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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
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New data types and functional objectives have largely expanded the scope
of public health surveillance
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New surveillance challenges and opportunities are
growing in complexity
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Outline of Presentation• Background and context for appreciation of
new complexities.
• Major themes and issues.
• Focus for this meeting
• Summary and discussion.
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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
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Surveillance System
Data Collection
Analysis
Dissemination
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Measurement and recording
Transactional data
Data Management•Quality checks•Editing
Data preprocessing for a specific purpose
(‘views’, ‘data marts’)
Analytical applications
Interpretation for associations,trends, unusual patterns, signals
Public health responsePopulation of interest which generates events
Surveillance System Components
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Conceptual TaxonomyPublic Health Surveillance
Disease
Traditional ‘Syndromic’Drug Vaccine
Birth defect Injuries
Other
Etc.
Infectious Disease
Medical Utilizationand Adverse Events
OtherProducts/Services
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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.
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Figure 1 published weekly in the MMWR
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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
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Surveillance System Components
Data View
‘Something unusual’ noted
in data
Reporting or recording anomaly
Data processing error
True increase in disease
Naturally occurring outbreak
Deliberate exposure event
Data collection and
preprocessing
Application of statistical algorithms
Epidemiological decisions
Requires information from other data sources
A
B
C
Statistical aberration due to natural variability
etc.
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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
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Potential Syndromic Surveillance Data Sources
• Day 1 - feels fine• Day 2 - headaches, • Day 3 - develops cough, • Day 4 – • Day 5 – Worsens,
• Day 6 - • Day 7 -• Day 8 -
Traditional Surveillance
Ambulance Dispatch (EMS) ED Logs
Managed Care Org Absenteeism
Nurse’s Hotline
Pharmaceutical Sales
*Farzad Mostashari, NYC DoH
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Messy Data
• Noisy, periodic (weekly, seasonally)
• Multiple data streams
• Duplicate records
• Syndromic coding not standardized
• Data quality
• Means for evaluation not well developed
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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
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Biosurveillance Data SpaceEARLY DETECTION LATER DETECTION
INTELLIGENCE BIOSENSORS MEDICALNON TRADITIONAL
ANIMALS CLINICAL DATA NON TRADITIONAL USESHUMAN BEHAVIORS
Test Results
Investi-gations
Sentinel MDEnvironmental
Test Results
Influenzaisolates
Diagnosis
Medical Examiner
OTC Pharm
Utilities
Absenteeism
Web Queries
Cafeteria
Coughs
Traffic
Newsgroup
Survey
Video Surv
Public Transport
Tests ordered
Poison Centers
Complaints
EMS Runs
Radiograph Reports
911 Calls
Nurse Calls
ER Visits
Prescriptions
Pollen counts
Temperature
Humidity
Wind Speed/direct.
Allergy Index
Pollution
Vets
Agribusiness
Zoos
Limited Utility
Some Potential
Promising
GOLDSTANDARDS
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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
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Common Interests/Challenges
CDC – BioSense
• Surveillance for BT• Non-traditional data• Early detection• Evaluation of algorithms• Privacy protection
DARPA – BioAlirt
• Surveillance for BT• Non-traditional data• Early detection• Evaluation of algorithms• Privacy protection
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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
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Themes (functionality)
• Timeliness for response potential
• Data quality factors
• System evaluation
• Data access
• Standards
• Signal detection thresholds
• Analytic methodologies
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Analytic Obstacles/Opportunities
• ‘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