Siev D
Center for Veterinary Biologics, U.S. Department of Agriculture, Ames, Iowa 50010, USA.
Adv Vet Med. 1999;41:749-74. doi: 10.1016/s0065-3519(99)80057-3.
Any analysis of spontaneous AER data must consider the many biases inherent in the observation and reporting of vaccine adverse events. The absence of a clear probability structure requires statistical procedures to be used in a spirit of exploratory description rather than definitive confirmation. The extent of such descriptions should be temperate, without the implication that they extend to parent populations. It is important to recognize the presence of overdispersion in selecting methods and constructing models. Important stochastic or systematic features of the data may always be unknown. Our attempts to delineate what constitutes an AER have not eliminated all the fuzziness in its definition. Some count every event in a report as a separate AER. Besides confusing the role of event and report, this introduces a complex correlational structure, since multiple event descriptions received in a single report can hardly be considered independent. The many events described by one reporter would then become inordinately weighted. The alternative is to record an AER once, regardless of how many event descriptions it includes. As a practical compromise, many regard the simultaneous submission of several report forms by one reporter as a single AER, and the next submission by that reporter as another AER. This method is reasonable when reporters submit AERs very infrequently. When individual reporters make frequent reports, it becomes difficult to justify the inconsistency of counting multiple events as a single AER when they are submitted together, but as separate AERs when they are reported at different times. While either choice is imperfect, the latter approach is currently used by the USDA and its licensed manufacturers in developing a mandatory postmarketing surveillance system for veterinary immunobiologicals in the United States. Under the proposed system, summaries of an estimated 10,000 AERs received annually by the manufacturers would be submitted to the USDA. In quantitative summaries, AERs received from lay consumers are usually weighted equally with those received from veterinary health professionals, although arguments have been advanced for separate classifications. The emphasis on AER rate estimation differentiates the surveillance of veterinary vaccines by the USDA CVB from the surveillance of veterinary drugs as practiced by the Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM). The FDA CVM does, in fact, perform a retrodictive causality assessment for individual AERs (Parkhie et al., 1995). This distinction reflects the differences between vaccines and drugs, as well as the difference in regulatory philosophy between the FDA and the USDA. The modified Kramer algorithm (Kramer et al., 1979) used by the FDA relies on features more appropriate to drug therapy than vaccination, such as an ongoing treatment regimen which allows evaluation of the response to dechallenge and rechallenge. In tracking AERs, the FDA has emphasized the inclusion of clinical manifestations on labels and inserts, while the USDA has been reluctant to have such information appear in product literature or to use postmarketing data for this purpose. The potential for the misuse of spontaneous AER data is great. Disinformation is likely when the nature of this type of data is misunderstood and inappropriate analytical methods blindly employed. A greater danger lies in the glib transformation of AER data into something else entirely. Since approval before publication is not required, advertisements for veterinary vaccines appear with claims such as "over 3 million doses, 99.9905% satisfaction rating," or "11,500,000 doses, 99.98% reaction free." These claims, presumably based on spontaneous AERs, are almost fraudulent in their deceptiveness. Are we to suppose that 11.5 million vaccinations were observed for reactions? In comparing the two advertisements, we find the second presumed AER rate is double the first. (ABSTRACT TRU
对自发的动物不良反应(AER)数据进行的任何分析,都必须考虑到疫苗不良事件观察和报告中固有的诸多偏差。由于缺乏清晰的概率结构,统计程序的运用应本着探索性描述的精神,而非确定性确认。此类描述的范围应适度,不应暗示其适用于总体人群。在选择方法和构建模型时,认识到过度离散的存在很重要。数据的一些重要随机或系统特征可能始终未知。我们界定什么构成动物不良反应的尝试,并未消除其定义中的所有模糊性。有些人将报告中的每个事件都视为一个单独的动物不良反应。这不仅混淆了事件和报告的作用,还引入了复杂的相关结构,因为在一份报告中收到的多个事件描述很难被视为独立的。那么,一位报告者描述的众多事件就会被过度加权。另一种方法是,无论一份报告包含多少事件描述,都只记录一次动物不良反应。作为一种实际的折衷办法,许多人将一位报告者同时提交的几份报告表格视为一个动物不良反应,而该报告者的下一次提交则视为另一个动物不良反应。当报告者很少提交动物不良反应报告时,这种方法是合理的。当个体报告者频繁报告时,就难以解释为何将多个事件一起提交时计为一个动物不良反应,而在不同时间报告时却计为单独的动物不良反应这种不一致性。虽然两种选择都不完美,但后一种方法目前被美国农业部及其持牌制造商用于在美国建立一个强制性的兽用免疫生物制品上市后监测系统。根据提议的系统,制造商每年收到的约10,000份动物不良反应报告的摘要将提交给美国农业部。在定量摘要中,从普通消费者那里收到的动物不良反应报告通常与从兽医健康专业人员那里收到的报告同等加权,尽管有人主张进行单独分类。对动物不良反应发生率估计的强调,使美国农业部兽医生物制品中心(CVB)对兽用疫苗的监测有别于美国食品药品监督管理局(FDA)兽医药品中心(CVM)对兽药的监测。事实上,FDA CVM确实对个别动物不良反应进行追溯因果关系评估(Parkhie等人,1995年)。这种区别反映了疫苗和药物之间的差异,以及FDA和美国农业部监管理念的差异。FDA使用的改良克莱默算法(Kramer等人,1979年)所依赖的特征更适合药物治疗而非疫苗接种,比如持续的治疗方案,这便于评估停药和再用药后的反应。在追踪动物不良反应时,FDA强调在标签和说明书中纳入临床表现,而美国农业部则不愿让此类信息出现在产品宣传资料中,也不愿为此目的使用上市后数据。自发的动物不良反应数据被滥用的可能性很大。当这类数据的性质被误解且盲目采用不适当的分析方法时,就可能出现虚假信息。更大的危险在于将动物不良反应数据轻率地转化为完全不同的东西。由于发布前无需批准,兽用疫苗的广告会出现诸如“超过300万剂,满意度评级99.9905%”或“1150万剂,无反应率99.98%”之类的宣称。这些宣称大概是基于自发的动物不良反应,其欺骗性几乎等同于欺诈。我们难道要假定观察了1150万次疫苗接种的反应情况吗?在比较这两则广告时,我们发现第二个假定的动物不良反应发生率是第一个的两倍。(摘要真实)