Risk of COVID-19 Hospitalization and Protection Associated With mRNA Vaccination Among US Adults With Psychiatric Disorders.

BACKGROUND: Although psychiatric disorders have been associated with reduced immune responses to other vaccines, it remains unknown whether they influence COVID-19 vaccine effectiveness (VE). This study evaluated risk of COVID-19 hospitalization and estimated mRNA VE stratified by psychiatric disorder status. METHODS: In a retrospective cohort analysis of the VISION Network in four US states, the rate of laboratory-confirmed COVID-19-associated hospitalization between December 2021 and August 2022 was compared across psychiatric diagnoses and by monovalent mRNA COVID-19 vaccination status using Cox proportional hazards regression. RESULTS: Among 2,436,999 adults, 22.1% had ≥1 psychiatric disorder. The incidence of COVID-19-associated hospitalization was higher among patients with any versus no psychiatric disorder (394 vs. 156 per 100,000 person-years, p < 0.001). Any psychiatric disorder (adjusted hazard ratio [aHR], 1.27; 95% CI, 1.18-1.37) and mood (aHR, 1.25; 95% CI, 1.15-1.36), anxiety (aHR, 1.33, 95% CI, 1.22-1.45), and psychotic (aHR, 1.41; 95% CI, 1.14-1.74) disorders were each significant independent predictors of hospitalization. Among patients with any psychiatric disorder, aHRs for the association between vaccination and hospitalization were 0.35 (95% CI, 0.25-0.49) after a recent second dose, 0.08 (95% CI, 0.06-0.11) after a recent third dose, and 0.33 (95% CI, 0.17-0.66) after a recent fourth dose, compared to unvaccinated patients. Corresponding VE estimates were 65%, 92%, and 67%, respectively, and were similar among patients with no psychiatric disorder (68%, 92%, and 79%). CONCLUSION: Psychiatric disorders were associated with increased risk of COVID-19-associated hospitalization. However, mRNA vaccination provided similar protection regardless of psychiatric disorder status, highlighting its benefit for individuals with psychiatric disorders.

Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer's disease with real-world clinical validation.

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: (1) Vanderbilt University Medical Center and (2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

Sustainable deployment of clinical prediction tools-a 360° approach to model maintenance.

BACKGROUND: As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time. OBJECTIVE: Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs. We describe a framework encompassing a 360° continuum of preventive, preemptive, responsive, and reactive approaches to address model monitoring and maintenance from critically different angles. DISCUSSION: We describe the complementary advantages and limitations of these four approaches and highlight the importance of such a coordinated strategy to help ensure the promise of clinical AI is not short-lived.

Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience.

Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur. Methods: Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers. Results: These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion: This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.

Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions.

INTRODUCTION: Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources. RECOMMENDATIONS: Here, we propose five independent fundamental dimensions of phenotyping algorithms-complexity, performance, efficiency, implementability, and maintenance-through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities.

Vaccine Effectiveness Against Pediatric Influenza-A-Associated Urgent Care, Emergency Department, and Hospital Encounters During the 2022-2023 Season: VISION Network.

BACKGROUND: During the 2022-2023 influenza season, the United States experienced the highest influenza-associated pediatric hospitalization rate since 2010-2011. Influenza A/H3N2 infections were predominant. METHODS: We analyzed acute respiratory illness (ARI)-associated emergency department or urgent care (ED/UC) encounters or hospitalizations at 3 health systems among children and adolescents aged 6 months-17 years who had influenza molecular testing during October 2022-March 2023. We estimated influenza A vaccine effectiveness (VE) using a test-negative approach. The odds of vaccination among influenza-A-positive cases and influenza-negative controls were compared after adjusting for confounders and applying inverse-propensity-to-be-vaccinated weights. We developed overall and age-stratified VE models. RESULTS: Overall, 13 547 of 44 787 (30.2%) eligible ED/UC encounters and 263 of 1862 (14.1%) hospitalizations were influenza-A-positive cases. Among ED/UC patients, 15.2% of influenza-positive versus 27.1% of influenza-negative patients were vaccinated; VE was 48% (95% confidence interval [CI], 44-52%) overall, 53% (95% CI, 47-58%) among children aged 6 months-4 years, and 38% (95% CI, 30-45%) among those aged 9-17 years. Among hospitalizations, 17.5% of influenza-positive versus 33.4% of influenza-negative patients were vaccinated; VE was 40% (95% CI, 6-61%) overall, 56% (95% CI, 23-75%) among children ages 6 months-4 years, and 46% (95% CI, 2-70%) among those 5-17 years. CONCLUSIONS: During the 2022-2023 influenza season, vaccination reduced the risk of influenza-associated ED/UC encounters and hospitalizations by almost half (overall VE, 40-48%). Influenza vaccination is a critical tool to prevent moderate-to-severe influenza illness in children and adolescents.

