8,994 research outputs found

    Clin Infect Dis

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    Importance:Reporting of adverse events (AEs) following vaccination can help identify rare or unexpected complications of immunizations and aid in characterizing potential vaccine safety signals.Objective:To create an electronic health record (EHR) module to assist clinicians with AE detection and reporting.Design:We developed an open-source, generalizable clinical decision system called Electronic Support for Public Health\u2013Vaccine Adverse Event Reporting System (ESP-VAERS) to facilitate automated AE detection and reporting using EHRs. ESP-VAERS prospectively monitors patients\u2019 electronic records for new diagnoses, changes in laboratory values and new allergies for up to 6 weeks following vaccinations. When suggestive events are found, ESP-VAERS sends a secure electronic message to the patient\u2019s clinician. The clinician is invited to affirm or refute the event, add comments, and if they wish, submit an automated, pre-populated electronic case report to the national VAERS. High probability AEs following vaccination are reported automatically even if the clinician does not respond.Setting:We implemented ESP-VAERS in December 2012 at the MetroHealth System, an inpatient and outpatient integrated healthcare system in Ohio with nearly 1 million encounters per year. We queried the VAERS database to determine MetroHealth\u2019s baseline reporting rates from 1/2009\u20133/2012 and then assessed changes in reporting rates with ESP-VAERS.Participants:All patients receiving vaccinations between 12/04/2012 and 08/03/2013 and their clinicians.Exposure:ESP-VAERSMain outcome and measure:The odds ratio of a VAERS report submission during the intervention period compared to the comparable pre-intervention period.Results:In the 8 months following implementation, 91,622 vaccinations were given. ESP-VAERS sent 1,385 messages to responsible clinicians describing potential AEs (15 per 1000 vaccinations, mean 0.4 alerts per clinician per month (range 0\u20138)). Clinicians reviewed 1,304 (94%) messages, responded to 209 (15%), and confirmed 16 for transmission to VAERS. An additional 16 high probability AEs were sent automatically. Reported events included seizure, pleural effusion, and lymphocytopenia. The odds of a VAERS report submission during the pilot period were 30.2 (95% CI, 9.52\u201395.5) times greater than the odds during the comparable pre-pilot period.Conclusion and relevance:An open-source EHR-based clinical decision support system can increase AE detection and reporting rates in VAERS.20152019-07-21T00:00:00ZCC999999/Intramural CDC HHS/United States200-2011-42037/PHS HHS/United States26060294PMC6642796689

    Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study

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    Introduction: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. Objective: The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Methods: Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Results: Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15–60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Conclusions: Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost–benefit of integrating these analyses at this stage of signal management requires further research

    BIG DATA ANALYTICS IN PHARMACOVIGILANCE - A GLOBAL TREND

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    Big data analysis has enhanced its demand nowadays in various sectors of health-care including pharmacovigilance. The exact definition of big data is not known to many people though it is routinely used by them. Big data refer to immense and voluminous computerized medical information which are obtained from electronic health records, administrative data, registries related to disease, drug monitoring, etc. This data are usually collected from doctors and pharmacists in a health-care facility. Analysis of big data in pharmacovigilance is useful for early raising of safety alerts, line listing them for signal detection of drugs and vaccines, and also for their validation. The present paper is intended to discuss big data analytics in pharmacovigilance focusing on global prospect and domestic country-India

    Comparison of Post-Licensure Safety Surveillance of 13-Valent Pneumococcal Conjugate Vaccine and 7-Valent Pneumococcal Conjugate Vaccine: Data from the Vaccine Advere Event Reporting System (Vaers)

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    Comparison of Post-licensure safety surveillance of 13-Valent Pneumococcal Conjugate vaccine and 7-Valent Pneumococcal Conjugate vaccine: Data from the Vaccine Adverse Event Reporting System (VAERS). Background: On February 24, 2010, Food and Drug Administration (FDA) licensed a 13-valent pneumococcal conjugate vaccine (Prevnar 13®, [PCV13]) for use among children aged 6 weeks--71 months. The Advisory Committee on Immunization Practices (ACIP) recommended PCV13 routine vaccination of all children aged 2--59 months, children aged 60--71 months with underlying medical conditions, with PCV13 replacing PCV7 for all doses. Methods: We searched case reports to the Vaccine Adverse Event Reporting System (VAERS), a US passive surveillance system, for adverse events (AEs) reported after immunization with PCV13 vaccine from February 24, 2010 through February 24, 2011 for persons vaccinated from February 24, 2010 through December 31, 2010 and compared them with AEs reported by persons who were vaccinated with PCV7. Results: VAERS received 1503 reports of AEs after PCV13; multiple vaccines were given in 79.0% of reports. One hundred eighty (11.9%) were coded as serious, including nineteen reports of death. The most frequently reported symptoms were injection site reactions, fever, irritability and vomiting. Seven hundred fifty-eight (50.4%) reports comprised males. Most reports (37.7%) were from children 1-2 years. Total number of reports received for PCV13 was very similar to those received after vaccination with PCV7. Conclusions: AEs reported to VAERS following 13-valent pneumococcal conjugate vaccine were consistent with AEs previously observed in pre-licensure trials. We did not identify any major safety concerns or outcomes

    BIRS Course: RNA Vaccine Manufacture and Assessment of Regulatory Documents for RNA Vaccines

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    This paper is in three segments: (A) Segment on Vaccine Manufacture; (B) Segment on Ready to Use (RTU) Fluid Path for Compounded Sterile Preparations, mRNA Vaccines, and Phage Therapy, (C) Segment on Competency Framework for Addressing Regulatory Review These segments can be used separately or in combination. Additionally, they can be presented in any order. The time devoted to each segment depends on the depth of the course coverage. These segments are interrelated and describe how to make vaccines, how to manufacture vaccines with a point-of-care system built from ready-to-use parts; and how to regulate vaccines. This is a timely review because of the importance of vaccines for the treatment of diseases. It is hoped that it will lead to new approaches to vaccine manufacture and regulation

    Use of Real-World Data in Pharmacovigilance Signal Detection

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    Use of Real-World Data in Pharmacovigilance Signal Detection

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    Can Digital Tools Be Used for Improving Immunization Programs?

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    In order to successfully control and eliminate vaccine-preventable infectious diseases, an appropriate vaccine coverage has to be achieved and maintained. This task requires a high level of effort as it may be compromised by a number of barriers. Public health agencies have issued specific recommendations to address these barriers and therefore improve immunization programs. In the present review, we characterize issues and challenges of immunization programs for which digital tools are a potential solution. In particular, we explore previously published research on the use of digital tools in the following vaccine-related areas: immunization registries, dose tracking, and decision support systems; vaccine-preventable diseases surveillance; surveillance of adverse events following immunizations; vaccine confidence monitoring; and delivery of information on vaccines to the public. Subsequently, we analyze the limits of the use of digital tools in such contexts and envision future possibilities and challenges

    Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

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    Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (β=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines
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