Impact of increased influenza vaccination in 2-3-year-old children on disease burden within the general population: A Bayesian model-based approach

Cost-utility analysis of increasing uptake of universal seasonal quadrivalent influenza vaccine (QIV) in children aged 6 months and older in Germany

Seasonal influenza causes many cases and related deaths in Europe annually, despite ongoing vaccination programs for older adults and people at high-risk of complications. Children have the highest risk of infection and play a key role in disease transmission. Our cost-utility analysis, based on a dynamic transmission model, estimated the impact of increasing the current vaccination coverage with inactivated quadrivalent influenza vaccine in Germany to all (healthy and high-risk) children under 5 years of age (40% uptake), or under 18 years (40% uptake), or only high-risk children under 18 years (90% uptake). Eight influenza complications were modeled, hospitalization and death rates were based on age and risk status. All three vaccination strategies provided more health benefits than the existing vaccination situation, reducing influenza cases, complications, hospitalizations and deaths across the entire population. The strategy targeting all children under 5 years was highly cost-effective (€6/quality-adjusted life-year gained, payer perspective). The other strategies were cost saving from the payer and societal perspectives. The vaccination strategy targeting all children under 18 years was estimated to provide the most health benefits (preventing on average 1.66 million cases, 179,000 complications, 14,000 hospitalizations and 3,600 deaths due to influenza annually) and the most cost savings (annually €20.5 million and €731.3 million from payer and societal perspectives, respectively). Our analysis provides policy decision-makers with evidence supporting strategies to expand childhood influenza vaccination, to directly protect children, and indirectly all other unvaccinated age groups, in order to reduce the humanistic and economic burden on healthcare systems and society.

Accounting for cross-immunity can improve forecast accuracy during influenza epidemics

Previous exposure to influenza viruses confers cross-immunity against future infections with related strains. However, this is not always accounted for explicitly in mathematical models used for forecasting during influenza outbreaks. We show that, if an influenza outbreak is due to a strain that is similar to one that has emerged previously, then accounting for cross-immunity explicitly can improve the accuracy of real-time forecasts. To do this, we consider two infectious disease outbreak forecasting models. In the first (the "1-group model"), all individuals are assumed to be identical and cross-immunity is not accounted for. In the second (the "2-group model"), individuals who have previously been infected by a related strain are assumed to be less likely to experience severe disease, and therefore recover more quickly, than immunologically naive individuals. We fit both models to estimated case notification data (including symptomatic individuals as well as laboratory-confirmed cases) from Japan from the 2009 H1N1 influenza pandemic, and then generate synthetic data for a future outbreak by assuming that the 2-group model represents the epidemiology of influenza infections more accurately. We use the 1-group model (as well as the 2-group model for comparison) to generate forecasts that would be obtained in real-time as the future outbreak is ongoing, using parameter values estimated from the 2009 epidemic as informative priors, motivated by the fact that without using prior information from 2009, the forecasts are highly uncertain. In the scenario that we consider, the 1-group model only produces accurate outbreak forecasts once the peak of the epidemic has passed, even when the values of important epidemiological parameters such as the lengths of the mean incubation and infectious periods are known exactly. As a result, it is necessary to use the more epidemiologically realistic 2-group model to generate accurate forecasts. Accounting for cross-immunity driven by exposures in previous outbreaks explicitly is expected to improve the accuracy of epidemiological modelling forecasts during influenza outbreaks.

Cost-effectiveness of live-attenuated influenza vaccination among school-age children

The current pediatric vaccination program in England and Wales administers Live-Attenuated Influenza Vaccine (LAIV) to children ages 2-16 years old. Annual administration of LAIV to this age group is costly and poses substantial logistical issues. This study aims to evaluate the cost-effectiveness of prioritizing vaccination to age groups within the 2-16 year old age range to mitigate the operational and resource challenges of the current strategy. We performed economic evaluations comparing the influenza vaccination program from 1995-2013 to seven alternative strategies targeted at low risk individuals along the school age divisions Preschool (2-4 years old), Primary school (5-11 years old), and Secondary school (12-16 years old). These extensions are evaluated incrementally on the status quo scenario (vaccinating subgroups at high risk of influenza-related complications and individuals 65+ years old). Impact of vaccination was assessed using a transmission model from a previously published study and updated with new data. At all levels of coverage, all strategies had a 100% probability of being cost-effective at the current National Health Service threshold, £20,000/QALY gained. The incremental analysis demonstrated vaccinating Primary School children was the most cost-efficient strategy compared incrementally against others with an Incremental Cost-Effectiveness Ratio of £639 spent per QALY gained (Net Benefit: 404 M£ [155, 795]). When coverage was varied between 30%, 55%, and 70% strategies which included Primary school children had a higher probability of being cost-effective at lower willingness-to-pay levels. Although children were the vaccine target the majority of QALY gains occurred in the 25-44 years old and 65+ age groups. Influenza strain A/H3N2 incurred the greatest costs and QALYs lost regardless of which strategy was used. Improvement could be made to the current LAIV pediatric vaccination strategy by eliminating vaccination of 2-4 year olds and focusing on school-based delivery to Primary and Secondary school children in tandem.

Vaccine hesitancy

In summer 2019 an extracurricular activity was started at the Medical University of Vienna (MUW) with the title: “Esoterism in Medicine”, where different chapters were evaluated by students. Here we present the subheading “Vaccine Hesitancy”. Three students formulated arguments from sceptic, hesitant or anti-vaccine groups and discussed the scientific literature to rebut it. Frequent objections were partly taken from the homepage of the German Robert-Koch-Institute, the home of the “Ständige Impfkommission”. Other objections were taken from blogs and social media. The students’ rebuttal was based on current scientific literature (preferentially pubmed), but also from other scientific sources like authorities.

