Syndromic Surveillance in Montreal: An Overview of Practice and Research David Buckeridge, MD PhD Epidemiology and Biostatistics, McGill University Surveillance.

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)

Population Under Surveillance

Intervention Decision

Intervention

GuidelinesPublic Health Action

3. Conveying information for action

Population Pattern

Definitions

Pattern Report

Pattern Detection Algorithm

2. Detecting population patterns

Event Report

s

Individual Event Definitions

Event Detection Algorithm

Data Describing Population

1. Identifying individual cases

Decision Algorithm

Knowledge

Telehealth911 CallsHospitalReportable

Telehealth911 CallsHospitalReportable

Counts, Native coding schemes, ISDS consensus syndromes

Counts, Native coding schemes, ISDS consensus syndromes

Routine SaTScan, alerts for shared addresses

Routine SaTScan, alerts for shared addresses

Daily review of analysis results, not clear protocol

Daily review of analysis results, not clear protocol

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

Population Under Surveillance

Intervention Decision

Intervention

GuidelinesPublic Health Action

3. Conveying information for action

Population Pattern

Definitions

Pattern Report

Pattern Detection Algorithm

2. Detecting population patterns

Event Report

s

Individual Event Definitions

Event Detection Algorithm

Data Describing Population

1. Identifying individual cases

Decision Algorithm

Knowledge

Subsets of admin data for ILI surveillance

Subsets of admin data for ILI surveillance

Looking for the Leading ILI Indicator in Billing Data

Syndromic Surveillance Research

Population Under Surveillance

Intervention Decision

Intervention

GuidelinesPublic Health Action

3. Conveying information for action

Population Pattern

Definitions

Pattern Report

Pattern Detection Algorithm

2. Detecting population patterns

Event Report

s

Individual Event Definitions

Event Detection Algorithm

Data Describing Population

1. Identifying individual cases

Decision Algorithm

Knowledge

Subsets of admin data for ILI surveillance

Subsets of admin data for ILI surveillance

Accuracy of ICD codes and syndromes in ambulatory practice

Accuracy of ICD codes and syndromes in ambulatory practice

Syndromic Surveillance Research

Population Under Surveillance

Intervention Decision

Intervention

GuidelinesPublic Health Action

3. Conveying information for action

Population Pattern

Definitions

Pattern Report

Pattern Detection Algorithm

2. Detecting population patterns

Event Report

s

Individual Event Definitions

Event Detection Algorithm

Data Describing Population

1. Identifying individual cases

Decision Algorithm

Knowledge

Subsets of admin data for ILI surveillance

Subsets of admin data for ILI surveillance

Accuracy of ICD codes and syndromes in ambulatory practice

Accuracy of ICD codes and syndromes in ambulatory practice

1. Selecting the best algorithm2.

3.

1. Selecting the best algorithm2.

3.

