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.

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.

Quality of antibiotic prescription during office hours and out-of-hours in Flemish primary care, using European quality indicators.

BACKGROUND: European disease-specific antibiotic prescribing quality indicators (APQI) were proposed for seven acute indications (bronchitis, upper respiratory infection, cystitis, tonsillitis, sinusitis, otitis media and pneumonia): (a) the percentage of patients prescribed an antibiotic; (b) the percentage of patients receiving the guideline recommended antibiotic; (c) the percentage of patients receiving quinolones. OBJECTIVES: To assess the feasibility of calculating values for these 21 APQI using primary care databases; and to assess the quality of antibiotic prescribing in office hours and out-of-hours general practice. METHODS: Data was extracted from a morbidity registration network ( http://www.intego.be ) and the out-of-hours service centre in Flanders. Within both databases diagnoses are labelled using the revised second edition of International Classification of Primary Care (ICPC-2-R) and antibiotic prescriptions using Anatomical Therapeutic Chemical (ATC) classification. RESULTS: Both databases allow calculation of APQI values and results are similar. Only for cystitis was the percentage of patients prescribed an antibiotic within the proposed acceptable range. For all indications, the percentage of recommended antibiotics was below the proposed acceptable range (80-100%). The percentage of quinolones was within the proposed acceptable range (0-5%) for otitis media, upper respiratory infection and tonsillitis. CONCLUSION: Primary care databases can produce APQI values. These values revealed huge opportunities to improve the quality of antibiotic prescribing in office hours and out-of-hours Flemish general practice, especially the prescription of recommended antibiotics.