Clinical Epidemiology and Risk Factors for Critical Outcomes Among Vaccinated and Unvaccinated Adults Hospitalized With COVID-19-VISION Network, 10 States, June 2021-March 2023.

BACKGROUND: The epidemiology of coronavirus disease 2019 (COVID-19) continues to develop with emerging variants, expanding population-level immunity, and advances in clinical care. We describe changes in the clinical epidemiology of COVID-19 hospitalizations and risk factors for critical outcomes over time. METHODS: We included adults aged ≥18 years from 10 states hospitalized with COVID-19 June 2021-March 2023. We evaluated changes in demographics, clinical characteristics, and critical outcomes (intensive care unit admission and/or death) and evaluated critical outcomes risk factors (risk ratios [RRs]), stratified by COVID-19 vaccination status. RESULTS: A total of 60 488 COVID-19-associated hospitalizations were included in the analysis. Among those hospitalized, median age increased from 60 to 75 years, proportion vaccinated increased from 18.2% to 70.1%, and critical outcomes declined from 24.8% to 19.4% (all P < .001) between the Delta (June-December, 2021) and post-BA.4/BA.5 (September 2022-March 2023) periods. Hospitalization events with critical outcomes had a higher proportion of ≥4 categories of medical condition categories assessed (32.8%) compared to all hospitalizations (23.0%). Critical outcome risk factors were similar for unvaccinated and vaccinated populations; presence of ≥4 medical condition categories was most strongly associated with risk of critical outcomes regardless of vaccine status (unvaccinated: adjusted RR, 2.27 [95% confidence interval {CI}, 2.14-2.41]; vaccinated: adjusted RR, 1.73 [95% CI, 1.56-1.92]) across periods. CONCLUSIONS: The proportion of adults hospitalized with COVID-19 who experienced critical outcomes decreased with time, and median patient age increased with time. Multimorbidity was most strongly associated with critical outcomes.

Influenza vaccine effectiveness against influenza-A-associated emergency department, urgent care, and hospitalization encounters among U.S. adults, 2022-2023.

BACKGROUND: The 2022-2023 United States influenza season had unusually early influenza activity with high hospitalization rates. Vaccine-matched A(H3N2) viruses predominated, with lower levels of A(H1N1)pdm09 activity also observed. METHODS: Using the test-negative design, we evaluated influenza vaccine effectiveness (VE) during the 2022-2023 season against influenza-A-associated emergency department/urgent care (ED/UC) visits and hospitalizations from October 2022-March 2023 among adults (age ≥18 years) with acute respiratory illness (ARI). VE was estimated by comparing odds of seasonal influenza vaccination among case-patients (influenza A test-positive by molecular assay) and controls (influenza test-negative), applying inverse-propensity-to-be-vaccinated weights. RESULTS: The analysis included 85,389 ED/UC ARI encounters (17.0% influenza-A-positive; 37.8% vaccinated overall) and 19,751 hospitalizations (9.5% influenza-A-positive; 52.8% vaccinated overall). VE against influenza-A-associated ED/UC encounters was 44% (95% confidence interval [95%CI]: 40-47%) overall and 45% and 41% among adults aged 18-64 and ≥65 years, respectively. VE against influenza-A-associated hospitalizations was 35% (95%CI: 27-43%) overall and 23% and 41% among adults aged 18-64 and ≥65 years, respectively. CONCLUSIONS: VE was moderate during the 2022-2023 influenza season, a season characterized with increased burden of influenza and co-circulation with other respiratory viruses. Vaccination is likely to substantially reduce morbidity, mortality, and strain on healthcare resources.

