Augmenting bacterial similarity measures using a graph-based genome representation.

Relationships between bacterial taxa are traditionally defined using 16S rRNA nucleotide similarity or average nucleotide identity. Improvements in sequencing technology provide additional pairwise information on genome sequences, which may provide valuable information on genomic relationships. Mapping orthologous gene locations between genome pairs, known as synteny, is typically implemented in the discovery of new species and has not been systematically applied to bacterial genomes. Using a data set of 378 bacterial genomes, we developed and tested a new measure of synteny similarity between a pair of genomes, which was scaled onto 16S rRNA distance using covariance matrices. Based on the input gene functions used (i.e., core, antibiotic resistance, and virulence), we observed varying topological arrangements of bacterial relationship networks by applying (i) complete linkage hierarchical clustering and (ii) K-nearest neighbor graph structures to synteny-scaled 16S data. Our metric improved clustering quality comparatively to state-of-the-art average nucleotide identity metrics while preserving clustering assignments for the highest similarity relationships. Our findings indicate that syntenic relationships provide more granular and interpretable relationships for within-genera taxa compared to pairwise similarity measures, particularly in functional contexts. IMPORTANCE: Given the prevalence and necessity of the 16S rRNA measure in bacterial identification and analysis, this additional analysis adds a functional and synteny-based layer to the identification of relatives and clustering of bacteria genomes. It is also of computational interest to model the bacterial genome as a graph structure, which presents new avenues of genomic analysis for bacteria and their closely related strains and species.

Characteristic Attribute Organization System (CAOS): Identifying Classification Rules Based on Phylogenetically Organized Sequences.

Classification is a technique that labels subjects based on the characteristics of the data. It often includes using prior learned information from preexisting data drawn from the same distribution or data type to make informed decisions per each given subject. The method presented here, the Characteristic Attribute Organization System (CAOS), uses a character-based approach to molecular sequence classification. Using a set of aligned sequences (either nucleotide or amino acid) and a maximum parsimony tree, CAOS will generate classification rules for the sequences based on tree structure and provide more interpretable results than other classification or sequence analysis protocols. The code is accessible at https://github.com/JuliaHealth/CAOS.jl/ .

Augmenting Bacterial Similarity Measures Using a Graph-Based Genome Representation.

Relationships between bacterial taxa are traditionally defined using 16S rRNA nucleotide similarity or average nucleotide identity. Improvements in sequencing technology provides additional pairwise information on genome sequences, which may provide valuable information on genomic relationships. Mapping orthologous gene locations between genome pairs, known as synteny, is typically implemented in the discovery of new species and has not been systematically applied to bacterial genomes. Using a dataset of 378 bacterial genomes, we developed and tested a new measure of synteny similarity between a pair of genomes, which was scaled onto 16S rRNA distance using covariance matrices. Based on the input gene functions used (i.e., core, antibiotic resistance, and virulence), we observed varying topological arrangements of bacterial relationship networks by applying (1) complete linkage hierarchical clustering and (2) KNN graph structures to syntenic-scaled 16S data. Our metric improved clustering quality comparatively to state-of-the-art ANI metrics while preserving clustering assignments for the highest similarity relationships. Our findings indicate that syntenic relationships provide more granular and interpretable relationships for within-genera taxa compared to pairwise similarity measures, particularly in functional contexts.

Interventions to Reduce Electronic Health Record-Related Burnout: A Systematic Review.

BACKGROUND: Electronic health records are a significant contributing factor in clinician burnout, which negatively impacts patient care. OBJECTIVES: To identify and appraise published solutions that aim to reduce EHR-related burnout in clinicians. METHODS: A literature search strategy was developed following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Six databases were searched for articles published between January 1950 and March 2023. The inclusion criteria were peer-reviewed, full-text, English language articles that described interventions targeting EHR-related burnout in any type of clinician, with reported outcomes related to burnout, wellness, EHR satisfaction, or documentation workload. Studies describing interventions without an explicit focus on reducing burnout or enhancing EHR-related satisfaction were excluded. RESULTS: We identified 44 articles describing interventions to reduce EHR-related burnout. These interventions included the use of scribes, EHR training, and EHR modifications. These interventions were generally well received by the clinicians and patients, with subjective improvements in documentation time and EHR satisfaction, although objective data were limited. CONCLUSION: The findings of this review underscore the potential benefits of interventions to reduce EHR-related burnout as well as the need for further research with more robust study designs involving randomized trials, control groups, longer study durations, and validated, objective outcome measurements.

Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma.

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Characterizing suicidal ideation, suicidal behaviors, and service utilization among unhoused individuals using a health information exchange.

INTRODUCTION: Unhoused individuals have high rates of suicidal ideation (SI) and suicidal behaviors (SB), but few have studied the relative timing of homelessness and SI/SB. Our study examines the potential to use state-wide electronic health record data from Rhode Island's health information exchange (HIE) to identify temporal relationships, service utilization, and associations of SI/SB among unhoused individuals. METHODS: We use timestamped HIE data for 5368 unhoused patients to analyze service utilization and the relative timing of homelessness versus SI/SB onset. Multivariable models identified associations of SI/SB, hospitalization, and repeat acute care utilization within 30 days from clinical features representing 10,000+ diagnoses captured within the HIE. RESULTS: The onset of SI typically precedes homelessness onset, while the onset of SB typically follows. Weekly rates of suicide-related service utilization increased over 25 times the baseline rate during the week before and after homelessness onset. Over 50% of encounters involving SI/SB result in hospitalization. Of those engaging in acute care for suicide-related reasons, we found high rates of repeat acute care encounters. CONCLUSION: HIEs are a particularly valuable resource for understudied populations. Our study demonstrates how longitudinal, multi-institutional data from an HIE can be used to characterize temporal associations, service utilization, and clinical associations of SI and behaviors among a vulnerable population at scale. Increasing access to services that address co-occurring SI/SB, mental health, and substance use is needed.

Transforming Health Data to Actionable Information: Recent Progress and Future Opportunities in Health Information Exchange.

OBJECTIVES: Provide a systematic review of literature pertaining to health information exchange (HIE) since 2018. Summarize HIE-associated literature for most frequently occurring topics, as well as within the context of the COVID-19 pandemic and health equity. Finally, provide recommendations for how HIE can advance the vision of a digital healthcare ecosystem. METHODS: A computer program was developed to mediate a literature search of primary literature indexed in MEDLINE that was: (1) indexed with "Health Information Exchange" MeSH descriptor as a major topic; and (2) published between January 2018 and December 2021. Frequency of MeSH descriptors was then used to identify and to rank topics associated with the retrieved literature. COVID-19 literature was identified using the general COVID-19 PubMed Clinical Query filter. Health equity literature was identified using additional MeSH descriptor-based searches. The retrieved literature was then reviewed and summarized. RESULTS: A total of 256 articles were retrieved and reviewed for this survey. The major thematic areas summarized were: (1) Information Dissemination; (2) Delivery of Health Care; (3) Hospitals; (4) Hospital Emergency Service; (5) COVID-19; (6) Health Disparities; and (7) Computer Security and Confidentiality. A common theme across all areas examined for this survey was the maturity of HIE to support data-driven healthcare delivery. Recommendations were developed based on opportunities identified across the reviewed literature. CONCLUSIONS: HIE is an essential advance in next generation healthcare delivery. The review of the recent literature (2018-2021) indicates that successful HIE improves healthcare delivery, often resulting in improved health outcomes. There remain major opportunities for expanded use of HIE, including the active engagement of clinical and patient stakeholders. The maturity of HIE reflects the maturity of the biomedical informatics and health data science fields.

Related amyloid burden and cortical atrophy in individuals with subtle cognitive decline.

