Evaluating spatial surveillance: detection of known outbreaks in real data.
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.
Kleinman
Ken
K
Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, USA. ken_kleinman@hms.harvard.edu
Abrams
Allyson
A
Katherine Yih
W
W
Platt
Richard
R
Kulldorff
Martin
M
D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks; D005767:Gastrointestinal Diseases / Q000453:epidemiology; D006801:Humans; D008910:Minnesota / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D013029:Space-Time Clustering