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Herbert C, Wang B, Lin H, Yan Y, Hafer N, Pretz C, Stamegna P, Wright C, Suvarna T, Harman E, Schrader S, Nowak C, Kheterpal V, Orvek E, Wong S, Zai A, Barton B, Gerber BS, Lemon SC, Filippaios A, Gibson L, Greene S, Colubri A, Achenbach C, Murphy R, Heetderks W, Manabe YC, O’Connor L, Fahey N, Luzuriaga K, Broach J, Roth K, McManus DD, Soni A. Performance of and Severe Acute Respiratory Syndrome Coronavirus 2 Diagnostics Based on Symptom Onset and Close Contact Exposure: An Analysis From the Test Us at Home Prospective Cohort Study. Open Forum Infect Dis 2024; 11:ofae304. [PMID: 38911947 PMCID: PMC11191649 DOI: 10.1093/ofid/ofae304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 05/29/2024] [Indexed: 06/25/2024] Open
Abstract
Background Understanding changes in diagnostic performance after symptom onset and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure within different populations is crucial to guide the use of diagnostics for SARS-CoV-2. Methods The Test Us at Home study was a longitudinal cohort study that enrolled individuals across the United States between October 2021 and February 2022. Participants performed paired antigen-detection rapid diagnostic tests (Ag-RDTs) and reverse-transcriptase polymerase chain reaction (RT-PCR) tests at home every 48 hours for 15 days and self-reported symptoms and known coronavirus disease 2019 exposures immediately before testing. The percent positivity for Ag-RDTs and RT-PCR tests was calculated each day after symptom onset and exposure and stratified by vaccination status, variant, age category, and sex. Results The highest percent positivity occurred 2 days after symptom onset (RT-PCR, 91.2%; Ag-RDT, 71.1%) and 6 days after exposure (RT-PCR, 91.8%; Ag-RDT, 86.2%). RT-PCR and Ag-RDT performance did not differ by vaccination status, variant, age category, or sex. The percent positivity for Ag-RDTs was lower among exposed, asymptomatic than among symptomatic individuals (37.5% (95% confidence interval [CI], 13.7%-69.4%) vs 90.3% (75.1%-96.7%). Cumulatively, Ag-RDTs detected 84.9% (95% CI, 78.2%-89.8%) of infections within 4 days of symptom onset. For exposed participants, Ag-RDTs detected 94.0% (95% CI, 86.7%-97.4%) of RT-PCR-confirmed infections within 6 days of exposure. Conclusions The percent positivity for Ag-RDTs and RT-PCR tests was highest 2 days after symptom onset and 6 days after exposure, and performance increased with serial testing. The percent positivity of Ag-RDTs was lowest among asymptomatic individuals but did not differ by sex, variant, vaccination status, or age category.
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Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Biqi Wang
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Yi Yan
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Pamela Stamegna
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Colton Wright
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | | | | | | | | | | | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Steven Wong
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Ben S Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Stephenie C Lemon
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Laura Gibson
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Sharone Greene
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Andres Colubri
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Robert Murphy
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, NIH, via contract with Kelly Services, Bethesda, Maryland, USA
| | - Yukari C Manabe
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Laurel O’Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Nisha Fahey
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Katherine Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - John Broach
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Kristian Roth
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - David D McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
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Dong S, Jutkowitz E, Giardina J, Bilinski A. Screening Strategies to Reduce COVID-19 Mortality in Nursing Homes. JAMA HEALTH FORUM 2024; 5:e240688. [PMID: 38669030 PMCID: PMC11065177 DOI: 10.1001/jamahealthforum.2024.0688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/03/2024] [Indexed: 05/01/2024] Open
Abstract
Importance Nursing home residents continue to bear a disproportionate share of COVID-19 morbidity and mortality, accounting for 9% of all US COVID-19 deaths in 2023, despite comprising only 0.4% of the population. Objective To evaluate the cost-effectiveness of screening strategies in reducing COVID-19 mortality in nursing homes. Design and Setting An agent-based model was developed to simulate SARS-CoV-2 transmission in the nursing home setting. Parameters were determined using SARS-CoV-2 virus data and COVID-19 data from the Centers for Medicare & Medicaid Services and US Centers for Disease Control and Prevention that were published between 2020 and 2023, as well as data on nursing homes published between 2010 and 2023. The model used in this study simulated interactions and SARS-CoV-2 transmission between residents, staff, and visitors in a nursing home setting. The population used in the simulation model was based on the size of the average US nursing home and recommended staffing levels, with 90 residents, 90 visitors (1 per resident), and 83 nursing staff members. Exposure Screening frequency (none, weekly, and twice weekly) was varied over 30 days against varying levels of COVID-19 community incidence, booster uptake, and antiviral use. Main Outcomes and Measures The main outcomes were SARS-CoV-2 infections, detected cases per 1000 tests, and incremental cost of screening per life-year gained. Results Nursing home interactions were modeled between 90 residents, 90 visitors, and 83 nursing staff over 30 days, completing 4000 to 8000 simulations per parameter combination. The incremental cost-effectiveness ratios of weekly and twice-weekly screening were less than $150 000 per resident life-year with moderate (50 cases per 100 000) and high (100 cases per 100 000) COVID-19 community incidence across low-booster uptake and high-booster uptake levels. When COVID-19 antiviral use reached 100%, screening incremental cost-effectiveness ratios increased to more than $150 000 per life-year when booster uptake was low and community incidence was high. Conclusions and Relevance The results of this cost-effectiveness analysis suggest that screening may be effective for reducing COVID-19 mortality in nursing homes when COVID-19 community incidence is high and/or booster uptake is low. Nursing home administrators can use these findings to guide planning in the context of widely varying levels of SARS-CoV-2 transmission and intervention measures across the US.
