<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Michiels</style></author><author><style face="normal" font="default" size="100%">Nguyen, Van Kinh</style></author><author><style face="normal" font="default" size="100%">Coenen, Samuel</style></author><author><style face="normal" font="default" size="100%">Philippe Ryckebosch</style></author><author><style face="normal" font="default" size="100%">Nathalie Bossuyt</style></author><author><style face="normal" font="default" size="100%">Hens, Niel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">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.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Infect Dis</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">After-Hours Care</style></keyword><keyword><style  face="normal" font="default" size="100%">Belgium</style></keyword><keyword><style  face="normal" font="default" size="100%">Data collection</style></keyword><keyword><style  face="normal" font="default" size="100%">Electronic Health Records</style></keyword><keyword><style  face="normal" font="default" size="100%">Epidemics</style></keyword><keyword><style  face="normal" font="default" size="100%">Epidemiological Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">general practitioners</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">incidence</style></keyword><keyword><style  face="normal" font="default" size="100%">Influenza, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">Retrospective Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Seasons</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 01 18</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;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.&lt;/p&gt;

&lt;p&gt;&lt;b&gt;METHOD: &lt;/b&gt;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.&lt;/p&gt;

&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;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&amp;nbsp;year&amp;nbsp;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&amp;nbsp;weeks) than the predicted duration of the next season.&lt;/p&gt;

&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;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.&lt;/p&gt;
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