Overview of ‘Syndromic
Surveillance’
presented as background to Multiple Data Source
Issue for
DIMACS Working Group on Adverse
Event/Disease Reporting, Surveillance, and
Analysis II
Henry R. Rolka, R.N., M.P.S., M.S.
Centers for Disease Control and Prevention
February 19, 2004
New data types and
functional objectives have
largely expanded the scope
of public health surveillance
New surveillance challenges
and opportunities are
growing in complexity
Outline of Presentation
• Background and context for appreciation of
new complexities.
• Major themes and issues.
• Focus for this meeting
• Summary and discussion.
Public Health Surveillance
“Ongoing systematic collection, analysis,
and interpretation of outcome-specific
data for use in the planning,
implementation, and evaluation of
public health practice.”
*Stephen Thacker, CDC
Surveillance System
 Data Collection
 Analysis
 Dissemination
Surveillance System Components
Population of interest
which generates events
Measurement
and recording
Transactional data
Data Management
•Quality checks
•Editing
Public health response
Interpretation for associations,
trends, unusual patterns, signals
Analytical applications
Data preprocessing for a
specific purpose
(‘views’, ‘data marts’)
Conceptual Taxonomy
Public Health Surveillance
Medical Utilization
and Adverse Events
Drug
Vaccine
Other
Products/Services
Disease
Traditional
Infectious Disease
Birth defect
‘Syndromic’
Other
Injuries
Etc.
NETSS
• Weekly data regarding cases of nationally
notifiable diseases.
• Core surveillance data: date, county, age,
sex, and race/ethnicity.
• Some disease-specific epidemiological
information.
• Transmitted electronically by the states and
territories to CDC each week.
Figure 1 published weekly in the MMWR
Syndromic Surveillance
“Monitoring frequency of illnesses
with a specified set of clinical
features in a given population,
without regard to the diagnoses.”
Arthur Reingold, UC Berkeley
Surveillance System Components
Data collection
and
preprocessing
A
Epidemiological decisions
Data View
Reporting or recording
anomaly
Application of
statistical algorithms
‘Something
unusual’ noted
in data
Data processing error
Statistical aberration due
to natural variability
etc.
B
True increase in
disease
Requires information from other data sources
Naturally
occurring
outbreak
Deliberate
exposure event
C
Non-traditional Data Types for Public
Health Surveillance
• Pre-diagnostic/chief complaint (text data)
• Over-the-counter sales transactions
– Drug store
– Grocery store
•
•
•
•
•
•
911-emergency calls
Ambulance dispatch data
Absenteeism data
ED discharge summaries
Managed care patient encounter data
Prescription/pharmaceuticals
Potential Syndromic Surveillance
Data Sources
•
•
•
•
•
Day 1 - feels fine
Day 2 - headaches, Pharmaceutical Sales
Day 3 - develops cough, Nurse’s Hotline
Day 4 – Managed Care Org
Absenteeism
Day 5 – Worsens, Ambulance Dispatch (EMS)
ED Logs
• Day 6 • Day 7 • Day 8 -
Traditional Surveillance
*Farzad Mostashari, NYC DoH
Messy Data
•
•
•
•
•
•
Noisy, periodic (weekly, seasonally)
Multiple data streams
Duplicate records
Syndromic coding not standardized
Data quality
Means for evaluation not well developed
Bio-ALIRT
• “Bio-Event Advanced Leading Indicator
Recognition Technology”
• Program to develop technology for early
detection of a covert biological attack
• Defense Advanced Research Projects Agency
(DARPA)
• Began in fy 2001
Biosurveillance Data Space
LATER DETECTION
EARLY DETECTION
INTELLIGENCE
ANIMALS
BIOSENSORS
HUMAN BEHAVIORS
NON TRADITIONAL
GOLD
MEDICAL STANDARDS
NON TRADITIONAL USES
CLINICAL DATA
Vets
OTC Pharm
Tests ordered
Zoos
Absenteeism
Complaints
Diagnosis
Poison Centers
Influenza
isolates
Agribusiness
Environmental
Pollen counts
Utilities
Coughs
Web Queries
911 Calls
Humidity
Traffic
EMS Runs
Temperature
Survey
Public
Transport
Cafeteria
Nurse Calls
Wind Speed/
direct.
Allergy Index
Pollution
Video Surv
Newsgroup
Test Results
ER Visits
Prescriptions
Medical
Examiner
Test
Results
Sentinel
MD
Investigations
Limited Utility
Some Potential
Promising
Radiograph Reports
BioSense (under development)
• Complementary project to President’s initiatives
BioWatch and BioShield.
• Focuses on disease symptoms related to syndromic
categories (BT agents)
• Data source examples:
–
–
–
–
Patient encounter (ICD9, outpatient)
OTC sales of home health remedies
Lab tests ordered
Nurse call line
Common Interests/Challenges
DARPA – BioAlirt
•
•
•
•
•
Surveillance for BT
Non-traditional data
Early detection
Evaluation of algorithms
Privacy protection
CDC – BioSense
•
•
•
•
•
Surveillance for BT
Non-traditional data
Early detection
Evaluation of algorithms
Privacy protection
Themes (system)
• Local vs. Regional vs. National vs. Global
focus
• Interoperability / Transportability
• Interdisciplinary science and technologies
– Culturalism
– Language
– Social networks
• Case/Adverse Event definitions
• Information/knowledge management
• Leadership
Themes (functionality)
•
•
•
•
•
•
•
Timeliness for response potential
Data quality factors
System evaluation
Data access
Standards
Signal detection thresholds
Analytic methodologies
Analytic Obstacles/Opportunities
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•
•
•
•
‘Opportunistic’ data
‘Syndromes’
Empirical inductive inference
Evaluation of utility and public health value
Multiple data streams in time
–
–
–
Multivariate time series ( uncharacterized transfer functions)
Time alignment
Differential quality