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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. 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. 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. 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.

The Global COVID Vaccine Safety (GCoVS) project was established in 2021 under the multinational Global Vaccine Data Network (GVDN) consortium to facilitate the rapid assessment of the safety of newly introduced vaccines. This study analyzed data from GVDN member sites on the background incidence rates of conditions designated as adverse events of special interest (AESI) for COVID-19 vaccine safety monitoring. Eleven GVDN global sites obtained data from national or regional healthcare databases using standardized methods. Incident events of 13 pre-defined AESI were included for a pre-pandemic period (2015–19) and the first pandemic year (2020). Background incidence rates (IR) and 95% confidence intervals (CI) were calculated for inpatient and emergency department encounters, stratified by age and sex, and compared between pre-pandemic and pandemic periods using incidence rate ratios. An estimated 197 million people contributed 1,189,652,926 person-years of follow-up time. Among inpatients in the pre-pandemic period (2015–19), generalized seizures were the most common neurological AESI (IR ranged from 22.15 [95% CI 19.01–25.65] to 278.82 [278.20–279.44] per 100,000 person-years); acute disseminated encephalomyelitis was the least common (<0.5 per 100,000 person-years at most sites). Pulmonary embolism was the most common thrombotic event (IR 45.34 [95% CI 44.85–45.84] to 93.77 [95% CI 93.46–94.08] per 100,000 person-years). The IR of myocarditis ranged from 1.60 [(95% CI 1.45–1.76) to 7.76 (95% CI 7.46–8.08) per 100,000 person-years. The IR of several AESI varied by site, healthcare setting, age and sex. The IR of some AESI were notably different in 2020 compared to 2015–19. Background incidence of AESIs exhibited some variability across study sites and between pre-pandemic and pandemic periods. These findings will contribute to global vaccine safety surveillance and research.

Ross River virus (RRV) is a mosquito-borne virus endemic to Australia. The disease, marked by arthritis, myalgia and rash, has a complex epidemiology involving several mosquito species and wildlife reservoirs. Outbreak years coincide with climatic conditions conducive to mosquito population growth. We developed regression models for human RRV notifications in the Mildura Local Government Area, Victoria, Australia with the objective of increasing understanding of the relationships in this complex system, providing trigger points for intervention and developing a forecast model. Surveillance, climatic, environmental and entomological data for the period July 2000–June 2011 were used for model training then forecasts were validated for July 2011–June 2015. Rainfall and vapour pressure were the key factors for forecasting RRV notifications. Validation of models showed they predicted RRV counts with an accuracy of 81%. Two major RRV mosquito vectors (Culex annulirostris and Aedes camptorhynchus) were important in the final estimation model at proximal lags. The findings of this analysis advance understanding of the drivers of RRV in temperate climatic zones and the models will inform public health agencies of periods of increased risk.

Primary human keratinocytes can be driven,in vitro, to differentiate, viaactivation of transglutaminases, by raisingthe culture medium calcium concentrationabove 1 mM. This results intransglutaminase regulated cross linking ofspecific amino acids with resultantcornified envelope formation. Thedifferentiation was monitored via theincorporation of fluorescein cadaverineinto the cornified envelops. Thisdifferentiation assay was combined withassessment of reductive capacity ofresazurin, as a measure of cellactivity/viability.One primary aim is to assess the effects ofTHz radiation on human skin, since medicalimaging of the body through the skin isenvisaged.Human keratinocytes, at passage 2 fromisolation, were grown to confluence, andtransported in a buffered salt solution at22 (°)C. The exposure to the THz sourcewas for 10, 20 or 30 minutes at roomtemperature.No donor specific inhibition or stimulationof cell activity, compared with non-exposedcells, was noted following exposure in therange 1 to 3 THz, at up to 0.45J/cm(2).The differentiation also occurred in anormal way, for exposed and non-exposedcells, with the FC incorporation increasingbetween day 3 and day 8, as previouslynoted.

