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Increased COVID-19 Testing Rates Following Combined Door-to-Door and Mobile Testing Facility Campaigns in Oslo, Norway, a Difference-in-Difference Analysis

Author

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  • Kristin Hestmann Vinjerui

    (Division for Health Services, Norwegian Institute of Public Health, 0213 Oslo, Norway)

  • Ingeborg Hess Elgersma

    (Division for Health Services, Norwegian Institute of Public Health, 0213 Oslo, Norway)

  • Atle Fretheim

    (Centre for Epidemic Interventions Research, Norwegian Institute of Public Health, 0213 Oslo, Norway
    Faculty of Health Sciences, Oslo Metropolitan University, 0130 Oslo, Norway)

Abstract

High testing rates limit COVID-19 transmission. Attempting to increase testing rates, Stovner District in Oslo, Norway, combined door-to-door campaigns with easy access testing facilities. We studied the intervention’s impact on COVID-19 testing rates. The Stovner District administration executed three door-to-door campaigns promoting COVID-19 testing accompanied by drop-in mobile COVID-19 testing facilities in different areas at 2-week intervals. We calculated testing rates pre- and post-campaigns using data from the Norwegian emergency preparedness register for COVID-19 (Beredt C19). We applied a difference-in-difference approach using ordinary least square regression models and robust standard errors to estimate changes in COVID-19 testing rates. Door-to-door visits reached around one of three households. Intervention and comparison areas had identical testing rates before the intervention, and we observed an increase in intervention areas after the campaigns. We estimate a 43% increase in testing rates over the first three days following the door-to-door campaigns ( p = 0.28), corresponding to an additional 79 (95% confidence interval, −54 to 175) people tested. Considering the shape of the time series curves and the large effect estimate, we find it highly likely that the campaigns had a substantial positive impact on COVID-19 testing rates, despite a p -value above the conventional levels for statistical significance. The results and the feasibility of the intervention suggest that it may be worth implementing in similar settings.

Suggested Citation

  • Kristin Hestmann Vinjerui & Ingeborg Hess Elgersma & Atle Fretheim, 2021. "Increased COVID-19 Testing Rates Following Combined Door-to-Door and Mobile Testing Facility Campaigns in Oslo, Norway, a Difference-in-Difference Analysis," IJERPH, MDPI, vol. 18(21), pages 1-11, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11078-:d:661654
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    References listed on IDEAS

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    1. Goodman-Bacon, Andrew & Marcus, Jan, 2020. "Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14(2), pages 153-158.
    2. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
    3. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    4. Andrew Goodman-Bacon & Jan Marcus, 2020. "Difference-in-Differences to Identify Causal Effects of COVID-19 Policies," Discussion Papers of DIW Berlin 1870, DIW Berlin, German Institute for Economic Research.
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