Long-term COVID-19 booster effectiveness by infection history and clinical vulnerability and immune imprinting

crossref(2022)

引用 0|浏览1
暂无评分
摘要
AbstractBackgroundLong-term effectiveness of COVID-19 mRNA boosters in populations with different prior infection histories and clinical vulnerability profiles is inadequately understood.MethodsA national, matched, retrospective, target trial cohort study was conducted in Qatar to investigate effectiveness of a third mRNA (booster) dose, relative to a primary series of two doses, against SARS-CoV-2 omicron infection and against severe COVID-19. Associations were estimated using Cox proportional-hazards regression models.ResultsBooster effectiveness relative to primary series was 41.1% (95% CI: 40.0-42.1%) against infection and 80.5% (95% CI: 55.7-91.4%) against severe, critical, or fatal COVID-19, over one-year follow-up after the booster. Among persons clinically vulnerable to severe COVID-19, effectiveness was 49.7% (95% CI: 47.8-51.6%) against infection and 84.2% (95% CI: 58.8-93.9%) against severe, critical, or fatal COVID-19. Effectiveness against infection was highest at 57.1% (95% CI: 55.9-58.3%) in the first month after the booster but waned thereafter and was modest at only 14.4% (95% CI: 7.3-20.9%) by the sixth month. In the seventh month and thereafter, coincident with BA.4/BA.5 and BA.2.75* subvariant incidence, effectiveness was progressively negative reaching -20.3% (95% CI: -55.0-29.0%) after one year of follow-up. Similar levels and patterns of protection were observed irrespective of prior infection status, clinical vulnerability, or type of vaccine (BNT162b2 versus mRNA-1273).ConclusionsBoosters reduced infection and severe COVID-19, particularly among those clinically vulnerable to severe COVID-19. However, protection against infection waned after the booster, and eventually suggested an imprinting effect of compromised protection relative to the primary series. However, imprinting effects are unlikely to negate the overall public health value of booster vaccinations.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要