Download Syndromic Surveillance in Montreal: An Overview of

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
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