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Herbert C, Shi Q, Baek J, Wang B, Kheterpal V, Nowak C, Suvarna T, Singh A, Hartin P, Durnam B, Schrader S, Harman E, Gerber B, Barton B, Zai A, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Hafer N, Broach J, Lin H, Heetderks W, McManus DD, Soni A. Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of COVID-19 rapid antigen tests in 5 US communities. BMC Public Health 2023; 23:1848. [PMID: 37735647 PMCID: PMC10515232 DOI: 10.1186/s12889-023-16642-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/29/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Many interventions for widescale distribution of rapid antigen tests for COVID-19 have utilized online, direct-to-consumer (DTC) ordering systems; however, little is known about the sociodemographic characteristics of home-test users. We aimed to characterize the patterns of online orders for rapid antigen tests and determine geospatial and temporal associations with neighborhood characteristics and community incidence of COVID-19, respectively. METHODS This observational study analyzed online, DTC orders for rapid antigen test kits from beneficiaries of the Say Yes! Covid Test program from March to November 2021 in five communities: Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; and Ann Arbor/Ypsilanti, Michigan. Using spatial autoregressive models, we assessed the geospatial associations of test kit distribution with Census block-level education, income, age, population density, and racial distribution and Census tract-level Social Vulnerability Index. Lag association analyses were used to measure the association between online rapid antigen kit orders and community-level COVID-19 incidence. RESULTS In total, 164,402 DTC test kits were ordered during the intervention. Distribution of tests at all sites were significantly geospatially clustered at the block-group level (Moran's I: p < 0.001); however, education, income, age, population density, race, and social vulnerability index were inconsistently associated with test orders across sites. In Michigan, Georgia, and Kentucky, there were strong associations between same-day COVID-19 incidence and test kit orders (Michigan: r = 0.89, Georgia: r = 0.85, Kentucky: r = 0.75). The incidence of COVID-19 during the current day and the previous 6-days increased current DTC orders by 9.0 (95% CI = 1.7, 16.3), 3.0 (95% CI = 1.3, 4.6), and 6.8 (95% CI = 3.4, 10.2) in Michigan, Georgia, and Kentucky, respectively. There was no same-day or 6-day lagged correlation between test kit orders and COVID-19 incidence in Indiana. CONCLUSIONS Our findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs.
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Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
- Center for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
- Center for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Jonggyu Baek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Biqi Wang
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | | | | | | | - Aditi Singh
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | - Paul Hartin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | | | | | | | - Ben Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Giselle Corbie-Smith
- Department of Social Medicine, Department of Medicine, Center for Health Equity Research, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Warren Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Juan Marquez
- Washtenaw County Health Department, Washtenaw, MI, USA
| | - Nathaniel Hafer
- Center for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, NIH, Via Contract With Kelly Services, Bethesda, MD, USA
| | - David D McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
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Soni A, Herbert C, Lin H, Yan Y, Pretz C, Stamegna P, Wang B, Orwig T, Wright C, Tarrant S, Behar S, Suvarna T, Schrader S, Harman E, Nowak C, Kheterpal V, Rao LV, Cashman L, Orvek E, Ayturk D, Gibson L, Zai A, Wong S, Lazar P, Wang Z, Filippaios A, Barton B, Achenbach CJ, Murphy RL, Robinson ML, Manabe YC, Pandey S, Colubri A, O'Connor L, Lemon SC, Fahey N, Luzuriaga KL, Hafer N, Roth K, Lowe T, Stenzel T, Heetderks W, Broach J, McManus DD. Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection : A Prospective Cohort Study. Ann Intern Med 2023; 176:975-982. [PMID: 37399548 PMCID: PMC10321467 DOI: 10.7326/m23-0385] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The performance of rapid antigen tests (Ag-RDTs) for screening asymptomatic and symptomatic persons for SARS-CoV-2 is not well established. OBJECTIVE To evaluate the performance of Ag-RDTs for detection of SARS-CoV-2 among symptomatic and asymptomatic participants. DESIGN This prospective cohort study enrolled participants between October 2021 and January 2022. Participants completed Ag-RDTs and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 every 48 hours for 15 days. SETTING Participants were enrolled digitally throughout the mainland United States. They self-collected anterior nasal swabs for Ag-RDTs and RT-PCR testing. Nasal swabs for RT-PCR were shipped to a central laboratory, whereas Ag-RDTs were done at home. PARTICIPANTS Of 7361 participants in the study, 5353 who were asymptomatic and negative for SARS-CoV-2 on study day 1 were eligible. In total, 154 participants had at least 1 positive RT-PCR result. MEASUREMENTS The sensitivity of Ag-RDTs was measured on the basis of testing once (same-day), twice (after 48 hours), and thrice (after a total of 96 hours). The analysis was repeated for different days past index PCR positivity (DPIPPs) to approximate real-world scenarios where testing initiation may not always coincide with DPIPP 0. Results were stratified by symptom status. RESULTS Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDTs twice 48 hours apart resulted in an aggregated sensitivity of 93.4% (95% CI, 90.4% to 95.9%) among symptomatic participants on DPIPPs 0 to 6. When singleton positive results were excluded, the aggregated sensitivity on DPIPPs 0 to 6 for 2-time serial testing among asymptomatic participants was lower at 62.7% (CI, 57.0% to 70.5%), but it improved to 79.0% (CI, 70.1% to 87.4%) with testing 3 times at 48-hour intervals. LIMITATION Participants tested every 48 hours; therefore, these data cannot support conclusions about serial testing intervals shorter than 48 hours. CONCLUSION The performance of Ag-RDTs was optimized when asymptomatic participants tested 3 times at 48-hour intervals and when symptomatic participants tested 2 times separated by 48 hours. PRIMARY FUNDING SOURCE National Institutes of Health RADx Tech program.
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Affiliation(s)
- Apurv Soni
- Program in Digital Medicine, Department of Medicine; Division of Health Systems Science, Department of Medicine; and Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.S.)
| | - Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Honghuang Lin
- Program in Digital Medicine and Division of Health Systems Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (H.L., B.W.)
| | - Yi Yan
- Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (Y.Y., K.R., T.L.)
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Pamela Stamegna
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Biqi Wang
- Program in Digital Medicine and Division of Health Systems Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (H.L., B.W.)
| | - Taylor Orwig
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Colton Wright
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Seanan Tarrant
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Stephanie Behar
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Thejas Suvarna
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Summer Schrader
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Emma Harman
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Chris Nowak
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Vik Kheterpal
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Lokinendi V Rao
- Quest Diagnostics, Marlborough, Massachusetts (L.V.R., L.C.)
| | - Lisa Cashman
- Quest Diagnostics, Marlborough, Massachusetts (L.V.R., L.C.)
| | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Didem Ayturk
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Laura Gibson
- Division of Infectious Diseases and Immunology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.G.)
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Steven Wong
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Ziyue Wang
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (Z.W.)
