Medication sales and syndromic surveillance, France

Abstract

Although syndromic surveillance systems using nonclinical data have been implemented in the United States, the approach has yet to be tested in France. We present the results of the first model based on drug sales that detects the onset of influenza season and forecasts its trend. Using weekly lagged sales of a selected set of medications, we forecast influenzalike illness (ILI) incidence at the national and regional level for 3 epidemic seasons (2000-01, 2001-02, and 2002-03) and validate the model with real-time updating on the fourth (2003-04). For national forecasts 1-3 weeks ahead, the correlation between observed ILI incidence and forecast was 0.85-0.96, an improvement over the current surveillance method in France. Our findings indicate that drug sales are a useful additional tool to syndromic surveillance, a complementary and independent source of information, and a potential improvement for early warning systems for both epidemic and pandemic planning.

Figure 1

Example of weekly consumption of expectorants and cephalosporins (provided by IMS France) in phase with weekly incidence of influenzalike illness (ILI) (data from French Sentinel Network) per 100,000 population.

Figure 2

Model construction for national influenzalike illness (ILI) incidence forecasting 1, 2, and 3 weeks ahead for the training dataset (week 36 of year 2000 to week 38 of year 2003) by using a jackknife reestimation procedure (95% confidence intervals [CI] for estimations are given). Forecasted incidence is per 100,000.

Figure 3

Evolution of regional influenzalike illness (ILI) incidence during the 2003–04 epidemic. The observed maps (first line) were constructed by using data from the French Sentinel Network. The forecast maps (for the first 6 epidemic weeks) 1, 2, and 3 weeks ahead show the results of the regional models when medication sales are used. The forecast horizon is indicated below each map. For example, for 2003(49), ILI predicted incidence is calculated by employing the model with data until week 47 of year 2003. Thus, the time forecasting horizon is 2 weeks.