Potential for early warning of viral influenza activity in the community by monitoring clinical diagnoses of influenza in hospital emergency departments.
BACKGROUND
Although syndromic surveillance systems are gaining acceptance as useful tools in public health, doubts remain about whether the anticipated early warning benefits exist. Many assessments of this question do not adequately account for the confounding effects of autocorrelation and trend when comparing surveillance time series and few compare the syndromic data stream against a continuous laboratory-based standard. We used time series methods to assess whether monitoring of daily counts of Emergency Department (ED) visits assigned a clinical diagnosis of influenza could offer earlier warning of increased incidence of viral influenza in the population compared with surveillance of daily counts of positive influenza test results from laboratories.


METHODS
For the five-year period 2001 to 2005, time series were assembled of ED visits assigned a provisional ED diagnosis of influenza and of laboratory-confirmed influenza cases in New South Wales (NSW), Australia. Poisson regression models were fitted to both time series to minimise the confounding effects of trend and autocorrelation and to control for other calendar influences. To assess the relative timeliness of the two series, cross-correlation analysis was performed on the model residuals. Modelling and cross-correlation analysis were repeated for each individual year.


RESULTS
Using the full five-year time series, short-term changes in the ED time series were estimated to precede changes in the laboratory series by three days. For individual years, the estimate was between three and 18 days. The time advantage estimated for the individual years 2003-2005 was consistently between three and four days.


CONCLUSIONS
Monitoring time series of ED visits clinically diagnosed with influenza could potentially provide three days early warning compared with surveillance of laboratory-confirmed influenza. When current laboratory processing and reporting delays are taken into account this time advantage is even greater.
Zheng
Wei
W
Centre for Epidemiology and Research, New South Wales Department of Health, Sydney, Australia. wzhen@doh.health.nsw.gov.au
Aitken
Robert
R
Muscatello
David J
DJ
Churches
Tim
T
D004196:Disease Outbreaks; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis* / Q000453:epidemiology / Q000821:virology; D009517:New South Wales / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D011634:Public Health / Q000379:methods*