Christian Holm Hansen

BACKGROUND: Influenza infection is a recognised trigger of acute myocardial infarction (AMI) and stroke, but whether influenza vaccination modifies this risk remains unclear. AIM: We aimed to quantify the short-term cardiovascular risk after laboratory-confirmed influenza infection and assess whether vaccination attenuates it. METHODS: We conducted a nationwide self-controlled case series study using Danish health registries (2014–2025) and included individuals aged ≥ 40 years with a first-ever hospital admission for AMI or stroke within ± 365 days of a PCR-confirmed influenza infection. We defined days 1–7 after specimen date as the risk period and excluded a 14-day pre-exposure period to reduce reverse causality, where influenza testing might be prompted by cardiovascular disease symptoms. We linked testing, hospitalisation, vaccination and mortality data deterministically via unique personal identifiers. We estimated incidence rate ratios (IRRs) and 95% confidence intervals (CI) with conditional Poisson regression. RESULTS: Among 1,221 individuals with a first-ever AMI (n = 429; 35%) or stroke (n = 792; 65%), median age was 75 years (interquartile range: 66–82); 561 (46%) were female. After calendar-month adjustment, the IRR for cardiovascular events during the risk period was 3.5 (95% CI: 2.6–4.7), higher for AMI (IRR = 4.7; 95% CI: 3.1–7.4) than stroke (IRR = 2.9; 95% CI: 2.0–4.2). Prior influenza vaccination during the same influenza season, recorded in 610 (50%) episodes, reduced the excess risk of AMI or stroke associated with influenza infection (interaction p = 0.020). CONCLUSIONS: Influenza infection conferred a transiently increased risk of first-time AMI and stroke. Vaccination substantially attenuated this risk, supporting its role in preventing cardiovascular complications after breakthrough infection.

Background: Electronic health record (EHR)-based observational studies can rapidly provide real-world data on vaccine effectiveness (VE), particularly key during the COVID-19 pandemic. However, EHR data may be prone to misclassification and unmeasured confounding, requiring systematic mitigation to ensure robust findings. Methods: In VEBIS-EHR, a retrospective multi-country COVID-19 VE cohort study, we examined unmeasured confounding using a negative control outcome (death not related to COVID-19) and misclassification from varying data extraction intervals. The evaluation spanned two periods (November-December 2023, January-February 2024), encompassing up to 18.7 million individuals across six EU/EEA countries. Vaccine confounding-adjusted hazard ratios (aHRs) were pooled using random-effects meta-analysis. Results: aHRs against non–COVID-19 mortality ranged from 0.35 (95% CI: 0.28–0.44) to 0.70 (0.66–0.73) when comparing vaccinated versus unvaccinated. Delaying EHR data extraction modestly increased the capture of outcome and exposure events, with some variation by vaccination status. Site-level fluctuations in aHRs did not meaningfully alter the overall pooled VE, suggesting stable estimates despite misclassification related to extraction timing. Conclusions: We observed some evidence of unmeasured confounding when using non-COVID-19 deaths as a negative outcome, though the specificity of our negative control must be considered. This result may suggest overestimation of VE, but also the need for further analysis with more specific negative control outcomes and confounding-adjustment techniques. Addressing such confounding using richer data sources and more refined approaches remains critical to ensure accurate, timely VE estimates when using real-world EHR-based data. Extending the delay between the end of observation and data extraction modestly improves the completeness of exposure and outcome data, with limited effect on pooled VE estimates. Key Messages: A Key Messages section should be added after the keywords and before the article’s introduction, with the key messages of the paper made in 3 bullet points that succinctly describe: What your research question was: Whether vaccine effectiveness estimates based on EHR data are internally valid according to analyses focused on two topics of concern related to EHR-based observational research – unmeasured confounding and misclassification. What you found: We observed unmeasured confounding in our estimates when using non-COVID19 deaths as negative outcome, and timing of data abstraction from source EHRs was found to have a small influence on the capture and classification of exposure and outcome. Why it is important: Our results reinforce earlier evidence of the healthy vaccinee phenomenon indicating possible biases in the estimates of vaccine effectiveness estimates obtained from EHR data and that there appears to be little influence of EHR extraction timing on data completeness.

Many vaccine effectiveness (VE) analyses of severe disease outcomes such as hospitalisation and death include ‘false’ cases that are not actually caused by the infection or disease under study. While the inclusion of such false cases inflate outcome rates in both vaccinated and unvaccinated populations, it is less obvious how they affect estimates of VE. Illustrating the main points through simple examples, this article shows how VE is underestimated when false cases are included as outcomes. Depending how the outcome indicator is defined, estimates of VE against severe disease outcomes, whose definition allows for the inclusion of false cases, will be biased downwards and may in certain circumstances approximate the same level as the VE against infection. The bias is particularly pronounced for vaccines that offer high levels of protection against severe disease outcomes but poor protection against infection. Analysing outcomes that are measured with low sensitivity generally does not cause bias in VE studies; defining outcome indicators that minimise the number of false cases rather than the number of missed cases is preferable in VE studies.

People with booster vaccinations appear more prone to reinfection than those with primary series vaccination only; however, selection bias in the study design complicates interpretation.