NOVA Medical School, NOVA University of Lisbon, Lisbon, Portugal.
Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA.
Eur J Clin Invest. 2024 Oct;54(10):e14286. doi: 10.1111/eci.14286. Epub 2024 Jul 30.
Annual vaccination is widely recommended for influenza and SARS-CoV-2. In this essay, we analyse and question the prevailing policymaking approach to these respiratory virus vaccines, especially in the United States. Every year, licensed influenza vaccines are reformulated to include specific strains expected to dominate in the season ahead. Updated vaccines are rapidly manufactured and approved without further regulatory requirement of clinical data. Novel vaccines (i.e. new products) typically undergo clinical trials, though generally powered for clinically unimportant outcomes (e.g. lab-confirmed infections, regardless of symptomatology or antibody levels). Eventually, the current and future efficacy of influenza and COVID-19 vaccines against hospitalization or death carries considerable uncertainty. The emergence of highly transmissible SARS-CoV-2 variants and waning vaccine-induced immunity led to plummeting vaccine effectiveness, at least against symptomatic infection, and booster doses have since been widely recommended. No further randomized trials were performed for clinically important outcomes for licensed updated boosters. In both cases, annual vaccine effectiveness estimates are generated by observational research, but observational studies are particularly susceptible to confounding and bias. Well-conducted experimental studies, particularly randomized trials, are necessary to address persistent uncertainties about influenza and COVID-19 vaccines. We propose a new research framework which would render results relevant to the current or future respiratory viral seasons. We demonstrate that experimental studies are feasible by adopting a more pragmatic approach and provide strategies on how to do so. When it comes to implementing policies that seriously impact people's lives, require substantial public resources and/or rely on widespread public acceptance, high evidence standards are desirable.
每年都广泛建议接种流感疫苗和 SARS-CoV-2 疫苗。在本文中，我们分析并质疑当前针对这些呼吸道病毒疫苗的决策制定方法，尤其是在美国。每年，许可的流感疫苗都会进行配方调整，以纳入预计在下一个季节占主导地位的特定菌株。更新后的疫苗会迅速制造和批准，无需进一步的临床数据监管要求。新型疫苗（即新产品）通常会进行临床试验，但通常针对临床不重要的结果进行（例如，实验室确诊的感染，无论症状或抗体水平如何）。最终，流感和 COVID-19 疫苗对住院或死亡的当前和未来疗效存在相当大的不确定性。高传染性 SARS-CoV-2 变异株的出现和疫苗诱导的免疫力减弱导致疫苗有效性大幅下降，至少对有症状感染而言如此，此后广泛建议接种加强针。对于许可的更新加强针针对临床重要结果，没有进行进一步的随机试验。在这两种情况下，年度疫苗有效性估计都是通过观察性研究得出的，但观察性研究特别容易受到混杂和偏倚的影响。需要进行精心设计的实验研究，特别是随机试验，以解决关于流感和 COVID-19 疫苗的持续不确定性。我们提出了一个新的研究框架，该框架将使结果与当前或未来的呼吸道病毒季节相关。我们通过采用更务实的方法证明了实验研究是可行的，并提供了如何进行的策略。当涉及到实施严重影响人们生活、需要大量公共资源和/或依赖广泛公众接受的政策时，高证据标准是可取的。
