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Cost and cost-effectiveness of four different SARS-CoV-2 active surveillance strategies: evidence from a randomised control trial in Germany

Author

Listed:
  • Hoa Thi Nguyen

    (Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University)

  • Claudia M. Denkinger

    (Heidelberg University Hospital
    German Center for Infection Research (DZIF))

  • Stephan Brenner

    (Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University)

  • Lisa Koeppel

    (Heidelberg University Hospital)

  • Lucia Brugnara

    (Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University
    evaplan GmbH at the University Hospital Heidelberg)

  • Robin Burk

    (Heidelberg University)

  • Michael Knop

    (Heidelberg University
    ZMBH Alliance)

  • Till Bärnighausen

    (Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University)

  • Andreas Deckert

    (Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University)

  • Manuela De Allegri

    (Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University)

Abstract

Introduction The COVID-19 pandemic has entered its third year and continues to affect most countries worldwide. Active surveillance, i.e. testing individuals irrespective of symptoms, presents a promising strategy to accurately measure the prevalence of SARS-CoV-2. We aimed to identify the most cost-effective active surveillance strategy for COVID-19 among the four strategies tested in a randomised control trial between 18th November 2020 and 23rd December 2020 in Germany. The four strategies included: (A1) direct testing of individuals; (A2) direct testing of households; (B1) testing conditioned on upstream COVID-19 symptom pre-screening of individuals; and (B2) testing conditioned on upstream COVID-19 symptom pre-screening of households. Methods We adopted a health system perspective and followed an activity-based approach to costing. Resource consumption data were collected prospectively from a digital individual database, daily time records, key informant interviews and direct observations. Our cost-effectiveness analysis compared each strategy with the status quo and calculated the average cost-effective ratios (ACERs) for one primary outcome (sample tested) and three secondary outcomes (responder recruited, case detected and asymptomatic case detected). Results Our results showed that A2, with cost per sample tested at 52,89 EURO, had the lowest ACER for the primary outcome, closely followed by A1 (63,33 EURO). This estimate was much higher for both B1 (243,84 EURO) and B2 (181,06 EURO). Conclusion A2 (direct testing at household level) proved to be the most cost-effective of the four evaluated strategies and should be considered as an option to strengthen the routine surveillance system in Germany and similar settings.

Suggested Citation

  • Hoa Thi Nguyen & Claudia M. Denkinger & Stephan Brenner & Lisa Koeppel & Lucia Brugnara & Robin Burk & Michael Knop & Till Bärnighausen & Andreas Deckert & Manuela De Allegri, 2023. "Cost and cost-effectiveness of four different SARS-CoV-2 active surveillance strategies: evidence from a randomised control trial in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(9), pages 1545-1559, December.
    • Handle: RePEc:spr:eujhec:v:24:y:2023:i:9:d:10.1007_s10198-022-01561-8
    DOI: 10.1007/s10198-022-01561-8
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    References listed on IDEAS

    as
    1. Alfonso Valenzuela Hurtado & Hoa Thi Nguyen & Viktoria Schenkel & Jonas Wachinger & Joachim Seybold & Claudia M. Denkinger & Manuela Allegri, 2022. "The economic cost of implementing antigen-based rapid diagnostic tests for COVID-19 screening in high-risk transmission settings: evidence from Germany," Health Economics Review, Springer, vol. 12(1), pages 1-10, December.
    2. Stephen F Weng & Jenna Reps & Joe Kai & Jonathan M Garibaldi & Nadeem Qureshi, 2017. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
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    More about this item

    Keywords

    COVID-19; Surveillance; Cost; Cost-effectiveness; Germany;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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