Scott A McDonald1, Michiel van Boven, Jacco Wallinga. 1. From the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Abstract
BACKGROUND: Estimation of the national-level incidence of seasonal influenza is notoriously challenging. Surveillance of influenza-like illness is carried out in many countries using a variety of data sources, and several methods have been developed to estimate influenza incidence. Our aim was to obtain maximally informed estimates of the proportion of influenza-like illness that is true influenza using all available data. METHODS: We combined data on weekly general practice sentinel surveillance consultation rates for influenza-like illness, virologic testing of sampled patients with influenza-like illness, and positive laboratory tests for influenza and other pathogens, applying Bayesian evidence synthesis to estimate the positive predictive value (PPV) of influenza-like illness as a test for influenza virus infection. We estimated the weekly number of influenza-like illness consultations attributable to influenza for nine influenza seasons, and for four age groups. RESULTS: The estimated PPV for influenza in influenza-like illness patients was highest in the weeks surrounding seasonal peaks in influenza-like illness rates, dropping to near zero in between-peak periods. Overall, 14.1% (95% credible interval [CrI]: 13.5%, 14.8%) of influenza-like illness consultations were attributed to influenza infection; the estimated PPV was 50% (95% CrI: 48%, 53%) for the peak weeks and 5.8% during the summer periods. CONCLUSIONS: The model quantifies the correspondence between influenza-like illness consultations and influenza at a weekly granularity. Even during peak periods, a substantial proportion of influenza-like illness-61%-was not attributed to influenza. The much lower proportion of influenza outside the peak periods reflects the greater circulation of other respiratory pathogens relative to influenza.
BACKGROUND: Estimation of the national-level incidence of seasonal influenza is notoriously challenging. Surveillance of influenza-like illness is carried out in many countries using a variety of data sources, and several methods have been developed to estimate influenza incidence. Our aim was to obtain maximally informed estimates of the proportion of influenza-like illness that is true influenza using all available data. METHODS: We combined data on weekly general practice sentinel surveillance consultation rates for influenza-like illness, virologic testing of sampled patients with influenza-like illness, and positive laboratory tests for influenza and other pathogens, applying Bayesian evidence synthesis to estimate the positive predictive value (PPV) of influenza-like illness as a test for influenza virus infection. We estimated the weekly number of influenza-like illness consultations attributable to influenza for nine influenza seasons, and for four age groups. RESULTS: The estimated PPV for influenza in influenza-like illness patients was highest in the weeks surrounding seasonal peaks in influenza-like illness rates, dropping to near zero in between-peak periods. Overall, 14.1% (95% credible interval [CrI]: 13.5%, 14.8%) of influenza-like illness consultations were attributed to influenza infection; the estimated PPV was 50% (95% CrI: 48%, 53%) for the peak weeks and 5.8% during the summer periods. CONCLUSIONS: The model quantifies the correspondence between influenza-like illness consultations and influenza at a weekly granularity. Even during peak periods, a substantial proportion of influenza-like illness-61%-was not attributed to influenza. The much lower proportion of influenza outside the peak periods reflects the greater circulation of other respiratory pathogens relative to influenza.
Authors: David T Gilbertson; Kenneth J Rothman; Glenn M Chertow; Brian D Bradbury; M Alan Brookhart; Jiannong Liu; Wolfgang C Winkelmayer; Til Stürmer; Keri L Monda; Charles A Herzog; Akhtar Ashfaq; Allan J Collins; James B Wetmore Journal: J Am Soc Nephrol Date: 2019-01-24 Impact factor: 10.121
Authors: Christian Garcia-Calavaro; Lee H Harrison; Darya Pokutnaya; Christina F Mair; Maria M Brooks; Wilbert van Panhuis Journal: Sci Rep Date: 2022-02-14 Impact factor: 4.379
Authors: Wilke Hendriks; Hendriek Boshuizen; Arnold Dekkers; Mirjam Knol; Ge A Donker; Arie van der Ende; Hester Korthals Altes Journal: Influenza Other Respir Viruses Date: 2017-01-03 Impact factor: 4.380