BACKGROUND AND PURPOSE: Subtle cognitive decline represents a stage of cognitive deterioration in which pathological biomarkers may be present, including early cortical atrophy and amyloid deposition. Using individual items from the Montreal Cognitive Assessment and k-modes cluster analysis, we previously identified three clusters of individuals without overt cognitive impairment: (1) High Performing (no deficits in performance), (2) Memory Deficits (lower memory performance), and (3) Compound Deficits (lower memory and executive function performance). In this study, we sought to understand the relationships found in our clusters between cortical atrophy on MR and amyloid burden on PET. METHODS: Data were derived from the Alzheimer's Disease Neuroimaging Initiative and comprised individuals from our previous analyses with available MR and amyloid PET scans (n = 272). Using multiple-group structural equation modeling, we regressed amyloid standardized uptake value ratio on volumetric regions to simultaneously evaluate unique associations within each cluster. RESULTS: In our Compound Deficits cluster, greater whole cerebral amyloid burden was significantly related to right entorhinal cortical and left hippocampal atrophy, rs  = -.412 (p = .005) and -.304 (p = .049), respectively. Within this cluster, right entorhinal cortical atrophy was significantly related to greater amyloid burden within multiple frontal regions. CONCLUSIONS: The Compound Deficits cluster, which represents a group potentially at higher risk for decline, was observed to have significantly more cortical atrophy, particularly within the entorhinal cortex and hippocampus, associated with whole brain and frontal lobe amyloid burden. These findings point to a pattern of early pathological deterioration that may place these individuals at risk for future decline.

GenBank as a source to monitor and analyze Host-Microbiome data.

MOTIVATION: Microbiome datasets are often constrained by sequencing limitations. GenBank is the largest collection of publicly available DNA sequences, which is maintained by the National Center of Biotechnology Information (NCBI). The metadata of GenBank records are a largely understudied resource and may be uniquely leveraged to access the sum of prior studies focused on microbiome composition. Here, we developed a computational pipeline to analyze GenBank metadata, containing data on hosts, microorganisms and their place of origin. This work provides the first opportunity to leverage the totality of GenBank to shed light on compositional data practices that shape how microbiome datasets are formed as well as examine host-microbiome relationships. RESULTS: The collected dataset contains multiple kingdoms of microorganisms, consisting of bacteria, viruses, archaea, protozoa, fungi, and invertebrate parasites, and hosts of multiple taxonomical classes, including mammals, birds and fish. A human data subset of this dataset provides insights to gaps in current microbiome data collection, which is biased towards clinically relevant pathogens. Clustering and phylogenic analysis reveals the potential to use these data to model host taxonomy and evolution, revealing groupings formed by host diet, environment and coevolution. AVAILABILITY AND IMPLEMENTATION: GenBank Host-Microbiome Pipeline is available at https://github.com/bcbi/genbank_holobiome. The GenBank loader is available at https://github.com/bcbi/genbank_loader. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Statewide evaluation of COVID-19 vaccine hesitancy in Rhode Island.

BACKGROUND: Vaccines are effective in preventing Coronavirus Disease 2019 (COVID-19). Vaccine hesitancy defined as delay of acceptance or refusal of the vaccine is a major barrier to effective implementation. METHODS: Participants were recruited statewide through an English and Spanish social media marketing campaign conducted by a local news station during a one-month period as vaccines were becoming available in Rhode Island (from December 21, 2020 to January 22, 2021). Participants completed an online survey about COVID-19 vaccines and vaccine hesitancy with constructs and items adopted from the Health Belief Model. RESULTS: A total of 2,007 individuals completed the survey. Eight percent (n = 161) reported vaccine hesitancy. The sample had a median age of 58 years (interquartile range [IQR]: 45, 67), were majority female (78%), White (96%), Non-Hispanic (94%), employed (58%), and reported an annual individual income of $50,000 (59%). COVID-19 vaccine hesitancy was associated with attitudes and behaviors related to COVID-19. A one unit increase in concern about COVID-19 was associated with a 69% (Adjusted Odds Ratio: 0.31, 95% CI: 0.26-0.37) decrease in vaccine hesitancy. A one-level increase in the likelihood of getting influenza vaccine was associated with a 55% (AOR: 0.45 95% CI: 0.41-0.50) decrease in vaccine hesitancy. CONCLUSIONS: COVID-19 vaccine hesitancy was relatively low in a state-wide survey in Rhode Island. Future research is needed to better understand and tailor messaging related to vaccine hesitancy.

Using the Montreal cognitive assessment to identify individuals with subtle cognitive decline.

