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Rapid Assessment and Characterization of Environmental Risks ( RACER) - A New Software Tool - Gary M. Marsh, Ph.D. Michael Cunningham, M.S. D epartment of Biostatistics Graduate School of Public Health Pittsburgh Environmental Summit - April 18, 2007. Problem - Approach.

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Rapid Assessment and Characterization of Environmental Risks (RACER) - A New Software Tool -Gary M. Marsh, Ph.D.Michael Cunningham, M.S.Department of BiostatisticsGraduate School of Public HealthPittsburgh Environmental Summit - April 18, 2007

problem approach
Problem - Approach
  • Under what conditions and to what extent can we detect elevations in the background rate of disease that were truly caused by an environmental exposure?
  • To provide a web-based software tool that will enable public health officials and others to evaluate feasibility of conducting health tracking activities or targeted investigations
feasibility factors statistical
Feasibility Factors (Statistical)
  • The background rate of the disease under study (i.e., the rate in the unexposed population)
  • The overall size of the potentially exposed population
  • The proportion of the population that is exposed to varying levels of the environmental hazard
  • The relative potencies of the various levels of exposure to elevate the background rate of disease
  • Observation time of exposed subpopulations and latency period of disease under study
Conceptual Model: Airborne Exposure to Point Source with Proportion of Subpopulations Exposed Downwind of Source
example 3 power as a function of exposed ew and time years comparison of 4 plots
Example 3: Power as a Function of % Exposed (ew) and Time (years) - Comparison of 4 Plots

RRee = 1.2

RRee = 1.4

RRee = 1.6

RRee = 1.8

possible future racer enhancements
Possible Future RACER Enhancements
  • Indirect and direct standardization procedures
  • Extend standardization to multiple factors
  • Account for geographic population mobility
  • Time dependent exposure-elevated relative risks
  • Account for latency/incubation period of health endpoints
  • Statistical methods for spatial (clustered) data

The investigators wish to thank the following groups for their support and guidance:

Centers for Disease Control and Prevention

Environmental Public Health Tracking Program Grant # 5U19EH000103-02 (Academic Partners for Excellence in Environmental Public Health Tracking)

University of Pittsburgh Academic Consortium for Excellence in Environmental Public Health Tracking (UPACE-EPHT) (a collaboration with Drexel University)