Risk Factors and Predictive Modeling of Adverse Events Following COVID-19 Booster Vaccination: A Prospective Cohort Study
Authors: Shahinur Islam, Bipul Nath, Subhashis Debnath
Indian Journal of Pharmacy Practice, Vol. 20, Issue 1, pp. 32-44, (2027)
Abstract
COVID-19 booster vaccination is essential for maintaining protection against severe disease, particularly among older adults; however, substantial interindividual variability exists in post-booster adverse-event responses. This study aimed to evaluate demographic, clinical, infection-related, and vaccination-associated factors influencing the post-booster adverse-event burden among adults aged ≥60≥60 years and develop a predictive score. A prospective observational cohort study was conducted with 500 recruited adults aged ≥60≥60 years, of whom 472 (94.4%) completed follow-up. Adverse events were recorded using a structured follow-up form and analyzed via a composite adverse-event score. Univariable and multivariable linear regression analyses were used to identify independent predictors, and an internally validated, simplified 0-5-point prediction score was constructed. The most commonly reported adverse events were injection-site pain (60.6%), fatigue (51.1%), and headache (41.9%). Previous vaccine reaction (standardized β=0.25β=0.25), female sex, previous COVID-19 infection, and shorter booster intervals were independently associated with a higher adverse-event burden. The prediction model explained 34% of outcome variability (development RMSE=1.12), and internal validation demonstrated an RMSE of 1.18 with 31% of outcome variability explained, yielding progressive risk stratification across the point-based score. The model provides a pragmatic exploratory tool to identify older adults at elevated risk of post-booster adverse events for tailored counseling and monitoring, although external validation in larger, independent cohorts is necessary before clinical implementation.
Keywords: Adverse events, COVID-19 booster, Internal validation, Older adults, Prediction model, Risk factors, Risk stratification
