title: Comparison of two signal detection methods in a coroner-based system for near real-time mortality surveillance.
abstract: OBJECTIVE
  This article describes and compares the performance characteristics of two approaches to outbreak detection in the context of a coroner-based mortality surveillance system using controlled feature set simulation.
  
  
  METHODS
  The comparative capabilities of the outbreak detection methods--the Epidemic Threshold and Cusum methods--were assessed by introducing a series of simulated signals, configured as nonoverlapping, three-day outbreaks, into historic surveillance data and assessing their respective performances. Treating each calendar day as a separate observation, sensitivity, predictive value positive, and predictive value negative were calculated for both signal detection methods at various outbreak magnitudes. Their relative performances were also assessed in terms of the overall percentage of outbreaks detected.
  
  
  RESULTS
  Both methods exhibited low sensitivity for small outbreaks and low to moderate sensitivity for larger ones. In terms of overall outbreak detection, large outbreaks were detected with moderate to high levels of reliability, while smaller ones were detected with low to moderate reliability for both methods. The Epidemic Threshold method performed significantly better than the Cusum method for overall outbreak detection.
  
  
  CONCLUSIONS
  The use of coroner data for mortality surveillance has both advantages and disadvantages, the chief advantage being the rapid availability of coroner data compared to vital statistics data, making near real-time mortality surveillance possible. Given the lack of sensitivity and limited outbreak detection reliability of the methods studied, the use of mortality surveillance for early outbreak detection appears to have limited usefulness. If it is used, it should be as an adjuvant in conjunction with other surveillance systems.
authors.affiliation: Louisville Metro Health Department, 400 East Gray St., P.O. Box 1704, Louisville, KY 40201, USA. matt.groenewold@louisvilleky.gov
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Groenewold
mesh_terms: D023482:Bioterrorism; D003198:Computer Simulation; D003334:Coroners and Medical Examiners; D004196:Disease Outbreaks; D006801:Humans; D009026:Mortality; D011159:Population Surveillance / Q000379:methods*; D012680:Sensitivity and Specificity