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Affiliation(s)
- Shirley Dong
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, Rhode Island
| | - Eric Jutkowitz
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, Rhode Island
- Center of Innovation in Long Term Services and Supports, Providence VA Medical Center, Providence, Rhode Island
- Evidence Synthesis Program Center Providence VA Medical Center, Providence, Rhode Island
| | - John Giardina
- Medical Practice Evaluation Center, Massachusetts General Hospital, Boston
| | - Alyssa Bilinski
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, Rhode Island
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island
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Kwiatkowska B, Krajewska-Włodarczyk M, Batko B, Maślińska M, Stajszczyk M, Świerkot J, Wiland P, Żuber Z, Tomasiewicz K. COVID-19 prophylaxis, diagnostics, and treatment in patients with rheumatic diseases. The Polish experts panel opinion. Reumatologia 2024; 62:4-17. [PMID: 38558893 PMCID: PMC10979375 DOI: 10.5114/reum/183469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 01/30/2024] [Indexed: 04/04/2024] Open
Abstract
As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolves, infection management in vulnerable populations requires formalized guidance. Although low-virulence variants of SARS-CoV-2 remain predominant, they pose an increased risk of severe illness in adults with rheumatic and musculoskeletal diseases (RMDs). Several disease-specific (chronic long-grade inflammation, concomitant immunosuppression) and individual (advanced age, multimorbidity, pregnancy, vaccination status) factors contribute to excess risk in RMD populations. Various post-COVID-19 manifestations are also increasingly reported and appear more commonly than in the general population. At a pathogenetic level, complex interplay involving innate and acquired immune dysregulation, viral persistence, and genetic predisposition shapes a unique susceptibility profile. Moreover, incident cases of SARS-CoV-2 infection as a trigger factor for the development of autoimmune conditions have been reported. Vaccination remains a key preventive strategy, and encouraging active education and awareness will be crucial for rheumatologists in the upcoming years. In patients with RMDs, COVID-19 vaccines' benefits outweigh the risks. Derivation of specialized diagnostic and therapeutic protocols within a comprehensive COVID-19 care plan represents an ideal scenario for healthcare system organization. Vigilance for symptoms of infection and rapid diagnosis are key for introducing antiviral treatment in patients with RMDs in a timely manner. This review provides updated guidance on optimal immunization, diagnosis, and antiviral treatment strategies.
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Affiliation(s)
- Brygida Kwiatkowska
- Early Arthritis Clinic, National Institute of Geriatrics, Rheumatology and Rehabilitation, Warsaw, Poland
| | | | - Bogdan Batko
- Department of Rheumatology and Immunology, Faculty of Medicine and Health Sciences, Andrzej Frycz Modrzewski University, Krakow, Poland
| | - Maria Maślińska
- Early Arthritis Clinic, National Institute of Geriatrics, Rheumatology and Rehabilitation, Warsaw, Poland
| | - Marcin Stajszczyk
- Department of Rheumatology and Autoimmune Diseases, Silesian Center for Rheumatology, Orthopedics and Rehabilitation, Ustron, Poland
| | - Jerzy Świerkot
- Department of Rheumatology and Internal Medicine, Wroclaw Medical University, Poland
| | - Piotr Wiland
- Department of Rheumatology and Internal Medicine, Wroclaw Medical University, Poland
| | - Zbigniew Żuber
- Department of Rheumatology, St. Louis Voivodeship Specialist Children’s Hospital, Krakow, Poland
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