1. 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

2. New data types and functional objectives have largely expanded the scope of public health surveillance

3. New surveillance challenges and opportunities are growing in complexity

4. Outline of Presentation Background and context for appreciation of new complexities. Major themes and issues. Focus for this meeting Summary and discussion.

5. 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

6. Surveillance System  Data Collection  Analysis  Dissemination

7. 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 response Population of interest which generates events Surveillance System Components

8. Conceptual Taxonomy Public Health Surveillance Disease Traditional‘Syndromic’ DrugVaccine Birth defectInjuries Other Etc. Infectious Disease Medical Utilization and Adverse Events Other Products/Services

9. 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.

10. Figure 1 published weekly in the MMWR

11. 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

12. 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.

13. 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

14. 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 OrgAbsenteeism Nurse’s Hotline Pharmaceutical Sales *Farzad Mostashari, NYC DoH

15. Messy Data Noisy, periodic (weekly, seasonally) Multiple data streams Duplicate records Syndromic coding not standardized Data quality Means for evaluation not well developed

16. 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

17. Biosurveillance Data Space

18. 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

19. 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

20. 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

21. Themes (functionality) Timeliness for response potential Data quality factors System evaluation Data access Standards Signal detection thresholds Analytic methodologies

22. 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