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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23015

Title: Influenza epidemic surveillance and prediction based on electronic health record data from an out-of-hours general practitioner cooperative: model development and validation on 2003–2015 data
Authors: Michiels, Barbara
Nguyen, Van Kinh
Coenen, Samuel
Ryckebosch, Philippe
Bossuyt, Nathalie
Hens, Niel
Issue Date: 2017
Citation: BMC INFECTIOUS DISEASES, 17, p. 1-8 (Art N° 84)
Abstract: Background: Annual influenza epidemics significantly burden health care. Anticipating them allows for timely preparation. The Scientific Institute of Public Health in Belgium (WIV-ISP) monitors the incidence of influenza and influenza-like illnesses (ILIs) and reports on a weekly basis. General practitioners working in out-of-hour cooperatives (OOH GPCs) register diagnoses of ILIs in an instantly accessible electronic health record (EHR) system. This article has two objectives: to explore the possibility of modelling seasonal influenza epidemics using EHR ILI data from the OOH GPC Deurne-Borgerhout, Belgium, and to attempt to develop a model accurately predicting new epidemics to complement the national influenza surveillance by WIV-ISP. Method: Validity of the OOH GPC data was assessed by comparing OOH GPC ILI data with WIV-ISP ILI data for the period 2003–2012 and using Pearson’s correlation. The best fitting prediction model based on OOH GPC data was developed on 2003–2012 data and validated on 2012–2015 data. A comparison of this model with other well-established surveillance methods was performed. A 1-week and one-season ahead prediction was formulated. Results: In the OOH GPC, 72,792 contacts were recorded from 2003 to 2012 and 31,844 from 2012 to 2015. The mean ILI diagnosis/week was 4.77 (IQR 3.00) and 3.44 (IQR 3.00) for the two periods respectively. Correlation between OOHs and WIV-ISP ILI incidence is high ranging from 0.83 up to 0.97. Adding a secular trend (5 year cycle) and using a first-order autoregressive modelling for the epidemic component together with the use of Poisson likelihood produced the best prediction results. The selected model had the best 1-week ahead prediction performance compared to existing surveillance methods. The prediction of the starting week was less accurate (±3 weeks) than the predicted duration of the next season. Conclusion: OOH GPC data can be used to predict influenza epidemics both accurately and fast 1-week and one-season ahead. It can also be used to complement the national influenza surveillance to anticipate optimal preparation.
Notes: [Michiels, Barbara; Coenen, Samuel; Ryckebosch, Philippe] Univ Antwerp, Fac Med & Hlth Sci, Ctr Gen Practice, Dept Primary & Interdisciplinary Care Antwerp ELI, Antwerp, Belgium. [Van Kinh Nguyen] Ho Chi Minh Univ Med & Pharm, Fac Publ Hlth, Dept Epidemiol, Ho Chi Minh, Vietnam. [Van Kinh Nguyen] Helmholtz Ctr Infect Res, Dept Syst Immunol, SMID, Braunschweig, Germany. [Coenen, Samuel] Univ Antwerp, Fac Med & Hlth Sci, Lab Med Microbiol, Vaccine & Infect Dis Inst VAXINFECTIO, Antwerp, Belgium. [Coenen, Samuel; Hens, Niel] Univ Antwerp, Fac Med & Hlth Sci, Epidemiol & Social Med ESOC, Antwerp, Belgium. [Bossuyt, Nathalie] Belgian Sci Inst Publ Hlth, Operat Directorate Publ Hlth & Surveillance, Unit Epidemiol Infect Dis, Brussels, Belgium. [Hens, Niel] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat IBIOSTA, Hasselt, Belgium. [Hens, Niel] Univ Antwerp, Fac Med & Hlth Sci, Ctr Hlth Econ Res & Modelling Infect Dis, Vaccine & Infect Dis Inst VAXINFECTIO, Antwerp, Belgium.
URI: http://hdl.handle.net/1942/23015
DOI: 10.1186/s12879-016-2175-x
ISI #: 000397347000002
ISSN: 1471-2334
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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