Safety signal detection with control of latent factors.

Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.

Lessons learned from annotation of VAERS reports on adverse events following influenza vaccination and related to Guillain-Barré syndrome.

BACKGROUND: Vaccine Adverse Events ReportingSystem (VAERS) is a promising resource of tracking adverse events following immunization. Medical Dictionary for Regulatory Activities (MedDRA) terminology used for coding adverse events in VAERS reports has several limitations. We focus on developing an automated system for semantic extraction of adverse events following vaccination and their temporal relationships for a better understanding of VAERS data and its integration into other applications. The aim of the present studyis to summarize the lessons learned during the initial phase of this project in annotating adverse events following influenza vaccination and related to Guillain-Barré syndrome (GBS). We emphasize on identifying the limitations of VAERS and MedDRA. RESULTS: We collected 282 VAERS reports documented between 1990 and 2016 and shortlisted those with at least 1,100 characters in the report. We used a subset of 50 reports for the preliminary investigation and annotated all adverse events following influenza vaccination by mapping to representative MedDRA terms. Associated time expressions were annotated when available. We used 16 System Organ Class (SOC) level MedDRA terms to map GBS related adverse events and expanded some SOC terms to Lowest Level Terms (LLT) for granular representation. We annotated three broad categories of events such as problems, clinical investigations, and treatments/procedures. The inter-annotator agreement of events achieved was 86%. Incomplete reports, typographical errors, lack of clarity and coherence, repeated texts, unavailability of associated temporal information, difficulty to interpret due to incorrect grammar, use of generalized terms to describe adverse events / symptoms, uncommon abbreviations, difficulty annotating multiple events with a conjunction / common phrase, irrelevant historical events and coexisting events were some of the challenges encountered. Some of the limitations we noted are in agreement with previous reports. CONCLUSIONS: We reported the challenges encountered and lessons learned during annotation of adverse events in VAERS reports following influenza vaccination and related to GBS. Though the challenges may be due to the inevitable limitations of public reporting systems and widely reported limitations of MedDRA, we emphasize the need to understand these limitations and extraction of other supportive information for a better understanding of adverse events following vaccination.

Vaccine-Associated Uveitis after COVID-19 Vaccination: Vaccine Adverse Event Reporting System Database Analysis.

PURPOSE: To assess the risk of vaccine-associated uveitis (VAU) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination and evaluate uveitis onset interval and clinical presentations in the patients. DESIGN: A retrospective study from December 11, 2020, to May 9, 2022, using the Centers for Disease Control and Prevention Vaccine Adverse Event Reporting System. PARTICIPANTS: Patients diagnosed with VAU after administration of BNT162b2 (Pfizer-BioNTech, Pfizer Inc/BioNTech SE), mRNA-1273 (Moderna, Moderna Therapeutics Inc), and Ad26.COV2.S (Janssen, Janssen Pharmaceuticals) vaccine worldwide. METHODS: A descriptive analysis of the demographics, clinical history, and presentation was performed. We evaluated the correlation among the 3 vaccines and continuous and categorical variables. A post hoc analysis was performed between uveitis onset interval after vaccination and age, dose, and vaccine type. Finally, a 30-day risk analysis for VAU onset postvaccination was performed. MAIN OUTCOME MEASURES: The estimated global crude reporting rate, observed to expected ratio of VAU in the United States, associated ocular and systemic presentations, and onset duration. RESULTS: A total of 1094 cases of VAU were reported from 40 countries with an estimated crude reporting rate (per million doses) of 0.57, 0.44, and 0.35 for BNT162b2, mRNA-1273, and Ad26.COV2.S, respectively. The observed to expected ratio of VAU was comparable for BNT162b2 (0.023), mRNA-1273 (0.025), and Ad26.COV2.S (0.027). Most cases of VAU were reported in patients who received BNT162b2 (n = 853, 77.97%). The mean age of patients with VAU was 46.24 ± 16.93 years, and 68.65% (n = 751) were women. Most cases were reported after the first dose (n = 452, 41.32%) and within the first week (n = 591, 54.02%) of the vaccination. The onset interval for VAU was significantly longer in patients who received mRNA-1273 (21.22 ± 42.74 days) compared with BNT162b2 (11.42 ± 23.16 days) and rAd26.COV2.S (12.69 ± 16.02 days) vaccines (P < 0.0001). The post hoc analysis revealed a significantly shorter interval of onset for the BNT162b2 compared with the mRNA 1273 vaccine (P < 0.0001). The 30-day risk analysis showed a significant difference among the 3 vaccines (P < 0.0001). CONCLUSIONS: The low crude reporting rate and observed to expected ratio suggest a low safety concern for VAU. This study provides insights into a possible temporal association between reported VAU events and SARS-CoV-2 vaccines; however, further investigations are required to delineate the associated immunological mechanisms.

