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Tobik ER, Kitfield-Vernon LB, Thomas RJ, Steel SA, Tan SH, Allicock OM, Choate BL, Akbarzada S, Wyllie AL. Saliva as a sample type for SARS-CoV-2 detection: implementation successes and opportunities around the globe. Expert Rev Mol Diagn 2022; 22:519-535. [PMID: 35763281 DOI: 10.1080/14737159.2022.2094250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Symptomatic testing and asymptomatic screening for SARS-CoV-2 continue to be essential tools for mitigating virus transmission. Though COVID-19 diagnostics initially defaulted to oropharyngeal or nasopharyngeal sampling, the worldwide urgency to expand testing efforts spurred innovative approaches and increased diversity of detection methods. Strengthening innovation and facilitating widespread testing remains critical for global health, especially as additional variants emerge and other mitigation strategies are recalibrated. AREAS COVERED A growing body of evidence reflects the need to expand testing efforts and further investigate the efficiency, sensitivity, and acceptability of saliva samples for SARS-CoV-2 detection. Countries have made pandemic response decisions based on resources, costs, procedures, and regional acceptability - the adoption and integration of saliva-based testing among them. Saliva has demonstrated high sensitivity and specificity while being less invasive relative to nasopharyngeal swabs, securing saliva's position as a more acceptable sample type. EXPERT OPINION Despite the accessibility and utility of saliva sampling, global implementation remains low compared to swab-based approaches. In some cases, countries have validated saliva-based methods but face challenges with testing implementation or expansion. Here, we review the localities that have demonstrated success with saliva-based SARS-CoV-2 testing approaches and can serve as models for transforming concepts into globally-implemented best practices.
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Affiliation(s)
- Emily R Tobik
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - Lily B Kitfield-Vernon
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - Russell J Thomas
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - Sydney A Steel
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - Steph H Tan
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.,Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA
| | - Orchid M Allicock
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - Brittany L Choate
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - Sumaira Akbarzada
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
| | - Anne L Wyllie
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA
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Savela ES, Winnett A, Romano AE, Porter MK, Shelby N, Akana R, Ji J, Cooper MM, Schlenker NW, Reyes JA, Carter AM, Barlow JT, Tognazzini C, Feaster M, Goh YY, Ismagilov RF. Quantitative SARS-CoV-2 viral-load curves in paired saliva and nasal swabs inform appropriate respiratory sampling site and analytical test sensitivity required for earliest viral detection. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.04.02.21254771. [PMID: 33851180 PMCID: PMC8043477 DOI: 10.1101/2021.04.02.21254771] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Early detection of SARS-CoV-2 infection is critical to reduce asymptomatic and pre-symptomatic transmission, curb the spread of variants by travelers, and maximize treatment efficacy. Low-sensitivity nasal-swab testing (antigen and some nucleic-acid-amplification tests) is commonly used for surveillance and symptomatic testing, but the ability of low-sensitivity nasal-swab tests to detect the earliest stages of infection has not been established. In this case-ascertained study, initially-SARS-CoV-2-negative household contacts of individuals diagnosed with COVID-19 prospectively self-collected paired anterior-nares nasal-swab and saliva samples twice daily for viral-load quantification by high-sensitivity RT-qPCR and digital-RT-PCR assays. We captured viral-load profiles from the incidence of infection for seven individuals and compared diagnostic sensitivities between respiratory sites. Among unvaccinated persons, high-sensitivity saliva testing detected infection up to 4.5 days before viral loads in nasal swabs reached the limit of detection of low-sensitivity nasal-swab tests. For most participants, nasal swabs reached higher peak viral loads than saliva, but were undetectable or at lower loads during the first few days of infection. High-sensitivity saliva testing was most reliable for earliest detection. Our study illustrates the value of acquiring early (within hours after a negative high-sensitivity test) viral-load profiles to guide the appropriate analytical sensitivity and respiratory site for detecting earliest infections. Such data are challenging to acquire but critical to design optimal testing strategies in the current pandemic and will be required for responding to future viral pandemics. As new variants and viruses emerge, up-to-date data on viral kinetics are necessary to adjust testing strategies for reliable early detection of infections.
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Affiliation(s)
- Emily S. Savela
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Alexander Winnett
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Anna E. Romano
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Michael K. Porter
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Natasha Shelby
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Reid Akana
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Jenny Ji
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Matthew M. Cooper
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Noah W. Schlenker
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Jessica A. Reyes
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Alyssa M. Carter
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Jacob T. Barlow
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
| | - Colten Tognazzini
- City of Pasadena Public Health Department, 1845 N. Fair Oaks Ave., Pasadena, CA, USA 91103
| | - Matthew Feaster
- City of Pasadena Public Health Department, 1845 N. Fair Oaks Ave., Pasadena, CA, USA 91103
| | - Ying-Ying Goh
- City of Pasadena Public Health Department, 1845 N. Fair Oaks Ave., Pasadena, CA, USA 91103
| | - Rustem F. Ismagilov
- California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, USA 91125
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