Impact of accounting for correlation between COVID-19 and influenza vaccination in a COVID-19 vaccine effectiveness evaluation using a test-negative design.

Test-negative-design COVID-19 vaccine effectiveness (VE) studies use symptomatic SARS-CoV-2-positive individuals as cases and symptomatic SARS-CoV-2-negative individuals as controls to evaluate COVID-19 VE. To evaluate the potential bias introduced by the correlation of COVID-19 and influenza vaccination behaviors, we assessed changes in estimates of VE of bivalent vaccines against COVID-19-associated hospitalizations and emergency department/urgent care (ED/UC) encounters when considering influenza vaccination status or including or excluding influenza-positive controls using data from the multi-state VISION vaccine effectiveness network. Analyses included encounters during October 2022 - February 2023, a period of SARS-CoV-2 and influenza cocirculation. When considering influenza vaccination status or including or excluding influenza-positive controls, COVID-19 VE estimates were robust, with most VE estimates against COVID-19-associated hospitalization and ED/UC encounters changing less than 5 percentage points. Higher proportions of influenza-positive patients among controls, influenza vaccination coverage, or VE could impact these findings; the potential bias should continue to be assessed.

Effectiveness of the Original Monovalent Coronavirus Disease 2019 Vaccines in Preventing Emergency Department or Urgent Care Encounters and Hospitalizations Among Adults With Disabilities: VISION Network, June 2021-September 2022.

Adults with disabilities are at increased risk for severe coronavirus disease 2019 (COVID-19). Using data across 9 states during Delta- and Omicron-predominant periods (June 2021-September 2022), we evaluated the effectiveness of the original monovalent COVID-19 messenger RNA vaccines among 521 206 emergency department/urgent care encounters (11 471 [2%] in patients with a documented disability) and 139 548 hospitalizations (16 569 [12%] in patients with a disability) for laboratory-confirmed COVID-19 illness in adults (aged ≥18 years). Across variant periods and for the primary series or booster doses, vaccine effectiveness was similar in those with and those without a disability. These findings highlight the importance of adults with disabilities staying up to date with COVID-19 vaccinations.

Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity.

Importance: US health professionals devote a large amount of effort to engaging with patients' electronic health records (EHRs) to deliver care. It is unknown whether patients with different racial and ethnic backgrounds receive equal EHR engagement. Objective: To investigate whether there are differences in the level of health professionals' EHR engagement for hospitalized patients according to race or ethnicity during inpatient care. Design, Setting, and Participants: This cross-sectional study analyzed EHR access log data from 2 major medical institutions, Vanderbilt University Medical Center (VUMC) and Northwestern Medicine (NW Medicine), over a 3-year period from January 1, 2018, to December 31, 2020. The study included all adult patients (aged ≥18 years) who were discharged alive after hospitalization for at least 24 hours. The data were analyzed between August 15, 2022, and March 15, 2023. Exposures: The actions of health professionals in each patient's EHR were based on EHR access log data. Covariates included patients' demographic information, socioeconomic characteristics, and comorbidities. Main Outcomes and Measures: The primary outcome was the quantity of EHR engagement, as defined by the average number of EHR actions performed by health professionals within a patient's EHR per hour during the patient's hospital stay. Proportional odds logistic regression was applied based on outcome quartiles. Results: A total of 243 416 adult patients were included from VUMC (mean [SD] age, 51.7 [19.2] years; 54.9% female and 45.1% male; 14.8% Black, 4.9% Hispanic, 77.7% White, and 2.6% other races and ethnicities) and NW Medicine (mean [SD] age, 52.8 [20.6] years; 65.2% female and 34.8% male; 11.7% Black, 12.1% Hispanic, 69.2% White, and 7.0% other races and ethnicities). When combining Black, Hispanic, or other race and ethnicity patients into 1 group, these patients were significantly less likely to receive a higher amount of EHR engagement compared with White patients (adjusted odds ratios, 0.86 [95% CI, 0.83-0.88; P < .001] for VUMC and 0.90 [95% CI, 0.88-0.92; P < .001] for NW Medicine). However, a reduction in this difference was observed from 2018 to 2020. Conclusions and Relevance: In this cross-sectional study of inpatient EHR engagement, the findings highlight differences in how health professionals distribute their efforts to patients' EHRs, as well as a method to measure these differences. Further investigations are needed to determine whether and how EHR engagement differences are correlated with health care outcomes.

