Preview
Sign-in for full Details 
Sign-in free and Explore the Exciting World of BiomedExperts:
- Over 1,800,000 Profiles
- More than 3,500 Organizations worldwide
- State of the Art Network Visualizations
- Manage your own Profile
- Locate Experts in your Country/Region
- Locate Experts in your 1. and 2. Level Network
- Connect to Experts Worldwide
find experts for
Sign-in to see more
2006:
Kleinman Ken; Abrams Allyson; Katherine Yih W; Platt Richard; Kulldorff Martin
Evaluating spatial surveillance: detection of known outbreaks in real data.
Statistics in medicine 2006;
25(
5):.
Since the anthrax attacks of October 2001 and the SARS outbreaks of recent years, there has been an increasing interest in developing surveillance systems to aid in the early detection of such illness. Systems have been established which do this is by monitoring primary health-care visits, pharmacy sales, absenteeism records, and other non-traditional sources of data. While many resources have been invested in establishing such systems, relatively little effort has as yet been expended in evaluating their performance. One way to evaluate a given surveillance system is to compare the signals it generates with known outbreaks identified in other systems. In public health practice, for example, public health departments investigate reports of illness and sometimes track hospital admissions. Comparison of new systems with extant systems cannot generate estimates of test characteristics such as sensitivity and specificity, since the actual number of positives and negatives cannot be known. However, the comparison can reveal whether a new or proposed system's signals match outbreaks detected by the existing system. This could help support or reject the new system as an alternative or complement to the extant system. We propose three methods to test the null hypothesis that the new system does not signal true outbreaks more often than would be expected by chance. The methods differ in the restrictiveness of the assumptions required. Each test may detect weaknesses in the new system, depending on the distribution of outbreaks and can be used to construct confidence limits on the agreement between the new system's signals and the outbreaks, given the distribution of the signals. They can be used to assess whether the new system works in that it detects the outbreaks better than chance would suggest and can also determine if the new systems' signals are generated earlier than an extant system.
Post to CiteULike 
Sign in free and see...
Visualized networks:
See your personal network in
sophisticated graphical views
GeoTargeted Searches:
Locate experts around the world
and connect with global collaborators
Research Profiles:
See the visualized research activity
of experts around the globe
Sign-in to see more