Introduction: The rapid deployment of COVID-19 vaccines resulted in unprecedented scale and speed of immunisations, necessitating identification of safety signals related to these new vaccines. Adverse events of special interest (AESI) are specific adverse events of concern due to severity, rarity, or potential association with specific drugs or vaccines. Close monitoring is required to ensure timely identification, impact on patient safety, and implications for public health interventions. Robust data collection systems with standardized reporting forms are crucial for AESI investigation. SAEFVIC is the reporting service for immunisation-related adverse events in Victoria meeting requirements for pharmacovigilance outlined by the World Health Organization (WHO). However, it lacks specific data fields for tracking details such as risk factors, demographics, laboratory, and imaging results. Epidemiological analysis, conducted by trained professionals, is crucial for assessing causality and implications of reports. Cross-institution collaboration enables timely detection, investigation, and appropriate actions. Specialists require secure access to de-identified patient data while complying with privacy constraints. Such analysis informs public health measures. Aim: Create a mechanism that enables the collection of data fields for specific AESI and facilitate secure access to de-identified data by cross-institution professionals. Method: SAEFVIC addressed these challenges by adopting REDCap, a secure and widely used application for survey and database management(1). Unlike SAEFVIC's standard reporting forms, which requires external vendors to make modifications at considerable time and cost, REDCap allows for the creation, deployment, and modification of questionnaires and databases without additional expenses and with greater speed. This flexibility enables the customization of forms to capture specific adverse events. REDCap's custom user access feature enables secure data sharing by allowing institutions to define and control user permissions and access levels ensuring compliance with privacy constraints and data protection regulations. Operational reports generated through REDCap also assist SAEFVIC's clinical teams in managing patients who experience AEFI, improving care and follow-up. Moreover, REDCap's data infrastructure can be leveraged to develop follow-up studies. If patients consent to research, the data can be used in transferred between projects. Questionnaires can be sent to patients and their responses automatically uploaded streamlining longitudinal analysis. Conclusion: Overall, the adoption of REDCap has strengthened SAEFVIC's safety surveillance capabilities. This integrated approach, characterized by streamlined data collection, enhanced collaboration among specialists, and comprehensive analysis, not only reinforces pharmacovigilance efforts for vaccines but provides a valuable framework for non-vaccine pharmacovigilance activities.

Introduction: Like many spontaneous adverse events following immunisation (AEFI) reporting systems, SAEFVIC in Victoria, Australia received a record number of reports following COVID-19 vaccine implementation with a 20-fold increase in 2021. Clinical review and patient follow up of AEFI reports through active and passive surveillance mechanisms are usually addressed within one business day. However, this process was overwhelmed during these times, leading to backlogs. We describe how SAEFVIC developed and refined a prioritisation system. Aim: The main objective of the system was to allow clinical staff to conduct a timely review of safety reports that could be adverse events of special interest (AESI), to inform signal investigation and epidemiological analyses that were urgent and ongoing at the time. There were heightened public interest, and necessity to inform vaccine policy on various newly emerging AESI such as thrombosis with thrombocytopenia syndrome (TTS) associated with Vaxzervria® (AstraZeneca) vaccine and myocarditis following mRNA vaccines Comirnaty® and Spikevax®. Methods: To address this challenge, we developed a clinical prioritisation R-based program. Pattern- matching techniques based initially on simple keywords were applied to free text in reports. The program automatically identified reports potentially matching 16 predefined categories then ranked from scale of 0-5 according to their clinical significance . For instance, mortality had the highest rating of 0. The positive impact was immediate, as it allowed clinical staff to follow up serious AEFI reports first. As volume and diversity of reports grew, we recognized the need for a systematic process. SAEFVIC informatics, clinical and epidemiology teams collaborated to improve the accuracy and utility of the program and its resulting list of potential reports. Management of communications and keyword changes with both teams were facilitated through shared online documentation, ensuring efficient case ascertainment of AEFI of concern. An iterative approach was used to continuously improve and expand the program based on identified keyword patterns, variations, combinations, and phrases. False positives and negatives were identified and addressed. Workflow changes to enable easier keyword modifications through an input file rather than code changes in R Studio was rolled out. Results: The implementation of these triaging techniques significantly improved efficiency in vaccine safety surveillance in Victoria, enabling prompt review and follow-up of potentially serious cases. Conclusions: The principles underlying these methods can be extended to pharmacovigilance efforts related to any drug or medical device. SAEFVIC is now in the process of developing a machine learning model to further enhance the triage process.

