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Confounder adjustment and subgroup analysis methods in observational studies of COVID-19 vaccine effectiveness in the UK

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Background: Observational data continue to play an important role in evaluating COVID-19 vaccine effectiveness (VE) across diverse populations, variants, and severe outcomes. Differences in confounder selection in VE studies imply different, potentially conflicting, causal identification assumptions, but these differences have not been mapped and explored. Methods: We conducted a descriptive review of COVID-19 VE studies undertaken in the United Kingdom between 22 February 2021 and 24 June 2024. Studies were identified from the VIEW-hub living literature review and complemented with studies known to the research team. From full texts, we extracted study characteristics, data sources, approaches to confounding control, and whether VE estimates were reported for predefined subgroups. Results: Forty-eight studies met the inclusion criteria. Age was included as a confounder in all studies. Sex or gender (97.9%), Ethnicity or race (68.8%), and area-level socioeconomic deprivation (72.9%) were frequently adjusted for, whereas individual-level socioeconomic indicators (18.8%) and disability (18.4%) were infrequently adjusted for. Clinical risk groups were adjusted for in 79.2% of studies, but there was considerable heterogeneity in their definition. Subgroup analyses were less frequent. Age-based analyses were reported in 60.4% of studies, and clinical risk analyses in 43.8%. Analyses by ethnicity, sex or gender, or socioeconomic position were rare. Conclusion: COVID-19 VE studies in the UK frequently adjusted for key demographic and clinical confounders, but in inconsistent ways. Subgroup-specific estimates were rarely reported, particularly for inequity axes. Expanding subgroup analyses could strengthen the evidence base for implementing equitable vaccination strategies.

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Sujets associés

SARS-CoV-2 and COVID-19 ResearchVaccine Coverage and HesitancyImmune responses and vaccinations

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