Objective: To estimate the effect of influenza-like illness (ILI) on outpatient visits and assess its impact on public health.
Methods: We analyzed the data of weekly number of ILI and outpatient visits in Departments of Internal Medicine, Pediatrics and Emergency at two influenza surveillance hospitals during a period of 137 weeks in Guangzhou. Spectral analysis and time-series analysis were performed to evaluate the variation of outpatient visits over time. The predictive model was fitted with weekly outpatient visits as the dependent variable and weekly number of ILI as the independent variable. The optimal model was established according to the coefficient of determination, Akaike-information criterion and residual analysis. The validity of the model was assessed prospectively using the 31-week data that were not used for the model establishment.
Results: The outpatient visits increased significantly over time and showed significant seasonality (P<0.001). A significant correlation was found between the weekly number of ILI and outpatient visits (r=0.568, P<0.001). The residuals of the fitted autoregression model were white-noise series and the coefficient of determination was 75% for the data used to establish the model and 56% for the subsequent 31-week data.
Conclusions: The autoregression model can be used to estimate the effect of weekly number of outpatient visits based on the weekly number of ILI and thus assess the effects of influenza on public health.