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Finding a needle in a haystack: The hidden costs of asymptomatic testing in a low incidence setting

Published online by Cambridge University Press:  24 June 2021

Vinay Srinivasan
Affiliation:
David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
Shruti K. Gohil
Affiliation:
Division of Infectious Diseases, Department of Medicine, University of California Irvine, Irvine, California
Shira R. Abeles
Affiliation:
Division of Infectious Diseases, Department of Medicine, University of California San Diego School of Medicine, San Diego, California
Deborah S. Yokoe
Affiliation:
Division of Infectious Diseases, Department of Medicine, University of California San Francisco, San Francisco, California
Stuart H. Cohen
Affiliation:
Division of Infectious Diseases, Department of Medicine, University of California Davis, Sacramento, California
Lynn Ramirez-Avila
Affiliation:
Division of Pediatric Infectious Diseases and Global Health, Department of Pediatrics, University of California San Francisco, San Francisco, California
Kavitha K. Prabaker
Affiliation:
Division of Infectious Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
Annabelle M. de St. Maurice*
Affiliation:
Division of Infectious Diseases, Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
*
Author for correspondence: Annabelle de St. Maurice, Email: adestmaurice@mednet.ucla.edu
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Abstract

Type
Letter to the Editor
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

To the Editor—Early in the coronavirus disease 2019 (COVID-19) pandemic, when testing was limited and the prevalence of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) was unknown, public health recommendations restricted testing for individuals at high risk for COVID-19. Risk factors included travel history, symptoms, and close contact with someone who had a history of COVID-19. As access to testing expanded and concerns for asymptomatic transmission mounted, healthcare facilities broadened COVID-19 surveillance strategies to include testing for all asymptomatic patients requiring hospital admission or aerosol-generating procedures.

Simultaneously, national and global PPE shortages amplified concerns about high risks for to healthcare providers (HCPs). Initial studies reported infection from patients as the primary mode of transmission in up to 60% of COVID-19 infections in healthcare workers before the pandemic was recognized.Reference Lai, Wang and Qin 1 Since that time, significant advances in testing capacity and PPE availability have been made, coupled with reassurance about the protective effects of PPE.Reference Jacob, Baker and Fridkin 2 Rates of COVID-19 positivity among asymptomatic patients presenting for surgery have been low throughout the pandemic: only 0.13% at an academic facility centered in one of the counties with the highest COVID-19 prevalence nationally. The total number of tests collected for asymptomatic surgical patients has exceeded 100,000 in the past 12 months. Reference Singer, Cheng and Murad3,Reference Lentz, Colt and Chen4 Data collected during this pandemic have demonstrated that healthcare workers are unlikely to become infected with COVID-19 when wearing appropriate PPE.Reference Jacob, Baker and Fridkin 2 Even in situations in which healthcare providers were performing an aerosol-generating procedure on a COVID-19–positive patient, the risk among those wearing a surgical mask and a respirator was equivalent.Reference Shah, Breeher, Hainy and Swift 5 As PPE supply has increased in the United States, many healthcare institutions have begun using respirators and eye protection for all aerosol-generating procedures regardless of a patient’s SARS-CoV-2 status, further decreasing the risk of unanticipated SARS-CoV-2 transmission.

Furthermore, as the incidence of a disease declines, the positive predictive value (PPV) of a test for that disease necessarily drops, even for tests with a high sensitivity and specificity. At low prevalence, the positive predictive value (PPV) of a given test is expected to be more sensitive to changes in underlying rates of disease. To illustrate this, we modeled the relationship between 7-day cumulative incidence of COVID-19 in the community (x-axis) and PPV (y-axis) for a PCR test with similar performance characteristics to those used at UCLA Health (ie, 96% sensitivity and 99% specificity), assuming a weekly testing strategy (Fig. 1). We show that false positives exceed the number of true positives when 7-day cumulative COVID-19 incidence is below 1,030 cases per 100,000 persons. These false positives can delay care, can cause unnecessary hospital and community-setting quarantines, and can lead to repeated retesting. In addition, direct and indirect costs accrue with testing asymptomatic individuals at low risk of having COVID-19. First, COVID-19 assays require expensive machines, reagents, and technologists’ labor. According to a recent Kaiser Family Foundation survey of 93 hospitals, the median cost of a COVID-19 test was $148.Reference Kurani, Pollitz, Cotliar, Ramirez and Cox 6 Additional labor and supply expenses are incurred by clinics and hospitals running COVID-19 testing sites for preoperative patients.