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Chad J Achenbach
- Division of Infectious Diseases, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.J.A., R.L.M.)
| | - Robert L Murphy
- Division of Infectious Diseases, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.J.A., R.L.M.)
| | - Matthew L Robinson
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.L.R., Y.C.M.)
| | - Yukari C Manabe
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.L.R., Y.C.M.)
| | - Shishir Pandey
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Andres Colubri
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.C.)
| | - Laurel O'Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.O., J.B.)
| | - Stephenie C Lemon
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Nisha Fahey
- Program in Digital Medicine, Department of Medicine; Department of Population and Quantitative Health Sciences; and Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, Massachusetts (N.F.)
| | - Katherine L Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science and Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (K.L.L., N.H.)
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science and Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (K.L.L., N.H.)
| | - Kristian Roth
- Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (Y.Y., K.R., T.L.)
| | - Toby Lowe
- Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (Y.Y., K.R., T.L.)
| | - Timothy Stenzel
- Division of Microbiology, Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (T.S.)
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland (W.H.)
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.O., J.B.)
| | - David D McManus
- Program in Digital Medicine, Division of Health Systems Science, and Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.D.M.)
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3
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Soni A, Herbert C, Pretz C, Stamegna P, Filippaios A, Shi Q, Suvarna T, Harman E, Schrader S, Nowak C, Schramm E, Kheterpal V, Behar S, Tarrant S, Ferranto J, Hafer N, Robinson M, Achenbach C, Murphy RL, Manabe YC, Gibson L, Barton B, O’Connor L, Fahey N, Orvek E, Lazar P, Ayturk D, Wong S, Zai A, Cashman L, Rao LV, Luzuriaga K, Lemon S, Blodgett A, Trippe E, Barcus M, Goldberg B, Roth K, Stenzel T, Heetderks W, Broach J, McManus D. Design and implementation of a digital site-less clinical study of serial rapid antigen testing to identify asymptomatic SARS-CoV-2 infection. J Clin Transl Sci 2023; 7:e120. [PMID: 37313378 PMCID: PMC10260333 DOI: 10.1017/cts.2023.540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 04/05/2023] [Accepted: 04/27/2023] [Indexed: 06/15/2023] Open
Abstract
Background Rapid antigen detection tests (Ag-RDT) for SARS-CoV-2 with emergency use authorization generally include a condition of authorization to evaluate the test's performance in asymptomatic individuals when used serially. We aim to describe a novel study design that was used to generate regulatory-quality data to evaluate the serial use of Ag-RDT in detecting SARS-CoV-2 virus among asymptomatic individuals. Methods This prospective cohort study used a siteless, digital approach to assess longitudinal performance of Ag-RDT. Individuals over 2 years old from across the USA with no reported COVID-19 symptoms in the 14 days prior to study enrollment were eligible to enroll in this study. Participants throughout the mainland USA were enrolled through a digital platform between October 18, 2021 and February 15, 2022. Participants were asked to test using Ag-RDT and molecular comparators every 48 hours for 15 days. Enrollment demographics, geographic distribution, and SARS-CoV-2 infection rates are reported. Key Results A total of 7361 participants enrolled in the study, and 492 participants tested positive for SARS-CoV-2, including 154 who were asymptomatic and tested negative to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 US states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide. Conclusions The digital site-less approach employed in the "Test Us At Home" study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19 and can be adapted across research disciplines to optimize study enrollment and accessibility.
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Affiliation(s)
- Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Pamela Stamegna
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | | | | | | | | | | | - Stephanie Behar
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Seanan Tarrant
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Julia Ferranto
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Matthew Robinson
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Robert L. Murphy
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yukari C. Manabe
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Laura Gibson
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Laurel O’Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Nisha Fahey
- Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Didem Ayturk
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Steven Wong
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | | | - Katherine Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Stephenie Lemon
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Allison Blodgett
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Elizabeth Trippe
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Mary Barcus
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Brittany Goldberg
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, 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, MD, USA
| | - Timothy Stenzel
- OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, NIH, Via Contract with Kelly Services, Bethesda, MD, USA
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - David McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
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Herbert C, Wang B, Lin H, Hafer N, Pretz C, Stamegna P, Tarrant S, Hartin P, Ferranto J, Behar S, Wright C, Orwig T, Suvarna T, Harman E, Schrader S, Nowak C, Kheterpal V, Orvek E, Wong S, Zai A, Barton B, Gerber B, Lemon SC, Filippaios A, D'Amore K, Gibson L, Greene S, Howard-Wilson S, Colubri A, Achenbach C, Murphy R, Heetderks W, Manabe YC, O'Connor L, Fahey N, Luzuriaga K, Broach J, McManus DD, Soni A. Performance of Rapid Antigen Tests Based on Symptom Onset and Close Contact Exposure: A secondary analysis from the Test Us At Home prospective cohort study. medRxiv 2023:2023.02.21.23286239. [PMID: 36865199 PMCID: PMC9980261 DOI: 10.1101/2023.02.21.23286239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Background The performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) in temporal relation to symptom onset or exposure is unknown, as is the impact of vaccination on this relationship. Objective To evaluate the performance of Ag-RDT compared with RT-PCR based on day after symptom onset or exposure in order to decide on 'when to test'. Design Setting and Participants The Test Us at Home study was a longitudinal cohort study that enrolled participants over 2 years old across the United States between October 18, 2021 and February 4, 2022. All participants were asked to conduct Ag-RDT and RT-PCR testing every 48 hours over a 15-day period. Participants with one or more symptoms during the study period were included in the Day Post Symptom Onset (DPSO) analyses, while those who reported a COVID-19 exposure were included in the Day Post Exposure (DPE) analysis. Exposure Participants were asked to self-report any symptoms or known exposures to SARS-CoV-2 every 48-hours, immediately prior to conducting Ag-RDT and RT-PCR testing. The first day a participant reported one or more symptoms was termed DPSO 0, and the day of exposure was DPE 0. Vaccination status was self-reported. Main Outcome and Measures Results of Ag-RDT were self-reported (positive, negative, or invalid) and RT-PCR results were analyzed by a central laboratory. Percent positivity of SARS-CoV-2 and sensitivity of Ag-RDT and RT-PCR by DPSO and DPE were stratified by vaccination status and calculated with 95% confidence intervals. Results A total of 7,361 participants enrolled in the study. Among them, 2,086 (28.3%) and 546 (7.4%) participants were eligible for the DPSO and DPE analyses, respectively. Unvaccinated participants were nearly twice as likely to test positive for SARS-CoV-2 than vaccinated participants in event of symptoms (PCR+: 27.6% vs 10.1%) or exposure (PCR+: 43.8% vs. 22.2%). The highest proportion of vaccinated and unvaccinated individuals tested positive on DPSO 2 and DPE 5-8. Performance of RT-PCR and Ag-RDT did not differ by vaccination status. Ag-RDT detected 78.0% (95% Confidence Interval: 72.56-82.61) of PCR-confirmed infections by DPSO 4. For exposed participants, Ag-RDT detected 84.9% (95% CI: 75.0-91.4) of PCR-confirmed infections by day five post-exposure (DPE 5). Conclusions and Relevance Performance of Ag-RDT and RT-PCR was highest on DPSO 0-2 and DPE 5 and did not differ by vaccination status. These data suggests that serial testing remains integral to enhancing the performance of Ag-RDT.
