Safety analysis of fluoroquinolone drugs in elderly patients over 65 based on FAERS.

OBJECTIVE: This study investigates adverse drug event (ADE) reports from the FAERS related to FQs drugs in patients aged 65 and older. The findings aim to guide the rational clinical use of these drugs in elderly patients. METHODS: We employed Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR) methods to analyze ADE reports for the representative FQ drugs from Q1 2015 to Q4 2023, covering 36 quarters. RESULTS: The analysis identified 6883 ADE cases for ciprofloxacin, 5866 for levofloxacin, 1498 for moxifloxacin, and 317 for ofloxacin. Moxifloxacin showed higher incidences of Cardiac disorders and Psychiatric disorders ADEs (4.01%, 23.11%). Ciprofloxacin and levofloxacin showed higher ADE rates in musculoskeletal and connective tissue diseases (20.18% and 26.97%) compared to moxifloxacin (3.62%) and ofloxacin (9.25%). Additionally, moxifloxacin and ofloxacin showed higher ADE rates for eye disorders (10.61% and 15.03%). CONCLUSION: Different FQs exhibit varying ADE profiles across cardiovascular, vascular and lymphatic, renal and urinary, psychiatric, musculoskeletal and connective tissue, and ocular systems. Patients with underlying systemic diseases should avoid FQs with higher ADE risks for their conditions. Personalized medication plans for elderly patients should also be strengthened.

SMS-Based Active Surveillance of Adverse Events following Immunization in Children: The VigiVax Study.

Underreporting is the main limitation of spontaneous reporting systems. This cohort-event monitoring study aims to examine the potential of short message service (SMS)-based surveillance compared to traditional surveillance systems. Using VigiVax software, parents of vaccinated children aged two years or younger, in the period March 2021-May 2022, received a single SMS inquiry about adverse events following immunization (AEFI). Responses were collected, validated by health operators and integrated with the information on electronic immunization registries. AEFI reports were automatically submitted to the Italian Pharmacovigilance system. Among 254,160 SMS messages sent, corresponding to 451,656 administered doses (AD), 71,643 responses were collected (28.2% response rate), and 21,231 of them (8.3%) reported AEFI. After a seriousness assessment based on clinical criteria, 50 reports (0.24%) were classified as serious. Among these, a causality assessment identified 31 reports at least potentially related to the vaccination (RR: 6.86/100,000 AD). Febrile seizures following MMRV (measles, mumps, rubella, varicella) vaccination accounted for 11 of these 31 cases, with an incidence of 32 per 100,000 AD. No fatal outcomes were reported. Our findings support the highly favorable risk profile of pediatric vaccinations and the possibility to improve spontaneous reporting through the integration of digital technologies.

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.

Heavy metals and trace elements in milk and dairy products in the Lazio region (Central Italy).

A study was carried out to determine the concentration of heavy metals and trace elements in milk and dairy products collected from local farms, supermarkets, or food retailers in the region of Lazio (Central Italy). Persistent exposure to metal contamination is of particular concern for human health, as it can cause different serious disorders. The monitoring of the matrices studied is therefore important, given their high consumption in the daily diet. The elements determined by ICP-MS (Inductively Coupled Plasma - Mass Spectrometry) were lead (Pb), arsenic (As), mercury (Hg), cadmium (Cd), chromium (Cr), nickel (Ni), molybdenum (Mo) and thallium (Tl), for a total of 151 measurements in 98 samples. The results showed that 11.3% of the measurements were quantifiable but below the legal maximum limits (MLs) set by EU regulations. The data obtained may be useful for dietary exposure information, inter-regional comparisons and for planning regional surveillance strategies.

Developing Causality and Severity Assessment Frameworks for Food Safety Signals Using Social Media Reviews: A Technical Report Based on Data From an Urban Indian Suburb.

