Background: The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends. Objective: To evaluate a complementary approach, change point analysis (CPA), for detecting changes in the incidence of ED visits due to ILI. Methodology and principal findings Data collected through the Distribute project (isdsdistribute.org), which aggregates data on ED visits for ILI from over 50 syndromic surveillance systems operated by state or local public health departments were used. The performance was compared of the cumulative sum (CUSUM) CPA method in combination with EARS and the performance of three CPA methods (CUSUM, structural change model and Bayesian) in detecting change points in daily time-series data from four contiguous US states participating in the Distribute network. Simulation data were generated to assess the impact of autocorrelation inherent in these time-series data on CPA performance. The CUSUM CPA method was robust in detecting change points with respect to autocorrelation in time-series data (coverage rates at 90% when −0.2≤ρ≤0.2 and 80% when −0.5≤ρ≤0.5). During the 2008–9 season...
STEINBERGER RALF; FUART FLAVIO; POULIQUEN BRUNO; VAN DER GOOT ERIK
Fonte: Global Risk Forum GRF DavosPublicador: Global Risk Forum GRF Davos
Tipo: Contributions to ConferencesFormato: CD-ROM
Relevância na Pesquisa
Most Western countries have an institution with the task of detecting and monitoring potential threats to Public Health (PH) in their countries, i.e. chemical, biological, radiological and nuclear (CBRN) threats, which can be natural (diseases), deliberate (terrorism) or accidental. One of the daily duties of these PH bodies includes monitoring the local, national and international media for reports on disease outbreaks, on the disappearance or the release of dangerous substances, etc. This typically involves identifying and analysing relevant news articles in up to half a million news reports per day in various languages.
In order to facilitate this task, the European Commission¿s Joint Research Centre (JRC) in collaboration with the EC¿s Directorate General for Health and Consumer Protection (DG SANCO) has developed the fully-automatic Medical Intelligence System MedISys, which takes away many of the routine tasks of this process and detects early warning signals that can be used as a starting point for the daily media review. MedISys gathers news reports in 42 languages from about 1,500 web portals and twenty commercial news providers, filters documents of potential interest to PH officials, categorises them, monitors trends and alerts users of an unexpected increase of articles in any of the fine-grained categories...