OBJECTIVE: Dementia is a devastating neurological disease that may be better managed if diagnosed earlier when subclinical neurodegenerative changes are already present, including subtle cognitive decline and mild cognitive impairment. In this study, we used item-level performance on the Montreal Cognitive Assessment (MoCA) to identify individuals with subtle cognitive decline. METHOD: Individual MoCA item data from the Alzheimer's Disease Neuroimaging Initiative was grouped using k-modes cluster analysis. These clusters were validated and examined for association with convergent neuropsychological tests. The clusters were then compared and characterized using multinomial logistic regression. RESULTS: A three-cluster solution had 77.3% precision, with Cluster 1 (high performing) displaying no deficits in performance, Cluster 2 (memory deficits) displaying lower memory performance, and Cluster 3 (compound deficits) displaying lower performance on memory and executive function. Age at MoCA (older in compound deficits), gender (more females in memory deficits), and marital status (fewer married in compound deficits) were significantly different among clusters. Age was not associated with increased odds of membership in the high-performing cluster compared to the others. CONCLUSIONS: We identified three clusters of individuals classified as cognitively unimpaired using cluster analysis. Individuals in the compound deficits cluster performed lower on the MoCA and were older and less often married than individuals in other clusters. Demographic analyses suggest that cluster identity was due to a combination of both cognitive and clinical factors. Identifying individuals at risk for future cognitive decline using the MoCA could help them receive earlier evidence-based interventions to slow further cognitive decline. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Adverse events following third dose of mRNA COVID-19 vaccination among nursing home residents who received the primary series.

BACKGROUND: We sought to compare rates of adverse events among nursing home residents who received an mRNA COVID-19 vaccine booster dose with those who had not yet received their booster. METHODS: We assessed a prospective cohort of 11,200 nursing home residents who received a primary COVID-19 mRNA vaccine series at least 6 months prior to September 22, 2021 and received a third "booster dose" between September 22, 2021 and February 2, 2022. Residents lived in 239 nursing homes operated by Genesis HealthCare, spanning 21 U.S. states. We screened electronic health records for 20 serious vaccine-related adverse events that are monitored following receipt of COVID-19 vaccination by the CDC's Vaccine Safety Datalink. We matched boosted and yet-to-be boosted residents during the same time period, comparing rates of events occurring 14 days after booster administration with those occurring 14 days prior to booster administration. To supplement previously reported background rates of adverse events, we report background rates of medical conditions among nursing home residents during 2020, before COVID-19 vaccines were administered in nursing homes. Events occurring in 2021-2022 were confirmed by physician chart review. We report unadjusted rates of adverse events and used a false discovery rate procedure to adjust for multiplicity of events tested. RESULTS: No adverse events were reported during the 14 days post-booster. A few adverse events occurred prior to booster (ischemic stroke: 49.4 per 100,000 residents, 95% CI: 21.2, 115.7; venous thromboembolism: 9.9 per 100,000 residents, 95% CI: 1.7, 56.0), though differences in event rates pre- versus post-booster were not statistically significant (p < 0.05) after adjusting for multiple comparisons. No significant differences were detected between post-booster vaccination rates and prior year 14-day background rates of medical conditions. CONCLUSIONS: No safety signals were detected following a COVID-19 mRNA vaccine booster dose in this large multi-state sample of nursing home residents.

Access to community-based reproductive health services and incidence of low birthweight delivery among refugee and displaced mothers: a retrospective study in the Thailand-Myanmar border region.

OBJECTIVES: Over 2.4 million people have been displaced within the Thailand-Myanmar border region since 1988. The efficacy of community-driven health models within displaced populations is largely unstudied. Here, we examined the relationship between maternal healthcare access and delivery outcomes to evaluate the impact of community-provided health services for marginalised populations. SETTING: Study setting was the Thailand-Myanmar border region's single largest provider of reproductive health services to displaced mothers. PARTICIPANTS: All women who had a delivery (n=34 240) between 2008 and 2019 at the study clinic were included in the performed retrospective analyses. PRIMARY AND SECONDARY OUTCOME MEASURES: Low birth weight was measured as the study outcome to understand the relationship between antenatal care access, family planning service utilisation, demographics and healthy deliveries. RESULTS: First trimester (OR=0.86; 95% CI=0.81 to 0.91) and second trimester (OR=0.86; 95% CI=0.83 to 0.90) antenatal care visits emerged as independent protective factors against low birthweight delivery, as did prior utilisation of family planning services (OR=0.82; 95% CI=0.73 to 0.92). Additionally, advanced maternal age (OR=1.36; 95% CI=1.21 to 1.52) and teenage pregnancy (OR=1.27, 95% CI=1.13 to 1.42) were notable risk factors, while maternal gravidity (OR=0.914; 95% CI=0.89 to 0.94) displayed a protective effect against low birth weight. CONCLUSION: Access to community-delivered maternal health services is strongly associated with positive delivery outcomes among displaced mothers. This study calls for further inquiry into how to best engage migrant and refugee populations in their own reproductive healthcare, in order to develop resilient models of care for a growing displaced population globally.