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Soni A, Herbert C, Lin H, Yan Y, Pretz C, Stamegna P, Wang B, Orwig T, Wright C, Tarrant S, Behar S, Suvarna T, Schrader S, Harman E, Nowak C, Kheterpal V, Rao LV, Cashman L, Orvek E, Ayturk D, Gibson L, Zai A, Wong S, Lazar P, Wang Z, Filippaios A, Barton B, Achenbach CJ, Murphy RL, Robinson M, Manabe YC, Pandey S, Colubri A, Oâ Connor L, Lemon SC, Fahey N, Luzuriaga KL, Hafer N, Roth K, Lowe T, Stenzel T, Heetderks W, Broach J, McManus DD. Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection. medRxiv 2023:2022.08.05.22278466. [PMID: 35982680 PMCID: PMC9387089 DOI: 10.1101/2022.08.05.22278466] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) varies over the course of an infection, and their performance in screening for SARS-CoV-2 is not well established. We aimed to evaluate performance of Ag-RDT for detection of SARS-CoV-2 for symptomatic and asymptomatic participants. Methods Participants >2 years old across the United States enrolled in the study between October 2021 and February 2022. Participants completed Ag-RDT and molecular testing (RT-PCR) for SARS-CoV-2 every 48 hours for 15 days. This analysis was limited to participants who were asymptomatic and tested negative on their first day of study participation. Onset of infection was defined as the day of first positive RT-PCR result. Sensitivity of Ag-RDT was measured based on testing once, twice (after 48-hours), and thrice (after 96 hours). Analysis was repeated for different Days Post Index PCR Positivity (DPIPP) and stratified based on symptom-status. Results In total, 5,609 of 7,361 participants were eligible for this analysis. Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDT twice 48-hours apart resulted in an aggregated sensitivity of 93.4% (95% CI: 89.1-96.1%) among symptomatic participants on DPIPP 0-6. Excluding singleton positives, aggregated sensitivity on DPIPP 0-6 for two-time serial-testing among asymptomatic participants was lower at 62.7% (54.7-70.0%) but improved to 79.0% (71.0-85.3%) with testing three times at 48-hour intervals. Discussion Performance of Ag-RDT was optimized when asymptomatic participants tested three-times at 48-hour intervals and when symptomatic participants tested two-times separated by 48-hours.
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Soni A, Herbert C, Pretz C, Stamegna P, Filippaios A, Shi Q, Suvarna T, Harman E, Schrader S, Nowak C, Schramm E, Kheterpal V, Behar S, Tarrant S, Ferranto J, Hafer N, Robinson M, Achenbach C, Murphy RL, Manabe YC, Gibson L, Barton B, O'Connor L, Fahey N, Orvek E, Lazar P, Ayturk D, Wong S, Zai A, Cashman L, Rao LV, Luzuriaga K, Lemon S, Blodgett A, Trippe E, Barcus M, Goldberg B, Roth K, Stenzel T, Heetderks W, Broach J, McManus D. Finding a Needle in a Haystack: Design and Implementation of a Digital Site-less Clinical Study of Serial Rapid Antigen Testing to Identify Asymptomatic SARS-CoV-2 Infection. medRxiv 2023:2022.08.04.22278274. [PMID: 35982663 PMCID: PMC9387154 DOI: 10.1101/2022.08.04.22278274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Rapid antigen tests (Ag-RDT) for SARS-CoV-2 with Emergency Use Authorization generally include a condition of authorization to evaluate the test's performance in asymptomatic individuals when used serially. Objective To describe a novel study design to generate regulatory-quality data to evaluate serial use of Ag-RDT in detecting SARS-CoV-2 virus among asymptomatic individuals. Design Prospective cohort study using a decentralized approach. Participants were asked to test using Ag-RDT and molecular comparators every 48 hours for 15 days. Setting Participants throughout the mainland United States were enrolled through a digital platform between October 18, 2021 and February 15, 2022. Ag-RDTs were completed at home, and molecular comparators were shipped to a central laboratory. Participants Individuals over 2 years old from across the U.S. with no reported COVID-19 symptoms in the 14 days prior to study enrollment were eligible to enroll in this study. Measurements Enrollment demographics, geographic distribution, and SARS-CoV-2 infection rates are reported. Key Results A total of 7,361 participants enrolled in the study, and 492 participants tested positive for SARS-CoV-2, including 154 who were asymptomatic and tested negative to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 U.S. states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide. Limitations New, complex workflows required significant operational and data team support. Conclusions: The digital site-less approach employed in the 'Test Us At Home' study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19, and can be adapted across research disciplines to optimize study enrollment and accessibility.
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Soni A, Herbert C, Filippaios A, Broach J, Colubri A, Fahey N, Woods K, Nanavati J, Wright C, Orwig T, Gilliam K, Kheterpal V, Suvarna T, Nowak C, Schrader S, Lin H, O'Connor L, Pretz C, Ayturk D, Orvek E, Flahive J, Lazar P, Shi Q, Achenbach C, Murphy R, Robinson M, Gibson L, Stamegna P, Hafer N, Luzuriaga K, Barton B, Heetderks W, Manabe YC, McManus D. Comparison of Rapid Antigen Tests' Performance Between Delta and Omicron Variants of SARS-CoV-2 : A Secondary Analysis From a Serial Home Self-testing Study. Ann Intern Med 2022; 175:1685-1692. [PMID: 36215709 PMCID: PMC9578286 DOI: 10.7326/m22-0760] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND It is important to document the performance of rapid antigen tests (Ag-RDTs) in detecting SARS-CoV-2 variants. OBJECTIVE To compare the performance of Ag-RDTs in detecting the Delta (B.1.617.2) and Omicron (B.1.1.529) variants of SARS-CoV-2. DESIGN Secondary analysis of a prospective cohort study that enrolled participants between 18 October 2021 and 24 January 2022. Participants did Ag-RDTs and collected samples for reverse transcriptase polymerase chain reaction (RT-PCR) testing every 48 hours for 15 days. SETTING The parent study enrolled participants throughout the mainland United States through a digital platform. All participants self-collected anterior nasal swabs for rapid antigen testing and RT-PCR testing. All Ag-RDTs were completed at home, whereas nasal swabs for RT-PCR were shipped to a central laboratory. PARTICIPANTS Of 7349 participants enrolled in the parent study, 5779 asymptomatic persons who tested negative for SARS-CoV-2 on day 1 of the study were eligible for this substudy. MEASUREMENTS Sensitivity of Ag-RDTs on the same day as the first positive (index) RT-PCR result and 48 hours after the first positive RT-PCR result. RESULTS A total of 207 participants were positive on RT-PCR (58 Delta, 149 Omicron). Differences in sensitivity between variants were not statistically significant (same day: Delta, 15.5% [95% CI, 6.2% to 24.8%] vs. Omicron, 22.1% [CI, 15.5% to 28.8%]; at 48 hours: Delta, 44.8% [CI, 32.0% to 57.6%] vs. Omicron, 49.7% [CI, 41.6% to 57.6%]). Among 109 participants who had RT-PCR-positive results for 48 hours, rapid antigen sensitivity did not differ significantly between Delta- and Omicron-infected participants (48-hour sensitivity: Delta, 81.5% [CI, 66.8% to 96.1%] vs. Omicron, 78.0% [CI, 69.1% to 87.0%]). Only 7.2% of the 69 participants with RT-PCR-positive results for shorter than 48 hours tested positive by Ag-RDT within 1 week; those with Delta infections remained consistently negative on Ag-RDTs. LIMITATION A testing frequency of 48 hours does not allow a finer temporal resolution of the analysis of test performance, and the results of Ag-RDTs are based on self-report. CONCLUSION The performance of Ag-RDTs in persons infected with the SARS-CoV-2 Omicron variant is not inferior to that in persons with Delta infections. Serial testing improved the sensitivity of Ag-RDTs for both variants. The performance of rapid antigen testing varies on the basis of duration of RT-PCR positivity. PRIMARY FUNDING SOURCE National Heart, Lung, and Blood Institute of the National Institutes of Health.
