tetano
Editor, Senior Moderator
BMC Infect Dis. 2017 Jan 18;17(1):84. doi: 10.1186/s12879-016-2175-x.
[h=1]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.[/h] Michiels B[SUP]1[/SUP], Nguyen VK[SUP]2,[/SUP][SUP]3[/SUP], Coenen S[SUP]4,[/SUP][SUP]5,[/SUP][SUP]6[/SUP], Ryckebosch P[SUP]4[/SUP], Bossuyt N[SUP]7[/SUP], Hens N[SUP]6,[/SUP][SUP]8,[/SUP][SUP]9[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] 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.
[h=4]METHOD:[/h] 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.
[h=4]RESULTS:[/h] 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.
[h=4]CONCLUSION:[/h] 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.
[h=4]KEYWORDS:[/h] Epidemics; Epidemiology; Influenza; Influenza-like illness; Out-of-hours; Prediction; Secular; Surveillance
PMID: 28100186 DOI: 10.1186/s12879-016-2175-x
[PubMed - in process] Free full text
[h=1]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.[/h] Michiels B[SUP]1[/SUP], Nguyen VK[SUP]2,[/SUP][SUP]3[/SUP], Coenen S[SUP]4,[/SUP][SUP]5,[/SUP][SUP]6[/SUP], Ryckebosch P[SUP]4[/SUP], Bossuyt N[SUP]7[/SUP], Hens N[SUP]6,[/SUP][SUP]8,[/SUP][SUP]9[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] 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.
[h=4]METHOD:[/h] 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.
[h=4]RESULTS:[/h] 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.
[h=4]CONCLUSION:[/h] 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.
[h=4]KEYWORDS:[/h] Epidemics; Epidemiology; Influenza; Influenza-like illness; Out-of-hours; Prediction; Secular; Surveillance
PMID: 28100186 DOI: 10.1186/s12879-016-2175-x
[PubMed - in process] Free full text