Effectiveness of Monovalent and Bivalent mRNA Vaccines in Preventing COVID-19-Associated Emergency Department and Urgent Care Encounters Among Children Aged 6 Months-5 Years - VISION Network, United States, July 2022-June 2023.

On June 19, 2022, the original monovalent mRNA COVID-19 vaccines were approved as a primary series for children aged 6 months-4 years (Pfizer-BioNTech) and 6 months-5 years (Moderna) based on safety, immunobridging, and limited efficacy data from clinical trials. On December 9, 2022, CDC expanded recommendations for use of updated bivalent vaccines to children aged ≥6 months. mRNA COVID-19 vaccine effectiveness (VE) against emergency department or urgent care (ED/UC) encounters was evaluated within the VISION Network during July 4, 2022-June 17, 2023, among children with COVID-19-like illness aged 6 months-5 years. Among children aged 6 months-5 years who received molecular SARS-CoV-2 testing during August 1, 2022-June 17, 2023, VE of 2 monovalent Moderna doses against ED/UC encounters was 29% (95% CI = 12%-42%) ≥14 days after dose 2 (median = 100 days after dose 2; IQR = 63-155 days). Among children aged 6 months-4 years with a COVID-19-like illness who received molecular testing during September 19, 2022-June 17, 2023, VE of 3 monovalent Pfizer-BioNTech doses was 43% (95% CI = 17%-61%) ≥14 days after dose 3 (median = 75 days after dose 3; IQR = 40-139 days). Effectiveness of ≥1 bivalent dose, comparing children with at least a complete primary series and ≥1 bivalent dose to unvaccinated children, irrespective of vaccine manufacturer, was 80% (95% CI = 42%-96%) among children aged 6 months-5 years a median of 58 days (IQR = 32-83 days) after the dose. All children should stay up to date with recommended COVID-19 vaccines, including initiation of COVID-19 vaccination immediately when they are eligible.

Effectiveness of COVID-19 vaccines at preventing emergency department or urgent care encounters and hospitalizations among immunocompromised adults: An observational study of real-world data across 10 US states from August-December 2021.

BACKGROUND: Immunocompromised (IC) persons are at increased risk for severe COVID-19 outcomes and are less protected by 1-2 COVID-19 vaccine doses than are immunocompetent (non-IC) persons. We compared vaccine effectiveness (VE) against medically attended COVID-19 of 2-3 mRNA and 1-2 viral-vector vaccine doses between IC and non-IC adults. METHODS: Using a test-negative design among eight VISION Network sites, VE against laboratory-confirmed COVID-19-associated emergency department (ED) or urgent care (UC) events and hospitalizations from 26 August-25 December 2021 was estimated separately among IC and non-IC adults and among specific IC condition subgroups. Vaccination status was defined using number and timing of doses. VE for each status (versus unvaccinated) was adjusted for age, geography, time, prior positive test result, and local SARS-CoV-2 circulation. RESULTS: We analyzed 8,848 ED/UC events and 18,843 hospitalizations among IC patients and 200,071 ED/UC events and 70,882 hospitalizations among non-IC patients. Among IC patients, 3-dose mRNA VE against ED/UC (73% [95% CI: 64-80]) and hospitalization (81% [95% CI: 76-86]) was lower than that among non-IC patients (ED/UC: 94% [95% CI: 93-94]; hospitalization: 96% [95% CI: 95-97]). Similar patterns were observed for viral-vector vaccines. Transplant recipients had lower VE than other IC subgroups. CONCLUSIONS: During B.1.617.2 (Delta) variant predominance, IC adults received moderate protection against COVID-19-associated medical events from three mRNA doses, or one viral-vector dose plus a second dose of any product. However, protection was lower in IC versus non-IC patients, especially among transplant recipients, underscoring the need for additional protection among IC adults.