Social media reviews are a valuable data source, reflecting consumer experiences and interactions with businesses. This study leverages such data to develop a passive surveillance framework for food safety in urban India. By employing a Bidirectional Encoder Representations from Transformers (BERT)-powered Aspect-Based Sentiment Analysis tool, branded as Eat At Right Place (ERP), the study analyses over 100,000 reviews from 93 restaurants to identify and assess food safety signals. The Causality Assessment Index (CAI) and Severity Assessment Score (SAS) are introduced to systematically evaluate potential risks. The CAI uses pattern recognition and temporal relationships to establish causality while the SAS quantifies severity based on sub-aspects such as cleanliness, food handling, and unintended health outcomes. Results indicate that 40% of the restaurants had a CAI above 1, highlighting significant food safety concerns. The framework successfully prioritizes corrective actions by grading the severity of issues, demonstrating its potential for real-time food safety management. This study underscores the importance of integrating innovative data-driven approaches into public health monitoring systems and suggests future improvements in natural language processing algorithms and data source expansion. The findings pave the way for enhanced food safety surveillance and timely regulatory interventions.

A national surveillance system for continuous monitoring of blood transfusion safety: German haemovigilance data.

BACKGROUND AND OBJECTIVES: Haemovigilance (HV) systems aim to improve transfusion outcomes in patients and donor safety. An important question for blood regulators is how to ensure an effective HV system. MATERIALS AND METHODS: We retrospectively analysed the HV reports submitted to Paul-Ehrlich-Institut over the last two decades. RESULTS: Between 2011 and 2020, 50.86 million units of blood components were used, and 8931 suspected serious donor and recipient adverse reactions (SARs), 874 serious adverse events (SAEs) and 12,073 donor look-backs were reported. Following implementation of specific risk-minimization measures (RMMs) between 2000 and 2010, SAR reporting rates decreased for transfusion-transmitted viral infections (TTVIs), transfusion-related acute lung injury (TRALI) and transfusion-transmitted bacterial infections (TTBIs), while increasing for other serious adverse transfusion reactions. Within this decade, the overall blood component use decreased. CONCLUSION: Long-term data collection forms the basis to establish trends and changes in reporting and to evaluate the effect of RMM. Standardized criteria for reaction types, seriousness and imputability assessments and availability of a denominator are important elements. Central data collection and independent assessment allow for monitoring HV data in a nationwide context over time. Stakeholder involvement and transparent feedback on the benefit of RMM will help to achieve the objectives of HV.

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

INTRODUCTION: The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans. METHODS: Drug X, a mature product with post-marketing data, and Drug Y, a recently approved drug in another therapeutic area, were selected. Gradient boosting-based ML approaches (e.g., XGBoost) were applied as the main modeling strategy. RESULTS: For Drug X, eight true signals were present in the test set. Among 12 potential new signals generated, four were true signals with a 50.0% sensitivity rate and a 33.3% positive predictive value (PPV) rate. Among the remaining eight potential new signals, one was confirmed as a signal and detected six months earlier than humans. For Drug Y, nine true signals were present in the test set. Among 13 potential new signals generated, five were true signals with a 55.6% sensitivity rate and a 38.5% PPV rate. Among the remaining eight potential new signals, none were confirmed as true signals upon human review. CONCLUSION: This model demonstrated acceptable accuracy for safety signal detection and potential for earlier detection when compared to humans. Expert judgment, flexibility, and critical thinking are essential human skills required for the final, accurate assessment of adverse event cases.

An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records.

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.

Online causal inference with application to near real-time post-market vaccine safety surveillance.

Streaming data routinely generated by social networks, mobile or web applications, e-commerce, and electronic health records present new opportunities to monitor the impact of an intervention on an outcome via causal inference methods. However, most existing causal inference methods have been focused on and applied to static data, that is, a fixed data set in which observations are pooled and stored before performing statistical analysis. There is thus a pressing need to turn static causal inference into online causal learning to support near real-time monitoring of treatment effects. In this paper, we present a framework for online estimation and inference of treatment effects that can incorporate new information as it becomes available without revisiting prior observations. We show that, under mild regularity conditions, the proposed online estimator is asymptotically equivalent to the offline oracle estimator obtained by pooling all data. Our proposal is motivated by the need for near real-time vaccine effectiveness and safety monitoring, and our proposed method is applied to a case study on COVID-19 vaccine safety surveillance.

