The Distributed Surveillance Taskforce for Real-time Influenza Burden Tracking and
Evaluation (DiSTRIBuTE) project began as a pilot effort initiated by the
International Society for Disease Surveillance (ISDS) in autumn 2006 to create a
collaborative electronic emergency department (ED) syndromic influenza-like illness
(ILI) surveillance network based on existing state and local systems and expertise.
DiSTRIBuTE brought together health departments that were interested in: 1) sharing
aggregate level data; 2) maintaining jurisdictional control; 3) minimizing barriers
to participation; and 4) leveraging the flexibility of local systems to create a
dynamic and collaborative surveillance network. This approach was in contrast to the
prevailing paradigm for surveillance where record level information was collected,
stored and analyzed centrally. The DiSTRIBuTE project was created with a distributed
design, where individual level data remained local and only summarized, stratified
counts were reported centrally, thus minimizing privacy risks. The project was
responsive to federal mandates to improve integration of federal...
Statistics, Politics, and Policy, 2, issue 1, article 1.; The article of record as published may be found at http://dx.doi.org/10.2202/2151-7509.1018; The Early Aberration Reporting System (EARS) is used by some local health departments
(LHDs) to monitor emergency room and clinic data for disease outbreaks. Using actual chief
complaint data from local public health clinics, we evaluate how EARS—both the baseline system
distributed by the CDC and two variants implemented by one LHD—perform at locally detecting
the 2009 influenza A H1N1 pandemic. We also compare the EARS methods to a CUSUM-based
method. We find that the baseline EARS system performed poorly in comparison to one of the
LHD variants and the CUSUM-based method. These results suggest that changes in how
syndromes are defined can substantially improve EARS performance. The results also show that
incorporating algorithms that use more historical data will improve EARS performance for routine
surveillance by local health departments.