The strengths of our model include the use of large nationally representative databases, which limited the potential for sampling error; inclusion of data over a long period that covered 14 seasons; and the use of control outcomes with no inherent seasonality and no association to influenza.
Age stratification allowed detailed estimation of the age-specific burden, controlled for the observed age effect on consultation rates and overcame possible confounding due to higher frequency of viral testing in some age groups. Finally, weekly occurrences of outcomes were assumed to be determined by the circulation of influenza, RSV, and other causes which follow a seasonality that was estimated using a combination of sine and cosine terms. The inclusion of both cyclical terms sine and cosine functions controlled for confounders for which data are not available.
Tri-mean smoothing of pathogens and outcomes series mitigated short-term effects such as national holidays and extreme weather conditions on viral testing and GP consultations. A potential limitation of the study is the exclusive analysis of influenza and RSV virus time-series with no consideration of other respiratory pathogens; if such pathogens consistently circulate at the same time as influenza, the influenza burden could be overestimated. We did not generate time-series for influenza subtypes as we did not have access to subtyped data. We did not evaluate the effect of pneumococcal vaccination in children or older age groups [ 30 ], or consider changes in herd immunity during the period of study, nor did we consider the effects of obesity [ 31 , 32 ].
The study is also dependent on the quality and consistency of routine recording in the practice network. The burden of illness as encountered in primary care, which is the primary objective of this study, includes all consulting persons regardless of their propensity to consult. We did not study the indirect burden of illness in individuals with influenza who did not consult.
While our study provides up-to-date and detailed information on health service utilization, the study does not provide information for evaluation of indirect medical costs associated with influenza. None of the potential limitations of the study are likely to have influenced the model estimates in any major way.
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In addition to influenza-attributable respiratory disease, we estimated the burden of influenza-attributable otitis media, which has often been regarded as a common complication of influenza [ 33 ]. The diverse range of presenting illnesses identified as attributable to influenza in our study questions the relevance of diagnostic criteria by which influenza is defined clinically. The potential misuse of antibiotics is a well-recognized cause for concern, particularly in relation to common respiratory infections [ 36 ]. A simple test that is able to discriminate between bacterial and viral infection would be a great step forward, as would timely local influenza surveillance data that would allow physicians to assess the probability that an acute respiratory infection is caused by influenza.
Health economic models can inform policy choices, but they depend on accurate and age-specific parameter estimates of the disease burden. Furthermore, it is critical to use estimates based on recent assessments of the burden: earlier publications could overestimate the current burden.
Because the effectiveness of influenza vaccination in preventing otitis media appears similar to that of pneumococcal conjugate vaccines [ 37 ], it is important that estimates of the otitis media burden and the potentially preventable fraction be included in cost-benefit estimates of influenza interventions in this age group. Our study provides a source for understanding the national influenza burden and undertaking cost evaluations. The authors thank the Sage Analytica team, in particular Lone Simonsen and Lewis Kim, for their contribution to the study.
GlaxoSmithKline Biologicals SA was the funding source and was involved in all stages of the study conduct and analysis. GlaxoSmithKline Biologicals SA also took in charge all costs associated with the development and publication of the present manuscript. The opinions expressed in this report are solely those of the authors. Dave J. Robert J.
Taylor, Roger L. Douglas M. Fleming reports personal fees from Sage Analytica, personal fees and non-financial support from the GSK Group of Companies during the conduct of the study. Read article at publisher's site DOI : PLoS One , 15 8 :e, 06 Aug Emerg Infect Dis , 26 1 , 01 Jan Dtsch Arztebl Int , 39 , 01 Sep Review Free to read. Influenza Other Respir Viruses , 24 Jul Epidemiol Infect , e, 01 Jan This data has been text mined from the article, or deposited into data resources.
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Lustig RL 2 ,. Search articles by 'G Matias'. Matias G 3. Affiliations 1 author 1. Share this article Share with email Share with twitter Share with linkedin Share with facebook. We used the Clinical Practice Research Datalink CPRD and surveillance data from Public Health England in a linear regression model to assess the number of persons consulting UK general practitioners GP episodes for respiratory illness, otitis media and antibiotic prescriptions attributable to influenza during 14 seasons, Cambridge University Press. Epidemiol Infect.
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Email: moc. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work. This article has been cited by other articles in PMC. Go to:. Key words: Epidemiology, GP surveillance systems, infectious disease, influenza, influenza vaccines. Study design Viral surveillance time-series regression models were used to estimate the number of persons consulting a GP for influenza-attributable respiratory disease in England in each season July in the index year through June the following year , from to , stratifying by age, risk status and vaccination status www.
Table 1. Open in a separate window. Vaccination and risk status The CPRD dataset contains unique patient identifiers, allowing patients to be followed longitudinally. Denominators The CPRD covers a population which is representative of England in age and gender distribution and is thus a suitable population-denominated database for the assessment of vaccination status and national patterns of comorbidity in age- and gender-specific groups [ 19 ].
Statistical methods Statistical analyses were performed using SAS v.
Time trends The onset of the influenza epidemic period usually followed RSV, and influenza A usually preceded influenza B illustrated for the 5—17 years age group in Fig. Model fit The goodness-of-fit was assessed using adjusted R 2. Table 2. Rate s. Comorbid risk status and vaccination Individuals with comorbid conditions that put them at high risk of severe influenza infection were more likely to consult a GP for an influenza-attributable respiratory disease than individuals at low risk Table 3.
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High risk s. Low risk s. Respiratory broadly defined 0—4 5—17 18—49 50—64 65 67 98 All ages Bronchitis and bronchiolitis 0—4 0 95 5—17 91 18—49 69 98 50—64 65 65 65 31 60 All ages 62 74 82 Influenza-like illness 0—4 5—17 18—49 97 50—64 60 55 65 21 26 All ages 99 81 91 Pneumonia and influenza 0—4 35 44 5—17 88 18—49 75 97 50—64 45 41 65 18 32 All ages 63 76 58 Clinical syndromes A wide spectrum of diseases that resulted in a GP episode was attributable to influenza A and B Fig.
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