San Diego County Public Health has been conducting syndromic surveillance for the past few years. Currently, the system has become largely automated and processes and analyzes data from a variety of disparate sources including hospital emergency departments, 911 call centers, prehospital transports, and over-the-counter drug sales. What has remained constant since the system’s initial conceptualization is the local opinion that the data should be analyzed and interpreted in a variety of ways, in anticipation for the variety of contexts in which events that are of public health interest may unfold. Relatively small increases in volume that are sustained over time will likely be detected by methods designed to detect “small process shifts”, and include the CUSUM and EWMA methods. Larger increases in volume that are not sustained over time will likely be detected by other employed methods (P-Chart in the event of a non-proportional increase in volume, U-Chart in the event of a proportional increase in volume). A retrospective analysis was conducted on historical data from various data sources to determine the frequency of signals and detected events as well as the context within which the alert occurred (i.e., the “shape” of the data). Findings regarding several actual public health events will also be discussed.
Objective
This paper describes the frequency, various “shapes” and magnitudes of data anomalies, and varying ways actual public health events may present themselves in syndromic data.