COVID-19 antibody seroprevalence in Santa Clara County, California

Author:

Bendavid Eran1ORCID,Mulaney Bianca2,Sood Neeraj3,Shah Soleil2,Bromley-Dulfano Rebecca2,Lai Cara2,Weissberg Zoe2,Saavedra-Walker Rodrigo4,Tedrow Jim5,Bogan Andrew6,Kupiec Thomas7,Eichner Daniel8,Gupta Ribhav9,Ioannidis John P A19,Bhattacharya Jay1

Affiliation:

1. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

2. Stanford University School of Medicine, Stanford, CA, USA

3. Sol Price School of Public Policy, University of Southern California, Los Angeles, CA, USA

4. Health Education is Power, Inc., Palo Alto, CA, USA

5. The Compliance Resource Group, Inc., Oklahoma City, OK, USA

6. Bogan Associates, LLC, Palo Alto, CA, USA

7. ARL BioPharma, Inc., Oklahoma City, OK, USA

8. Sports Medicine Research and Testing Laboratory, Salt Lake City, UT, USA

9. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA

Abstract

Abstract Background Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County. Methods On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity. Results The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1–2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2–99.7%) and sensitivity was 82.8% (95% CI 76.0–88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7–1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3–4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000–35 000) using unweighted prevalence] people were infected in Santa Clara County by late March—many more than the ∼1200 confirmed cases at the time. Conclusion The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

Funder

Stanford COVID-19 Seroprevalence Studies Fund

Publisher

Oxford University Press (OUP)

Subject

General Medicine,Epidemiology

Reference31 articles.

1. Estimating case fatality rates of COVID-19;Spychalski;Lancet Infect Dis,2020

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