Invasive pneumococcal disease (IPD) notifications are used to monitor IPD vaccination programmes. We conducted sequential deterministic data-linkage between IPD notifications and hospitalization data in Victoria, Australia, in order to determine whether all diagnosed cases were being reported. The proportion of each relevant hospital admission ICD-10-AM code that could be linked to notified cases was calculated. Total and age-specific annual rates were calculated and compared for notified and non-notified cases. Total incidence was estimated using data-linkage results and application of a two-source capture–recapture method. The first 2 years of IPD surveillance in Victoria missed at least one-sixth of laboratory-confirmed IPD cases. Estimated annual IPD rate increased from 9·0 to 10·7/100 000 and rose even higher, to 11·5/100 000, with age-specific rates possibly reaching 90·0/100 000 children aged <2 years, when using capture–recapture. Strategies to improve notification and coding of hospitalized cases of IPD are required.

Three outbreaks of respiratory illness associated with human coronavirus HCoV-OC43 infection occurred in geographically unrelated aged-care facilities in Melbourne, Australia during August and September 2002. On clinical and epidemiological grounds the outbreaks were first thought to be caused by influenza virus. HCoV-OC43 was detected by RT–PCR in 16 out of 27 (59%) specimens and was the only virus detected at the time of sampling. Common clinical manifestations were cough (74%), rhinorrhoea (59%) and sore throat (53%). Attack rates and symptoms were similar in residents and staff across the facilities. HCoV-OC43 was also detected in surveillance and diagnostic respiratory samples in the same months. These outbreaks establish this virus as a cause of morbidity in aged-care facilities and add to increasing evidence of the significance of coronavirus infections.

Tagne et al discuss the Australian Childhood Travel Outcomes Registry (ACTOR) for travel vaccine safety. Vaccine safety pharmacovigilance is of utmost importance in protecting the health and well-being of childhood travelers worldwide. Ethical considerations play a crucial role in clinical trials involving children. A limited availability of comprehensive and reliable data for assessing vaccine safety presents as a significant challenge, particularly in vulnerable pediatric populations, such as childhood travel. The results revealed that concerns around vaccine safety are the main driver of vaccine hesitancy, increasing the risk many traveling children may not receive recommended travel vaccines. There is a need to strengthen vaccine safety surveillance, particularly in the context of travel-related immunizations.

Introduction: With vaccine hesitancy related to safety concerns remaining high despite the success of COVID-19 vaccines, the ability to rapidly detect and confirm vaccine safety signals has become increasingly important. Vaccine safety surveillance systems typically rely on spontaneous adverse events following immunisation (AEFI) reports, which are useful in providing reaction details [1]; but most AEFI either go unreported or occur after the vaccinee has sought healthcare. This means that information on common AEFI can be missed. Secondary use of nurse-based telephone hotline record data [2] can log AEFI at earlier time-points. SAEFVIC, Victoria's vaccine safety surveillance service, has previously demonstrated that the Farrington algorithm [3] retrospectively applied to state-specific Victorian telephone Healthline data detected vaccine safety signals before authorities were alerted with traditional methods [4]. This study expands that pilot to Healthdirect Australia [5] a multi-jurisdictional government-funded health information and advice service operating in all Australian jurisdictions other than Victoria and Queensland. Aim: To determine the potential of Healthdirect [5] phone record data to provide near real-time vaccine safety signal detection. Methods: We applied the Farrington algorithm to de-identified Healthdirect data from 2015-2019 (ethics approval HREC/18/MonH/ 345). Available triage codes (one per caller, as logged by nurse) were: immunisation reaction; fever (infant & toddler); fever (adult & child); allergic reaction and seizure. The proportional reporting ratio (PRR) [6] of AEFI reports reported to SAEFVIC in the same period were analysed for comparison. Results: Healthdirect recorded 156,162 calls for the triage codes listed. Of these, 21,319 were explicit immunisation reactions. SAEFVIC received 8,313 AEFI reports for the same period. When applied, the Farrington algorithm indicated 4 instances of immunisation reaction alert for 3+ consecutive weeks with one period during April-June 2019 also coinciding with alerts of fever in both adults and infants. PRR analysis of AEFI reported to SAEFVIC did not indicate any signals during this period. The algorithm appeared able to distinguish community background from immunisation reaction and did not raise an undue number of spurious signals. Conclusion: With near real-time availability, prospective surveillance of national telephone healthline data will provide an additional, costeffective, and sensitive supplementary method for vaccine safety signal detection. Receiving background information on circulating conditions (eg. fever/allergy) provides valuable context for assessing the presence of signal in immunisation reactions. With increasing uptake and more sophisticated data models of nurse triage hotline services, their secondary use provides a population-level adjunct to strengthen vaccine pharmacovigilance and community confidence in vaccines.