A broad assessment of covid-19 vaccine safety using tree-based data-mining in the vaccine safety datalink

Tinnitus after COVID-19 vaccination: Findings from the vaccine adverse event reporting system and the vaccine safety datalink

PURPOSE

To assess the occurrence of tinnitus following COVID-19 vaccination using data mining and descriptive analyses in two U.S. vaccine safety surveillance systems.

METHODS

Reports of tinnitus after COVID-19 vaccination to the Vaccine Adverse Event Reporting System (VAERS) from 2020 through 2024 were examined using empirical Bayesian data mining and by calculating reporting rates. In the Vaccine Safety Datalink (VSD) population, ICD-10 coded post-vaccination medical visits were examined using tree-based data mining, and tinnitus visit incidence rates during post-vaccination days 1-140 were calculated by age group for COVID-19 vaccines and for comparison, influenza vaccine.

RESULTS

VAERS data mining did not find disproportionate reporting of tinnitus for any COVID-19 vaccine. VAERS received up to 84.82 tinnitus reports per million COVID-19 vaccine doses administered. VSD tree-based data mining found no signals for tinnitus. VSD tinnitus visit incidence rates after COVID-19 vaccines were similar to those after influenza vaccine except for the group aged ≥65 years (Moderna COVID-19 vaccine, 165 per 10,000 person-years; Pfizer-BioNTech COVID-19 vaccine, 154; influenza vaccine, 135).

CONCLUSIONS

Overall, these findings do not support an increased risk of tinnitus following COVID-19 vaccination but cannot definitively exclude the possibility. Descriptive comparisons between COVID-19 and influenza vaccines were limited by lack of adjustment for potential confounding factors.

Current Approaches in Postapproval Vaccine Safety Studies Using Real-World Data: A Systematic Review of Published Literature

PURPOSE

Well-designed observational postmarketing studies using real-world data (RWD) are critical in supporting an evidence base and bolstering public confidence in vaccine safety. This systematic review presents current research methodologies in vaccine safety research in postapproval settings, technological advancements contributing to research resources and capabilities, and their major strengths and limitations.

METHODS

A comprehensive search was conducted using PubMed to identify relevant articles published from January 1, 2019, to December 31, 2022. Eligible studies were summarized overall by study design and other study characteristics (eg, country, vaccine studied, types of data source, and study population). An in-depth review of select studies representative of conventional or new designs, analytical approaches, or data collection methods was conducted to summarize current methods in vaccine safety research.

FINDINGS

Out of 977 articles screened for inclusion, 135 were reviewed. The review shows that recent advancements in scientific methods, digital technology, and analytic approaches have significantly contributed to postapproval vaccine safety studies using RWD. "Near real-time surveillance" using large datasets (via collaborative or distributed databases) has been used to facilitate rapid signal detection that complements passive surveillance. There was increasing appreciation for self-controlled case-only designs (self-controlled case series and self-controlled risk interval) to assess acute-onset safety outcomes, artificial intelligence, and natural language processing to improve outcome accuracy and study timeliness and emerging artificial intelligence-based analysis to capture adverse events from social media platforms.

IMPLICATIONS

Continued development in the area of vaccine safety research methodologies using RWD is warranted. The future of successful vaccine safety research, especially evaluation of rare safety events, is likely to comprise digital technologies including linking RWD networks, machine learning, and advanced analytic methods to generate rapid and robust real-world safety information.

Molecular cloning, biophysical and in silico studies of Human papillomavirus 33 E2 DNA binding domain

Human papillomavirus 33, a high-risk HPV strain, is mainly responsible for HPV infection and cervical cancer in Asian countries. The E2 protein of HPV 33 is a DNA-binding protein that plays a crucial role in viral replication and transcription. We have cloned, overexpressed, and purified the DNA binding domain of the E2 protein. Size exclusion chromatography results suggested that the protein exists in a homodimeric state in the native form. Circular dichroism data showed that the protein has a higher content of β-sheet. The melting temperature obtained from differential scanning calorimetry is 52.59 °C, and the protein is stable at pH 8 and is in a dimeric form at basic pH. The protein is monomeric or unfolded at a very low pH. Chemical denaturation studies suggested that the protein denatured and dissociated simultaneously. The DNA binding activity of the protein was also confirmed and it showed binding affinity in the order of 106 M-1. The protein structure was modeled using homology modeling and other bioinformatic tools. The virtual screening and molecular dynamic simulation studies were performed to find compounds that can act as potent inhibitors against E2 DBD. This study expands the understanding of the conserved structural and binding properties of HPV33 E2 DBD and provides the first report on the characterization of the viral protein.Communicated by Ramaswamy H. Sarma.

