Characterization of the Differential Adverse Event Rates by Race/Ethnicity Groups for HPV Vaccine by Integrating Data From Different Sources

Adverse effect investigation using application software after vaccination against SARS-CoV-2 for healthcare workers

INTRODUCTION

The usefulness of smartphone-based application software as a way to manage adverse events (AEs) after vaccination is well known. The purpose of this study is to clarify the usefulness and precautions of employing a smartphone application for collecting AEs after the administration of Comirnaty®️.

METHODS

Healthcare workers (HCWs) who were vaccinated with Comirnaty®️ were asked to register for the application software and to report AEs for 14 days after vaccination. AEs were self-reported according to severity. The software was set to output an alert in case of fever.

RESULTS

The number of HCWs who received the first dose was 2,551, and 2,406 (94.3%) reported their vaccinations. 2,547 received the second dose, and 2,347 (92.1%) reported their vaccinations. With the first dose, the reporting rate stayed above 83.3% until the final day. On the other hand, that of the second dose decreased rapidly after 6 days. The most frequent symptom was "pain at injection site" (more than 70%). Severe AEs were 6.6% after the second dose, with 0.6% visiting a clinic. Many AEs peaked on the day after administration and disappeared within 1 week. There were few reports of fever.

CONCLUSION

Smartphone applications can be used to collect information on AEs after vaccination. Application settings and dissemination are necessary to maintain the reporting rate of HCWs.

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.

Adverse Effects on Work and Daily Life Interference among Healthcare Workers after the First and Second ChAdOx1 and BNT162b2 COVID-19 Vaccine Doses

In this study, we assessed the adverse effects and the work and daily life interference associated with each dose of the ChAdOx1 and BNT162b2 COVID-19 vaccines. Questionnaires were distributed to workers after they received both doses; only those who worked the day after receiving the vaccine were included in the analysis. Overall, 368 ChAdOx1-vaccinated and 27 BNT162b2-vaccinated participants were included. Among the ChAdOx1-vaccinated participants, the incidence of adverse effects was significantly lower after the second dose than after the first dose. Among the BNT162b2-vaccinated participants, however, no differences in adverse effects or work and daily life interference were found between the doses. After the first and second dose, the numeric scale score (0–10) for interference with work was 3.9 ± 2.9 and 1.6 ± 1.9 for the ChAdOx1 and 3.2 ± 2.5 and 3.6 ± 3.0 for the BNT162b2 vaccine, respectively. A similar trend was observed for interference with daily life. Factors associated with work and daily life interference in the multivariate model were age, vaccine dose (first or second), and the interaction term of vaccine type and dose. These results could be used to inform the general population of the adverse effects associated with these vaccinations.

Methodological frontiers in vaccine safety: qualifying available evidence for rare events, use of distributed data networks to monitor vaccine safety issues, and monitoring the safety of pregnancy interventions

While vaccines are rigorously tested for safety and efficacy in clinical trials, these trials do not include enough subjects to detect rare adverse events, and they generally exclude special populations such as pregnant women. It is therefore necessary to conduct postmarketing vaccine safety assessments using observational data sources. The study of rare events has been enabled in through large linked databases and distributed data networks, in combination with development of case-centred methods. Distributed data networks necessitate common protocols, definitions, data models and analytics and the processes of developing and employing these tools are rapidly evolving. Assessment of vaccine safety in pregnancy is complicated by physiological changes, the challenges of mother-child linkage and the need for long-term infant follow-up. Potential sources of bias including differential access to and utilisation of antenatal care, immortal time bias, seasonal timing of pregnancy and unmeasured determinants of pregnancy outcomes have yet to be fully explored. Available tools for assessment of evidence generated in postmarketing studies may downgrade evidence from observational data and prioritise evidence from randomised controlled trials. However, real-world evidence based on real-world data is increasingly being used for safety assessments, and new tools for evaluating real-world evidence have been developed. The future of vaccine safety surveillance, particularly for rare events and in special populations, comprises the use of big data in single countries as well as in collaborative networks. This move towards the use of real-world data requires continued development of methodologies to generate and assess real world evidence.

Anti-NMDA Receptor Encephalitis, Vaccination and Virus

Anti-N-methyl-d-aspartate (Anti-NMDA) receptor encephalitis is an acute autoimmune disorder. The symptoms range from psychiatric symptoms, movement disorders, cognitive impairment, and autonomic dysfunction. Previous studies revealed that vaccination might induce this disease. A few cases were reported to be related to H1N1 vaccine, tetanus/diphtheria/pertussis and polio vaccine, and Japanese encephalitis vaccine. Although vaccination is a useful strategy to prevent infectious diseases, in a low risk, it may trigger serious neurological symptoms. In addition to anti-NMDA receptor encephalitis, other neurological diseases were reported to be associated with a number of vaccines. In this paper, the anti-NMDA receptor encephalitis cases related to a number of vaccines and other neurological symptoms that might be induced by these vaccines were reviewed. In addition, anti-NMDA receptor encephalitis cases that were induced by virus infection were also reviewed.

Investigating safety profiles of human papillomavirus vaccine across group differences using VAERS data and MedDRA

Background

The safety of vaccines is a critical factor in maintaining public trust in national vaccination programs. This study aimed to evaluate the safety profiles of human papillomavirus (HPV) vaccines with regard to the distribution of adverse events (AE) across gender and age, and the correlations across various AEs using the Food and Drug Administration/Centers for Disease Control and Prevention Vaccine Adverse Event Reporting System (VAERS).

Methods

For analyses, 27,348 patients aged between 9 and 25 years old with at least one AE reported in VAERS between the year of 2006 and 2017 were included. AEs were summarized into two levels: the lower level preferred term (PT) and higher level system organ classes (SOCs) based on the structure of Medical Dictionary for Regulatory Activities (MedDRA). A series of statistical analyses were applied on both levels of AEs. Zero-truncated Poisson regression and multivariate logistic regression models were first developed to assess the rate and risk of SOCs across age groups and genders. Pairwise Pearson correlation analyses and hierarchical clustering analyses were then conducted to explore the interrelationships and clustering pattern among AEs.

Results

We identified 27,337 unique HPV vaccine reports between 2006 and 2017. Disproportional reporting of AEs was observed across age and gender in 21 SOCs (p < 0.05). The correlation analyses found most SOCs demonstrate weak positive correlations except for five pairs which were negatively correlated: skin and subcutaneous tissue disorders + injury poisoning and procedural complications; skin and subcutaneous tissue disorders + nervous system disorders; Skin and subcutaneous tissue disorders + pregnancy, puerperium and perinatal conditions; nervous system disorders + pregnancy, puerperium and perinatal conditions; pregnancy, puerperium and perinatal conditions + general disorders and administration site conditions. Nervous system disorders had the most AEs which contributed to 12,448 (46%) cases. In the further analyses of correlations between PT in nervous system disorders, the three most strongly correlated AEs were psychiatric disorders (r = 0.35), gastrointestinal disorders (r = 0.215), and musculoskeletal and connective tissue disorders (r = 0.261). We observed an inter-SOCs correlation of the PTs among AE pairs by nervous system disorders/psychiatric disorders/gastrointestinal disorders/musculoskeletal and connective tissue disorders.

Conclusions

The analyses revealed a different distribution pattern of AEs across gender and age subgroups in 21 SOC level AEs. Correlation analyses and hierarchical clustering analyses further revealed several correlated patterns across various AEs. However, findings from this study should be interpreted with caution. Further clinical studies are needed to understand the heterogeneity of AEs reporting across subgroups and the biological pathways among the statistically correlated AEs.