The effect of levetiracetam treatment on survival in patients with glioblastoma: a systematic review and meta-analysis.

BACKGROUND: Levetiracetam (LEV) is an anti-epileptic drug (AED) that sensitizes glioblastoma (GBM) to temozolomide (TMZ) chemotherapy by inhibiting O6-methylguanine-DNA methyltransferase (MGMT) expression. Adding LEV to the standard of care (SOC) for GBM may improve TMZ efficacy. This study aimed to pool the existing evidence in the literature to quantify LEV's effect on GBM survival and characterize its safety profile to determine whether incorporating LEV into the SOC is warranted. METHOD: A search of CINAHL, Embase, PubMed, and Web of Science from inception to May 2021 was performed to identify relevant articles. Hazard ratios (HR), median overall survival, and adverse events were pooled using random-effect models. Meta-regression, funnel plots, and the Newcastle-Ottawa Scale were utilized to identify sources of heterogeneity, bias, and statistical influence. RESULTS: From 20 included studies, 5804 GBM patients underwent meta-analysis, of which 1923 (33%) were treated with LEV. Administration of LEV did not significantly improve survival in the entire patient population (HR 0.89, p = 0.094). Significant heterogeneity was observed during pooling of HRs (I2 = 75%, p < 0.01). Meta-regression determined that LEV treatment effect decreased with greater rates of MGMT methylation (RC = 0.03, p = 0.02) and increased with greater proportions of female patients (RC = - 0.05, p = 0.002). Concurrent LEV with the SOC for GBM did not increase odds of adverse events relative to other AEDs. CONCLUSIONS: Levetiracetam treatment may not be effective for all GBM patients. Instead, LEV may be better suited for treating specific molecular profiles of GBM. Further studies are necessary to identify optimal GBM candidates for LEV.

LYRUS: a machine learning model for predicting the pathogenicity of missense variants.

SUMMARY: Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents 〈Lai Yang Rubenstein Uzun Sarkar〉 (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53. AVAILABILITY AND IMPLEMENTATION: LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.

An Unsupervised Cluster Analysis of Post-COVID-19 Mental Health Outcomes and Associated Comorbidities.

The COVID-19 pandemic continues to be widespread, and little is known about mental health impacts from dealing with the disease itself. This retrospective study used a deidentified health information exchange (HIE) dataset of electronic health record data from the state of Rhode Island and characterized different subgroups of the positive COVID-19 population. Three different clustering methods were explored to identify patterns of condition groupings in this population. Increased incidence of mental health conditions was seen post-COVID-19 diagnosis, and these individuals exhibited higher prevalence of comorbidities compared to the negative control group. A self-organizing map cluster analysis showed patterns of mental health conditions in half of the clusters. One mental health cluster revealed a higher comorbidity index and higher severity of COVID-19 disease. The clinical features identified in this study motivate the need for more in-depth analysis to predict and identify individuals at high risk for developing mental illness post-COVID-19 diagnosis.

Predicting open wound mortality in the ICU using machine learning.

