An automated, broad-based, near real-time public health surveillance system using presentations to hospital Emergency Departments in New South Wales, Australia.
BACKGROUND
In a climate of concern over bioterrorism threats and emergent diseases, public health authorities are trialling more timely surveillance systems. The 2003 Rugby World Cup (RWC) provided an opportunity to test the viability of a near real-time syndromic surveillance system in metropolitan Sydney, Australia. We describe the development and early results of this largely automated system that used data routinely collected in Emergency Departments (EDs).


METHODS
Twelve of 49 EDs in the Sydney metropolitan area automatically transmitted surveillance data from their existing information systems to a central database in near real-time. Information captured for each ED visit included patient demographic details, presenting problem and nursing assessment entered as free-text at triage time, physician-assigned provisional diagnosis codes, and status at departure from the ED. Both diagnoses from the EDs and triage text were used to assign syndrome categories. The text information was automatically classified into one or more of 26 syndrome categories using automated "naïve Bayes" text categorisation techniques. Automated processes were used to analyse both diagnosis and free text-based syndrome data and to produce web-based statistical summaries for daily review. An adjusted cumulative sum (cusum) was used to assess the statistical significance of trends.


RESULTS
During the RWC the system did not identify any major public health threats associated with the tournament, mass gatherings or the influx of visitors. This was consistent with evidence from other sources, although two known outbreaks were already in progress before the tournament. Limited baseline in early monitoring prevented the system from automatically identifying these ongoing outbreaks. Data capture was invisible to clinical staff in EDs and did not add to their workload.


CONCLUSIONS
We have demonstrated the feasibility and potential utility of syndromic surveillance using routinely collected data from ED information systems. Key features of our system are its nil impact on clinical staff, and its use of statistical methods to assign syndrome categories based on clinical free text information. The system is ongoing, and has expanded to cover 30 EDs. Results of formal evaluations of both the technical efficiency and the public health impacts of the system will be described subsequently.
Muscatello
David J
DJ
Centre for Epidemiology and Research, New South Wales Department of Health, North Sydney NSW 2059, Australia. dmusc@doh.health.nsw.gov.au
Churches
Tim
T
Kaldor
Jill
J
Zheng
Wei
W
Chiu
Clayton
C
Correll
Patricia
P
Jorm
Louisa
L
D000846:Anniversaries and Special Events; D001499:Bayes Theorem; D003141:Communicable Diseases / Q000175:diagnosis / Q000453:epidemiology*; D019992:Databases as Topic; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D006751:Hospital Information Systems; D006801:Humans; D020407:Internet; D008498:Medical Record Linkage; D009517:New South Wales / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D040442:Public Health Informatics; D013577:Syndrome; D018511:Systems Integration; D014218:Triage; D014504:Urban Health