Overview of

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Overview of Syndromic Surveillancepresented
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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Surveillance System Components
Population of interest which generates events
Public health response
Interpretation for associations, trends, unusual
patterns, signals
Measurement and recording
Analytical applications
Transactional data

• Data Management
• Quality checks
• Editing

Data preprocessing for a specific purpose
(views, data marts)
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Conceptual Taxonomy
Public Health Surveillance
Disease
Medical Utilization and Adverse Events
Drug
Vaccine
Other Products/Services
Traditional
Syndromic
Other
Infectious Disease
Birth defect
Injuries
Etc.
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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
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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 -

Pharmaceutical Sales
Nurses Hotline
Managed Care Org
Absenteeism
Ambulance Dispatch (EMS) ED Logs
Traditional Surveillance
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 Space
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BioSense (under development)

• Complementary project to Presidents 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