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Affiliation(s)
- Apurv Soni
- Program in Digital Medicine and Division of Clinical Informatics, Department of Medicine, and Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.S.)
| | - Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (J.B., L.O.)
| | - Andres Colubri
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.C.)
| | - Nisha Fahey
- Program in Digital Medicine, Department of Medicine, Department of Population and Quantitative Health Sciences, and Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, Massachusetts (N.F.)
| | - Kelsey Woods
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Janvi Nanavati
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Colton Wright
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Taylor Orwig
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Karen Gilliam
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Vik Kheterpal
- CareEvolution, Ann Arbor, Michigan (V.K., T.S., C.N., S.S.)
| | - Thejas Suvarna
- CareEvolution, Ann Arbor, Michigan (V.K., T.S., C.N., S.S.)
| | - Chris Nowak
- CareEvolution, Ann Arbor, Michigan (V.K., T.S., C.N., S.S.)
| | | | - Honghuang Lin
- Program in Digital Medicine and Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (H.L.)
| | - Laurel O'Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (J.B., L.O.)
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Didem Ayturk
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - Julie Flahive
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, Department of Population and Quantitative Health Sciences, and University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts (Q.S.)
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.A., R.M.)
| | - Robert Murphy
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.A., R.M.)
| | - Matthew Robinson
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.R., Y.C.M.)
| | - Laura Gibson
- Department of Pediatrics and Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.G.)
| | - Pamela Stamegna
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts (P.S., N.H.)
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts (P.S., N.H.)
| | - Katherine Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science and Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (K.L.)
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland (W.H.)
| | - Yukari C Manabe
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.R., Y.C.M.)
| | - David McManus
- Program in Digital Medicine and Division of Cardiology, Department of Medicine, and Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.M.)
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Herbert C, Broach J, Heetderks W, Qashu F, Gibson L, Pretz C, Woods K, Kheterpal V, Suvarna T, Nowak C, Lazar P, Ayturk D, Barton B, Achenbach C, Murphy R, McManus D, Soni A. Feasibility of At-Home Serial Testing Using Over-the-Counter SARS-CoV-2 Tests With a Digital Smartphone App for Assistance: Longitudinal Cohort Study. JMIR Form Res 2022; 6:e35426. [PMID: 36041004 PMCID: PMC9580993 DOI: 10.2196/35426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The ongoing SARS-CoV-2 pandemic necessitates the development of accurate, rapid, and affordable diagnostics to help curb disease transmission, morbidity, and mortality. Rapid antigen tests are important tools for scaling up testing for SARS-CoV-2; however, little is known about individuals' use of rapid antigen tests at home and how to facilitate the user experience. OBJECTIVE This study aimed to describe the feasibility and acceptability of serial self-testing with rapid antigen tests for SARS-CoV-2, including need for assistance and the reliability of self-interpretation. METHODS A total of 206 adults in the United States with smartphones were enrolled in this single-arm feasibility study in February and March 2021. All participants were asked to self-test for COVID-19 at home using rapid antigen tests daily for 14 days and use a smartphone app for testing assistance and to report their results. The main outcomes were adherence to the testing schedule, the acceptability of testing and smartphone app experiences, and the reliability of participants versus study team's interpretation of test results. Descriptive statistics were used to report the acceptability, adherence, overall rating, and experience of using the at-home test and MyDataHelps app. The usability, acceptability, adherence, and quality of at-home testing were analyzed across different sociodemographic, age, and educational attainment groups. RESULTS Of the 206 enrolled participants, 189 (91.7%) and 159 (77.2%) completed testing and follow-up surveys, respectively. In total, 51.3% (97/189) of study participants were women, the average age was 40.7 years, 34.4% (65/189) were non-White, and 82% (155/189) had a bachelor's degree or higher. Most (n=133/206, 64.6%) participants showed high testing adherence, meaning they completed over 75% of the assigned tests. Participants' interpretations of test results demonstrated high agreement (2106/2130, 98.9%) with the study verified results, with a κ score of 0.29 (P<.001). Participants reported high satisfaction with self-testing and the smartphone app, with 98.7% (157/159) reporting that they would recommend the self-test and smartphone app to others. These results were consistent across age, race/ethnicity, and gender. CONCLUSIONS Participants' high adherence to the recommended testing schedule, significant reliability between participants and study staff's test interpretation, and the acceptability of the smartphone app and self-test indicate that self-tests for SARS-CoV-2 with a smartphone app for assistance and reporting is a highly feasible testing modality among a diverse population of adults in the United States.
