Date Published: June 27, 2019
Publisher: Public Library of Science
Author(s): Chiara Chiavenna, Anne M. Presanis, Andre Charlett, Simon de Lusignan, Shamez Ladhani, Richard G. Pebody, Daniela De Angelis, Aziz Sheikh
Abstract: BackgroundMeasures of the contribution of influenza to Streptococcus pneumoniae infections, both in the seasonal and pandemic setting, are needed to predict the burden of secondary bacterial infections in future pandemics to inform stockpiling. The magnitude of the interaction between these two pathogens has been difficult to quantify because both infections are mainly clinically diagnosed based on signs and symptoms; a combined viral–bacterial testing is rarely performed in routine clinical practice; and surveillance data suffer from confounding problems common to all ecological studies. We proposed a novel multivariate model for age-stratified disease incidence, incorporating contact patterns and estimating disease transmission within and across groups.Methods and findingsWe used surveillance data from England over the years 2009 to 2017. Influenza infections were identified through the virological testing of samples taken from patients diagnosed with influenza-like illness (ILI) within the sentinel scheme run by the Royal College of General Practitioners (RCGP). Invasive pneumococcal disease (IPD) cases were routinely reported to Public Health England (PHE) by all the microbiology laboratories included in the national surveillance system. IPD counts at week t, conditional on the previous time point t−1, were assumed to be negative binomially distributed. Influenza counts were linearly included in the model for the mean IPD counts along with an endemic component describing some seasonal background and an autoregressive component mimicking pneumococcal transmission. Using age-specific counts, Akaike information criterion (AIC)-based model selection suggested that the best fit was obtained when the endemic component was expressed as a function of observed temperature and rainfall. Pneumococcal transmission within the same age group was estimated to explain 33.0% (confidence interval [CI] 24.9%–39.9%) of new cases in the elderly, whereas 50.7% (CI 38.8%–63.2%) of incidence in adults aged 15–44 years was attributed to transmission from another age group. The contribution of influenza on IPD during the 2009 pandemic also appeared to vary greatly across subgroups, being highest in school-age children and adults (18.3%, CI 9.4%–28.2%, and 6.07%, CI 2.83%–9.76%, respectively). Other viral infections, such as respiratory syncytial virus (RSV) and rhinovirus, also seemed to have an impact on IPD: RSV contributed 1.87% (CI 0.89%–3.08%) to pneumococcal infections in the 65+ group, whereas 2.14% (CI 0.87%–3.57%) of cases in the group of 45- to 64-year-olds were attributed to rhinovirus. The validity of this modelling strategy relies on the assumption that viral surveillance adequately represents the true incidence of influenza in the population, whereas the small numbers of IPD cases observed in the younger age groups led to significant uncertainty around some parameter estimates.ConclusionsOur estimates suggested that a pandemic wave of influenza A/H1N1 with comparable severity to the 2009 pandemic could have a modest impact on school-age children and adults in terms of IPD and a small to negligible impact on infants and the elderly. The seasonal impact of other viruses such as RSV and rhinovirus was instead more important in the older population groups.
Partial Text: Just one century ago, the “1918 Spanish Influenza” is thought to have caused at least 50 million deaths worldwide despite influenza often naively being considered to be a nonsevere disease. Hence, a number of researchers in recent decades have tried to understand the drivers of such severity in the fear of a new pandemic [2–4]. Viral–bacterial synergism, in particular with S. pneumoniae, is considered to have played a major role in the observed mortality rate, as postmortem examinations revealed the presence of bacteria in the lungs of many influenza-infected individuals .
A total of 62,679 ILI consultations within the sentinel scheme and of 45,601 IPD cases nationwide have been notified over 9 years. Fig 1 displays the temporal trend of all ILI and influenza-confirmed consultation rates respectively, where influenza-confirmed counts (referred to as ‘Flu’ from now on) were obtained as described in the Methods. A clear seasonal pattern is visible, with regular outbreaks in the winter months and epidemics lasting 10–15 weeks, except for 2009, when the A/H1N1 pandemic started in spring. Virological testing is not systematically performed during the summer; hence, the Flu data are quite sparse off-season. Nonetheless, it is evident how, even during winter, the influenza cases do not closely mimic the ILI curve, confirming the nonspecificity of the ILI diagnosis. In the IPD time series (Fig 1, bottom panel), peaks appear to be similar across seasons both in terms of amplitude and timing, with a gradual increase of cases from autumn to a winter peak, followed by a decline in summer. The incidence rate per 1,000,000 population is plotted in this case, as IPD is rare.
Using English surveillance data, we quantified the magnitude of the interaction between influenza virus and S. pnuemoniae in seasonal and pandemic settings by proposing a multivariate extension of the HHH modelling framework. Such interaction was estimated to be quite small when looking at population-wide counts (model D). These results are consistent with previous research, showing a small association at aggregate level . We found evidence to support the hypothesis of an age-specific interaction , the contribution of Flu towards IPD being significant in school-age children and adults aged 15–44 but not in other age groups (model I). Moreover, the components of IPD explained by influenza were strikingly higher during the 2009 pandemic period in the same age groups. This supports findings of Weinberger and colleagues . Other viruses also appeared to interact with S. pneumoniae with various intensities across age groups: both RSV and rhinovirus played an important role in 45- to 64- and 65+-year-olds (models F and K respectively). Such findings support previous evidence of interplay among these pathogens, with differential behaviour across ages [50, 51].