Reportable Disease Surveillance Modeling Dispersion of Microorganisms Dispersion Modeling Infection: Mobility Mobility-Weighted Infection Probability by Home Address Modeling Disease, Visits, Testing, Reporting to Public Health Evaluating the Effect of Surveillance Enhancements For more information…