Fig. 1. Relationship between the positive predictive value (PPV) of COVID-19 tests and the 7-day COVID-19 cumulative incidence (cases per 100,000 persons). The blue line represents the PPV of a COVID-19 PCR test as the 7-day cumulative incidence of COVID-19 changes in a community, assuming a sensitivity of 96% and specificity of 99%. The dashed line represents the point at which the PPV is 50%.

The highest 7-day average of COVID-19 cases in the United States was 533 of 100,000 in January 2020Reference Kurani, Pollitz, Cotliar, Ramirez and Cox 6 ; however, rates varied substantially by locality. Currently, the 7-day average incidence is 13.65 of 100,000 in Los Angeles County and 48.95 of 100,000 nationally, 7 with continued steady declines every day, suggesting that at this juncture in the US COVID-19 pandemic, the risk of false-positive tests and their associated consequences far outweigh the benefits of mass COVID-19 testing. The justification for maintaining such time and resource-intensive surveillance programs becomes more complex in the context of widespread use of highly effective vaccines in healthcare providers. It is time to rethink the strategy of testing asymptomatic individuals entering hospitals or receiving procedures.

Acknowledgments

Financial support

No financial support was provided relevant to this article.

Conflicts of interest

All authors report no conflicts of interest relevant to this article.

References

Lai, X, Wang, M, Qin, C, et al. Coronavirus disease 2019 (COVID-2019) infection among healthcare workers and implications for prevention measures in a tertiary hospital in Wuhan, China. JAMA Network Open 2020;3:e209666.CrossRefGoogle Scholar
Jacob, JT, Baker, JM, Fridkin, SK, et al. Risk factors associated with SARS-CoV-2 seropositivity among US healthcare personnel. JAMA Network Open 2021;4:e211283.CrossRefGoogle Scholar
Singer, JS, Cheng, EM, Murad, DA, et al. Low prevalence (0.13%) of COVID-19 infection in asymptomatic pre-operative/pre-procedure patients at a large, academic medical center informs approaches to perioperative care. Surgery 2020;168:980986.CrossRefGoogle Scholar
Lentz, RJ, Colt, H, Chen, H, et al. Assessing coronavirus disease 2019 (COVID-19) transmission to healthcare personnel: the global ACT-HCP case–control study. Infect Control Hosp Epidemiol 2021;42:381387.CrossRefGoogle ScholarPubMed
Shah, VP, Breeher, LE, Hainy, CM, Swift, MD. Evaluation of healthcare personnel exposures to patients with SARS-CoV-2 associated with personal protective equipment use. Infect Control Hosp Epidemiol 2021. doi: 10.1017/ice.2021.219.CrossRefGoogle Scholar
Kurani, NP, Pollitz, K, Cotliar, D, Ramirez, G, Cox, C. COVID-19 test prices and payment policy. Health System Tracker website. https://www.healthsystemtracker.org/brief/covid-19-test-prices-and-payment-policy/#:˜:text=Data%20from%2093%20hospitals%20with,with%20a%20median%20of%20%24148. Published 2021. Accessed May 25, 2021.Google Scholar
COVID Data Tracker. Centers for Disease Control and Prevention website. https://covid.cdc.gov/covid-data-tracker/#trends_dailytrendscases. Published 2021. Accessed May 25, 2021.Google Scholar
Figure 0

Fig. 1. Relationship between the positive predictive value (PPV) of COVID-19 tests and the 7-day COVID-19 cumulative incidence (cases per 100,000 persons). The blue line represents the PPV of a COVID-19 PCR test as the 7-day cumulative incidence of COVID-19 changes in a community, assuming a sensitivity of 96% and specificity of 99%. The dashed line represents the point at which the PPV is 50%.

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