Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Ave, Boston, MA 02215, USA.
Pediatrics. 2011 May;127 Suppl 1:S54-64. doi: 10.1542/peds.2010-1722I. Epub 2011 Apr 18.
To describe the Vaccine Safety Datalink (VSD) project's experience with population-based, active surveillance for vaccine safety and draw lessons that may be useful for similar efforts.
The VSD comprises a population of 9.2 million people annually in 8 geographically diverse US health care organizations. Data on vaccinations and diagnoses are updated and extracted weekly. The safety of 5 vaccines was monitored, each with 5 to 7 prespecified outcomes. With sequential analytic methods, the number of cases of each outcome was compared with the number of cases observed in a comparison group or the number expected on the basis of background rates. If the test statistic exceeded a threshold, it was a signal of a possible vaccine-safety problem. Signals were investigated by using temporal scan statistics and analyses such as logistic regression.
Ten signals appeared over 3 years of surveillance: 1 signal was reported to external stakeholders and ultimately led to a change in national vaccination policy, and 9 signals were found to be spurious after rigorous internal investigation. Causes of spurious signals included imprecision in estimated background rates, changes in true incidence or coding over time, other confounding, inappropriate comparison groups, miscoding of outcomes in electronic medical records, and chance. In the absence of signals, estimates of adverse-event rates, relative risks, and attributable risks from up-to-date VSD data have provided rapid assessment of vaccine safety to policy-makers when concerns about a specific vaccine have arisen elsewhere.
Care with data quality, outcome definitions, comparison groups, and length of surveillance are required to enable detection of true safety problems while minimizing false signals. Some causes of false signals in the VSD system were preventable and have been corrected, whereas others will be unavoidable in any active surveillance system. Temporal scan statistics, analyses to control for confounding, and chart review are indispensable tools in signal investigation. The VSD's experience may inform new systems for active safety surveillance.
描述疫苗安全数据链(VSD)项目在基于人群的主动监测疫苗安全性方面的经验,并从中吸取可能对类似工作有用的经验教训。
VSD 由美国 8 个地理位置不同的医疗保健组织每年的 920 万人组成。每周更新和提取疫苗接种和诊断数据。监测了 5 种疫苗的安全性,每种疫苗都有 5 到 7 个预先指定的结果。通过顺序分析方法,将每种结果的病例数与对照组中的病例数或基于背景率的预期病例数进行比较。如果检验统计量超过阈值,则表示可能存在疫苗安全性问题。通过时间扫描统计和逻辑回归等分析方法对信号进行调查。
在 3 年的监测中发现了 10 个信号:1 个信号报告给了外部利益相关者,最终导致国家疫苗接种政策发生了变化,经过严格的内部调查,发现 9 个信号是虚假的。虚假信号的原因包括背景率估计不准确、真实发病率或编码随时间变化、其他混杂因素、不合适的对照组、电子病历中结果的错误编码以及偶然性。在没有信号的情况下,来自最新 VSD 数据的不良事件发生率、相对风险和归因风险的估计值为政策制定者提供了快速评估疫苗安全性的方法,当其他地方对特定疫苗产生担忧时。
需要注意数据质量、结果定义、对照组和监测时间,以在尽量减少假信号的同时,检测到真正的安全问题。VSD 系统中一些虚假信号的原因是可以预防的,已经得到纠正,而在任何主动监测系统中,其他原因是不可避免的。时间扫描统计、控制混杂因素的分析和图表审查是信号调查不可或缺的工具。VSD 的经验可能为新的主动安全监测系统提供信息。