Bayesian generalized linear low rank regression models for the detection of vaccine-adverse event associations.

We propose a generalized linear low-rank mixed model (GLLRM) for the analysis of both high-dimensional and sparse responses and covariates where the responses may be binary, counts, or continuous. This development is motivated by the problem of identifying vaccine-adverse event associations in post-market drug safety databases, where an adverse event is any untoward medical occurrence or health problem that occurs during or following vaccination. The GLLRM is a generalization of a generalized linear mixed model in that it integrates a factor analysis model to describe the dependence among responses and a low-rank matrix to approximate the high-dimensional regression coefficient matrix. A sampling procedure combining the Gibbs sampler and Metropolis and Gamerman algorithms is employed to obtain posterior estimates of the regression coefficients and other model parameters. Testing of response-covariate pair associations is based on the posterior distribution of the corresponding regression coefficients. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the proposed procedures on binary and count outcomes. We further illustrate the GLLRM via a real data example based on the Vaccine Adverse Event Reporting System.

Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system.

Many statistical methods have been applied to VAERS (vaccine adverse event reporting system) database to study the safety of COVID-19 vaccines. However, none of these methods considered the adverse event (AE) ontology. The AE ontology contains important information about biological similarities between AEs. In this paper, we develop a model to estimate vaccine-AE associations while incorporating the AE ontology. We model a group of AEs using the zero-inflated negative binomial model and then estimate the vaccine-AE association using the empirical Bayes approach. This model handles the AE count data with excess zeros and allows borrowing information from related AEs. The proposed approach was evaluated by simulation studies and was further illustrated by an application to the Vaccine Adverse Event Reporting System (VAERS) dataset. The proposed method is implemented in an R package available at https://github.com/umich-biostatistics/zGPS.AO.

Imprecision in vaccine adverse event reporting and a methodological analysis of reporting systems to improve pharmacovigilance and public health.

INTRODUCTION: This study documents imprecision in Japanese reports of adverse events following immunization (AEFI). In doing so, it presents methods to analyze this imprecision. METHODS: These methods include use of unique Japanese data on the validity of certain AEFIs. They also include ways to estimate AEFI rates, which allow comparison of AEFI data between countries. Using US AEFI data for comparison, we show how differences in AEFI reporting systems likely influence AEFI statistics. RESULTS: Although our comparisons of AEFI rates are not precise, many of the difference we detected between Japanese and US statistics make sense and reflect differences in the societal and medical perspectives on various vaccines or can be explained by differences in the reporting systems including reporting sources. For example, differences in societal and medical perspective probably underly the extraordinarily high Japanese rates of anaphylaxis and other AEs following HPV immunizations from 2010 to 2016 compared to US rates and to Japanese rates for other vaccines. High US rates of reported Guillain-Barré syndrome following influenza vaccination relative to Japanese rates and to rates for other US vaccines are consistent with data suggesting that the index of suspicion for such reactions could affect AEFI rates. The findings that over half of Japanese anaphylaxis reports for every vaccine are erroneous, and that close to half of "serious" Japanese AEFI cases probably are not serious may be due in part not only to explanations unique to Japan, but also to factors that apply to the USA and other countries. Differences in reporting systems account for a much higher rate of non-serious AEFI reports in the USA compared to Japan. Japanese marketing authorization holders are probably at least as assiduous and timely in their reporting of AEFIs as health care providers, though granular level differences are apparent in reporting by various sources. CONCLUSION: The methods we used to analyze the validity of Japanese statistics can be used to analyze the validity of AEFI reports from other countries and aid the harmonization of adverse event reporting systems. Eventually, similar reporting systems might be adapted for drugs and medical devices.

