Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA.
Kaiser Permanente Vaccine Study Center, Oakland, CA, USA.
Drug Saf. 2020 Oct;43(10):1057-1065. doi: 10.1007/s40264-020-00967-8.
Childhood immunization schedules often involve multiple vaccinations per visit. When increased risk of an adverse event is observed after simultaneous (same-day) vaccinations, it can be difficult to ascertain which triggered the adverse event. This methods paper discusses a systematic process to determine which of the simultaneously administered vaccine(s) are most likely to have caused an observed increase in risk of an adverse event.
We use an example from the literature where excess risk of seizure was observed 1 day after vaccination, but same-day vaccination patterns made it difficult to discern which vaccine(s) may trigger the adverse event. We illustrate the systematic identification process using a simulation that retained the observed pattern of simultaneous vaccination in an empirical cohort of vaccinated children. We simulated "true" effects for diphtheria-tetanus-acellular pertussis (DTaP) and pneumococcal conjugate (PCV) on risk of seizure the day after vaccination. We varied the independent and interactive effects of vaccines (on the multiplicative scale). After applying the process to simulated data, we evaluated risk of seizure 1 day after vaccination in the empirical cohort.
In all simulations, we were able to determine which vaccines contributed to excess risk. In the empirical data, we narrowed the association with seizure from all vaccines in the schedule to three likely candidates, DTaP, PCV, and/or Haemophilus influenzae type B (HiB) (p < 0.01, attributable risk when all three were administered together: five per 100,000). Disentangling their associations with seizure would require a larger sample or more variation in the combinations administered. When none of these three were administered, no excess risk was observed.
The process outlined could provide valuable information on the magnitude of potential risk from individual and simultaneousvaccinations. Associations should be further investigated with independent data as well as biologically based, statistically independent hypotheses.
儿童免疫接种计划通常每次就诊都涉及多种疫苗接种。当同时（同一天）接种疫苗后观察到不良事件风险增加时，很难确定是哪种疫苗引发了不良事件。本方法论文讨论了一种系统的流程，以确定同时给予的疫苗中哪一种最有可能导致观察到的不良事件风险增加。
我们使用文献中的一个例子，其中在接种疫苗后 1 天观察到癫痫发作的风险增加，但同时接种疫苗的模式使得难以辨别哪种疫苗可能引发不良事件。我们使用接种疫苗儿童的实证队列中的模拟数据，说明了系统识别过程。我们模拟了白喉-破伤风-无细胞百日咳（DTaP）和肺炎球菌结合疫苗（PCV）对接种疫苗后第 1 天癫痫发作风险的“真实”影响。我们改变了疫苗的独立和交互作用（在乘法尺度上）。在将该过程应用于模拟数据后，我们评估了实证队列中接种疫苗后第 1 天癫痫发作的风险。
在所有模拟中，我们都能够确定哪些疫苗导致了风险增加。在实证数据中，我们将与癫痫发作相关的所有疫苗从时间表中缩小到三个可能的候选者，即 DTaP、PCV 和/或乙型流感嗜血杆菌（HiB）（p<0.01，当所有三种疫苗同时接种时的归因风险：每 10 万 5 例）。要确定它们与癫痫发作的关联，需要更大的样本量或给予的组合中更多的变化。当这三种疫苗都未接种时，未观察到风险增加。
所概述的流程可以提供有关个体和同时接种疫苗潜在风险的重要信息。应使用独立数据以及基于生物学和统计学独立的假设进一步调查这些关联。
