With the spread of the new SARS-CoV-2 variants, many countries have begun COVID-19 vaccine booster programs with the mix-and-match strategy. However, research on the adverse events (AE) of booster doses is still scarce. The aim of our study was to analyze the reported incidence rate (IR), and factors associated with AE, including short-term serious adverse events (SAE) and short-term non-serious adverse events (NSAE), among different vaccine products through the hospital-based Vaccine Adverse Event Reporting System (VAERS). A total of 7432 records were collected during the three-month study period. While more than half of the responses (52.2%) reported the presence of AE after receiving a booster dose, only a few AE were considered SAE (2.4%). AE were significantly higher among women and people of younger age, and the brand of vaccines is the strongest factor associated with post-booster dose AE. The incidence of AE in mRNA1273 is higher than in BNT162b2 and MVC-COV1901 (IRR mRNA1273 vs. BNT162b2: 1.22, 95% CI: 1.11–1.34; BNT162b2 vs. MVC-COV1901: 2.77, 95% CI: 2.27–3.39). The IR of different groups were calculated to support the decision making of the booster vaccine. Although AE were not uncommon for booster vaccines, almost all AE were not serious and predictable using estimated IR. This result can be used to optimize booster vaccine decision making.
BACKGROUND Drug reference apps play various distinct and vital roles through the medication use process. A drug reference app that carried out comprehensive localized drug information would greatly improve the efficiency and quality of work for physicians, nurses, pharmacists, and patients. OBJECTIVE This current study aimed to describe a systematic and stepwise process to identify drug reference apps with localized drug information in Taiwan. Moreover, we assessed the quality of these apps by using a reliable quality assessment tool and further analyzing the influential factors for user ratings. METHODS A two-step algorithm (KESS) consisting of keyword growing and systematic search was proposed. Apps were divided into two groups: higher user ratings and lower user ratings. Seven independent reviewers were trained to evaluate these apps using Mobile App Rating Scale (MARS). A logistic regression model was fitted, and average marginal effects (AME) were calculated to identify the effects of factors for higher user ratings. A p-value< 0.05 was considered statistically significant. RESULTS A total of 23 drug reference apps in Taiwan had been identified and analyzed. Ten apps had higher user star ratings (>=4 stars), and 13 apps had lower user star ratings ( < 4 stars). These drug reference apps had acceptable quality with an average MARS score of 3.23. Apps with higher user star ratings had higher MARS scores than the lowers (engagement (2.70 v.s. 2.50, P= .005), functionality (3.85 v.s. 3.49, P= .003), aesthetics (3.39 v.s. 2.98, P < .001), and information (3.55 v.s. 3.25, P= .005)). The regression model showed five influential factors for higher user ratings (navigation, AME, 13.15%; performance, AME, 11.03%; visual appeal, AME, 10.87%; credibility, AME, 10.67%; quantity of information, AME, 10.42%). CONCLUSIONS The proposed KESS algorithm could be a valuable and unbiased framework for systematic search for app. While the higher engagement, more functionality, better aesthetics, and more information associated with higher user ratings, there are five most influential factors, navigation, performance, visual appeal, credibility, and quantity of information among the four elements.
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