Building the Knowledge-Base for Algorithm Selection

1. Model the aberrancy detection process

2. Evaluate modeled algorithms using high throughput software

3. Use machine learning to identify and model the determinants of detection

Syndromic Surveillance Research

Population Under Surveillance

Intervention Decision

Intervention

GuidelinesPublic Health Action

3. Conveying information for action

Population Pattern

Definitions

Pattern Report

Pattern Detection Algorithm

2. Detecting population patterns

Event Report

s

Individual Event Definitions

Event Detection Algorithm

Data Describing Population

1. Identifying individual cases

Decision Algorithm

Knowledge

Subsets of admin data for ILI surveillance

Subsets of admin data for ILI surveillance

Accuracy of ICD codes and syndromes in ambulatory practice

Accuracy of ICD codes and syndromes in ambulatory practice

1. Selecting the best algorithm2. Looking for connected cases3.

1. Selecting the best algorithm2. Looking for connected cases3.

System Architecture

Python, R-Server, SaTScan

Statistical Analysis Server

PostGreSQL / PostGIS DB

Spatial Database

Apache + PHP, MapServer +

MapScript

Mapping and Web

ServerWeb Client

Firefox, Explorer

Current Case Management System

DCIMI Database

DCIMI Client

Oracle

Oracle Forms

Web-based Cartography Software

Organizing Data by Person, Place and Time

PostGreSQL / PostGIS DB

Spatial Database

PersonMADONameBirthdate…

PlaceAddressX, YPlace Type (Residence, Workplace)…

EpisodeOnset DateDisease Type…

Contact

SituationRole (Home, Work, School, …)Active Date…

Address Validation and Correction in a Public Health System

Dracones – Query Form

PersonPerson

TimeTime

PlacePlace

Dracones – SaTScan Results

Syndromic Surveillance Research

Population Under Surveillance

Intervention Decision

Intervention

GuidelinesPublic Health Action

3. Conveying information for action

Population Pattern

Definitions

Pattern Report

Pattern Detection Algorithm

2. Detecting population patterns

Event Report

s

Individual Event Definitions

Event Detection Algorithm

Data Describing Population

1. Identifying individual cases

Decision Algorithm

Knowledge

Optimal decision making after an alarm

Optimal decision making after an alarm

Subsets of admin data for ILI surveillance

Subsets of admin data for ILI surveillance

Accuracy of ICD codes and syndromes in ambulatory practice

Accuracy of ICD codes and syndromes in ambulatory practice

1. Selecting the best algorithm2. Looking for connected cases3. Spatial TB clusters

1. Selecting the best algorithm2. Looking for connected cases3. Spatial TB clusters

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 DataKnowledgeKnowledge

Detection Method

Detection Method

Investigate

Investigate

ConfirmConfirm

WaitWait

Review RecordsReview Records No

OutbreakNo

Outbreak

Alert No AlertInterventionNo

Intervention

Yes

Yes

Yes

No

No

No

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

Do nothing

Review

records

Investigate

cases

Declare

outbreak

Actio

n

Transitio

n

(Izadi M & Buckeridge DL, 2007)

POMDP Policy Dominates Ad Hoc Policy

Syndromic Surveillance Research

Population Under Surveillance

Intervention Decision

Intervention

GuidelinesPublic Health Action

3. Conveying information for action

Population Pattern

Definitions

Pattern Report

Pattern Detection Algorithm

2. Detecting population patterns

Event Report

s

Individual Event Definitions

Event Detection Algorithm

Data Describing Population

1. Identifying individual cases

Decision Algorithm

Knowledge

Optimal decision making after an alarm

Optimal decision making after an alarm

Evaluating Syndromic Surveillance in Public Health Practice: Detecting Waterborne Outbreaks

Evaluating Syndromic Surveillance in Public Health Practice: Detecting Waterborne Outbreaks

Subsets of admin data for ILI surveillance

Subsets of admin data for ILI surveillance

Accuracy of ICD codes and syndromes in ambulatory practice

Accuracy of ICD codes and syndromes in ambulatory practice

1. Selecting the best algorithm2. Looking for connected cases3. Spatial TB clusters

1. Selecting the best algorithm2. Looking for connected cases3. Spatial TB clusters

Automated and ‘Traditional’ Surveillance for Waterborne Outbreaks

Dispersion Exposure

Latent InfectedLatent

Infected

Infectious (Symptom

atic)

Infectious (Symptom

atic)

Infectious (Asympto

matic)

Infectious (Asympto

matic)

Tele-healthTele-health

EDED

Out-patientOut-

patient

Stool TestStool Test

Analysis by Public

Health

Analysis by Public

Health

Disease Health Care Utilization Reportable Disease Surveillance

Syndromic Surveillance

Analysis by Public

Health

Analysis by Public

Health

Outbreak DetectionOutbreak Detection

Historical Tele-health and ED Data

Historical Case Reports

O

S

S

S

R

R

RR

R

O O S,R

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

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