Early detection of disease outbreaks.

For disease outbreak detection, the public health community has historically relied on the watchful eyes of doctors and other health care workers. The increased availability of electronic health care data, however, raises the possibility of more automated and earlier outbreak detection and subsequent intervention. Besides diagnoses of known diseases, prediagnostic syndromic indicators--such as the primary complaints of patients coming to the emergency room or calling a nurse hotline--are being collected in electronic formats and could be analyzed if suitable methods existed. Martin Kulldorff and co-authors have been developing such methods, and in the March 2005 issue of PloS Medicine, they report a new, very flexible approach for prospective infectious-disease outbreak surveillance.

Their method, which they call the "space-time permutation scan statistic," is an extension of a method called scan statistic. All previously developed scan statistics require either 1) a uniform population at risk (with the same number of expected disease cases in every square kilometer), 2) a control group (such as emergency visits not due to the disease of interest), or 3) other data that provide information about the geographical and temporal distribution of the underlying population at risk, such as census numbers. The new method, because of a different probability model, can be used for the early detection of disease outbreaks when only the number of cases is available. It also corrects for missing data and makes minimal assumptions about the spatio-temporal characteristics of an outbreak. So that it will be widely accessible, the method has been implemented as a feature of the freely available SaTScan software at www.satscan.org.

Since November 2003, the space-time permutation scan statistic has been used daily to analyze emergency department data in New York City in parallel with other methods, and it seems to perform well. Like any other surveillance method, it has limitations. Because it adjusts for purely temporal clusters, the method can detect outbreaks only if they start locally (not simultaneously across the entire surveillance area). The less geographically compact an outbreak is, the less power there is to detect it. Also, some outbreaks--for example, those caused by exposure to an infectious agent in the subway--will be hard to cluster by place of residence or choice of emergency department.

Kulldorff and colleagues have applied their method to infectious-disease surveillance in a metropolitan area in the United States. As they state, however, "the ability to perform disease surveillance without population-at-risk data is especially important in developing countries, where these data may be hard to obtain."

[Adapted from "Early Detection of Disease Outbreaks," PLoS Medicine 2(3): e65, http://medicine.plosjournals.org (2005).]