Reporting rates for VAERS death reports following COVID-19 vaccination, December 14, 2020-November 17, 2021.

PURPOSE: Despite widely available safety information for the COVID-19 vaccines, vaccine hesitancy remains a challenge. In some cases, vaccine hesitancy may be related to concerns about the number of reports of death to the Vaccine Adverse Event Reporting System (VAERS). We aimed to provide information and context about reports of death to VAERS following COVID-19 vaccination. METHODS: This is a descriptive study evaluating reporting rates for VAERS death reports for COVID-19 vaccine recipients in the United States between December 14, 2020, and November 17, 2021. Reporting rates were calculated as death events per million persons vaccinated and compared to expected all-cause (background) death rates. RESULTS: 9201 death events were reported for COVID-19 vaccine recipients aged 5 years and older (or age unknown). Reporting rates for death events increased with increasing age, and males generally had higher reporting rates than females. For death events within 7 days and 42 days of vaccination, respectively, observed reporting rates were lower than the expected all-cause death rates. Reporting rates for Ad26.COV2.S vaccine were generally higher than for mRNA COVID-19 vaccines, but still lower than the expected all-cause death rates. Limitations of VAERS data include potential reporting bias, missing or inaccurate information, lack of a control group, and reported diagnoses, including deaths, are not causally verified diagnoses. CONCLUSIONS: Reporting rates for death events were lower than the all-cause death rates expected in the general population. Trends in reporting rates reflected known trends in background death rates. These findings do not suggest an association between vaccination and overall increased mortality.

VAERS-reported new-onset seizures following use of COVID-19 vaccinations as compared to influenza vaccinations.

The incidence of new-onset seizures, which we defined as de novo seizures occurring within 4 weeks of receiving any of the US Food and Drug Administration-approved COVID-19 vaccinations as reported in patient-reported data compiled in the US Centers for Disease Control and Prevention Vaccine Adverse Events Reporting System Data (CDC VAERS), has not been explored. The VAERS database contains de-identified patient-reported adverse events following vaccination and represents post-marketing surveillance and analysis of vaccine safety. After adjusting for time at risk, this resulted in estimated incidence rates of 3.19 seizures per 100 000 persons per year for the COVID-19 vaccine and 0.090 seizures per 100 000 persons per year for the influenza vaccines. A data-driven, individualized dataset that is comprehensive and coupled with a longitudinal follow-up in larger numbers of vaccinated individuals is needed to expand on our preliminary findings of vaccine-related seizures.

Thromboembolic events after Ad.26.COV2.S COVID-19 vaccine: Reports to the Vaccine Adverse Event Reporting System.

PURPOSE: The Food and Drug Administration (FDA) has identified a potential safety concern for thromboembolic events (TEEs) after Ad.26.COV2.S COVID-19 Vaccine. We sought to characterize the frequency, severity, type, and anatomic location of TEEs reported to the Vaccine Adverse Event Reporting System (VAERS) following Ad.26.COV2.S. METHODS: Reports of TEEs after Ad.26.COV2.S were identified in VAERS, and demographics, clinical characteristics, and relevant medical history were summarized. For a subset of reports, physicians reviewed available medical records and evaluated clinical presentation, diagnostic evaluation, risk factors, and treatment. The crude reporting rate of TEEs was estimated based on case counts in VAERS and vaccine administration data. RESULTS: Through February 28, 2022, FDA identified 3790 reports of TEEs after Ad.26.COV2.S. Median age was 56 years, and 1938 individuals (51.1%) were female. Most reports, 2892 (76.3%), were serious, including 421 deaths. Median time to onset was 12 days post-vaccination. Obesity and ischemia were among the most commonly documented risk factors. Thrombocytopenia (platelet count less than 150 000/µl) was documented in 63 records (11.5%) and anti-platelet 4 antibodies in 25 (4.6%). Medical review identified cases of severe clot burden (e.g., bilateral, saddle, or other massive pulmonary embolism with or without cor pulmonale; lower extremity thrombus involving the external iliac, common femoral, popliteal, posterior tibial, peroneal, and gastrocnemius veins). The crude reporting rate was ~20.7 cases of TEE per 100 000 doses of Ad.26.COV2.S administered. CONCLUSIONS: Life-threatening or fatal TEEs have been reported after Ad.26.COV2.S, including bilateral massive pulmonary embolism or other severe clot burden.

