Estimating age-stratified influenza-associated invasive pneumococcal disease in England: A time-series model based on population surveillance data

Background
Measures of the contribution of influenza to S.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 Findings
We 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 ILI within the sentinel scheme run by the RCGP. IPD cases were routinely reported to 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, 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% (CI 24.9%-39.9%) of new cases in the elderly, while 50.7% (CI 38.8%-63.2%) of incidence in adults aged 15-44 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 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, while 2.14% (CI 0.87%-3.57%) of cases in the 45-64 years old group 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, while the small numbers of IPD cases observed in the younger age groups led to significant uncertainty around some parameter estimates.
Conclusions
Our 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