Unmeasured confounding and misclassification in studies estimating vaccine effectiveness against hospitalisation and death using electronic health records (EHRs): an evaluation of a multi-country European retrospective cohort study
Year: 2025
Type: article
Abstract: BACKGROUND: Electronic health record (EHR)-based observational studies can rapidly provide real-world data on vaccine effectiveness (VE), though EHR data may be prone to misclassification and unmeasured confounding. 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 due to timing of data extraction. 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 based on retrospective cohorts constructed using registry 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.
Source: BMC Medical Research Methodology
Authors: James Humphreys, Nathalie Nicolay, Toon Braeye, Izaak Van Evercooren, Christian Holm Hansen, Ida Rask Moustsen-Helms, Chiara Sacco, Alberto Mateo-Urdiales, Jesús Castilla, Iván Martínez-Baz, Ausenda Machado, Patricia Soares, Brechje de Gier, Hinta Meijerink, Susana Monge, Sabrina Bacci, Baltazar Nunes, VEBIS-EHR working group
Institutions: European Centre for Disease Prevention and Control, Sciensano (Belgium), Statens Serum Institut, Istituto Superiore di Sanità, Instituto de Salud Pública de Navarra, Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, National Institute of Health Dr. Ricardo Jorge, National Institute for Public Health and the Environment, Norwegian Institute of Public Health, Instituto de Salud Carlos III, Centro Nacional de Epidemiología, Centro de Investigación Biomédica en Red
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,090
Award: G8298862964
OpenAlex ID: W4417423286