COVID-19 vaccine and menstrual conditions in female: data analysis of the Vaccine Adverse Event Reporting System (VAERS).

BACKGROUND: In reports of adverse reactions following vaccination with the coronavirus disease 2019(COVID-19) vaccines, there have been fewer reports of concern for menstrual disorders in female. OBJECTIVE: Our study employed Vaccine Adverse Event Reporting System (VAERS) to investigate and analyze the relationship between COVID-19 Vaccines and menstrual disorders in female. METHODS: We collected reports of menstrual disorders in VAERS from July 2, 1990 to November 12, 2021, and performed a stratified analysis. The potential relationship between COVID-19 vaccine and reports of menstrual disorders was evaluated using the Reporting Odds Ratio (ROR) method. RESULTS: A total of 14,431 reports of menstrual disorders were included in the study, and 13,118 were associated with COVID-19 vaccine. The ROR was 7.83 (95% confidence interval [95%CI]: 7.39-8.28). The most commonly reported event was Menstruation irregular (4998 reports), and a higher percentage of female aged 30-49 years reported menstrual disorders (42.55%) after exposure to COVID-19 Vaccines. Both for all reports of menstrual disorders (ROR = 5.82; 95%CI: 4.93-6.95) and excluding reports of unknown age (ROR = 13.02; 95%CI: 10.89-15.56),suggest that female age may be associated with menstrual disorders after vaccination with the COVID-19 Vaccines. CONCLUSION: There is a potential safety signal when the COVID-19 vaccine is administered to young adult female (30-49 years old), resulting in menstrual disorders in. However, due to the well-known limitations of spontaneous reporting data, it is challenging to explicity classify menstrual disorders as an adverse event of the COVID-19 Vaccines, and reports of adverse reactions to COVID-19 Vaccines in this age group should continue to be tracked.

Hearing disorder following COVID-19 vaccination: A pharmacovigilance analysis using the Vaccine Adverse Event Reporting System.

WHAT IS KNOWN AND OBJECTIVE: Evidence on whether the coronavirus disease 2019 (COVID-19) vaccination could cause hearing-related adverse events is still conflicting. This study aims to access the association between COVID-19 vaccine and hearing disorder. METHODS: The Vaccine Adverse Event Reporting System (VAERS) was queried between January 2020 to November 2021. The disproportionality pattern for hearing impairment of COVID-19 vaccine was accessed by calculating the reporting odds ratio (ROR) and proportional reporting ratio (PRR). A further subgroup analysis based on the type of COVID-19 vaccine and the doses administered was performed. In addition, the disproportionalities for hearing dysfunction between COVID-19 and influenza vaccines were compared. RESULTS AND DISCUSSION: A total of 14,956 reports of hearing-related adverse events were identified with COVID-19 vaccination and 151 with influenza vaccine during the analytic period in VAERS. The incidence of hearing disorder following COVID-19 vaccination was 6.66 per 100,000. The results of disproportionality analysis revealed that the adverse events of hearing impairment, after administration of COVID-19 vaccine, was significantly highly reported (ROR 2.38, 95% confidence interval [CI] 2.20-2.56; PRR: 2.35, χ2 537.58), for both mRNA (ROR 2.37, 95% CI 2.20-2.55; PRR 2.34, χ2 529.75) and virus vector vaccines (ROR 2.50, 95% CI 2.28-2.73; PRR 2.56, χ2 418.57). While the disproportional level for hearing dysfunction was quite lower in influenza vaccine (ROR 0.36, 95% CI 0.30-0.42; PRR 0.36, χ2 172.24). WHAT IS NEW AND CONCLUSION: This study identified increased risk for hearing disorder following administration of both mRNA and virus vector COVID-19 vaccines compared to influenza vaccination in real-world settings.

Acute kidney injury after COVID-19 vaccines: a real-world study.

