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The DIMACS Working Group on Disease and
Adverse Event Surveillance
Henry Rolka and David Madigan
Background• WG Objective: Bring together researchers in
adverse event monitoring and disease surveillance
• Part of a 5-year special focus on computational and mathematical epidemiology
• 50+ WG members: epidemiologists, public health professionals, biostatisticians, etc.
• Focus on analytic/statistical methods• Two WG meetings plus week-long tutorial (02-
03)• Coordinated closely with National Syndromic
Surveillance Conferences
Disease Surveillance
Drug Safety Surveillance
Syndromic Surveillance
Vaccine SafetySurveillance
Areas of Common Interest
Representation
• Carnegie-Mellon University
• FDA• Quintiles Inc.• CDC• Rutgers University• Emergint, Inc.• AT&T Labs• NJ State
• NYC Dept. of Health• University of
Pennsylvania• Aventis• ATSDR• University of Connecticut• Los Alamos National Lab• Lincoln Technologies• SAS Institute
Background, cont.
• WG conceived before September 11, 2001• Surveillance landscape has changed
drastically• Major public health effort directed at
bioterrorism detection• Proliferation of novel surveillance projects
in response to national threat• “Good for detecting outbreaks of various
kinds”
New Data Types for Public Health Surveillance
• Managed care patient encounter data• 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 • Prescription/pharmaceuticals• Adverse event reports
New Analytic Methods and Approaches
• Spatial-temporal scan statistics • Statistical process control (SPC) • Bayesian applications • Market-basket association analysis • Text mining• Rule-based surveillance• Change-point techniques
ANALYTIC METHODS IN USE
• Scan statistics (e.g., Kulldorff’s SaTScan)
• Statistical process control (e.g., Hutwagner’s EARS)
• Association rule mining (e.g., Moore’s WSARE)
• Bayesian shrinkage (e.g., DuMouchel’s MGPS)
• Generalized linear mixed models (e.g., Kleinman)
• Sequential probability ratio tests (e.g., Spiegelhalter, Evans)
SCAN STATISTICS
• Martin Kulldorff’s SaTScan - Spatial and Space-Time Scan Statistics - software.• e.g., spatial scan – using Poisson model computes likelihood of all possible circles compared with likelihood under the null distribution• Picks the circle with the biggest likelihood ratio • P-value computed via Monte Carlo
• Big literature on disease clustering: Besag & Newell, Diggle, Moran test, Turnbull’s method, Cuzick & Edwards, etc.• Need methodology for multiple sources
Farzad Mostashari
BAYESIAN SHRINKAGE ESTIMATION
• DuMouchel’s GPS/MGPS
• Compares observed counts of “market baskets” to expected counts under some (simple) model. For example, saw 30 cases in the ER today with G.I. syndrome AND fever AND work in Newark compared with an expectation of 3 cases
• 30-to-3 is more convincing than 3-to-0.3 but less convincing that 300-to-30. Idea: shrink the smaller ones towards one.
log RR
log
EB
GM
0 1 2 3 4 5 6
01
23
45
6
12351050-100
number of reports
GPS SHRINKAGE – AERS DATA
BAYESIAN SHRINKAGE ESTIMATION
• Issues:
Appropriate amount of shrinkage?
Where do the expected values come from?
Temporal dimension?
Covariate information
Simpson’s paradox (“innocent bystander”)
SEQUENTIAL PROBABILITY RATIO TESTS
• Classical much-studied statistical method dating back to Wald (1948)
NATURAL LANGUAGE
• Important sources of health data begin life as free text “chief complaints” (ED visits, primary care encounters, adverse event reports, e-mail, etc.)
“Approximately 5 minutes after receiving flu and pneumonia vaccine pt began hollering, "Oh, Oh my neck is hurting.
Feels like a knot in my throat, a medicine taste." Complained of chest pain moving to back and leg
numbness.”
• Some (successful) work on automated coding of free text.
• Little work on direct surveillance of text data
CONCLUSION
• Analytic methods for surveillance have a long history in Statistics but currently attract substantial new interest from researchers in both CS and Statistics
• Urgently need new methods for multivariate, multi-data type streams
• Data availability a bottleneck; simulation non-trivial.