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Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.,UMass Memorial Medical Center, Worcester, MA, United States
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, United States
| | - Felicia Qashu
- National Institutes of Health, Bethesda, MD, United States
| | - Laura Gibson
- Division of Infectious Disease and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kelsey Woods
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | | | | | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Didem Ayturk
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Robert Murphy
- Division of Infectious Disease, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - David McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.,Department of Cardiology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.,Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
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9
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Waggoner JJ, Vos MB, Tyburski EA, Nguyen PV, Ingersoll JM, Miller C, Sullivan J, Griffiths M, Stone C, Benoit M, Benedit L, Seitter B, Jerris R, Levy JM, Kraft CS, Farmer S, Peagler A, Wood A, Westbrook AL, Morris CR, Sathian UN, Heetderks W, Li L, Roth K, Barcus M, Stenzel T, Martin GS, Lam WA. Concordance of SARS-CoV-2 Results in Self-collected Nasal Swabs vs Swabs Collected by Health Care Workers in Children and Adolescents. JAMA 2022; 328:935-940. [PMID: 36018570 PMCID: PMC9419070 DOI: 10.1001/jama.2022.14877] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE Despite the expansion of SARS-CoV-2 testing, available tests have not received Emergency Use Authorization for performance with self-collected anterior nares (nasal) swabs from children younger than 14 years because the effect of pediatric self-swabbing on SARS-CoV-2 test sensitivity is unknown. OBJECTIVE To characterize the ability of school-aged children to self-collect nasal swabs for SARS-CoV-2 testing compared with collection by health care workers. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study of 197 symptomatic children and adolescents aged 4 to 14 years old. Individuals were recruited based on results of testing in the Children's Healthcare of Atlanta system from July to August 2021. EXPOSURES Children and adolescents were given instructional material consisting of a short instructional video and a handout with written and visual steps for self-swab collection. Participants first provided a self-collected nasal swab. Health care workers then collected a second specimen. MAIN OUTCOMES AND MEASURES The primary outcome was SARS-CoV-2 detection and relative quantitation by cycle threshold (Ct) in self- vs health care worker-collected nasal swabs when tested with a real-time reverse transcriptase-polymerase chain reaction test with Emergency Use Authorization. RESULTS Among the study participants, 108 of 194 (55.7%) were male and the median age was 9 years (IQR, 6-11). Of the 196 participants, 87 (44.4%) tested positive for SARS-CoV-2 and 105 (53.6%) tested negative by both self- and health care worker-collected swabs. Two children tested positive by self- or health care worker-collected swab alone; 1 child had an invalid health care worker swab. Compared with health care worker-collected swabs, self-collected swabs had 97.8% (95% CI, 94.7%-100.0%) and 98.1% (95% CI, 95.6%-100.0%) positive and negative percent agreement, respectively, and SARS-CoV-2 Ct values did not differ significantly between groups (mean [SD] Ct, self-swab: 26.7 [5.4] vs health care worker swab: 26.3 [6.0]; P = .65). CONCLUSIONS AND RELEVANCE After hearing and seeing simple instructional materials, children and adolescents aged 4 to 14 years self-collected nasal swabs that closely agreed on SARS-CoV-2 detection with swabs collected by health care workers.
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Affiliation(s)
| | - Miriam B. Vos
- Emory University School of Medicine, Atlanta, Georgia
- Children’s Healthcare of Atlanta, Atlanta, Georgia
| | | | | | | | | | | | | | - Cheryl Stone
- Children’s Healthcare of Atlanta, Atlanta, Georgia
| | | | | | | | | | | | | | | | | | - Anna Wood
- Pediatric Biostatistics Core, Department of Pediatrics, Emory University, Atlanta, Georgia
| | - Adrianna L. Westbrook
- Pediatric Biostatistics Core, Department of Pediatrics, Emory University, Atlanta, Georgia
| | - Claudia R. Morris
- Emory University School of Medicine, Atlanta, Georgia
- Children’s Healthcare of Atlanta, Atlanta, Georgia
| | | | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland
| | - Li Li
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - 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
| | - Mary Barcus
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Timothy Stenzel
- OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | | | - Wilbur A. Lam
- Emory University School of Medicine, Atlanta, Georgia
- Children’s Healthcare of Atlanta, Atlanta, Georgia
- Georgia Institute of Technology, Atlanta
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Herbert C, Shi Q, Kheterpal V, Nowak C, Suvarna T, Durnan B, Schrader S, Behar S, Naeem S, Tarrant S, Kalibala B, Singh A, Gerber B, Barton B, Lin H, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Baek J, Hafer N, Gibson L, O’Connor L, Broach J, Heetderks W, McManus D, Soni A. Use of a Digital Assistant to Report COVID-19 Rapid Antigen Self-test Results to Health Departments in 6 US Communities. JAMA Netw Open 2022; 5:e2228885. [PMID: 36018589 PMCID: PMC9419013 DOI: 10.1001/jamanetworkopen.2022.28885] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/06/2022] [Indexed: 11/14/2022] Open
Abstract
Importance Widespread distribution of rapid antigen tests is integral to the US strategy to address COVID-19; however, it is estimated that few rapid antigen test results are reported to local departments of health. Objective To characterize how often individuals in 6 communities throughout the United States used a digital assistant to log rapid antigen test results and report them to their local departments of health. Design, Setting, and Participants This prospective cohort study is based on anonymously collected data from the beneficiaries of the Say Yes! Covid Test program, which distributed more than 3 000 000 rapid antigen tests at no cost to residents of 6 communities (Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; Ann Arbor and Ypsilanti, Michigan; and Chattanooga, Tennessee) between April and October 2021. A descriptive evaluation of beneficiary use of a digital assistant for logging and reporting their rapid antigen test results was performed. Interventions Widespread community distribution of rapid antigen tests. Main Outcomes and Measures Number and proportion of tests logged and reported to the local department of health through the digital assistant. Results A total of 313 000 test kits were distributed, including 178 785 test kits that were ordered using the digital assistant. Among all distributed kits, 14 398 households (4.6%) used the digital assistant, but beneficiaries reported three-quarters of their rapid antigen test results to their state public health departments (30 965 tests reported of 41 465 total test results [75.0%]). The reporting behavior varied by community and was significantly higher among communities that were incentivized for reporting test results vs those that were not incentivized or partially incentivized (90.5% [95% CI, 89.9%-91.2%] vs 70.5%; [95% CI, 70.0%-71.0%]). In all communities, positive tests were less frequently reported than negative tests (60.4% [95% CI, 58.1%-62.8%] vs 75.5% [95% CI, 75.1%-76.0%]). Conclusions and Relevance These results suggest that application-based reporting with incentives may be associated with increased reporting of rapid tests for COVID-19. However, increasing the adoption of the digital assistant may be a critical first step.
