Journal article
A syndromic surveillance system for clinical and non-clinical health data
IIE transactions on healthcare systems engineering, Vol.1(1), pp.37-48
2011
DOI: 10.1080/19488300.2011.555877
Abstract
The data used in health care and public health monitoring and surveillance are likely to be nonstationary and highly auto-correlated. These data may come from clinical and nonclinical sources, the latter being increasingly used in an attempt to supplement traditional surveillance. We propose an alternative approach applicable to multiple sets of health data that combines a method of preconditioning (ARMA filtering), a forecasting method (the method of analogues) and a monitoring method (the change-point). This procedure is applied to Influenza-Like Illness (ILI) surveillance in France using both clinical data (time series of weekly ILI incidence at the national level and in 22 regions) and nonclinical data (over-the-counter and prescribed weekly medication sales data at the national level). The outcomes show that multivariate monitoring provides an improved ability to recognize warning signals and drug sales data provide an indirect but earlier indication of influenza outbreaks. The proposed method has the advantage of being both practical and relatively simple to implement and could be easily adapted to develop adequate surveillance tools for specific diseases.
Details
- Title: Subtitle
- A syndromic surveillance system for clinical and non-clinical health data
- Creators
- Min-Jung Kim - Pennsylvania State UniversityHarriet Black Nembhard - University of PennsylvaniaBruno Lambert - IMS Heath , Puteaux, FranceClément Turbelin - Marie CurieAntoine Flahault - École des Hautes Études en Santé PubliqueElisabeta Vergu - Institut National de la Recherche Agronomique
- Resource Type
- Journal article
- Publication Details
- IIE transactions on healthcare systems engineering, Vol.1(1), pp.37-48
- DOI
- 10.1080/19488300.2011.555877
- ISSN
- 1948-8300
- eISSN
- 1948-8319
- Language
- English
- Date published
- 2011
- Academic Unit
- Industrial and Systems Engineering; Engineering Administration
- Record Identifier
- 9984187058302771
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