DIMACS Working Group on Adverse Event/Disease Reporting, Surveillance, and Analysis
October 16 - 18, 2002
DIMACS Center, CoRE Building, Rutgers University
Presented under the auspices of the of the Special Focus
on Computational and Mathematical Epidemiology. Co-sponsored by the American Statistical Association, Section on Statistics in
- Donald Hoover, Rutgers, Statistics, email@example.com
- David Madigan, Rutgers, Statistics, firstname.lastname@example.org
- Henry Rolka, (CDC), email@example.com
DIMACS Subgroup on Adverse Event/Disease Reporting, Surveillance, and Analysis
DIMACS Working Group on Adverse Event/Disease Reporting, Surveillance, and Analysis II
Disease or event reporting and surveillance systems represent a
primary epidemiological data source for the study of/alert to adverse
reactions to medication, emerging diseases, or bioterrorist
attacks. These systems synthesize data from millions of reports. The
working group will bring together pharmacoepidemiologists,
statisticians and computer scientists to investigate current major
issues confronting adverse event/disease reporting, surveillance, and
analysis. We describe the issues for drug reaction reports; those for
disease or symptom reports are similar though they raise their own set
of issues as well. In the US, the two major reporting/surveillance
systems are AERS and VAERS. AERS, the Adverse Event Reporting System
(http://www.fda.gov/cder/aers/) is a data base of drug adverse
reactions reported by health professionals and others. AERS is
administered by the Food and Drug Administration (FDA). The system
contains adverse reactions detected and reported after marketing of a
drug for a specified time period. AERS contains over two million
cases. VAERS, the Vaccine Adverse Event Reporting System
(http://www.vaers.org), is a Cooperative Program for Vaccine Safety of
the FDA and the Centers for Disease Control and Prevention
(CDC). VAERS is a post-marketing safety surveillance program,
collecting information about adverse events that occur after the
administration of US licensed vaccines. Reports are provided by all
concerned individuals: patients, parents, health care providers,
pharmacists, and vaccine manufacturers. The VAERS database is publicly
available and contains over 100,000 reports. Analyses of AERS and
VAERS data must confront several difficulties including adverse event
recognition, underreporting, biases, estimation of population
exposure, report quality, and, most importantly, no denominator or
control group of persons not taking the medication. In many cases it
is difficult to discern whether or not a reported adverse reaction was
from the medication or instead was a consequence of the underlying
conditions that necessitated the medication (see, for example,
[Koch-Weser, Sellers, and Zacest (1977),
Strom and Tugwell (1990)]). Several methodological issues relating to these
reporting mechanisms and others emphasizing disease/symptom reporting
will form the agenda for this working group. These include application
of computational and statistical methods for early detection of
emerging trends; modification of algorithms of streaming data analysis
designed to set off early warning alarms; application of data mining
methods; development of causal inferential methods in the absence of
controls; study of ways to eliminate bias; design of verification
methodology. This last issue is especially pressing since large-scale
medical record databases that now exist in certain sub-populations
(e.g., HMOs, military) can provide a basis both for assessing the
quality of AERS and VAERS data and for validating analyses. Still
another set of research issues for the working group arises from the
use of natural language in reporting systems: Devise effective methods
for translating natural language input into formats suitable for
statistical analysis (prior work on machine natural language
processing and information retrieval is relevant); develop
computationally efficient methods to provide automated responses
consisting of follow-up questions; develop semi-automatic systems to
generate queries based on dynamically changing data, indicating
developing epidemiological trends. Relevant to these questions is work
in [VanLehn and Niu (to appear)] on interpreting natural language reports based on
probabilistic models of context, work in
[Langlotz, Shortliffe and Fagan (1986),
McConachy, Korb and Zuckerman (1998),
Zuckerman, McConachy and Korb (1998)] on
communicating uncertain information and summarizing rough trends, and
[Horvitz and Paek (1999),
Horvitz and Paek (2001),
Walker (2000)] on decision-theoretic methods for asking followup
questions in natural language processing. Earlier work on electronic
surveillance reporting from public health reference laboratories is
very relevant here
(e.g., [Bean and Martin (2001),
Bean, Martin and Bradford (1992),
Hutwagner, Maloney, Bean, Slutsker and Martin (1997),
Mahon, Rohn, Pack and Tauxe (1995)]).
Subgroups might be formed to concentrate on drug reactions, emerging diseases, or bioterrorism.
Bean, N.H., and Martin, M. (2001), "Implementing a network for electronic
surveillance reporting from public health reference laboratories: An international
perspective," Emerging Infectious Diseases, 7,
Bean, N.H., Martin, M., and Bradford, H. (1992), "PHLIS: An electronic system for reporting public health data from remote sites," Am. J. Public Health, 82, 1273-1276.
Horvitz, E. and Paek, T. (1999), "A computational architecture for conversation," User Modeling Conference, 201-210.
Horvitz, E. and Paek, T. (2001), "Harnessing models of users' goals to mediate clarification dialog in spoken language systems," User Modeling Conference.
Hutwagner, L.C., Maloney, E.K., Bean, N.H., Slutsker, L., and Martin, S.M. (1997), "Using laboratory-based surveillance data for prevention: An algorithm for detecting salmonella outbreaks," Emerging Infectious Diseases, 3, 395-400.
Koch-Weser, J., Sellers, E.M., and Zacest, R. (1977), "The ambiguity of adverse drug reactions,"
Eur. J. Clin. Pharmacol., 11, 75-78.
Langlotz, C.P., Shortliffe, E.H., and Fagan, L.M. (1986), "A methodology for computer-based explanation of decision analysis," Stanford University, KSL-86-57.
Mahon, B.E., Rohn, D.D., Pack, S.R., and Tauxe, R.V. (1995),
"Electronic communication facilitates investigation of a highly dispersed foodborne outbreak: Salmonella on the superhighway,"
Emerging Infectious Diseases, 1, 94-95.
McConachy, R., Korb, K.B., and Zuckerman, I. (1998),
"Deciding what not to say: An attentional probabilistic approach to argument presentation,"
Proceedings of the Twentieth Annual Conference of the Cognitive Science Society (CogSci 98).
Rawlins, M.D. (1994), "Pharmacovigilance: Paradise lost, regained or postponed?" The William Withering Lecture. Appeared in J. R. Coll. Physicians London, 29, (1995), 41-49.
Strom, B.L., and Tugwell, P. (1990), "Pharmacoepidemiology: Current status, prospects, and problems,"
Ann. Intern. Med., 113, 179-181.
VanLehn, K., and Niu, Z. (to appear), "Bayesian student modeling, user interfaces and feedback: A sensitivity analysis,"
International Journal of Artificial Intelligence in Education, 12.
Walker, M.A. (2000), "An application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email," Journal of Artificial Intelligence Research, 12, 387-416.
Zuckerman, I., McConachy, R., and Korb, K.B. (1998),
"Bayesian reasoning in an abductive mechanism for argument generation and analysis,"
Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI 98).
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Document last modified on December 10, 2001.