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Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
- Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester
| | | | | | | | | | | | - Stephanie Behar
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Syed Naeem
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Seanan Tarrant
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Ben Kalibala
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Aditi Singh
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Ben Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | | | - Giselle Corbie-Smith
- Center for Health Equity Research, Department of Social Medicine, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill
| | - Warren Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Juan Marquez
- Washtenaw County Health Department, Washtenaw, Michigan
| | - Jonggyu Baek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
| | - Nathaniel Hafer
- Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester
| | - Laura Gibson
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Laurel O’Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, via contract with Kelly Services, Bethesda, Maryland
| | - David McManus
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
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11
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Soni A, Herbert C, Baek J, Shi Q, Marquez J, Harman E, Kheterpal V, Nowak C, Suvarna T, Lin H, Heetderks W, Zai A, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Gerber BS, Hafer N, Barton B, Broach J, McManus D. Association of Mass Distribution of Rapid Antigen Tests and SARS-CoV-2 Prevalence: Results from NIH-CDC funded Say Yes! Covid Test program in Michigan. medRxiv 2022:2022.03.26.22272727. [PMID: 35411342 PMCID: PMC8996630 DOI: 10.1101/2022.03.26.22272727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Importance Wide-spread distribution of diagnostics is an integral part of the United States’ COVID-19 strategy; however, few studies have assessed the effectiveness of this intervention at reducing transmission of community COVID-19. Objective To assess the impact of the Say Yes! Covid Test (SYCT!) Michigan program, a population-based program that distributed 20,000 free rapid antigen tests within Ann Arbor and Ypsilanti, Michigan in June-August 2021, on community prevalence of SARS-CoV-2. Design This ecological study analyzed cases of SARS-CoV-2 from March to October 2021 reported to the Washtenaw County Health Department. Setting Washtenaw County, Michigan. Participants All residents of Washtenaw County. Interventions Community-wide distribution of 500,000 rapid antigen tests for SARS-CoV-2 to residents of Ann Arbor and Ypsilanti, Michigan. Each household was limited to one test kit containing 25 rapid antigen tests. Main Outcome and Measures Community prevalence of SARS-CoV-2, as measured through 7-day average cases, in Ann Arbor and Ypsilanti was compared to the rest of Washtenaw County. A generalized additive model was fitted with non-parametric trends for control and relative differences of trends in the pre-intervention, intervention, and post-intervention periods to compare intervention municipalities of Ann Arbor and Ypsilanti to the rest of Washtenaw County. Model results were used to calculate average cases prevented in the post-intervention period. Results In the post-intervention period, there were significantly lower standardized average cases in the intervention communities of Ann Arbor/Ypsilanti compared to the rest of Washtenaw County (p<0.001). The estimated standardized relative difference between Ann Arbor/Ypsilanti and the rest of Washtenaw County was -0.016 cases per day (95% CI: -0.020 to -0.013), implying that the intervention prevented 40 average cases per day two months into the post-intervention period if trends were consistent. Conclusions and Relevance Mass distribution of rapid antigen tests may be a useful mitigation strategy to combat community transmission of SARS-CoV-2, especially given the recent relaxation of social distancing and masking requirements.
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12
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Herbert C, Shi Q, Kheterpal V, Nowak C, Suvarna T, Durnam B, Schrader S, Behar S, Naeem S, Tarrant S, Kalibala B, Singh A, Gerber B, Barton B, Lin H, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Baek J, Hafer N, Gibson L, O'Connor L, Broach J, Heetderks W, McManus D, Soni A. If you build it, will they use it? Use of a Digital Assistant for Self-Reporting of COVID-19 Rapid Antigen Test Results during Large Nationwide Community Testing Initiative. medRxiv 2022:2022.03.31.22273242. [PMID: 35411338 PMCID: PMC8996627 DOI: 10.1101/2022.03.31.22273242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Importance Wide-spread distribution of rapid-antigen tests is integral to the United States' strategy to address COVID-19; however, it is estimated that few rapid-antigen test results are reported to local departments of health. Objective To characterize how often individuals in six communities throughout the United States used a digital assistant to log rapid-antigen test results and report them to their local Department of Health. Design This prospective cohort study is based on anonymously collected data from the beneficiaries of The Say Yes! Covid Test program, which distributed 3,000,000 rapid antigen tests at no cost to residents of six communities between April and October 2021. We provide a descriptive evaluation of beneficiaries' use of digital assistant for logging and reporting their rapid antigen test results. Main Outcome and Measures Number and proportion of tests logged and reported to the Department of Health through the digital assistant. Results A total of 178,785 test kits were ordered by the digital assistant, and 14,398 households used the digital assistant to log 41,465 test results. Overall, a small proportion of beneficiaries used the digital assistant (8%), but over 75% of those who used it reported their rapid antigen test results to their state public health department. The reporting behavior varied between communities and was significantly different for communities that were incentivized for reporting test results (p < 0.001). In all communities, positive tests were less reported than negative tests (60.4% vs 75.5%; p<0.001). Conclusions and Relevance These results indicate that app-based reporting with incentives may be an effective way to increase reporting of rapid tests for COVID-19; however, increasing the adoption of the digital assistant is a critical first step.
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13
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Herbert C, Kheterpal V, Suvarna T, Broach J, Marquez JL, Gerber B, Schrader S, Nowak C, Harman E, Heetderks W, Fahey N, Orvek E, Lazar P, Ferranto J, Noorishirazi K, Valpady S, Shi Q, Lin H, Marvel K, Gibson L, Barton B, Lemon S, Hafer N, McManus D, Soni A. Design and Preliminary Findings from Self-Testing for Our Protection from COVID-19 (STOP COVID-19): a prospective digital study of adherence to a risk-based testing protocol (Preprint). JMIR Form Res 2022; 6:e38113. [PMID: 35649180 PMCID: PMC9205422 DOI: 10.2196/38113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/11/2022] [Accepted: 05/29/2022] [Indexed: 01/15/2023] Open
Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | | | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | - Ben Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | | | - Emma Harman
- CareEvolution, Inc, Ann Arbor, MI, United States
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Kelly Services, Bethesda, MD, United States
| | - Nisha Fahey
- Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Julia Ferranto
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kamran Noorishirazi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Shivakumar Valpady
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kathryn Marvel
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Laura Gibson
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Stephenie Lemon
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - David McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
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14
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Soni A, Herbert C, Filippaios A, Broach J, Colubri A, Fahey N, Woods K, Nanavati J, Wright C, Orwig T, Gilliam K, Kheterpal V, Suvarna T, Nowak C, Schrader S, Lin H, O'Connor L, Pretz C, Ayturk D, Orvek E, Flahive J, Lazar P, Shi Q, Achenbach C, Murphy R, Robinson M, Gibson L, Stamegna P, Hafer N, Luzuriaga K, Barton B, Heetderks W, Manabe YC, McManus D. Comparison of Rapid Antigen Tests' Performance between Delta (B.1.61.7; AY.X) and Omicron (B.1.1.529; BA1) Variants of SARS-CoV-2: Secondary Analysis from a Serial Home Self-Testing Study. medRxiv 2022. [PMID: 35262091 DOI: 10.1101/2022.02.27.22271090] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background There is a need to understand the performance of rapid antigen tests (Ag-RDT) for detection of the Delta (B.1.61.7; AY.X) and Omicron (B.1.1.529; BA1) SARS-CoV-2 variants. Methods Participants without any symptoms were enrolled from October 18, 2021 to January 24, 2022 and performed Ag-RDT and RT-PCR tests every 48 hours for 15 days. This study represents a non-pre-specified analysis in which we sought to determine if sensitivity of Ag-RDT differed in participants with Delta compared to Omicron variant. Participants who were positive on RT-PCR on the first day of the testing period were excluded. Delta and Omicron variants were defined based on sequencing and date of first RT-PCR positive result (RT-PCR+). Comparison of Ag-RDT performance between the variants was based on sensitivity, defined as proportion of participants with Ag-RDT+ results in relation to their first RT-PCR+ result, for different duration of testing with rapid Ag-RDT. Subsample analysis was performed based on the result of participants' second RT-PCR test within 48 hours of the first RT-PCR+ test. Results From the 7,349 participants enrolled in the parent study, 5,506 met the eligibility criteria for this analysis. A total of 153 participants were RT-PCR+ (61 Delta, 92 Omicron); among this group, 36 (23.5%) tested Ag-RDT+ on the same day, and 84 (54.9%) tested Ag-RDT+ within 48 hours as first RT-PCR+. The differences in sensitivity between variants were not statistically significant (same-day: Delta 16.4% [95% CI: 8.2-28.1] vs Omicron 28.2% [95% CI: 19.4-38.6]; and 48-hours: Delta 45.9% [33.1-59.2] vs. Omicron 60.9% [50.1-70.9]). This trend continued among the 86 participants who had consecutive RT-PCR+ result (48-hour sensitivity: Delta 79.3% [60.3-92.1] vs. Omicron: 89.5% [78.5-96.0]). Conversely, the 38 participants who had an isolated RT-PCR+ remained consistently negative on Ag-RDT, regardless of the variant. Conclusions The performance of Ag-RDT is not inferior among individuals infected with the SARS-CoV-2 Omicron variant as compared to the Delta variant. The improvement in sensitivity of Ag-RDT noted with serial testing is consistent between Delta and Omicron variant. Performance of Ag-RDT varies based on duration of RT-PCR+ results and more studies are needed to understand the clinical and public health significance of individuals who are RT-PCR+ for less than 48 hours.
