AI Article Synopsis
- Serious illnesses or deaths can sometimes occur after childhood vaccinations, making it challenging to determine if these events are related to the vaccines or coincidental due to other conditions.
- The Global Advisory Committee for Vaccine Safety (GACVS) has commissioned experts to create a standardized tool for assessing the causality of Adverse Events Following Immunization (AEFI) to help healthcare providers accurately interpret these incidents.
- This tool includes a checklist and decision support algorithm to classify AEFI into four categories (consistent, inconsistent, indeterminate, or unclassifiable), aiding public health decisions and enhancing overall vaccine safety monitoring.
Article Abstract
Serious illnesses or even deaths may rarely occur after childhood vaccinations. Public health programs are faced with great challenges to establish if the events presenting after the administration of a vaccine are due to other conditions, and hence a coincidental presentation, rather than caused by the administered vaccines. Given its priority, the Global Advisory Committee for Vaccine Safety (GACVS) commissioned a group of experts to review the previously published World Health Organization (WHO) Adverse Event Following Immunization (AEFI) causality assessment methodology and aide-memoire, and to develop a standardized and user friendly tool to assist health care personnel in the processing and interpretation of data on individual events, and to assess the causality after AEFIs. We describe a tool developed for causality assessment of individual AEFIs that includes: (a) an eligibility component for the assessment that reviews the diagnosis associated with the event and identifies the administered vaccines; (b) a checklist that systematically guides users to gather available information to feed a decision algorithm; and (c) a decision support algorithm that assists the assessors to come to a classification of the individual AEFI. Final classification generated by the process includes four categories in which the event is either: (1) consistent; (2) inconsistent; or (3) indeterminate with respect of causal association; or (4) unclassifiable. Subcategories are identified to assist assessors in resulting public health decisions that can be used for action. This proposed tool should support the classification of AEFI cases in a standardized, transparent manner and to collect essential information during AEFI investigation. The algorithm should provide countries and health officials at the global level with an instrument to respond to vaccine safety alerts, and support the education, research and policy decisions on immunization safety.
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| http://dx.doi.org/10.1016/j.vaccine.2013.08.087 | DOI Listing |
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