Dataset Information

Spontaneous Adverse Event Reporting by COVID-19 Vaccinated Healthcare Professionals Through an Electronic Form Implemented by the Hospital Pharmacy.

ABSTRACT: Aim: Implementation of a web-form based pharmacovigilance plan for the spontaneous notification of adverse events to the Comirnaty® COVID-19 vaccine during its administration to hospital healthcare professionals. Methods: An electronic pharmacovigilance form was developed containing 8 pre-defined event options, an open answer option for the description of other events and/or symptoms, and a question about the overall intensity of symptoms. The adverse events reports were standardized according to physiological and pathological condition. Results: A total of 4119 adverse events notifications were obtained with a 45% rate of electronic notification. The most clinically relevant events reported were:tachycardia (n = 19), dyspnea (n = 7), chest pain (n = 6), facial/labial edema (n = 6), lipothymia (n = 5), bronchospasm (n = 2), herpetic infection (n = 2), vasculitis (n = 2), arrhythmia (n = 1), difficult to control arterial hypertension (n = 1), gastritis (n = 1), and spontaneous abortion (n = 1). Regarding the intensity of symptoms (n = 2928), 70.0% were reported as mild, 25.8% as moderate, and 4.27% as severe, with higher intensity in the second dose compared to first dose. The highest frequency of severe events were reported in the groups from 40 to 59 years in both vaccination periods. During the vaccination process, no hospitalizations and no deaths were notified and/or recorded. Conclusion: In this real world study, comparing with Comirnaty clinical trials program, it was observed a higher frequency of adenomegaly and gastrointestinal disorders. Noteworthy, the notification of a case of miscarriage. The use of hospital pharmacy pharmacovigilance electronic forms, seemed to be relevant to notification adherence and to obtain a greater and faster knowledge of COVID-19 vaccine safety profile.

SUBMITTER: da Cruz JPGL

PROVIDER: S-EPMC9631018 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Spontaneous Adverse Event Reporting by COVID-19 Vaccinated Healthcare Professionals Through an Electronic Form Implemented by the Hospital Pharmacy.

da Cruz João Paulo Garcia Lopes JPGL de Carvalho Cristina da Conceição Ribeiro CDCR da Cruz Silva Paula Alexandre PA Guerreiro Luis Filipe Campos LFC Bento Tatiana Vedes TV Costa Leila Vanessa Carmo Cardoso Martins LVCCM Simões Raquel Filipa Miguel Margarido Duarte RFMMD Marques Rui Pedro Pinheiro Gonçalves RPPG Castro Fernandes Ana Cristina AC Galaio Luís Miguel Costa de Mendonça LMCM Correia Ana Isabel B AIB Leite Resende Ema Maria S EMS Gonçalves João Manuel Braz JMB

Hospital pharmacy 20220715 6

<b>Aim:</b> Implementation of a web-form based pharmacovigilance plan for the spontaneous notification of adverse events to the Comirnaty® COVID-19 vaccine during its administration to hospital healthcare professionals. <b>Methods:</b> An electronic pharmacovigilance form was developed containing 8 pre-defined event options, an open answer option for the description of other events and/or symptoms, and a question about the overall intensity of symptoms. The adverse events reports were standardiz ...[more]

PMID: 36340632

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Project description:BackgroundDrug toxicity does not affect patients equally; the toxicity may only exert in patients who possess certain attributes of susceptibility to specific drug properties (i.e., drug-host interaction). This concept is crucial for personalized drug safety but remains under-studied, primarily due to methodological challenges and limited data availability. By monitoring a large volume of adverse event reports in the postmarket stage, spontaneous adverse event reporting systems provide an unparalleled resource of information for adverse events and could be utilized to explore risk disparities of specific adverse events by age, sex, and other host factors. However, well-formulated statistical methods to formally address such risk disparities are currently lacking.MethodsIn this paper, we present a statistical framework to explore spontaneous adverse event reporting databases for drug-host interactions and detect risk disparities in adverse drug events by various host factors, adapting methods for safety signal detection. We proposed four different methods, including likelihood ratio test, normal approximation test, and two tests using subgroup ratios. We applied our proposed methods to simulated data and Food and Drug Administration (FDA) Adverse Event Reporting Systems (FAERS) and explored sex-/age-disparities in reported liver events associated with specific drug classes.ResultsThe simulation result demonstrates that two tests (likelihood ratio, normal approximation) can detect disparities in adverse drug events associated with host factors while controlling the family wise error rate. Application to real data on drug liver toxicity shows that the proposed method can be used to detect drugs with unusually high level of disparity regarding a host factor (sex or age) for liver toxicity or to determine whether an adverse event demonstrates a significant unbalance regarding the host factor relative to other events for the drug.ConclusionThough spontaneous adverse event reporting databases require careful data processing and inference, the sheer size of the databases with diverse data from different countries provides unique resources for exploring various questions for drug safety that are otherwise impossible to address. Our proposed methods can be used to facilitate future investigation on drug-host interactions in drug toxicity using a large number of reported adverse events.

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Project description:BackgroundSpontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals' privacy. We have previously proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, θ*)-bounding.ObjectiveWe investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data.MethodsWe identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, θ*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio).ResultsThe best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal.ConclusionsThe proposed PPMS(k, θ*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection.

Dataset Information

Spontaneous Adverse Event Reporting by COVID-19 Vaccinated Healthcare Professionals Through an Electronic Form Implemented by the Hospital Pharmacy.

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Spontaneous Adverse Event Reporting by COVID-19 Vaccinated Healthcare Professionals Through an Electronic Form Implemented by the Hospital Pharmacy.

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