DIMACS Working Group on Adverse Event/Disease Reporting, Surveillance, and Analysis III
A DIMACS Panel at the Tenth Biennial CDC/ATSDR Symposium on Statistical Methods

March 2, 2005
Bethesda, Maryland

Organizers:
Donald Hoover, Rutgers University, drhoover@stat.rutgers.edu
David Madigan, Rutgers University, madigan@stat.rutgers.edu
Henry Rolka, CDC, hrr2@cdc.gov
Presented under the auspices of the of the Special Focus on Computational and Mathematical Epidemiology.

Tenth Biennial CDC/ATSDR Symposium on Statistical Methods

DIMACS Working Group on Adverse Event/Disease Reporting, Surveillance, and Analysis I

DIMACS Working Group on Adverse Event/Disease Reporting, Surveillance, and Analysis II

DIMACS Subgroup on Adverse Event/Disease Reporting, Surveillance, and Analysis


The concept for this session was promulgated by the Working Group on Adverse Event and Disease Reporting, Surveillance and Analysis (http://dimacs.rutgers.edu/Workshops/AdverseEvent/index.html), which was formed as part of a five-year Special Focus on Computational and Mathematical Epidemiology http://dimacs.rutgers.edu/SpecialYears/2002_Epid at the Center for Discrete Mathematics and Computer Science (DIMACS). A subgroup of participants from the Working Group will moderate the session and provide the presentations: Howard Burkom (John Hopkins University Applied Physics Laboratory), Gregory F. Cooper (University of Pittsburgh), Martin Kulldorff (Harvard Medical School), David Madigan (Rutgers University) and Henry Rolka (Centers for Disease Control and Prevention).

A variety of analytic approaches have arisen and are in use for performing Bioterrorism (BT) surveillance using social and other public health indicators from various types of data (e.g., pre-diagnostic/chief complaint ambulatory care encounters, nurse call line data, over-the-counter sales, absenteeism, Emergency Department discharge summaries, prescription pharmaceutical sales, 911-emergency calls; etc.). The value of data anomaly investigation and signal detection as technologies in surveillance can be enhanced by more formalized application of data pre-processing methods and applied probabilistic decision science concepts and principles. A full characterization of the usefulness as well corresponding development of analytic methods for exploiting opportunistic data are rich areas for research; especially in the context of information system integration. The specific focus of this session is on the analytic surveillance component where multiple sources of data are utilized to assess the health-related temporal and geographic status of human health risk.

The purpose of the session is to provide (1) an overview of the problem, (2) a view of current practices in operation (2 presentations), (3) evolving application areas of promise (1 presentation) and (4) to elicit feedback and discussion. Ideas and conceptual threads from the discussion will be explored and developed into a one-to-five year research agenda for addressing this area.


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Document last modified on May 23, 2005.