BACKGROUND: Acute kidney injury (AKI), a rare adverse event, cannot be ignored as millions of doses of coronavirus disease 2019 (COVID-19) vaccinations. We aimed to investigate the occurrence of post-vaccine AKI reported to the Vaccine Adverse Event Reporting System (VAERS). METHODS: After data mapping from December 2020 to June 2021, we summarized demographic and clinical features and outcomes of reported cases from three vaccines (Pfizer-BNT, MODERNA, and JANSSEN). The Bayesian and nonproportional analyses explored the correlations between COVID-19 vaccines and AKI. RESULTS: We identified 1133 AKI cases. Pfizer-BNT appeared to have a stronger AKI correlation than MODERNA and JANSSEN, based on the highest reporting odds ratio (ROR = 2.15, 95% confidence interval = 1.97, 2.36). We observed the differences in ages, comorbidities, current illnesses, post-vaccine AKI causes, and time to AKI onset (all p＜.05) among three vaccines. Most patients are elderly, with the highest age in MODERNA (68.41 years) and lowest in JANSSEN (59.75 years). Comorbidities were noticed in 58.83% of the cases and active infections in over 20% of cases. The leading cause of post-vaccine AKI was volume depletion (40.78%), followed by sepsis (11.74%). Patients in Pfizer-BNT had the worst outcome with 19.78% deaths, following 17.78% in MODERNA and 12.36% in JANSSEN (p = .217). The proportion of patients on dialysis was higher in JANSSEN than in Pfizer-BNT and MODERNA (14.61% vs. 6.54%, 10.62%, p = .008). CONCLUSION: AKI could occur after the COVID-19 vaccines, predominantly in elderly patients. However, the causality needs further identification.

Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines.

Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted >50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).

Vaccine adverse event reporting system (VAERS): Evaluation of 31 years of reports and pandemics' impact.

Background: Vaccine adverse event reporting system (VAERS) was established in the United States (U.S.) as an early warning system with a main purpose of collecting post-marketing Adverse events following immunizations (AEFIs) reports to monitor the vaccine safety and to mitigate the risks from vaccines. During the coronavirus diseases 2019 (COVID-19) pandemic, VAERS got more attention as its important role in monitoring the safety of the vaccines. The aim of this study was to investigate VAERS patterns, reported AEFI, vaccines, and impact of different pandemics since its inception. Methods: This was an observational study using VARES data from 2/7/1990 to 12/11/2021. Patterns of reports over years were first described, followed by a comparison of reports statistics per year. Furthermore, a comparison of incidents (death, ER visits, etc.) statistics over years, in addition to statistics of each vaccine were calculated. Moreover, each incident's statistics for each vaccine were calculated and top vaccines were reported. All analyses were conducted using R (Version 1.4.1717) and Excel for Microsoft 365. Results: There were 1,396,280 domestic and 346,210 non-domestic reports during 1990-2021, including 228 vaccines. For both domestic and non-domestic reports, year of 2021 had the highest reporting rate (48.52 % and 70.33 %), in addition a notable change in AEFIs patterns were recorded during 1991, 1998, 2000, 2006, 2009, 2011, and 2017. AEFIs were as follow: deaths (1.00 % and 4.08 %), ER or doctor visits (13.37 % and 2.27 %), hospitalizations (5.84 % and 27.78 %), lethal threat (1.42 % and 4.38 %), and disabilities (1.4 % and 7.96 %). Pyrexia was the top reported symptom during the past 31 years, except for 2021 where headache was the top one. COVID-19 vaccines namely Moderna, Pfizer-Biontech, and Janssen were the top 3 reported vaccines with headache, pyrexia, and fatigue as the top associated AEFIs. Followed by Zoster, Seasonal Influenza, Pneumococcal, and Human papillomavirus vaccines. Conclusions: The large data available in VARES make it a useful tool for detecting and monitoring vaccine AEFIs. However, its usability relies on understating the limitations of this surveillance system, the impact of governmental regulations, availability of vaccines, and public health recommendations on the reporting rate.

Vaccine adverse event enrichment tests.

Vaccination safety is critical for individual and public health. Many existing methods have been used to conduct safety studies with the VAERS (Vaccine Adverse Event Reporting System) database. However, these methods frequently identify many adverse event (AE) signals and they are often hard to interpret in a biological context. The AE ontology introduces biologically meaningful structures to the Vaccine Adverse Event Reporting System (VAERS) database by connecting similar AEs, which provides meaningful interpretation for the underlying safety issues. In this paper, we develop rigorous statistical methods to identify "interesting" AE groups by performing AE enrichment analysis. We extend existing gene enrichment tests to perform AE enrichment analysis, while incorporating the special features of the AE data. The proposed methods were evaluated using simulation studies and were further illustrated on two studies using VAERS data. The proposed methods were implemented in R package AEenrich and can be installed from the Comprehensive R Archive Network, CRAN, and source code are available at https://github.com/umich-biostatistics/AEenrich.

