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

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

Conceptual TaxonomyPublic Health Surveillance

Disease

Traditional ‘Syndromic’Drug Vaccine

Birth defect Injuries

Other

Etc.

Infectious Disease

Medical Utilizationand Adverse Events

OtherProducts/Services

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 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.

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, • 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

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 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

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

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

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

• ‘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