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Impact of virus testing on COVID-19 case fatality rate: estimate using a fixed-effects model

Author

Listed:
  • Anthony Terriau

    (GAINS - Groupe d'Analyse des Itinéraires et des Niveaux Salariaux - UM - Le Mans Université)

  • Arthur Poirier

    (GAINS - Groupe d'Analyse des Itinéraires et des Niveaux Salariaux - UM - Le Mans Université)

  • Julien Albertini

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Quentin Le Bastard

    (MiHAR - Microbiotes, Hôtes, Antibiotiques et Résistances bactériennes (MiHAR) - UFR MEDECINE - Université de Nantes - UFR de Médecine et des Techniques Médicales - UN - Université de Nantes)

Abstract

In response to the coronavirus disease (COVID-19) pandemic, governments have adopted a variety of public health measures. In this study, we aimed to evaluate the impact of testing on the fatality rate. We use data on inpatients across French geographic areas and propose a novel methodology that exploits policy discontinuities at region borders to estimate the effect of testing symptomatic individuals on the case-fatality rate in France. Our identification strategy is based on the fact that, in France, testing policies are determined regionally by the Regional Public Health Agencies. We compare all contiguous department pairs located on the opposite sides of a region border. Department pairs have different testing rates but share similar health trends. The heterogeneity in testing rate between department pairs together with the similarities in other dimensions allow us to mimic the existence of treatment and control groups and to identify the impact of testing on the mortality rate. We find that in France, the increase of one percentage point in the test rate is associated with a decrease of 0.001 percentage point in the death rate. Putting this number into perspective involves that for each additional 1000 tests, one person would have remained alive.

Suggested Citation

  • Anthony Terriau & Arthur Poirier & Julien Albertini & Quentin Le Bastard, 2020. "Impact of virus testing on COVID-19 case fatality rate: estimate using a fixed-effects model," Working Papers halshs-02559354, HAL.
    • Handle: RePEc:hal:wpaper:halshs-02559354
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02559354
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Testing

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    Cited by:

    1. Aparicio Fenoll, Ainoa & Grossbard, Shoshana, 2020. "Intergenerational residence patterns and Covid-19 fatalities in the EU and the US," Economics & Human Biology, Elsevier, vol. 39(C).
    2. Amuedo-Dorantes, Catalina & Borra, Cristina & Rivera-Garrido, Noelia & Sevilla, Almudena, 2021. "Early adoption of non-pharmaceutical interventions and COVID-19 mortality," Economics & Human Biology, Elsevier, vol. 42(C).

    More about this item

    Keywords

    tests; Covid-19; Case-fatality rate; Fixed-effects model;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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