An introduction to analytical methods for the postmarketing surveillance of veterinary vaccines

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