Interim Effectiveness of Updated 2023–2024 (Monovalent XBB.1.5) COVID-19 Vaccines Against COVID-19–Associated Emergency Department and Urgent Care Encounters and Hospitalization Among Immunocompetent Adults Aged ≥18 Years — VISION and IVY Networks, September 2023–January 2024

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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

Ruth Link-Gelles, Zachary A. Weber, Sarah E. Reese … (2023)

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Effects of Confounding Bias in Coronavirus Disease 2019 (COVID-19) and Influenza Vaccine Effectiveness Test-Negative Designs Due to Correlated Influenza and COVID-19 Vaccination Behaviors

Margaret Doll, Stacy Pettigrew, Julia Ma … (2022)

Abstract Background The test-negative design is commonly used to estimate influenza and coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE). In these studies, correlated COVID-19 and influenza vaccine behaviors may introduce a confounding bias where controls are included with the other vaccine-preventable acute respiratory illness (ARI). We quantified the impact of this bias on VE estimates in studies where this bias is not addressed. Methods We simulated study populations under varying vaccination probabilities, COVID-19 VE, influenza VE, and proportions of controls included with the other vaccine-preventable ARI. Mean bias was calculated as the difference between estimated and true VE. Absolute mean bias in VE estimates was classified as low (<10%), moderate (10% to <20%), and high (≥20%). Results Where vaccination probabilities are positively correlated, COVID-19 and influenza VE test-negative studies with influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ARI controls, respectively, underestimate VE. For COVID-19 VE studies, mean bias was low for all scenarios where influenza represented ≤25% of controls. For influenza VE studies, mean bias was low for all scenarios where SARS-CoV-2 represented ≤10% of controls. Although bias was driven by the conditional probability of vaccination, low VE of the vaccine of interest and high VE of the confounding vaccine increase its magnitude. Conclusions Where a low percentage of controls is included with the other vaccine-preventable ARI, bias in COVID-19 and influenza VE estimates is low. However, influenza VE estimates are likely more susceptible to bias. Researchers should consider potential bias and its implications in their respective study settings to make informed methodological decisions in test-negative VE studies.

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Fast evolution of SARS-CoV-2 BA.2·86 to JN.1 under heavy immune pressure

Sijie Yang, Yuanling Yu, Yanli Xu … (2023)

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Author and article information

Contributors

Shekhar Ghamande

Robert Gottlieb

Tresa McNeal

Catherine Raver

William Bender

Linda Fletcher

Phillip Heaton

Sheryl Kane

Charlene McEvoy

Sunita Thapa

Gabriela Vazquez-Benitez

Anne Frosch

Lois E Lamerato

Mayur Ramesh

Julie Arnofer

Bradley Crane

Padma Dandamudi

Kristin Goddard

John Hansen

Julius Timbol

Ousseny Zerbo

Katie Allen

Thomas Duszynski

William Fadel

Colin Rogerson

Nida Qadir

Catia Chavez

Bryant Doyle

David Mayer

Suchitra Rao

Carolina Rivas

Nicholas J. Johnson

Adrienne Baughman

Cara T. Lwin

Jillian P. Rhoads

Kelsey N. Womack

Margaret Dunne

Allison Ciesla

Josephine Mak

Morgan Najdowski

Caitlin Ray

Journal

Journal ID (nlm-ta): MMWR Morb Mortal Wkly Rep

Journal ID (iso-abbrev): MMWR Morb Mortal Wkly Rep

Journal ID (publisher-id): WR

Title: Morbidity and Mortality Weekly Report

Publisher: Centers for Disease Control and Prevention

ISSN (Print): 0149-2195

ISSN (Electronic): 1545-861X

Publication date (Electronic): 29 February 2024

Publication date Collection: 29 February 2024

Volume: 73

Issue: 8

Pages: 180-188

Affiliations

Coronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, CDC; Vanderbilt University Medical Center, Nashville, Tennessee; Westat, Rockville, Maryland; Division of Infectious Diseases and Clinical Epidemiology, Intermountain Healthcare, Salt Lake City, Utah; HealthPartners Institute, Minneapolis, Minnesota; Kaiser Permanente Center for Health Research, Portland, Oregon; Indiana University School of Medicine, Indianapolis, Indiana; Regenstrief Institute Center for Biomedical Informatics, Indianapolis, Indiana; University of Colorado School of Medicine, Aurora, Colorado; Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York; New York-Presbyterian Hospital, New York, New York; General Dynamics Information Technology, Falls Church, Virginia; University of Michigan, Ann Arbor, Michigan; Baylor Scott & White Health, Texas; Baylor College of Medicine, Temple, Texas; Intermountain Medical Center, Murray, Utah; University of Utah, Salt Lake City, Utah; University of Iowa, Iowa City, Iowa; Wake Forest School of Medicine, Winston-Salem, North Carolina; Johns Hopkins University School of Medicine, Baltimore, Maryland; Hennepin County Medical Center, Minneapolis, Minnesota; Montefiore Medical Center, Albert Einstein College of Medicine, New York, New York; University of Washington, Seattle, Washington; Baystate Medical Center, Springfield, Massachusetts; Oregon Health & Science University, Portland, Oregon; Emory University, Atlanta, Georgia; Cleveland Clinic, Cleveland, Ohio; Stanford University School of Medicine, Stanford, California; Ronald Reagan UCLA Medical Center, Los Angeles, California; University of Miami, Miami, Florida; Washington University in St. Louis, St. Louis, Missouri; The Ohio State University, Columbus, Ohio; Texas A&M University College of Medicine, Dallas, Texas; Henry Ford Health, Detroit, Michigan; Yale University School of Medicine, New Haven, Connecticut; University of Arizona, Tucson, Arizona; Influenza Division, National Center for Immunization and Respiratory Diseases, CDC.

Baylor Scott & White Health

Emory University

HealthPartners

Hennepin County Medical Center

Henry Ford Health

Intermountain Healthcare

Kaiser Permanente Center for Health Research

Kaiser Permanente Northern California

Regenstrief Institute

University of California, Los Angeles

University of Colorado

University of Miami

University of Washington

Vanderbilt University Medical Center

Westat

CDC

CDC.

Author notes

Corresponding author: Jennifer DeCuir, media@ 123456cdc.gov .

Article

Publisher ID: mm7308a5

DOI: 10.15585/mmwr.mm7308a5

PMC ID: 10907041

PubMed ID: 38421945

SO-VID: 2656f762-a8d4-4d05-8286-fb84d3594442

License:

All material in the MMWR Series is in the public domain and may be used and reprinted without permission; citation as to source, however, is appreciated.