Adverse drug reaction early warning using user search data

Vaccine Vigilance System: Considerations on the Effectiveness of Vigilance Data Use in COVID-19 Vaccination

(1) Background: The safety of medicines has been receiving increased attention to ensure that the risks of taking medicines do not outweigh the benefits. This is the reason why, over several decades, the pharmacovigilance system has been developed. The post-authorization pharmacovigilance system is based on reports from healthcare professionals and patients on observed adverse reactions. The reports are collected in databases and progressively evaluated. However, there are emerging concerns about the effectiveness of the established passive pharmacovigilance system in accelerating circumstances, such as the COVID-19 pandemic, when billions of doses of new vaccines were administered without a long history of use. Currently, health professionals receive fragmented new information on the safety of medicines from competent authorities after a lengthy evaluation process. Simultaneously, in the context of accelerated mass vaccination, health professionals need to have access to operational information-at least on organ systems at higher risk. Therefore, the aim of this study was to perform a primary data analysis of publicly available data on suspected COVID-19 vaccine-related adverse reactions in Europe, in order to identify the predominant groups of reported medical conditions after vaccination and their association with vaccine groups, as well as to evaluate the data accessibility on specific syndromes. (2) Methods: To achieve the objectives, the data publicly available in the EudraVigilance European Database for Suspected Adverse Drug Reaction Reports were analyzed. The following tasks were defined to: (1) Identify the predominant groups of medical conditions mentioned in adverse reaction reports; (2) determine the relative frequency of reports within vaccine groups; (3) assess the feasibility of obtaining information on a possibly associated syndrome-myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). (3) Results: The data obtained demonstrate that the predominant medical conditions induced after vaccination are relevant to the following categories: (1) "General disorders and administration site conditions", (2) "nervous system disorders", and (3) "musculoskeletal and connective tissue disorders". There are more reports for mRNA vaccines, but the relative frequency of reports per dose administered, is lower for this group of vaccines. Information on ME/CFS was not available, but reports of "chronic fatigue syndrome" are included in the database and accessible for primary analysis. (4) Conclusions: The information obtained on the predominantly reported medical conditions and the relevant vaccine groups may be useful for health professionals, patients, researchers, and medicine manufacturers. Policymakers could benefit from reflecting on the design of an active pharmacovigilance model, making full use of modern information technologies, including big data analysis of social media and networks for the detection of primary signals and building an early warning system.

Global research output in 'pharmacovigilance' during 2010-2020

The rapid spread of Covid-19 pandemic globally has thrust drugs safety into the spotlight and the public is now more aware of the role of healthcare professionals and health regulators. The present study aimed to measure the global research landscape on pharmacovigilance (PV) indexed in Scopus database for eleven years period spanning from 2010–2020. The study has sought to use quantitative and visualization technologies for data analysis and interpretation. The search strategy accumulated a total of 2052 global publications data on PV. The findings disclose that the global research productivity on PV registered 8.74% average growth rate (AGR) and 7.38% compound average growth rate (CAGR). The mean relative growth rate (RGR) and doubling time (DT) of PV global publications for the 11 years is 0.27 and 3.03, respectively. The average number of authors per paper (AAPP) is 1.52 and average productivity per author (PPA) is 0.68. The authorship patterns in PV research shows collaborative trend as most of the publications have been published by multiple authors (80.75%). The mean values of degree of collaboration (DC), collaboration index (CI), collaboration coefficient (CC) and modified collaboration coefficient (MCC) during the selected period of study are 0.79, 2.74, 0.72, and 0.73, respectively which highly significant and indicates the better authorship collaborations. France is the bellwether in PV related scientific research as produced the highest number of publications.