Prediction of post-vaccination Guillain-Barré syndrome using data from a passive surveillance system.

PURPOSE: Severe adverse events (AEs), such as Guillain-Barré syndrome (GBS) occur rarely after influenza vaccination. We identify highly associated AEs with GBS and develop prediction models for GBS using the US Vaccine Adverse Event Reporting System (VAERS) reports following trivalent influenza vaccination (FLU3). METHODS: This study analyzed 80 059 reports from the US VAERS between 1990 and 2017. Several AEs were identified as highly associated with GBS and were used to develop the prediction model. Some common and mild AEs that were suspected to be underreported when GBS occurred simultaneously were removed from the final model. The analyses were validated using European influenza vaccine AEs data from EudraVigilance. RESULTS: Of the 80 059 reports, 1185 (1.5%) were annotated as GBS related. Twenty-four AEs were identified as having strong association with GBS. The full prediction model, using age, sex, and all 24 AEs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 85.4% (90% CI: [83.8%, 86.9%]). After excluding the nine (e.g., pruritus, rash, injection site pain) likely underreported AEs, the final AUC became 77.5% (90% CI: [75.5%, 79.6%]). Two hundred and one (0.25%) reports were predicted as of high risk of GBS (predicted probability >25%) and 84 actually developed GBS. CONCLUSION: The prediction performance demonstrated the potential of developing risk-prediction models utilizing the VAERS cohort. Excluding the likely underreported AEs sacrificed some prediction power but made the model more interpretable and feasible. The high absolute risk of even a small number of AE combinations suggests the promise of GBS prediction within the VAERS dataset.

Vaccine hesitancy: evidence from an adverse events following immunization database, and the role of cognitive biases.

BACKGROUND: Vaccine hesitancy has been a growing challenge for public health in recent decades. Among factors contributing to vaccine hesitancy, concerns regarding vaccine safety and Adverse Events (AEs) play the leading role. Moreover, cognitive biases are critical in connecting such concerns to vaccine hesitancy behaviors, but their role has not been comprehensively studied. In this study, our first objective is to address concerns regarding vaccine AEs to increase vaccine acceptance. Our second objective is to identify the potential cognitive biases connecting vaccine hesitancy concerns to vaccine-hesitant behaviors and identify the mechanism they get triggered in the vaccine decision-making process. METHODS: First, to mitigate concerns regarding AEs, we quantitatively analyzed the U.S. Vaccine Adverse Event Reporting System (VAERS) from 2011 to 2018 and provided evidence regarding the non-severity of the AEs that can be used as a communicable summary to increase vaccine acceptance. Second, we focused on the vaccination decision-making process. We reviewed cognitive biases and vaccine hesitancy literature to identify the most potential cognitive biases that affect vaccine hesitancy and categorized them adopting the Precaution Adoption Process Model (PAPM). RESULTS: Our results show that the top frequent AEs are expected mild reactions like injection site erythema (4.29%), pyrexia (3.66%), and injection site swelling (3.21%). 94.5% of the reports are not serious and the average population-based serious reporting rate over the 8 years was 25.3 reports per 1 million population. We also identified 15 potential cognitive biases that might affect people's vaccination decision-making and nudge them toward vaccine hesitancy. We categorized these biases based on the factors that trigger them and discussed how they contribute to vaccine hesitancy. CONCLUSIONS: This paper provided an evidence-based communicable summary of VAERS. As the most trusted sources of vaccine information, health practitioners can use this summary to provide evidence-based vaccine information to vaccine decision-makers (patients/parents) and mitigate concerns over vaccine safety and AEs. In addition, we identified 15 potential cognitive biases that might affect the vaccination decision-making process and nudge people toward vaccine hesitancy. Any plan, intervention, and message to increase vaccination uptake should be modified to decrease the effect of these potential cognitive biases.

Safety of bivalent human papillomavirus vaccine in the US vaccine adverse event reporting system (VAERS), 2009-2017.