• DARPA currently staging a competition
THE IDEA OF A COMPETITION
Thesis: Rapid growth in the number of deployed health surveillance systems and increasing complexity require new analytic methodologies
Goal: Stimulate mainstream Computer Science and Statistics researchers to focus on this area
How: A signal detection competition
Examples: the Message Understanding Conferences (MUC), Text Retrieval Conferences (TREC), KDD Cup, M3 Time Series competition
COMPETITION STATUS
•DIMACS Working Group on Adverse Event and Disease Reporting, Surveillance, Analysis
•Subgroup focused on competition; applied for funding; identified data sources
•Key challenge: appropriate methods for inserting signals into real data (“spiking”)
•Other groups face the same challenge (e.g. BioStorm)
ANALYTIC METHODS IN USE
• Scan statistics (e.g., Kulldorff’s SaTScan)
• Statistical process control (e.g., Hutwagner’s EARS)
• Association rule mining (e.g., Moore’s WSARE)
• Bayesian shrinkage (e.g., DuMouchel’s MGPS)
• Generalized linear mixed models (e.g., Kleinman)
• Sequential probability ratio tests (e.g., Spiegelhalter, Evans)
SCAN STATISTICS
• Martin Kulldorff’s SaTScan - Spatial and Space-Time Scan Statistics - software.• e.g., spatial scan – using Poisson model computes a likelihood ratio for all possible circles comparing event counts inside and outside• Picks the circle with the biggest likelihood ratio • P-value computed via Monte Carlo
• Big literature on disease clustering: Besag & Newell, Cuzick & Edwards, Diggle, Moran test, Pagano, Turnbull’s method,, etc.• Need methodology for multiple sources
Farzad Mostashari
BAYESIAN SHRINKAGE ESTIMATION
• DuMouchel’s GPS/MGPS
• Compares observed counts of “market baskets” to expected counts under some (simple) model. For example, saw 30 cases in the ER today with G.I. syndrome AND fever AND work in Newark compared with an expectation of 3 cases
• 30-to-3 is more convincing than 3-to-0.3 but less convincing that 300-to-30. Idea: shrink the smaller ones towards one.
log RR
log
EB
GM
0 1 2 3 4 5 6
01
23
45
6
12351050-100
number of reports
GPS SHRINKAGE – AERS DATA
BAYESIAN SHRINKAGE ESTIMATION
• Issues:
Appropriate amount of shrinkage?
Where do the expected values come from?
Temporal dimension?
Covariate information
SEQUENTIAL PROBABILITY RATIO TESTS
• Classical much-studied statistical method dating back to Wald (1948). Mostly univariate.
NATURAL LANGUAGE
• Important sources of health data begin life as free text “chief complaints” (ED visits, primary care encounters, adverse event reports, e-mail, etc.)
“Approximately 5 minutes after receiving flu and pneumonia vaccine pt began hollering, "Oh, Oh my neck is hurting.
Feels like a knot in my throat, a medicine taste." Complained of chest pain moving to back and leg
numbness.”
• Some (successful) work on automated coding of free text.
• Little work on direct surveillance of text data
THE IDEA OF A COMPETITION
Thesis: Rapid growth in the number of deployed health surveillance systems and increasing complexity require new analytic methodologies
Goal: Stimulate mainstream Computer Science and Statistics researchers to focus on this area
How: A signal detection competition
Examples: the Message Understanding Conferences (MUC), Text Retrieval Conferences (TREC), KDD Cup, M3 Time Series competition
• Definitions of signals.
• Test data sets for refining signal detection procedures.
• Modular, interoperable signal generation algorithms.
• Computing efficiencies for Monte Carlo simulations of signal detection events in large complex data.
• Multidimensional graphical displays to interpret results and evaluate algorithms.
• Multivariate statistical techniques for evaluating signal detection profiles across multiple data sources.
HOW CAN THIS BE ACCOMPLISHED
COMPETITION STATUS
•DIMACS Working Group on Adverse Event and Disease Reporting, Surveillance, Analysis
•Subgroup focused on competition; applied for funding; identified data sources
•Key challenge: appropriate methods for inserting signals into real data (“spiking”)
•Other groups face the same challenge (e.g. BioStorm)
CONCLUSION
• Short-term goals/benefits:•Promote coordination and collaboration
• Long-term goals/benefits• Stimulate methodological research• Provide objective evaluation of competing algorithms• Produce high quality spiking algorithms
ANALYTICAL METHODS FOR HEALTH
SURVEILLANCE
DAVID MADIGAN
DEPARTMENT OF STATISTICS
RUTGERS UNIVERSITY
Novel Surveillance Applications Methodologies
• Early Aberration Reporting System (EARS), CDC
• What’s Strange About Recent Events? (WSARE), U of Pittsburgh and Carnegie-Mellon U
• Spatial and Space-Time Scan Statistics (SaTScanTM – Kulldorff)
• Web Visual Data Mining Environment (WebVDME), Lincoln Technologies, Inc.
Novel Surveillance Applications Projects
• Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE I&II), DOD
• Real-time Outbreak and Disease Surveillance (RODS), U of Pittsburgh
• Biological Spatio-Temporal Outbreak Reasoning Module (BioSTORM), Stanford U
• Rapid Syndrome Validation Project (RSVP), Sandia NL, NM
• Alternative Surveillance Alert Program (ASAP), Health Canada
• Syndromic Surveillance Project, NYC
• Bioterrorism Syndromic Surveillance Demonstration Program, CDC/Harvard
Conceptual Taxonomy
Public Health Surveillance
Adverse event(to intervention exposure)
Disease
Traditional SyndromicDrug Vaccine
Birth defect Injuries
Other
Etc.
Infectious disease