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BackgroundWhen a randomized controlled trial fails to demonstrate statistically significant efficacy against the primary endpoint, a potentially costly new trial would need to be conducted to receive licensure. Incorporating data from previous trials might allow for the conduct of more efficient follow-up trials to demonstrate efficacy, speeding the availability of effective vaccines.MethodsBased on the outcomes from a failed trial of a maternal vaccine against respiratory syncytial virus (RSV), we simulated data for a new Bayesian group-sequential trial. The data were analyzed either ignoring data from the previous trial (i.e., weakly informative prior distributions) or using prior distributions that incorporate the historical data into the analysis. We evaluated scenarios where the efficacy in the new trial was the same, greater than, or less than the efficacy in the original trial. For each scenario, we evaluated the statistical power and type I error rate for estimating the vaccine effect following interim analyses.ResultsIf a stringent threshold is used to control the type I error rate, the analyses that incorporated historical data had a small advantage over trials that did not. If control of type I error is less important (e.g., in a post-licensure evaluation), the incorporation of historical data can provide a substantial boost in efficiency.ConclusionsDue to the need to control the type I error rate in trials used to license a vaccine, the incorporation of historical data provides little additional benefit in terms of stopping the trial early. However, these statistical approaches could be promising in evaluations that use real-world evidence following licensure.

Measles elimination hinges on vaccination coverage remaining above 95% to retain sufficient community protection. Recent declines in routine measles vaccinations due to the COVID-19 pandemic coupled with prior models indicating the country was close to the 92% herd immunity benchmark are a cause for concern. We evaluated population-level measles susceptibility in the US, including sensitivity analyses accounting for pandemic-related impacts on immunization. We estimated the number of children aged 0-18 currently susceptible to measles and modeled susceptibility proportions in decreased vaccination scenarios. Participants were respondents to the NIS-Teen survey between 2008 and 2017 that also had provider-verified vaccination documentation. The exposure of interest was vaccination with a measles-containing vaccine (MCV), and the age at which they were vaccinated for all doses given. Using age at vaccination, we estimated age-based probabilities of vaccination and modeled population levels of MCV immunization and immunity vs. susceptibility. Currently, 9,145,026 children (13.1%) are estimated to be susceptible to measles. With pandemic level vaccination rates, 15,165,221 children (21.7%) will be susceptible to measles if no attempt at catch-up is made, or 9,454,436 children (13.5%) if catch-up vaccinations mitigate the decline by 2-3%. Models based on increased vaccine hesitancy also show increased susceptibility at national levels, with a 10% increase in hesitancy nationally resulting in 14,925,481 children (21.37%) susceptible to measles, irrespective of pandemic vaccination levels. Current levels of measles immunity remain below herd immunity thresholds. If pandemic-era reductions in childhood immunization are not rectified, population-level immunity to measles is likely to decline further.

Many studies have examined the effectiveness of non-pharmaceutical interventions (NPIs) on SARS-CoV-2 transmission worldwide. However, less attention has been devoted to understanding the limits of NPIs across the course of the pandemic and along a continuum of their stringency. In this study, we explore the relationship between the growth of SARS-CoV-2 cases and an NPI stringency index across Canada before the accelerated vaccine roll-out.We conducted an ecological time-series study of daily SARS-CoV-2 case growth in Canada from February 2020 to February 2021. Our outcome was a back-projected version of the daily growth ratio in a stringency period (i.e., a 10-point range of the stringency index) relative to the last day of the previous period. We examined the trends in case growth using a linear mixed-effects model accounting for stringency period, province, and mobility in public domains.Case growth declined rapidly by 20-60% and plateaued within the first month of the first wave, irrespective of the starting values of the stringency index. When stringency periods increased, changes in case growth were not immediate and were faster in the first wave than in the second. In the first wave, the largest decreasing trends from our mixed effects model occurred in both early and late stringency periods, depending on the province, at a geometric mean index value of 30⋅1 out of 100. When compared with the first wave, the stringency periods in the second wave possessed little association with case growth.The minimal association in the first wave, and the lack thereof in the second, is compatible with the hypothesis that NPIs do not, per se, lead to a decline in case growth. Instead, the correlations we observed might be better explained by a combination of underlying behaviors of the populations in each province and the natural dynamics of SARS-CoV-2. Although there exist alternative explanations for the equivocal relationship between NPIs and case growth, the onus of providing evidence shifts to demonstrating how NPIs can consistently have flat association, despite incrementally high stringency.

We evaluated the cycle threshold (CT) values of 1,160 influenza A positive and 806 influenza B positive specimens from two seasons of the US Flu VE Network to identify factors associated with CT values. Low CT values (high genomic load) were associated with shorter intervals between illness onset and specimen collection, young age (ages 3-8 years old), and self-rated illness severity for both influenza A and B. Low CT values were also associated with reported fever/feverishness and age ≥65 years for influenza A.

In the United States, annual vaccination against seasonal influenza is recommended for all persons aged ≥6 months. Each season since 2004-05, CDC has estimated the effectiveness of seasonal influenza vaccine to prevent influenza-associated, medically attended acute respiratory infection (ARI). This season, early data from 1,155 children and adults with ARI enrolled during December 3, 2012-January 2, 2013 were used to estimate the overall effectiveness of seasonal influenza vaccine for preventing laboratory-confirmed influenza virus infection associated with medically attended ARI. After adjustment for study site, but not for other factors, the estimated vaccine effectiveness (VE) was 62% (95% confidence intervals [CIs] = 51%-71%). This interim estimate indicates moderate effectiveness, and is similar to a summary VE estimate from a meta-analysis of randomized controlled clinical trial data; final estimates likely will differ slightly. As of January 11, 2013, 24 states and New York City were reporting high levels of influenza-like illness, 16 states were reporting moderate levels, five states were reporting low levels, and one state was reporting minimal levels. CDC and the Advisory Committee on Immunization Practices routinely recommend that annual influenza vaccination efforts continue as long as influenza viruses are circulating. Persons aged ≥6 months who have not yet been vaccinated this season should be vaccinated. However, these early VE estimates underscore that some vaccinated persons will become infected with influenza; therefore, antiviral medications should be used as recommended for treatment in patients, regardless of vaccination status. In addition, these results highlight the importance of continued efforts to develop more effective vaccines.