Leveraging Generative AI to Prioritize Drug Repurposing Candidates: Validating Identified Candidates for Alzheimer's Disease in Real-World Clinical Datasets.

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

Leveraging Generative AI to Prioritize Drug Repurposing Candidates: Validating Identified Candidates for Alzheimer's Disease in Real-World Clinical Datasets.

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

ChatGPT and the clinical informatics board examination: the end of unproctored maintenance of certification?

We aimed to assess ChatGPT's performance on the Clinical Informatics Board Examination and to discuss the implications of large language models (LLMs) for board certification and maintenance. We tested ChatGPT using 260 multiple-choice questions from Mankowitz's Clinical Informatics Board Review book, omitting 6 image-dependent questions. ChatGPT answered 190 (74%) of 254 eligible questions correctly. While performance varied across the Clinical Informatics Core Content Areas, differences were not statistically significant. ChatGPT's performance raises concerns about the potential misuse in medical certification and the validity of knowledge assessment exams. Since ChatGPT is able to answer multiple-choice questions accurately, permitting candidates to use artificial intelligence (AI) systems for exams will compromise the credibility and validity of at-home assessments and undermine public trust. The advent of AI and LLMs threatens to upend existing processes of board certification and maintenance and necessitates new approaches to the evaluation of proficiency in medical education.

Number needed to vaccinate with a COVID-19 booster to prevent a COVID-19-associated hospitalization during SARS-CoV-2 Omicron BA.1 variant predominance, December 2021-February 2022, VISION Network: a retrospective cohort study.

Background: Understanding the usefulness of additional COVID-19 vaccine doses-particularly given varying disease incidence-is needed to support public health policy. We characterize the benefits of COVID-19 booster doses using number needed to vaccinate (NNV) to prevent one COVID-19-associated hospitalization or emergency department encounter. Methods: We conducted a retrospective cohort study of immunocompetent adults at five health systems in four U.S. states during SARS-CoV-2 Omicron BA.1 predominance (December 2021-February 2022). Included patients completed a primary mRNA COVID-19 vaccine series and were either eligible to or received a booster dose. NNV were estimated using hazard ratios for each outcome (hospitalization and emergency department encounters), with results stratified by three 25-day periods and site. Findings: 1,285,032 patients contributed 938 hospitalizations and 2076 emergency department encounters. 555,729 (43.2%) patients were aged 18-49 years, 363,299 (28.3%) 50-64 years, and 366,004 (28.5%) ≥65 years. Most patients were female (n = 765,728, 59.6%), White (n = 990,224, 77.1%), and non-Hispanic (n = 1,063,964, 82.8%). 37.2% of patients received a booster and 62.8% received only two doses. Median estimated NNV to prevent one hospitalization was 205 (range 44-615) and NNV was lower across study periods for adults aged ≥65 years (110, 46, and 88, respectively) and those with underlying medical conditions (163, 69, and 131, respectively). Median estimated NNV to prevent one emergency department encounter was 156 (range 75-592). Interpretation: The number of patients needed to receive a booster dose was highly dependent on local disease incidence, outcome severity, and patient risk factors for moderate-to-severe disease. Funding: Funding was provided by the Centers for Disease Control and Prevention though contract 75D30120C07986 to Westat, Inc. and contract 75D30120C07765 to Kaiser Foundation Hospitals.