Overview of U.S. COVID-19 vaccine safety surveillance systems.

The U.S. COVID-19 vaccination program, which commenced in December 2020, has been instrumental in preventing morbidity and mortality from COVID-19 disease. Safety monitoring has been an essential component of the program. The federal government undertook a comprehensive and coordinated approach to implement complementary safety monitoring systems and to communicate findings in a timely and transparent way to healthcare providers, policymakers, and the public. Monitoring involved both well-established and newly developed systems that relied on both spontaneous (passive) and active surveillance methods. Clinical consultation for individual cases of adverse events following vaccination was performed, and monitoring of special populations, such as pregnant persons, was conducted. This report describes the U.S. government's COVID-19 vaccine safety monitoring systems and programs used by the Centers for Disease Control and Prevention, the U.S. Food and Drug Administration, the Department of Defense, the Department of Veterans Affairs, and the Indian Health Service. Using the adverse event of myocarditis following mRNA COVID-19 vaccination as a model, we demonstrate how the multiple, complementary monitoring systems worked to rapidly detect, assess, and verify a vaccine safety signal. In addition, longer-term follow-up was conducted to evaluate the recovery status of myocarditis cases following vaccination. Finally, the process for timely and transparent communication and dissemination of COVID-19 vaccine safety data is described, highlighting the responsiveness and robustness of the U.S. vaccine safety monitoring infrastructure during the national COVID-19 vaccination program.

Development of a Safety Surveillance Plan for the Academic Medicine Sponsor Performing First-in-Human Cellular Therapy Clinical Trials: A Report from the Consortium for Pediatric Cellular Immunotherapy.

Pharmacovigilance (PV), also known as drug safety, is the science of risk management involving the detection, assessment, understanding, and prevention of adverse effects related to a medication. This discipline has traditionally focused on the postmarketing period, with less attention to early-phase clinical trials. However, during the immunotherapy and cellular therapy investigational stage, regulatory agencies are increasingly emphasizing the need to identify and characterize safety signals earlier in clinical development as part of a comprehensive safety surveillance plan. Compliance with PV and safety regulations are further heightened as cell and gene therapy (CGT) trials grow in complexity and scope owing to ever-changing and increasingly rigorous regulatory mandates. Based on this changing landscape, a critical aspect of early-phase trials of cellular products where significant safety events are anticipated is to ensure that every effort is made to protect clinical trial participants by maximizing attention to the risk-versus-benefit profile. This includes the development of robust plans for safety surveillance that provide a continual assessment of safety signals to enable safety reporting to regulatory bodies and the Food and Drug Administration, a regular analysis of aggregate safety data, and a plan to communicate safety findings. This report focuses on PV in early-phase clinical trials of first-in-human investigational products sponsored by academic centers in which the availability of PV resources and subject matter experts is limited. To more fully understand the challenges of CGT PV oversight within pediatric academic medical centers conducting early-phase clinical trials, a working group from institutions participating in the Consortium for Pediatric Cellular Immunotherapy composed of faculty and regulatory professionals was convened to compare experiences, identify best practices, and review published literature to identify commonalities and opportunities for alignment. Here we present guidelines on PV planning in early-phase CGT clinical trials occurring in academic medical centers and offer strategies to mitigate risk to trial participants. Standards to address regulatory requirements and governance for safety signal identification and risk assessment are discussed.

Counting Farm Injuries and Fatalities: An Assessment of Irish Occupational Health and Safety Surveillance Data Systems.