COVID-19 Vaccine Safety Technical (VaST) Work Group: Enhancing vaccine safety monitoring during the pandemic

During the COVID-19 pandemic, candidate COVID-19 vaccines were being developed for potential use in the United States on an unprecedented, accelerated schedule. It was anticipated that once available, under U.S. Food and Drug Administration (FDA) Emergency Use Authorization (EUA) or FDA approval, COVID-19 vaccines would be broadly used and potentially administered to millions of individuals in a short period of time. Intensive monitoring in the post-EUA/licensure period would be necessary for timely detection and assessment of potential safety concerns. To address this, the Centers for Disease Control and Prevention (CDC) convened an Advisory Committee on Immunization Practices (ACIP) work group focused solely on COVID-19 vaccine safety, consisting of independent vaccine safety experts and representatives from federal agencies - the ACIP COVID-19 Vaccine Safety Technical Work Group (VaST). This report provides an overview of the organization and activities of VaST, summarizes data reviewed as part of the comprehensive effort to monitor vaccine safety during the COVID-19 pandemic, and highlights selected actions taken by CDC, ACIP, and FDA in response to accumulating post-authorization safety data. VaST convened regular meetings over the course of 29 months, from November 2020 through April 2023; through March 2023 FDA issued EUAs for six COVID-19 vaccines from four different manufacturers and subsequently licensed two of these COVID-19 vaccines. The independent vaccine safety experts collaborated with federal agencies to ensure timely assessment of vaccine safety data during this time. VaST worked closely with the ACIP COVID-19 Vaccines Work Group; that work group used safety data and VaST's assessments for benefit-risk assessments and guidance for COVID-19 vaccination policy. Safety topics reviewed by VaST included those identified in safety monitoring systems and other topics of scientific or public interest. VaST provided guidance to CDC's COVID-19 vaccine safety monitoring efforts, provided a forum for review of data from several U.S. government vaccine safety systems, and assured that a diverse group of scientists and clinicians, external to the federal government, promptly reviewed vaccine safety data. In the event of a future pandemic or other biological public health emergency, the VaST model could be used to strengthen vaccine safety monitoring, enhance public confidence, and increase transparency through incorporation of independent, non-government safety experts into the monitoring process, and through strong collaboration among federal and other partners.

Risk of cardiac arrhythmia and cardiac arrest after primary and booster COVID-19 vaccination in England: A self-controlled case series analysis

Background

Various cardiac arrhythmias have been reported after COVID-19 infection and vaccination. We assessed the risk after primary immunisation with the ChAdOx1 adenovirus vectored vaccine, and primary and booster immunisation with an mRNA vaccine in 40 million vaccinated adults with 121 million doses (33.9% ChAdOx1 and 66.1% mRNA) in England.

Methods

Hospital admissions for a cardiac arrhythmia and emergency care attendance for a cardiac arrest in individuals aged 18 years and older on the 31st March 2021 were linked to the national COVID-19 immunisation register. The incidence of events 1-14 and 15-28 days after vaccination relative to a post-vaccination control period was estimated using the self-controlled case series method modified for fatal events. Outcomes were stratified by arrhythmia type, vaccine type, age group and dose number (up to five). Elevated relative incidence (RI) estimates with p < 0.001 were considered strong evidence of an association.

Findings

There was an increased risk of admission for arrhythmia events that were largely palpitations without myocarditis within 14 days of a second priming dose of an mRNA vaccine in 18-49 year olds with an RI of 1.66 (95 % confidence interval 1.47,1.86) for BNT162b2 and 3.75 (2.52,5.57) for mRNA-1273 (p < 0.001) and also after a first booster dose, 1.34 (1.17,1.53) and 1.75 (1.43,2.15) respectively (p < 0.001). No other cardiac arrhythmia, including cardiac arrest, showed an elevated incidence within 28 days of vaccination for any dose, age group or vaccine type. In contrast the risk of a cardiac arrhythmia of all types, including a cardiac arrest, was consistently elevated in those testing positive for SARS-CoV-2 infection.