Estimates of Bivalent mRNA Vaccine Durability in Preventing COVID-19-Associated Hospitalization and Critical Illness Among Adults with and Without Immunocompromising Conditions - VISION Network, September 2022-April 2023.

On September 1, 2022, CDC's Advisory Committee on Immunization Practices (ACIP) recommended a single bivalent mRNA COVID-19 booster dose for persons aged ≥12 years who had completed at least a monovalent primary series. Early vaccine effectiveness (VE) estimates among adults aged ≥18 years showed receipt of a bivalent booster dose provided additional protection against COVID-19-associated emergency department and urgent care visits and hospitalizations compared with that in persons who had received only monovalent vaccine doses (1); however, insufficient time had elapsed since bivalent vaccine authorization to assess the durability of this protection. The VISION Network* assessed VE against COVID-19-associated hospitalizations by time since bivalent vaccine receipt during September 13, 2022-April 21, 2023, among adults aged ≥18 years with and without immunocompromising conditions. During the first 7-59 days after vaccination, compared with no vaccination, VE for receipt of a bivalent vaccine dose among adults aged ≥18 years was 62% (95% CI = 57%-67%) among adults without immunocompromising conditions and 28% (95% CI = 10%-42%) among adults with immunocompromising conditions. Among adults without immunocompromising conditions, VE declined to 24% (95% CI = 12%-33%) among those aged ≥18 years by 120-179 days after vaccination. VE was generally lower for adults with immunocompromising conditions. A bivalent booster dose provided the highest protection, and protection was sustained through at least 179 days against critical outcomes, including intensive care unit (ICU) admission or in-hospital death. These data support updated recommendations allowing additional optional bivalent COVID-19 vaccine doses for certain high-risk populations. All eligible persons should stay up to date with recommended COVID-19 vaccines.

Effectiveness of BNT162b2 COVID-19 Vaccination in Children and Adolescents.

OBJECTIVES: We assessed BNT162b2 vaccine effectiveness (VE) against mild to moderate and severe coronavirus disease 2019 (COVID-19) in children and adolescents through the Omicron BA.4/BA.5 period. METHODS: Using VISION Network records from April 2021 to September 2022, we conducted a test-negative, case-control study assessing VE against COVID-19-associated emergency department/urgent care (ED/UC) encounters and hospitalizations using logistic regression, conditioned on month and site, adjusted for covariates. RESULTS: We compared 9800 ED/UC cases with 70 232 controls, and 305 hospitalized cases with 2612 controls. During Delta, 2-dose VE against ED/UC encounters at 12 to 15 years was initially 93% (95% confidence interval 89 to 95), waning to 77% (69% to 84%) after ≥150 days. At ages 16 to 17, VE was initially 93% (86% to 97%), waning to 72% (63% to 79%) after ≥150 days. During Omicron, VE at ages 12 to 15 was initially 64% (44% to 77%), waning to 13% (3% to 23%) after ≥150 days; at ages 16 to 17 VE was 31% (10% to 47%) during days 60 to 149, waning to 7% (-8 to 20%) after 150 days. A monovalent booster increased VE to 54% (40% to 65%) at ages 12 to 15 and 46% (30% to 58%) at ages 16 to 17. At ages 5 to 11, 2-dose VE was 49% (33% to 61%) initially and 41% (29% to 51%) after 150 days. During Delta, VE against hospitalizations at ages 12 to 17 was high (>97%), and at ages 16 to 17 remained 98% (73% to 100%) beyond 150 days; during Omicron, hospitalizations were too infrequent to precisely estimate VE. CONCLUSIONS: BNT162b2 protected children and adolescents against mild to moderate and severe COVID-19. VE was lower during Omicron predominance including BA.4/BA.5, waned after dose 2 but increased after a monovalent booster. Children and adolescents should receive all recommended COVID-19 vaccinations.