Algorithm to assess causality after individual adverse events following immunizations

Assessing individual reports of adverse events following immunizations (AEFI) can be challenging. Most published reviews are based on expert opinions, but the methods and logic used to arrive at these opinions are neither well described nor understood by many health care providers and scientists. We developed a standardized algorithm to assist in collecting and interpreting data, and to help assess causality after individual AEFI. Key questions that should be asked during the assessment of AEFI include: Is the diagnosis of the AEFI correct? Does clinical or laboratory evidence exist that supports possible causes for the AEFI other than the vaccine in the affected individual? Is there a known causal association between the AEFI and the vaccine? Is there strong evidence against a causal association? Is there a specific laboratory test implicating the vaccine in the pathogenesis? An algorithm can assist with addressing these questions in a standardized, transparent manner which can be tracked and reassessed if additional information becomes available. Examples in this document illustrate the process of using the algorithm to determine causality. As new epidemiologic and clinical data become available, the algorithm and guidelines will need to be modified. Feedback from users of the algorithm will be invaluable in this process. We hope that this algorithm approach can assist with educational efforts to improve the collection of key information on AEFI and provide a platform for teaching about causality assessment.

► Causality assessments are often performed without taking all factors into account.
► An algorithm provides standardized, transparent evaluations.
► An algorithm can be an effective tool to teach the science of causality assessment.