无论如何会发生什么？在估计免疫后不良事件背景发生率以告知疫苗安全性时，人群数据来源的考虑因素。

What would have happened anyway? Population data source considerations when estimating background incident rates of adverse events following immunisation to inform vaccine safety.

机构信息

Health Informatics, Centre for Health Analytics, Melbourne Children's Campus, 50 Flemington Road, Parkville, Victoria, Australia; SAEFVIC, Infection and Immunity, Murdoch Children's Research Institute, 50 Flemington Road, Parkville, Victoria, Australia; Melbourne School of Population & Global Health, University of Melbourne, Grattan Street, Parkville, Victoria, Australia; Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, Victoria, Australia.

出版信息

Vaccine. 2024 Feb 15;42(5):1108-1115. doi: 10.1016/j.vaccine.2024.01.025. Epub 2024 Jan 22.

DOI:10.1016/j.vaccine.2024.01.025

PMID:38262811

Abstract

INTRODUCTION

Understanding background incident rates of adverse events following immunisation (AEFI) is essential to rapidly detect, evaluate, respond to, and communicate about vaccine safety concerns, especially for new vaccines. Creating estimates based on geographic specific population level data is increasingly important, as new AEFI presentations will be subject to the same local influences of population demography, exposures, health system variations and level of health care sought.

METHODS

We conducted a retrospective cohort analysis of hospital admissions, emergency department presentations and general practice consultations from 2015 to 2019-before introduction of COVID-19, Mpox or Shingrix vaccination-to estimate background incident rates for 37 conditions considered potential AEFI of special interest (AESI). Background incident rates per 100,000 population were calculated and presented as cases expected to occur coincidentally 1 day, 1 week and 6 weeks post-vaccination, by life-stage age-groups and presenting healthcare setting. We then assessed the proportional contribution of each data source to inform each AESI background rate estimate.

RESULTS

16,437,156 episodes of the 37 AESI were identified. Hospital admissions predominantly informed 19 (51%) of AESI, including exclusively ADEM and CVST; 8 AESI (22%) by primary care, and 10 (27%) a mix. Four AESI (allergic urticaria, Bell's palsy, erythema multiform and sudden death) were better informed by emergency presentations than admissions, but conversely 11 AESI (30%) were not captured in ICD-10 coded emergency presentations at all.

CONCLUSIONS

Emergent safety concerns are inevitable in population-wide implementation of new vaccines, therefore understanding local background rates aids both safety signal detection as well as maintaining public confidence in vaccination. Hospital and primary care data sources can be interrogated to inform expected background incident rates of adverse events that may occur following vaccination. However, it is necessary to understand which data-source provides best intelligence according to nature of condition and presenting healthcare setting.

摘要

简介

了解疫苗接种后不良反应（AEFI）的背景发生率对于快速发现、评估、应对和交流疫苗安全问题至关重要，尤其是对于新疫苗。基于特定地理区域人群水平数据来创建估计值变得越来越重要，因为新的 AEFI 表现将受到人群人口统计学、暴露、卫生系统差异和寻求医疗保健水平等当地因素的影响。

方法

我们对 2015 年至 2019 年（在引入 COVID-19、Mpox 或 Shingrix 疫苗之前）的住院、急诊就诊和全科医生就诊进行了回顾性队列分析，以估计 37 种被认为是特殊关注的 AEFI（AESI）的潜在 AEFI 的背景发生率。计算了每 10 万人的背景发生率，并按生命阶段年龄组和就诊医疗保健机构展示了接种疫苗后 1 天、1 周和 6 周时预计会偶然发生的病例数。然后，我们评估了每个数据源对每个 AESI 背景率估计的贡献程度。

结果

共确定了 37 种 AESI 中的 16437156 例病例。住院治疗主要为 19 种（51%）AESI 提供了信息，包括 ADEM 和 CVST；8 种（22%）由初级保健提供信息，10 种（27%）为混合提供信息。4 种 AESI（过敏性荨麻疹、贝尔氏面瘫、多形性红斑和猝死）通过急诊就诊比通过住院治疗提供的信息更好，但相反，11 种 AESI（30%）根本没有在 ICD-10 编码的急诊就诊中记录。