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

Abstract

BackgroundAnnual 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.MethodValidity 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.ResultsIn 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.ConclusionOOH 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.

Evaluation of the secondary use of electronic health records to detect seasonal, holiday-related, and rare events related to traumatic injury and poisoning

“…For instance, Melamed et al showed the utility of EHRs to link diseases to seasonal trends [16]. Other seasonal detection methods using EHR data have been used to model seasonal influenza outbreaks, seasonal blood pressure controls, and seasonal effects on early child development [17][18][19]. While these studies show that EHRs can be used for accurate population health trends, each of these have looked at only one category of disease at a time.…”

“…In this paper, we explore the utility of the EHR as a generalizable event and trend detection platform. In contrast to previous studies, we don't look for seasonal trends of specific diseases, but rather look for unusual coding trends for all traumatic injuries because they have known seasonal trends [16][17][18] and gold standard events by which we can validate a generalizable event detection method (e.g., we expect the 4th of July to have a spike in firework accidents). Our goal is to test whether a general event detection method can use a live EHR system to alert public health officials to possible actionable environmental events.…”

Evaluation of the secondary use of electronic health records to detect seasonal, holiday-related, and rare events related to traumatic injury and poisoning

“…[16] Other seasonal detection methods using EHR data have been used to model seasonal influenza outbreaks, seasonal blood pressure controls, and seasonal effects on early child development. [17][18][19] While these studies show that EHRs can be used for accurate population health trends, each of these have looked at only one category of disease at a time.…”

“…In contrast to previous studies, we don't look for seasonal trends of specific diseases, but rather look for unusual coding trends for all traumatic injuries because they have known seasonal trends [16][17][18] and gold standard events by which we can validate a generalizable event detection method (e.g., we expect the 4th of July to have a spike in firework accidents). Our goal is to test whether a general event detection method can use a live EHR system to alert public health officials to possible actionable environmental events.…”

Comparison between influenza coded primary care consultations and national influenza incidence obtained by the General Practitioners Sentinel Network in Portugal from 2012 to 2017

“…Sentinel surveillance systems for medical practices are recognized to be the most efficient way to perform influenza surveillance [ 7 ]. Sentinel surveillance aims to promptly identify and monitor influenza activity in the community, presenting a central role in support of public health interventions [ 5 , 8 , 9 ].…”