BACKGROUND: Open wounds have a significant impact on the health of patients causing pain, loss of function, and death. Labeled as a comorbid condition, open wounds represent a "silent epidemic" that affect a large portion of the US population. Due to their burden of care, open wound patients face an increased risk of ICU stay and mortality. There is a dearth of studies that investigate mortality among wound patients in the ICU. We sought to develop a model that predicts the risk of mortality among wound patients in the ICU. METHODS: Random forest and binomial logistic regression models were developed to predict the risk of mortality among open wound patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. MIMIC-III includes de-identified data for patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. Six variables were used to develop the model (wound location, gender, age, admission type, minimum platelet count and hyperphosphatemia). The Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index were used to assess model strength. RESULTS: A total of 3,937 patients were included with a mean age of 76.57. Of those, 3,372 (85%) survived and 565 (15%) died during their ICU stay. The random forest model achieved an area under the curve (AUC) of 0.924. The CCI and Elixhauser models resulted in AUC of 0.528 and 0.565, respectively. CONCLUSIONS: Machine learning models may allow clinicians to provide better care and management to open wound patients in the ICU.

Adverse Events Following One Dose of mRNA COVID-19 Vaccination Among US Nursing Home Residents With and Without a Previous SARS-CoV-2 Infection.

OBJECTIVES: To compare rates of adverse events following Coronavirus Disease 2019 (COVID-19) vaccination among nursing home residents with and without previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. DESIGN: Prospective cohort. SETTING AND PARTICIPANTS: A total of 20,918 nursing home residents who received the first dose of messenger RNA COVID-19 vaccine from December 18, 2020, through February 14, 2021, in 284 facilities within Genesis Healthcare, a large nursing home provider spanning 24 US states. METHODS: We screened the electronic health record for adverse events, classified by the Brighton Collaboration, occurring within 15 days of a resident's first COVID-19 vaccine dose. All events were confirmed by physician chart review. To obtain risk ratios, multilevel logistic regression model that accounted for clustering (variability) across nursing homes was implemented. To balance the probability of prior SARS-CoV-2 infection (previous positive test or diagnosis by the International Classification of Diseases, 10th Revision, Clinical Modification) more than 20 days before vaccination, we used inverse probability weighting. To adjust for multiplicity of adverse events tested, we used a false discovery rate procedure. RESULTS: Statistically significant differences existed between those without (n = 13,163) and with previous SARS-CoV-2 infection [symptomatic (n = 5617) and asymptomatic (n = 2138)] for all baseline characteristics assessed. Only 1 adverse event was reported among those with previous SARS-CoV-2 infection (asymptomatic), venous thromboembolism [46.8 per 100,000 residents 95% confidence interval (CI) 8.3-264.5], which was not significantly different from the rate reported for those without previous infection (30.4 per 100,000 95% CI 11.8-78.1). Several other adverse events were observed for those with no previous infection, but were not statistically significantly higher than those reported with previous infection after adjustments for multiple comparisons. CONCLUSIONS AND IMPLICATIONS: Although reactogenicity increases with preexisting immunity, we did not find that vaccination among those with previous SARS-CoV-2 infection resulted in higher rates of adverse events than those without previous infection. This study stresses the importance of monitoring novel vaccines for adverse events in this vulnerable population.

Adverse events following mRNA SARS-CoV-2 vaccination among U.S. nursing home residents.

BACKGROUND: The devastating impact of the SARS-CoV-2 pandemic prompted the development and emergency use authorization of two mRNA vaccines in early 2020. Vaccine trials excluded nursing home (NH) residents, limiting adverse event data that directly apply to this population. METHODS: To prospectively monitor for potential adverse events associated with vaccination, we used Electronic Health Record (EHR) data from Genesis HealthCare, the largest NH provider in the United States. EHR data on vaccinations and pre-specified adverse events were updated daily and monitored for signal detection among residents of 147 facilities who received the first dose of vaccine between December 18, 2020 and January 3, 2021. For comparison, unvaccinated residents during the same time period were included from 137 facilities that started vaccinating at least 15 days after the vaccinating-facilities. RESULTS: As of January 3, 2021, 8553 NH residents had received one dose of SARS-CoV-2 vaccine and by February 20, 2021, 8371 residents had received their second dose of vaccine; 11,072 were included in the unvaccinated comparator group. No significant associations were noted for neurologic outcomes, anaphylaxis, or cardiac events. CONCLUSIONS: No major safety problems were detected following the first or second dose of the vaccine to prevent COVID-19 in the study cohort from December 18, 2020 through March 7, 2021.