A syndrome cannot be created to identify every possible cluster of potential public health significance. A method is needed to identify clusters without pre-classification into syndromes. This could include clusters of signs or symptoms, clusters of place names (e.g. mentioning a specific restaurant), clusters of events (e.g. mentioning a specific fair, concert, etc.). Example provided below.
How this issue is currently being addressed:
North Carolina has implemented a partial solution that uses Time of Arrival Analysis (TOA) developed by the Johns Hopkins Applied Physics Lab to detect clusters of interest based solely on visit counts (no syndromes) (Li et al, 2013). TOA detects clusters based on arrival date and time. The user can then scan the line listing of these time-based clusters to see if the line listing data reveal any commonalities among the visits that might be of public health significance. This can be time-consuming depending on the number of clusters detected by TOA. In addition, this method will not detect clusters of events that occur over a longer time period. For example, if 5 people come into an emergency department complaining about “Larry’s hot dog shack” but these visits are scattered over a 24-hour period, TOA will not group these into a cluster.
Updated on Tuesday, March 19, 2013 @ 21:25