OBJECTIVE: Generation of reliable data underpins the effectiveness of Occupational Health and Safety (OHS) surveillance systems. Despite the importance of understanding OHS data systems, there are few papers that provide overviews of their structure and/or content. This paper introduces a basic framework for assessing OHS data systems that will be of use to researchers internationally. We applied this approach to assess the Irish OHS data system by undertaking a data mapping exercise. METHOD: We developed a checklist based on recommendations of monitoring and measurement of OHS proposed by the National Academies of Sciences, Engineering, and Medicine (USA). An assessment of published reports that present systematic OHS surveillance data was undertaken to identify the institutions or organisations responsible for collecting and curating the data, their remit, and, associated with this, their respective case definitions. We then provide an overview of the variables collected and these are then mapped against the checklist. RESULTS: The assessment highlights that whilst the farm fatalities dataset provides complete coverage of all fatalities, regardless of age or employment status, the same is not true of the three non-fatal injuries datasets reviewed. There are important differences in the data collection methods and, associated with this, which populations are covered. PRACTICAL APPLICATION: The assessment approach provides valuable insights into the strengths and weaknesses of a critical element of OHS surveillance systems, namely the production of datasets. This knowledge is important for researchers as understanding the data that informs their research is fundamental to good science. It is critical for policy-makers and other stakeholders to understand the strengths and weaknesses on which OHS policy, strategies, or education and training interventions are developed.

A scoping review of active, participant centred, digital adverse events following immunization (AEFI) surveillance of WHO approved COVID-19 vaccines: A Canadian immunization Research Network study.

This scoping review examines the role of digital solutions in active, participant-centered surveillance of adverse events following initial release of COVID-19 vaccines. The goals of this paper were to examine the existing literature surrounding digital solutions and technology used for active, participant centered, AEFI surveillance of novel COVID-19 vaccines approved by WHO. This paper also aimed to identify gaps in literature surrounding digital, active, participant centered AEFI surveillance systems and to identify and describe the core components of active, participant centered, digital surveillance systems being used for post-market AEFI surveillance of WHO approved COVID-19 vaccines, with a focus on the digital solutions and technology being used, the type of AEFI detected, and the populations under surveillance. The findings highlight the need for customized surveillance systems based on local contexts and the lessons learned to improve future vaccine monitoring and pandemic preparedness.

COVID-19 vaccines and adverse events of special interest: A multinational Global Vaccine Data Network (GVDN) cohort study of 99 million vaccinated individuals.

BACKGROUND: The Global COVID Vaccine Safety (GCoVS) Project, established in 2021 under the multinational Global Vaccine Data Network™ (GVDN®), facilitates comprehensive assessment of vaccine safety. This study aimed to evaluate the risk of adverse events of special interest (AESI) following COVID-19 vaccination from 10 sites across eight countries. METHODS: Using a common protocol, this observational cohort study compared observed with expected rates of 13 selected AESI across neurological, haematological, and cardiac outcomes. Expected rates were obtained by participating sites using pre-COVID-19 vaccination healthcare data stratified by age and sex. Observed rates were reported from the same healthcare datasets since COVID-19 vaccination program rollout. AESI occurring up to 42 days following vaccination with mRNA (BNT162b2 and mRNA-1273) and adenovirus-vector (ChAdOx1) vaccines were included in the primary analysis. Risks were assessed using observed versus expected (OE) ratios with 95 % confidence intervals. Prioritised potential safety signals were those with lower bound of the 95 % confidence interval (LBCI) greater than 1.5. RESULTS: Participants included 99,068,901 vaccinated individuals. In total, 183,559,462 doses of BNT162b2, 36,178,442 doses of mRNA-1273, and 23,093,399 doses of ChAdOx1 were administered across participating sites in the study period. Risk periods following homologous vaccination schedules contributed 23,168,335 person-years of follow-up. OE ratios with LBCI > 1.5 were observed for Guillain-Barré syndrome (2.49, 95 % CI: 2.15, 2.87) and cerebral venous sinus thrombosis (3.23, 95 % CI: 2.51, 4.09) following the first dose of ChAdOx1 vaccine. Acute disseminated encephalomyelitis showed an OE ratio of 3.78 (95 % CI: 1.52, 7.78) following the first dose of mRNA-1273 vaccine. The OE ratios for myocarditis and pericarditis following BNT162b2, mRNA-1273, and ChAdOx1 were significantly increased with LBCIs > 1.5. CONCLUSION: This multi-country analysis confirmed pre-established safety signals for myocarditis, pericarditis, Guillain-Barré syndrome, and cerebral venous sinus thrombosis. Other potential safety signals that require further investigation were identified.

