Early Detection of Influenza-Like Illness Through Medication Sales

Abstract

Monitoring sales of medications is a potential candidate for an early signal of a seasonal influenza epidemic. To test this theory, the data from a traditional, consultation-oriented influenza surveillance system were compared to medication sales and a predictive model was developed. Weekly influenza-like incidence rates from the National Influenza Sentinel Surveillance System were compared to sales of seven groups of medications (nasal decongestants, medicines for sore throat (MST), antitussives, mucolytics, analgo-antipyretics, non-steroidal anti-inflamatory drugs (NSAIDs), betalactam antibiotics, and macrolide antibiotics) to determine the correlation of medication sales with the sentinel surveillance system -and therefore their predictive power. Poisson regression and regression tree approaches were used in the statistical analyses. The fact that NSAIDs do not exhibit any seasonality and that prescription of antibiotics requires a visit to the doctor's office makes the two medication groups inappropriate for predictive purposes. The influenza-like illness (ILI) curve is the best matched by the mucolytics and antitussives sales curves. Distinct seasonality is also observed with MST and decongestants. The model including these four medication groups performed best in prediction of ILI incidence rate using the Poisson regression model. Sales of antitussives proved to be the best single predictive variable for regression tree model. Sales of medication groups included in the model were demonstrated to have a predictive potential for early detection of influenza season. The quantitative information on medication sales proves to be a useful supplementary system, complementing the traditional consultation-oriented surveillance system.

The spatiotemporal association of non-prescription retail sales with cases during the 2009 influenza pandemic in Great Britain

“…The use of sales information for adult and child remedy products has been suggested as a useful augmentation to traditional surveillance mechanisms,8 13 14 but has not been tested within the UK. Previous studies have suggested that localised retail sales data is more reflective of surveyed influenza patterns than national level data 11 12 28.…”

Surveillance for emerging respiratory viruses

“…Medication sales [74][75][76][77][78][79] Over-the-counter drug sales correlated with infl uenza activity Self-reporting participatory systems [81][82][83] Online-based surveillance system relying on voluntary participation Informal surveillance and epidemic intelligence [85][86][87][88][89][90][91][92][93]94 Detect relevant information from the internet, nationally and internationally The Global Public Health Intelligence Network (GPHIN) is a Canadian initiative that draws on the capacity of the internet and worldwide news coverage of health events. 93 GPHIN creates an early warning of outbreaks by monitoring internet media, including news wires and websites, to detect and report disease outbreaks.…”

Epidemiological and statistical basis for detection and prediction of influenza epidemics

“…Following this, the potential of using several different syndromic data sources as a tool in local influenza surveillance was examined at the beginning of this thesis (in the eHealth study). The findings were promising and corresponded to the recent outcomes worldwide (Kim et al 2013, Timpka et al 2014a, Nagel et al 2013, Yom-Tov et al 2014, Kirian & Weintraub 2010, Socan et al 2012). However, it was concluded that further longitudinal research incorporating prospective evaluations of actionable alerts (Milinovich et al 2014) is required before eHealth surveillance systems can be used in routine public health practice.…”

“…In the past few years, a considerable amount of research has focused on the use of interactive health information technology-referred to as eHealth systems-to improve the effectiveness of infectious disease surveillance (Castillo-Salgado 2010). Researchers have focused on the use of Internet search engines (Kim et al 2013, Sharpe et al 2016, Pollett et al 2017, telenursing data (Timpka et al 2014a), mini-blogs (Nagel et al 2013, YomTov et al 2014, Sharpe et al 2016, and records of over-the-counter drug sales (Kirian & Weintraub 2010, Socan et al 2012. This implies that the area where the need for knowledge is most immediate is the detection and prediction of influenza activity at local levels (Shaman et al 2013).…”