Influenza epidemiology in patients admitted to sentinel Australian hospitals in 2018: the Influenza Complications Alert Network (FluCAN)

The Influenza Complications Alert Network (FluCAN) is a sentinel hospital-based surveillance program that operates at sites in all jurisdictions in Australia. This report summarises the epidemiology of hospitalisations with laboratory-confirmed influenza during the 2018 influenza season. In this observational surveillance system, cases were defined as patients admitted to any of the 17 sentinel hospitals with influenza confirmed by nucleic acid detection. Data were also collected on a frequency-matched control group of influenza-negative patients admitted with acute respiratory infection. During the period 3 April to 31 October 2018 (the 2018 influenza season), 769 patients were admitted with confirmed influenza to one of 17 FluCAN sentinel hospitals. Of these, 30% were elderly (≥65 years), 28% were children (<16 years), 6.4% were Aboriginal and Torres Strait Islander peoples, 2.2% were pregnant and 66% had chronic comorbidities. A small proportion of FluCAN admissions were due to influenza B (13%). Estimated vaccine coverage was 77% in the elderly (≥65 years), 45% in non-elderly adults with medical comorbidities and 26% in children (<16 years) with medical comorbidities. The estimated vaccine effectiveness (VE) in the target population was 52% (95% CI: 37%, 63%). There were a smaller number of hospital admissions detected with confirmed influenza in this national observational surveillance system in 2018 than in 2017, with the demographic profile reflecting the change in circulating subtype from A/H3N2 to A/H1N1.

Seasonal influenza: Modelling approaches to capture immunity propagation

Seasonal influenza poses serious problems for global public health, being a significant contributor to morbidity and mortality. In England, there has been a long-standing national vaccination programme, with vaccination of at-risk groups and children offering partial protection against infection. Transmission models have been a fundamental component of analysis, informing the efficient use of limited resources. However, these models generally treat each season and each strain circulating within that season in isolation. Here, we amalgamate multiple data sources to calibrate a susceptible-latent-infected-recovered type transmission model for seasonal influenza, incorporating the four main strains and mechanisms linking prior season epidemiological outcomes to immunity at the beginning of the following season. Data pertaining to nine influenza seasons, starting with the 2009/10 season, informed our estimates for epidemiological processes, virological sample positivity, vaccine uptake and efficacy attributes, and general practitioner influenza-like-illness consultations as reported by the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). We performed parameter inference via approximate Bayesian computation to assess strain transmissibility, dependence of present season influenza immunity on prior protection, and variability in the influenza case ascertainment across seasons. This produced reasonable agreement between model and data on the annual strain composition. Parameter fits indicated that the propagation of immunity from one season to the next is weaker if vaccine derived, compared to natural immunity from infection. Projecting the dynamics forward in time suggests that while historic immunity plays an important role in determining annual strain composition, the variability in vaccine efficacy hampers our ability to make long-term predictions.

Influenza, the flu, is a highly infectious respiratory disease that can cause serious health complications. Characterised by seasonal outbreaks, a key challenge for policy-makers is implementing measures to successfully lessen the public health burden on an annual basis. Seasonal influenza vaccine programmes are an established method to deliver cost-effective prevention against influenza and its complications. Transmission models have been a fundamental component of vaccine programme analysis, informing the efficient use of limited resources. However, these models generally treat each influenza season and each strain circulating within that season in isolation. By developing a mathematical model explicitly including multiple immunity propagation mechanisms, then fit to influenza-related vaccine and epidemiological data from England via statistical methods, we sought to quantify the extent that epidemiological events in the previous influenza season alter susceptibility at the onset of the following season. The findings suggest that susceptibility in the next season to a given influenza strain type is modulated to the greatest extent through natural infection by that strain type in the current season. Residual vaccine immunity has a lesser role. Prospectively, the adoption of influenza transmission modelling frameworks with immunity propagation would provide a comprehensive manner to assess the impact of seasonal vaccination programmes.

Influenza epidemiology in patients admitted to sentinel Australian hospitals in 2017: the Influenza Complications Alert Network (FluCAN)

The Influenza Complications Alert Network (FluCAN) is a sentinel-hospital-based surveillance program that operates at sites in all jurisdictions in Australia. This report summarises the epidemiology of hospitalisations with laboratory-confirmed influenza during the 2017 influenza season. In this observational surveillance system, cases were defined as patients admitted to any of the 17 sentinel hospitals with influenza confirmed by nucleic acid detection. Data are also collected on a frequency-matched control group of influenza-negative patients admitted with acute respiratory infection. During the period 3 April to 31 October 2017 (the 2017 influenza season), 4,359 patients were admitted with confirmed influenza to one of 17 FluCAN sentinel hospitals. Of these, 52% were elderly (≥65 years), 14% were children (<16 years), 6.5% were Aboriginal and Torres Strait Islander peoples, 1.6% were pregnant and 78% had chronic comorbidities. A significant proportion were due to influenza B (31%). Estimated vaccine coverage was 72% in the elderly (≥65 years), 50% in non-elderly adults with medical comorbidities and 24% in children (<16 years) with medical comorbidities. The estimated vaccine effectiveness (VE) in the target population was 23% (95% CI: 7%, 36%). There were a large number of hospital admissions detected with confirmed influenza in this national observational surveillance system in 2017, with case numbers more than twice that reported in 2016.