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Citywide serosurveillance of the initial SARS-CoV-2 outbreak in San Francisco using electronic health records

Author

Listed:
  • Isobel Routledge

    (University of California San Francisco)

  • Adrienne Epstein

    (University of California San Francisco)

  • Saki Takahashi

    (University of California San Francisco)

  • Owen Janson

    (University of California San Francisco)

  • Jill Hakim

    (University of California San Francisco)

  • Elias Duarte

    (University of California San Francisco)

  • Keirstinne Turcios

    (University of California San Francisco)

  • Joanna Vinden

    (University of California San Francisco)

  • Kirk Sujishi

    (University of California San Francisco)

  • Jesus Rangel

    (University of California San Francisco)

  • Marcelina Coh

    (University of California San Francisco)

  • Lee Besana

    (University of California San Francisco)

  • Wai-Kit Ho

    (University of California San Francisco)

  • Ching-Ying Oon

    (University of California San Francisco)

  • Chui Mei Ong

    (University of California San Francisco)

  • Cassandra Yun

    (University of California San Francisco)

  • Kara Lynch

    (University of California San Francisco)

  • Alan H. B. Wu

    (University of California San Francisco)

  • Wesley Wu

    (Chan Zuckerberg Biohub)

  • William Karlon

    (University of California San Francisco)

  • Edward Thornborrow

    (University of California San Francisco)

  • Michael J. Peluso

    (University of California San Francisco)

  • Timothy J. Henrich

    (University of California San Francisco)

  • John E. Pak

    (Chan Zuckerberg Biohub)

  • Jessica Briggs

    (University of California San Francisco)

  • Bryan Greenhouse

    (University of California San Francisco)

  • Isabel Rodriguez-Barraquer

    (University of California San Francisco)

Abstract

Serosurveillance provides a unique opportunity to quantify the proportion of the population that has been exposed to pathogens. Here, we developed and piloted Serosurveillance for Continuous, ActionabLe Epidemiologic Intelligence of Transmission (SCALE-IT), a platform through which we systematically tested remnant samples from routine blood draws in two major hospital networks in San Francisco for SARS-CoV-2 antibodies during the early months of the pandemic. Importantly, SCALE-IT allows for algorithmic sample selection and rich data on covariates by leveraging electronic health record data. We estimated overall seroprevalence at 4.2%, corresponding to a case ascertainment rate of only 4.9%, and identified important heterogeneities by neighborhood, homelessness status, and race/ethnicity. Neighborhood seroprevalence estimates from SCALE-IT were comparable to local community-based surveys, while providing results encompassing the entire city that have been previously unavailable. Leveraging this hybrid serosurveillance approach has strong potential for application beyond this local context and for diseases other than SARS-CoV-2.

Suggested Citation

  • Isobel Routledge & Adrienne Epstein & Saki Takahashi & Owen Janson & Jill Hakim & Elias Duarte & Keirstinne Turcios & Joanna Vinden & Kirk Sujishi & Jesus Rangel & Marcelina Coh & Lee Besana & Wai-Kit, 2021. "Citywide serosurveillance of the initial SARS-CoV-2 outbreak in San Francisco using electronic health records," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23651-6
    DOI: 10.1038/s41467-021-23651-6
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    Cited by:

    1. Isobel Routledge & Saki Takahashi & Adrienne Epstein & Jill Hakim & Owen Janson & Keirstinne Turcios & Jo Vinden & John Tomas Risos & Margaret Rose Baniqued & Lori Pham & Clara Di Germanio & Michael B, 2022. "Using sero-epidemiology to monitor disparities in vaccination and infection with SARS-CoV-2," Nature Communications, Nature, vol. 13(1), pages 1-7, December.

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