Interpretation

Our study provides reassuring evidence of the safety of the ChAdOx1 and mRNA COVID-19 vaccines with respect to serious cardiac arrhythmias and of the favourable risk benefit of mRNA booster vaccination.

Comparison of Venous Thromboembolism Outcomes after COVID-19 and Influenza Vaccinations

Background  Published data on the risk of venous thromboembolism (VTE) with coronavirus disease 2019 (COVID-19) vaccines are scarce and inconclusive, leading to an unmet need for further studies. Methods  A retrospective, multicentered study of adult patients vaccinated for one of the three approved COVID-19 vaccines in the United States of America and a pre-COVID-19 cohort of patients vaccinated for influenza at two institutions: Mayo Clinic Enterprise sites and the Medical College of Wisconsin, looking at rate of VTE over 90 days. VTE was identified by applying validated natural language processing algorithms to relevant imaging studies. Kaplan-Meier curves were used to evaluate rate of VTE and Cox proportional hazard models for incident VTE after vaccinations. Sensitivity analyses were performed for age, sex, outpatient versus inpatient status, and type of COVID-19 vaccine. Results  A total of 911,381 study subjects received COVID-19 vaccine (mean age: 56.8 [standard deviation, SD: 18.3] years, 55.3% females) and 442,612 received influenza vaccine (mean age: 56.5 [SD: 18.3] years, 58.7% females). VTE occurred within 90 days in 1,498 (0.11%) of the total 1,353,993 vaccinations: 882 (0.10%) in the COVID-19 and 616 (0.14%) in the influenza vaccination cohort. After adjusting for confounding variables, there was no difference in VTE event rate after COVID-19 vaccination compared with influenza vaccination (adjusted hazard ratio: 0.95 [95% confidence interval: 0.85-1.05]). No significant difference in VTE rates was observed between the two cohorts on sensitivity analyses. Conclusion  In this large cohort of COVID-19-vaccinated patients, risk of VTE at 90 days was low and no different than a pre-COVID-19 cohort of influenza-vaccinated patients.

Safety signal identification for COVID-19 bivalent booster vaccination using tree-based scan statistics in the Vaccine Safety Datalink

BACKGROUND

Traditional active vaccine safety monitoring involves pre-specifying health outcomes and biologically plausible outcome-specific time windows of concern, limiting the adverse events that can be evaluated. In this study, we used tree-based scan statistics to look broadly for >60,000 possible adverse events after bivalent COVID-19 vaccination.

METHODS

Vaccine Safety Datalink enrollees aged ≥5 years receiving Moderna or Pfizer-BioNTech bivalent COVID-19 vaccine through November 2022 were followed for 56 days post-vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within the hierarchical ICD-10-CM diagnosis code "tree" and temporally within post-vaccination follow-up. The conditional self-controlled tree-temporal scan statistic was used, conditioning on total number of cases of each diagnosis and total number of cases of any diagnosis occurring during the scanning risk window across the entire tree. P = 0.01 was the pre-specified cut-off for statistical significance.

RESULTS

Analysis included 352,509 doses of Moderna and 979,189 doses of Pfizer-BioNTech bivalent vaccines. After Moderna vaccination, no statistically significant clusters were found. After Pfizer-BioNTech, there were clusters of unspecified adverse events (Days 1-3, p = 0.0001-0.0007), influenza (Days 35-56, p = 0.0001), cough (Days 44-55, p = 0.0002), and COVID-19 (Days 52-56, p = 0.0004).

CONCLUSIONS

For Pfizer-BioNTech only, we detected clusters of: (1) unspecified adverse effects, as have been observed in other vaccine studies using this method, and (2) respiratory disease toward the end of follow-up. The respiratory clusters were likely due to overlap of follow-up with the spread of respiratory syncytial virus, influenza, and COVID-19, i.e., confounding by seasonality. The untargeted nature of the method and its inherent adjustment for the many diagnoses and risk intervals evaluated are unique advantages. Limitations include susceptibility to time-varying confounding, lower statistical power for assessing risks of specific outcomes than in traditional studies targeting fewer outcomes, and the possibility of missing adverse events not strongly clustered in time or within the "tree."