Estimation of hospital emergency room data using otc pharmaceutical sales and least mean square filters

BACKGROUND: Surveillance of Over-the-Counter pharmaceutical (OTC) sales as a potential early indicator of developing public health conditions, in particular in cases of interest to Bioterrorism, has been suggested in the literature. The data streams of interest are quite non-stationary and we address this problem from the viewpoint of linear adaptive filter theory: the clinical data is the primary channel which is to be estimated from the OTC data that form the reference channels. METHOD: The OTC data are grouped into a few categories and we estimate the clinical data using each individual category, as well as using a multichannel filter that encompasses all the OTC categories. The estimation (in the least mean square sense) is performed using an FIR (Finite Impulse Response) filter and the normalized LMS algorithm. RESULTS: We show all estimation results and present a table of effectiveness of each OTC category, as well as the effectiveness of the combined filtering operation. Individual group results clearly show the effectiveness of each particular group in estimating the clinical hospital data and serve as a guide as to which groups have sustained correlations with the clinical data. CONCLUSION: Our results indicate that Multichannle adaptive FIR least squares filtering is a viable means of estimating public health conditions from OTC sales, and provide quantitative measures of time dependent correlations between the clinical data and the OTC data channels.