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15
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Marjenin T, Scott P, Bajaj A, Bansal T, Berne B, Bowsher K, Costello A, Doucet J, Franca E, Ghosh C, Govindarajan A, Gutowski S, Gwinn K, Hinckley S, Keegan E, Lee H, Mathews B, Misra S, Patel S, Tang X, Heetderks W, Hoffmann M, Pena C. FDA Perspectives on the Regulation of Neuromodulation Devices. Neuromodulation 2020; 23:3-9. [PMID: 31965667 DOI: 10.1111/ner.13085] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 09/16/2019] [Accepted: 10/07/2019] [Indexed: 11/30/2022]
Abstract
The United States Food and Drug Administration (FDA) ensures that patients in the United States have access to safe and effective medical devices. The division of neurological and physical medicine devices reviews medical technologies that interface with the nervous system, including many neuromodulation devices. This article focuses on neuromodulation devices and addresses how to navigate the FDA's regulatory landscape to successfully bring devices to patients.
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Affiliation(s)
- Timothy Marjenin
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Pamela Scott
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Anita Bajaj
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Tushar Bansal
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Bernard Berne
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Kristen Bowsher
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Ann Costello
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - John Doucet
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Eric Franca
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Chandramallika Ghosh
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Anupama Govindarajan
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Stacie Gutowski
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Katrina Gwinn
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Stephen Hinckley
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Erin Keegan
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Hyung Lee
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Binoy Mathews
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Sanjay Misra
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Shyama Patel
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Xiaorui Tang
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - William Heetderks
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Michael Hoffmann
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Carlos Pena
- Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
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16
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Peña C, Anderson L, Brooks C, Bydon M, Fusco M, Heetderks W, Herrmann R, Hoffmann M, Loftus C, Raben S, Seog J, Noonan P, Smith M, Williams D, Zheng X. Update to Food and Drug Administration Regulation of Stroke Neurological Devices. Stroke 2019; 50:524-528. [DOI: 10.1161/strokeaha.118.021078] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Carlos Peña
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Leigh Anderson
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Claudette Brooks
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Mohamad Bydon
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Matthew Fusco
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - William Heetderks
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Robert Herrmann
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Michael Hoffmann
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Christopher Loftus
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Samuel Raben
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Joonil Seog
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Patrick Noonan
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Myra Smith
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Dhanya Williams
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
| | - Xiaolin Zheng
- From the Division of Neurological and Physical Medicine Devices, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD
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17
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Loftus CM, Hoffmann M, Heetderks W, Zheng X, Peña C. Regulation of Neurological Devices and Neurointerventional Endovascular Approaches for Acute Ischemic Stroke. Front Neurol 2018; 9:320. [PMID: 29988408 PMCID: PMC6024113 DOI: 10.3389/fneur.2018.00320] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 04/23/2018] [Indexed: 12/01/2022] Open
Abstract
The United States Food and Drug Administration (FDA) Center for Devices and Radiological Health (CDRH) is charged with ensuring patients in the US have timely access to high-quality, safe, and effective medical devices of public health importance. Within CDRH, the Division of Neurological and Physical Medicine Devices reviews medical technologies that interface with the central and peripheral nervous system (neurotechnologies), including neurointerventional medical devices that are used in the treatment of stroke. Endovascular treatments have demonstrated recent advances in reaching the marketplace and providing more options for patients with acute ischemic stroke and intracranial aneurysms specifically. Depending upon the pathway chosen for regulatory approval, and the evidentiary standard for different regulatory pathways, neurotechnologies can have well-established safety and effectiveness profiles, varying degrees of scientific and clinical uncertainty regarding safety and effectiveness, or when a humanitarian use exists, need only demonstrate a probable benefit and safety to the patient so potentially life-saving treatments can reach the marketplace. Reperfusion therapies have had specific advances in the treatment of stroke patients that originally had limited or no treatment options and for preventative treatments in providing care to patients with intracranial aneurysms to avoid potentially more catastrophic outcomes. Collaboration in multiple forums and environments will be important to continue to foster the neurointerventional technology sector and positively impact clinical medicine, from diagnosing and treating a neurological disorder, to potentially altering the progression of disease, and in many ways, contemporary approved devices have brought a new sense of hope and optimism that serious and otherwise disabling neurological diseases can be treated and in many cases cured with modern therapy. We present here the scope of FDA’s regulatory landscape for neurological devices and neurointerventional endovascular approaches for acute ischemic stroke; this is essential information for those seeking to successfully translate medical device neurotechnologies for patient and consumer use.