Aggregate IND Safety Reporting for Smaller Companies and Programs.

The United States (US) Food and Drug Administration (FDA) Investigational New Drug (IND) Final Rule (US FDA, Final rule: Investigational new drug safety reporting requirements for human drug and biological products and safety reporting requirements for bioavailability and bioequivalence studies in humans, 2010) applies to all human drugs and biological products being studied under an IND. The Final Rule specifies that a sponsor must file an IND safety report for any Suspected Unexpected Serious Adverse Reaction (SUSAR) of a medicinal product being investigated. To make a proper SUSAR classification, sponsors need to go beyond conventional Data Monitoring Committees (DMCs) with an interdisciplinary effort, using all relevant data (including data outside clinical trials), to make judgments on the possibility of serious adverse events being caused by the study drug-rather than the underlying condition of the patient or a concomitant therapy. Ball et al. (Ball et al. in Ther Innov Regul Sci 55:705-716, 2021) have reported on how the Final Rule has been implemented by large pharmaceutical companies. This paper explores the experiences of small sponsor companies regarding the Final Rule, to understand the current challenges that they have been facing to meet aggregate IND safety reporting requirements.

Bayesian safety surveillance with adaptive bias correction.

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis.

Long term follow up and outcomes of Covid-19 vaccine associated myocarditis in Victoria, Australia: A clinical surveillance study.

BACKGROUND: Myocarditis and myopericarditis are well described adverse events of special interest (AESI) following COVID-19 vaccinations. Although reports are reassuring regarding initial clinical outcomes, information about longer term outcomes remains limited. We aimed to further this knowledge and report outcomes to 6 months post diagnosis from a single population cohort. METHODS: Reports of myocarditis following COVID-19 vaccination were followed up by SAEFVIC (Surveillance of Adverse Events Following Vaccination in the Community), the state-wide vaccine safety service for Victoria, Australia. Confirmed myocarditis cases (Brighton Collaboration Criteria levels 1-3) were followed up via surveys at 1, 3 and 6 months post symptom onset. Responses received between 22 February 2021 and 30 September 2022 were analysed. RESULTS: 87.5 % (N = 182) of eligible participants completed at least 1 survey report. 377 reports were analysed. 76.9 % of completed reports were from male patients. The median age of patients was 21 years [IQR: 16 to 32]. 54.8 % (n = 74) of survey reports at 6 months, reported ongoing symptoms. At all follow-up time points, females were significantly more likely to have ongoing symptoms. At 6 months, 51.9 % of male respondents reported symptom resolution compared to 22.6 % of female patients (p = 0.002). Females were also more likely to continue medication and have ongoing exercise restrictions. However, males were significantly more likely to have higher initial peak troponin results and abnormal initial cardiac imaging investigations. CONCLUSIONS: There appears to be a significant proportion of patients who experience ongoing symptoms to 6 months post onset amongst patients that experience these AESI. Male patients were more likely to report earlier and more complete symptom recovery, despite significantly higher average initial peak troponin. This difference in phenotypic presentation in females compared to males warrants further investigation and there is a need for longer term follow up data.

Three seasons of enhanced safety surveillance of a cell culture-based quadrivalent influenza vaccine.

