IDEAS home Printed from https://ideas.repec.org/a/spr/eujhec/v24y2023i9d10.1007_s10198-022-01561-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10198-022-01561-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10198-022-01561-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mirza Rizwan Sajid & Bader A. Almehmadi & Waqas Sami & Mansour K. Alzahrani & Noryanti Muhammad & Christophe Chesneau & Asif Hanif & Arshad Ali Khan & Ahmad Shahbaz, 2021. "Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches," IJERPH, MDPI, vol. 18(23), pages 1-16, November.
    2. Salvatore Tedesco & Martina Andrulli & Markus Åkerlund Larsson & Daniel Kelly & Antti Alamäki & Suzanne Timmons & John Barton & Joan Condell & Brendan O’Flynn & Anna Nordström, 2021. "Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults," IJERPH, MDPI, vol. 18(23), pages 1-18, December.
    3. Ajay Dev & Sanjay Kumar Malik, 2021. "Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data: ABC-DNN for Prediction of Stroke," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 12(5), pages 67-83, September.
    4. Feihan Lu & Yao Zheng & Harrington Cleveland & Chris Burton & David Madigan, 2018. "Bayesian hierarchical vector autoregressive models for patient-level predictive modeling," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-27, December.
    5. Shinya Suzuki & Takeshi Yamashita & Tsuyoshi Sakama & Takuto Arita & Naoharu Yagi & Takayuki Otsuka & Hiroaki Semba & Hiroto Kano & Shunsuke Matsuno & Yuko Kato & Tokuhisa Uejima & Yuji Oikawa & Minor, 2019. "Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-14, September.
    6. Ying Wang & Zhicheng Du & Wayne R. Lawrence & Yun Huang & Yu Deng & Yuantao Hao, 2019. "Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population," IJERPH, MDPI, vol. 16(23), pages 1-13, December.
    7. Francesco Cappelli & Gianfranco Castronuovo & Salvatore Grimaldi & Vito Telesca, 2024. "Random Forest and Feature Importance Measures for Discriminating the Most Influential Environmental Factors in Predicting Cardiovascular and Respiratory Diseases," IJERPH, MDPI, vol. 21(7), pages 1-21, July.
    8. Shelda Sajeev & Stephanie Champion & Alline Beleigoli & Derek Chew & Richard L. Reed & Dianna J. Magliano & Jonathan E. Shaw & Roger L. Milne & Sarah Appleton & Tiffany K. Gill & Anthony Maeder, 2021. "Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    9. Emily J MacKay & Michael D Stubna & Corey Chivers & Michael E Draugelis & William J Hanson & Nimesh D Desai & Peter W Groeneveld, 2021. "Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-14, June.
    10. Woo Suk Hong & Adrian Daniel Haimovich & R Andrew Taylor, 2018. "Predicting hospital admission at emergency department triage using machine learning," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-13, July.
    11. Adrian Richter & Julia Truthmann & Jean-François Chenot & Carsten Oliver Schmidt, 2021. "Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data—An Interpretable Machine Learning Approach," IJERPH, MDPI, vol. 18(22), pages 1-14, November.
    12. Gian Luca Di Tanna & Heidi Wirtz & Karen L Burrows & Gary Globe, 2020. "Evaluating risk prediction models for adults with heart failure: A systematic literature review," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-23, January.
    13. Rafał Niemiec & Irmina Morawska & Maria Stec & Wiktoria Kuczmik & Andrzej S. Swinarew & Arkadiusz Stanula & Katarzyna Mizia-Stec, 2022. "ARNI in HFrEF—One-Centre Experience in the Era before the 2021 ESC HF Recommendations," IJERPH, MDPI, vol. 19(4), pages 1-12, February.
    14. Dohyun Kim & Sungmin You & Soonwon So & Jongshill Lee & Sunhyun Yook & Dong Pyo Jang & In Young Kim & Eunkyoung Park & Kyeongwon Cho & Won Chul Cha & Dong Wook Shin & Baek Hwan Cho & Hoon-Ki Park, 2018. "A data-driven artificial intelligence model for remote triage in the prehospital environment," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
    15. Sharan Srinivas, 2020. "A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
    16. Syed Waseem Abbas Sherazi & Jang-Whan Bae & Jong Yun Lee, 2021. "A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary ," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    17. Stephen F Weng & Luis Vaz & Nadeem Qureshi & Joe Kai, 2019. "Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-22, March.
    18. Alexander Engels & Katrin C Reber & Ivonne Lindlbauer & Kilian Rapp & Gisela Büchele & Jochen Klenk & Andreas Meid & Clemens Becker & Hans-Helmut König, 2020. "Osteoporotic hip fracture prediction from risk factors available in administrative claims data – A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-14, May.
    19. Cynthia Rudin & Berk Ustun, 2018. "Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice," Interfaces, INFORMS, vol. 48(5), pages 449-466, October.
    20. Pablo Gonzalez Ginestet & Ales Kotalik & David M. Vock & Julian Wolfson & Erin E. Gabriel, 2021. "Stacked inverse probability of censoring weighted bagging: A case study in the InfCareHIV Register," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 51-65, January.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eujhec:v:24:y:2023:i:9:d:10.1007_s10198-022-01561-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.