Introduction The detection of adverse events following immunisation (AEFI) fundamentally depends on how these events are classified. Standard methods impose a choice between either grouping similar events together to gain power or splitting them into more specific definitions. We demonstrate a method of medically guided Bayesian information sharing that avoids grouping or splitting the data, and we further combine this with the standard epidemiological tools of stratification and multivariate regression.
Methods All spontaneous reports of gastrointestinal AEFI in children under 18 years old in the WHO (Uppsala Monitoring Centre) Vigibase© were used to calculate reporting ORs for each AEFI and vaccine combination. After testing for effect modification these were then reestimated using multivariable logistic regression adjusting for age, gender, year and country of report. A medically guided hierarchy of AEFI terms was then derived to allow information sharing in a Bayesian model.
Results A crude analysis identified 132 signals from 655 reported combinations of gastrointestinal AEFI. Adjusting for confounding, where appropriate, reduced the number of signals identified to 88. The addition of a Bayesian hierarchical model identified four further signals and removed three. Effect modification by age and gender was identified for six vaccines.
Conclusion This study demonstrated a sequence of methods for routinely analysing spontaneous report databases that was easily understandable and reproducible. The combination of classical and Bayesian methods in this study help to focus the limited resources for hypothesis testing studies towards the adverse events with the strongest support from the data.
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