AIMS: Human papillomavirus (HPV) vaccines prevent infection with oncogenic virus types. We analysed reports to the US Vaccine Adverse Event Reporting System (VAERS) of adverse events (AE) following bivalent HPV vaccine (2vHPV). METHODS: We conducted descriptive analysis of 2vHPV reports, reviewed individual reports, calculated crude AE reporting rates and conducted empirical Bayesian data mining. RESULTS: Of 241 2vHPV reports, 158 were in females, 64 in males (2vHPV is approved for females only) and 19 with unknown sex; 95.8% were classified as nonserious. Dizziness, headache, nausea and injection site reactions were the most common symptoms. Crude AE reporting rates were 33.3 reports per 100 000 doses distributed overall, and 1.4 per 100 000 for serious reports. Empirical Bayesian data mining identified disproportional reporting for three types of medical errors; assessment indicated findings that were probably driven by inadvertent 2vHPV use in males. CONCLUSIONS: We did not identify any new or unexpected safety concerns in our review of 2vHPV reports to VAERS.

Logistic Regression Likelihood Ratio Test Analysis for Detecting Signals of Adverse Events in Post-market Safety Surveillance.

The Vaccine Adverse Event Reporting System (VAERS) and other product surveillance systems compile reports of product-associated adverse events (AEs), and these reports may include a wide range of information including age, gender, and concomitant vaccines. Controlling for possible confounding variables such as these is an important task when utilizing surveillance systems to monitor post-market product safety. A common method for handling possible confounders is to compare observed product-AE combinations with adjusted baseline frequencies where the adjustments are made by stratifying on observable characteristics. Though approaches such as these have proven to be useful, in this article we propose a more flexible logistic regression approach which allows for covariates of all types rather than relying solely on stratification. Indeed, a main advantage of our approach is that the general regression framework provides flexibility to incorporate additional information such as demographic factors and concomitant vaccines. As part of our covariate-adjusted method, we outline a procedure for signal detection that accounts for multiple comparisons and controls the overall Type 1 error rate. To demonstrate the effectiveness of our approach, we illustrate our method with an example involving febrile convulsion, and we further evaluate its performance in a series of simulation studies.

A signal detection method for temporal variation of adverse effect with vaccine adverse event reporting system data.

BACKGROUND: To identify safety signals by manual review of individual report in large surveillance databases is time consuming; such an approach is very unlikely to reveal complex relationships between medications and adverse events. Since the late 1990s, efforts have been made to develop data mining tools to systematically and automatically search for safety signals in surveillance databases. Influenza vaccines present special challenges to safety surveillance because the vaccine changes every year in response to the influenza strains predicted to be prevalent that year. Therefore, it may be expected that reporting rates of adverse events following flu vaccines (number of reports for a specific vaccine-event combination/number of reports for all vaccine-event combinations) may vary substantially across reporting years. Current surveillance methods seldom consider these variations in signal detection, and reports from different years are typically collapsed together to conduct safety analyses. However, merging reports from different years ignores the potential heterogeneity of reporting rates across years and may miss important safety signals. METHOD: Reports of adverse events between years 1990 to 2013 were extracted from the Vaccine Adverse Event Reporting System (VAERS) database and formatted into a three-dimensional data array with types of vaccine, groups of adverse events and reporting time as the three dimensions. We propose a random effects model to test the heterogeneity of reporting rates for a given vaccine-event combination across reporting years. The proposed method provides a rigorous statistical procedure to detect differences of reporting rates among years. We also introduce a new visualization tool to summarize the result of the proposed method when applied to multiple vaccine-adverse event combinations. RESULT: We applied the proposed method to detect safety signals of FLU3, an influenza vaccine containing three flu strains, in the VAERS database. We showed that it had high statistical power to detect the variation in reporting rates across years. The identified vaccine-event combinations with significant different reporting rates over years suggested potential safety issues due to changes in vaccines which require further investigation. CONCLUSION: We developed a statistical model to detect safety signals arising from heterogeneity of reporting rates of a given vaccine-event combinations across reporting years. This method detects variation in reporting rates over years with high power. The temporal trend of reporting rate across years may reveal the impact of vaccine update on occurrence of adverse events and provide evidence for further investigations.