Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
Integrating Genomic Data into Test-negative Designs for Estimating Lineage-specific COVID-19 Vaccine Effectiveness.
СтатьяResearch Support, N.I.H., IntramuralНазвание и аннотация: Перевод пока не подготовлен
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Ma Kevin C, Surie Diya, Dean Natalie E, Paden Clinton R, Thornburg Natalie J, Dawood Fatimah S
Аннотация
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage-specific coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE) studies can inform decision-making on whether vaccine composition updates are needed to maintain effectiveness against severe disease as SARS-CoV-2 continues to evolve. Lineage assignment methods in VE test-negative design (TND) studies include sequence-based (whole-genome sequencing), proxy-based (eg, S-gene target failure during polymerase chain reaction), and time period-based (using variant predominance thresholds) approaches.
METHODS: We first summarize benefits, challenges (including cost and timeliness), and methodologic considerations for estimating lineage-specific COVID-19 VE using these different assignment approaches. We then use a deterministic model to illustrate the potential impact of lineage misclassification error on VE estimates in a TND using period-based versus sequence-based lineage assignment across different variant emergence scenarios.
RESULTS: Our model suggests period-based analyses may sometimes underestimate differences in VE between 2 lineages due to lineage misclassification error. This effect is most evident during prolonged variant cocirculation or in early time periods following new variant takeover. Using higher predominance thresholds can reduce VE estimate bias in period-based analyses but at the expense of sample size, reducing precision or outright precluding estimation under some scenarios. Period-based VE analyses should therefore consider including sensitivity analyses to characterize robustness of VE estimates to different predominance thresholds.
CONCLUSIONS: TND studies using sequence-, proxy-, and period-based lineage assignment have contributed substantially toward understanding SARS-CoV-2 variant-mediated vaccine escape, but biases that can affect each study design vary. Our results identify analytic considerations for robust estimation and suggest principles that may translate to other pathogens that undergo continuous antigenic drift.
METHODS: We first summarize benefits, challenges (including cost and timeliness), and methodologic considerations for estimating lineage-specific COVID-19 VE using these different assignment approaches. We then use a deterministic model to illustrate the potential impact of lineage misclassification error on VE estimates in a TND using period-based versus sequence-based lineage assignment across different variant emergence scenarios.
RESULTS: Our model suggests period-based analyses may sometimes underestimate differences in VE between 2 lineages due to lineage misclassification error. This effect is most evident during prolonged variant cocirculation or in early time periods following new variant takeover. Using higher predominance thresholds can reduce VE estimate bias in period-based analyses but at the expense of sample size, reducing precision or outright precluding estimation under some scenarios. Period-based VE analyses should therefore consider including sensitivity analyses to characterize robustness of VE estimates to different predominance thresholds.
CONCLUSIONS: TND studies using sequence-, proxy-, and period-based lineage assignment have contributed substantially toward understanding SARS-CoV-2 variant-mediated vaccine escape, but biases that can affect each study design vary. Our results identify analytic considerations for robust estimation and suggest principles that may translate to other pathogens that undergo continuous antigenic drift.