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Affiliation(s)
- Christopher M Loftus
- Division of Neurological and Physical Medicine Devices (DNPMD), Center for Devices and Radiological Health (CDRH), United States Food and Drug Administration (FDA), Silver Spring, MD, United States
| | - Michael Hoffmann
- Division of Neurological and Physical Medicine Devices (DNPMD), Center for Devices and Radiological Health (CDRH), United States Food and Drug Administration (FDA), Silver Spring, MD, United States
| | - William Heetderks
- Division of Neurological and Physical Medicine Devices (DNPMD), Center for Devices and Radiological Health (CDRH), United States Food and Drug Administration (FDA), Silver Spring, MD, United States
| | - Xiaolin Zheng
- Division of Neurological and Physical Medicine Devices (DNPMD), Center for Devices and Radiological Health (CDRH), United States Food and Drug Administration (FDA), Silver Spring, MD, United States
| | - Carlos Peña
- Division of Neurological and Physical Medicine Devices (DNPMD), Center for Devices and Radiological Health (CDRH), United States Food and Drug Administration (FDA), Silver Spring, MD, United States
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18
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Anderson L, Antkowiak P, Asefa A, Ballard A, Bansal T, Bello A, Berne B, Bowsher K, Blumenkopf B, Broverman I, Bydon M, Chao K, Como P, Cork K, Costello A, De Laurentis K, DeMarco A, Dean H, Doucet J, Dworak B, Epperson L, Franca E, Ghassemian N, Ghosh C, Govindarajan A, Gupta J, Gutowski S, Herrmann R, Hoffmann M, Heetderks W, Hsu S, Kaufman D, Keegan E, Kittlesen G, Khuu K, Lee H, Lo L, Marcus I, Marjenin T, Mathews B, Misra S, Pinto V, Ramos V, Raben S, Russell A, Saha D, Seog J, Shenouda C, Smith M, Tang X, Wachrathit K, Waterhouse J, Williams D, Zheng X, Peña C. FDA Regulation of Neurological and Physical Medicine Devices: Access to Safe and Effective Neurotechnologies for All Americans. Neuron 2016; 92:943-948. [DOI: 10.1016/j.neuron.2016.10.036] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 10/18/2016] [Accepted: 10/18/2016] [Indexed: 10/20/2022]
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Klonoff DC, Zimliki CL, Stevens LA, Beaston P, Pinkos A, Choe SY, Arreaza-Rubín G, Heetderks W. Innovations in technology for the treatment of diabetes: clinical development of the artificial pancreas (an autonomous system). J Diabetes Sci Technol 2011; 5:804-26. [PMID: 21722597 PMCID: PMC3192648 DOI: 10.1177/193229681100500336] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The Food and Drug Administration in collaboration with the National Institutes of Health presented a public workshop to facilitate medical device innovation in the development of the artificial pancreas (or autonomous system) for the treatment of diabetes mellitus on November 10, 2010 in Gaithersburg, Maryland. The purpose of the workshop was to discuss four aspects of artificial pancreas research and development, including: (1) the current state of device systems for autonomous systems for the treatment of diabetes mellitus; (2) challenges in developing this expert device system using existing technology; (3) clinical expectations for these systems; and (4) development plans for the transition of this device system toward an outpatient setting. The patients discussed how clinical science, system components, and regulatory policies will all need to harmonize in order to achieve the goal of seeing an AP product brought forward to the marketplace for patients to use.
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Affiliation(s)
- David C Klonoff
- Mills-Peninsula Health Services, San Mateo, California, USA.
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Pettigrew RI, Peterson KP, Heetderks W, Seto B. The National Institute of Biomedical Imaging and Bioengineering marks its first five years. Acad Radiol 2007; 14:1448-54. [PMID: 18172950 DOI: 10.1016/j.acra.2007.10.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Narayan RK, Michel ME, Ansell B, Baethmann A, Biegon A, Bracken MB, Bullock MR, Choi SC, Clifton GL, Contant CF, Coplin WM, Dietrich WD, Ghajar J, Grady SM, Grossman RG, Hall ED, Heetderks W, Hovda DA, Jallo J, Katz RL, Knoller N, Kochanek PM, Maas AI, Majde J, Marion DW, Marmarou A, Marshall LF, McIntosh TK, Miller E, Mohberg N, Muizelaar JP, Pitts LH, Quinn P, Riesenfeld G, Robertson CS, Strauss KI, Teasdale G, Temkin N, Tuma R, Wade C, Walker MD, Weinrich M, Whyte J, Wilberger J, Young AB, Yurkewicz L. Clinical trials in head injury. J Neurotrauma 2002; 19:503-57. [PMID: 12042091 PMCID: PMC1462953 DOI: 10.1089/089771502753754037] [Citation(s) in RCA: 645] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Traumatic brain injury (TBI) remains a major public health problem globally. In the United States the incidence of closed head injuries admitted to hospitals is conservatively estimated to be 200 per 100,000 population, and the incidence of penetrating head injury is estimated to be 12 per 100,000, the highest of any developed country in the world. This yields an approximate number of 500,000 new cases each year, a sizeable proportion of which demonstrate significant long-term disabilities. Unfortunately, there is a paucity of proven therapies for this disease. For a variety of reasons, clinical trials for this condition have been difficult to design and perform. Despite promising pre-clinical data, most of the trials that have been performed in recent years have failed to demonstrate any significant improvement in outcomes. The reasons for these failures have not always been apparent and any insights gained were not always shared. It was therefore feared that we were running the risk of repeating our mistakes. Recognizing the importance of TBI, the National Institute of Neurological Disorders and Stroke (NINDS) sponsored a workshop that brought together experts from clinical, research, and pharmaceutical backgrounds. This workshop proved to be very informative and yielded many insights into previous and future TBI trials. This paper is an attempt to summarize the key points made at the workshop. It is hoped that these lessons will enhance the planning and design of future efforts in this important field of research.
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Affiliation(s)
- Raj K Narayan
- Department of Neurosurgery, Temple University Hospital, Philadelphia, Pennsylvania 19140, USA.
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Grill WM, McDonald JW, Peckham PH, Heetderks W, Kocsis J, Weinrich M. At the interface: convergence of neural regeneration and neural prostheses for restoration of function. J Rehabil Res Dev 2001; 38:633-9. [PMID: 11767971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
The rapid pace of recent advances in development and application of electrical stimulation of the nervous system and in neural regeneration has created opportunities to combine these two approaches to restoration of function. This paper relates the discussion on this topic from a workshop at the International Functional Electrical Stimulation Society. The goals of this workshop were to discuss the current state of interaction between the fields of neural regeneration and neural prostheses and to identify potential areas of future research that would have the greatest impact on achieving the common goal of restoring function after neurological damage. Identified areas include enhancement of axonal regeneration with applied electric fields, development of hybrid neural interfaces combining synthetic silicon and biologically derived elements, and investigation of the role of patterned neural activity in regulating various neuronal processes and neurorehabilitation. Increased communication and cooperation between the two communities and recognition by each field that the other has something to contribute to their efforts are needed to take advantage of these opportunities. In addition, creative grants combining the two approaches and more flexible funding mechanisms to support the convergence of their perspectives are necessary to achieve common objectives.
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Affiliation(s)
- W M Grill
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106-4912, USA
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