The objective of this paper is to summarize annual enhanced safety surveillance activity across three seasons (2019/20-2021/22) for cell culture-based quadrivalent influenza vaccine (QIVc; Flucelvax® Tetra) in all age groups. This activity was conducted in primary care setting in Genoa (Italy) during the seasons 2019/20, 2020/21 and 2021/22. All adverse events registered within the first seven days following immunization were analyzed by season, type, age group and seriousness. Over three seasons, 3,603 QIVc exposures were recorded within the enhanced passive safety surveillance activity. No safety signals were identified. The overall reporting rates of individual case safety reports for the seasons 2019/20, 2020/21 and 2021/22 were 1.75%, 0.48% and 0.40%, respectively. The average number of adverse events per individual case safety report was similar (range 3.3-3.8 adverse events per case report) across the three seasons. Most adverse events were reactogenic in nature. The rate of adverse events was similarly low in all age groups. Enhanced passive safety surveillance activity is a feasible approach for the post-marketing monitoring of seasonal influenza vaccines. Within its limitations, results of this study support the favorable safety profile of QIVc. These safety data could further bolster public trust in influenza vaccines with the goal to increase vaccination uptake in all target groups.

Thrombosis with thrombocytopenia syndrome (TTS) following adenovirus vector COVID-19 vaccination in Canada.

INTRODUCTION: Identifying and monitoring adverse events following vaccination contributed to the safety and effectiveness of COVID-19 mass vaccination campaigns. In March 2021, international reports emerged of an adverse event following vaccination with adenovirus vector COVID-19 vaccines (ChAdOx1-S [recombinant] and Ad26.COV2.S) of thrombosis with low platelet counts, referred to as thrombosis with thrombocytopenia syndrome (TTS). We described TTS reports in Canada following adenovirus vector COVID-19 vaccines and investigated whether the observed number of events were higher than expected. METHODS: Reports of TTS following receipt of ChAdOx1-S [recombinant] or Ad26.COV2.S meeting the Canadian case definition for TTS and diagnostic certainty levels 1-3 of the Brighton Collaboration case definition, submitted to the Canadian Adverse Events Following Immunization Surveillance System and Canada Vigilance Database between February 26, 2021 and October 31, 2022 were included. Demographics and characteristics of the TTS reports are described along with an analysis comparing the observed number of reports to the expected number. RESULTS: As of October 31, 2022, 56 reports of TTS following administration of ChAdOx1-S [recombinant] and no reports following Ad26.COV2.S vaccines were reported in Canada, of which 37 had functionally positive anti-PF4 antibodies. The median age was 56 years; males accounted for 54 % of reports. Five deaths were reported. The observed number of reports exceeded the expected for all ages and sexes combined, as well as for males aged 30-49 and 60-69 years, and females aged 40-59 years. CONCLUSION: Based on international surveillance data, Canada evaluated a statistical signal of TTS following adenovirus vector vaccines. The investigation of this signal demonstrated how post-market vaccine safety surveillance systems were successful in investigating rare adverse events during the rollout of COVID-19 vaccines in Canada. As adenovirus vector vaccines continue to be administered, characterization of the association between the vaccine and TTS informs immunization programs and policies.

Signal detection statistics of adverse drug events in hierarchical structure for matched case-control data.

The tree-based scan statistic is a data mining method used to identify signals of adverse drug reactions in a database of spontaneous reporting systems. It is particularly beneficial when dealing with hierarchical data structures. One may use a retrospective case-control study design from spontaneous reporting systems (SRS) to investigate whether a specific adverse event of interest is associated with certain drugs. However, the existing Bernoulli model of the tree-based scan statistic may not be suitable as it fails to adequately account for dependencies within matched pairs. In this article, we propose signal detection statistics for matched case-control data based on McNemar's test, Wald test for conditional logistic regression, and the likelihood ratio test for a multinomial distribution. Through simulation studies, we demonstrate that our proposed methods outperform the existing approach in terms of the type I error rate, power, sensitivity, and false detection rate. To illustrate our proposed approach, we applied the three methods and the existing method to detect drug signals for dizziness-related adverse events related to antihypertensive drugs using the database of the Korea Adverse Event Reporting System.