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Background: The test-negative design is commonly used to estimate influenza and coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE). In these studies, correlated COVID-19 and influenza vaccine behaviors may introduce a confounding bias where controls are included with the other vaccine-preventable acute respiratory illness (ARI). We quantified the impact of this bias on VE estimates in studies where this bias is not addressed., Methods: We simulated study populations under varying vaccination probabilities, COVID-19 VE, influenza VE, and proportions of controls included with the other vaccine-preventable ARI. Mean bias was calculated as the difference between estimated and true VE. Absolute mean bias in VE estimates was classified as low (<10%), moderate (10% to <20%), and high (≥20%)., Results: Where vaccination probabilities are positively correlated, COVID-19 and influenza VE test-negative studies with influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ARI controls, respectively, underestimate VE. For COVID-19 VE studies, mean bias was low for all scenarios where influenza represented ≤25% of controls. For influenza VE studies, mean bias was low for all scenarios where SARS-CoV-2 represented ≤10% of controls. Although bias was driven by the conditional probability of vaccination, low VE of the vaccine of interest and high VE of the confounding vaccine increase its magnitude., Conclusions: Where a low percentage of controls is included with the other vaccine-preventable ARI, bias in COVID-19 and influenza VE estimates is low. However, influenza VE estimates are likely more susceptible to bias. Researchers should consider potential bias and its implications in their respective study settings to make informed methodological decisions in test-negative VE studies., (© The Author(s) 2022. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Pregnant women are vaccinated against influenza less frequently than other high-risk groups. To design effective vaccination strategies, we must understand how decisions regarding vaccination may vary by trimester and over vaccination campaigns. We used a Cox model indexed by calendar time to estimate the effect of gestational trimester and other factors on vaccination uptake in a large cohort of pregnant women in Catalonia (Spain) during 2008-09 to 2012-13 influenza vaccination campaigns. We analyzed 247,316 pregnancies. Vaccination coverage was 3.7%, 5.2%, 4.8%, 5.6% and 4.6% from 2008-09 to 2012-13 seasonal vaccination campaigns and 8.3% for the 2009 pandemic vaccination campaign. Pregnant women previously vaccinated had higher uptake than women not previously vaccinated and the hazard ratios (HRs) comparing these 2 groups decreased from 10, the first day of seasonal campaigns, to 1.3 the last day. During the pandemic campaign, HRs decreased over the course of the campaign from 8.6 to 1.9. Women in second and third trimester had higher uptake than women in first trimester, with HR=2.8 and 2.3, respectively, at the start of seasonal campaigns. Influenza vaccination coverage among this cohort of pregnant women was alarmingly low. Our analysis reveals that gestational and calendar time have distinct and interacting effects on vaccination uptake; women in their second trimester and third trimester and previously vaccinated were more prone to be vaccinated, but this effect wanes as the influenza season progresses., (Copyright © 2017. Published by Elsevier Inc.)

Background: In response to the 2009 H1N1 influenza pandemic, Canada undertook the largest vaccination campaign in its history. The effort mobilized thousands of healthcare workers, cost many hundreds of millions of dollars, and vaccinated more than 40% of the population. Despite the large investment in mass vaccination internationally, little is known about the factors that drive the timing of vaccination uptake., Purpose: Data from 2009 were used to investigate three potential determinants of vaccination uptake in Montreal, Canada., Methods: Poisson regression was used to analyze daily vaccination before and after a telephone intervention targeting households in 12 of the city's 29 health neighborhoods. The effect of an eligibility strategy based on risk groups, and of weather, on uptake was then estimated. Data were analyzed in 2013., Results: Considerable variation in daily mass vaccination was observed, with the peak day (30,204 individuals) accounting for nearly five times the uptake of the slowest day (6298 individuals). No evidence was found that the telephone intervention led to a significant increase in vaccination. Daily vaccination was associated significantly with weather conditions, including mean temperature (relative risk [RR]=1.28, 95% CI=1.12, 1.46) and heavy precipitation (RR=0.63, 95% CI=0.45, 0.89), even after accounting for changes to eligibility, which also were associated with increased vaccination., Conclusions: Considerable temporal variation in uptake can occur during mass vaccination efforts. Targeted interventions to increase vaccination should be evaluated further, as a large intervention had no observable effect. Mass vaccination campaigns should, however, attempt to optimize priority sequences and account for weather when estimating vaccine demand., (© 2013 American Journal of Preventive Medicine.)

Neighborhood-level analyses of influenza vaccination can identify the characteristics of vulnerable neighborhoods, which can inform public health strategy for future pandemics. In this study, the authors analyzed rates of 2009 pandemic A/H1N1 influenza vaccination in Montreal, Quebec, Canada, using individual-level vaccination records from a vaccination registry with census, survey, and administrative data to estimate the population at risk. The neighborhood socioeconomic and demographic determinants of vaccination were identified using Bayesian ecologic logistic regression, with random effects to account for spatial autocorrelation. A total of 918,773 (49.9%) Montreal residents were vaccinated against pandemic A/H1N1 influenza from October 22, 2009, through April 8, 2010. Coverage was greatest among females, children under age 5 years, and health-care workers. Neighborhood vaccine coverage ranged from 33.6% to 71.0%. Neighborhoods with high percentages of immigrants (per 5% increase, odds ratio = 0.90, 95% credible interval: 0.86, 0.95) and material deprivation (per 1-unit increase in deprivation score, odds ratio = 0.93, 95% credible interval: 0.88, 0.98) had lower vaccine coverage. Half of the Montreal population was vaccinated; however, considerable heterogeneity in coverage was observed between neighborhoods and subgroups. In future vaccination campaigns, neighborhoods that are materially deprived or have high percentages of immigrants may benefit from focused interventions.