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Low-income neighbourhood was a key determinant of severe COVID-19 incidence during the first wave of the epidemic in Paris
  1. Anne-Sophie Jannot1,2,3,
  2. Hector Countouris1,
  3. Alexis Van Straaten1,
  4. Anita Burgun1,2,3,
  5. Sandrine Katsahian1,2,3,
  6. Bastien Rance1,2,3
  7. AP-HP/Universities/Inserm COVID-19 research collaboration
    1. 1 Hôpital Européen Georges Pompidou, Service d'informatique médicale, biostatistiques et santé publique, AP-HP, Paris, France
    2. 2 Université de Paris, Paris, France
    3. 3 Centre de Recherche des Cordeliers, Inserm, Paris, France
    1. Correspondence to Dr Anne-Sophie Jannot, Hôpital Européen Georges Pompidou, Service d'informatique médicale, biostatistiques et santé publique, AP-HP, Paris 75015, Île-de-France, France; annesophie.jannot{at}aphp.fr

    Abstract

    Background Previous studies have demonstrated that socioeconomic factors are associated with COVID-19 incidence. In this study, we analysed a broad range of socioeconomic indicators in relation to hospitalised cases in the Paris area.

    Methods We extracted 303 socioeconomic indicators from French census data for 855 residential units in Paris and assessed their association with COVID-19 hospitalisation risk.

    Findings The indicators most associated with hospitalisation risk were the third decile of population income (OR=9.10, 95% CI 4.98 to 18.39), followed by the primary residence rate (OR=5.87, 95% CI 3.46 to 10.61), rate of active workers in unskilled occupations (OR=5.04, 95% CI 3.03 to 8.85) and rate of women over 15 years old with no diploma (OR=5.04, 95% CI 3.03 to 8.85). Of note, population demographics were considerably less associated with hospitalisation risk. Among these indicators, the rate of women aged between 45 and 59 years (OR=2.17, 95% CI 1.40 to 3.44) exhibited the greatest level of association, whereas population density was not associated. Overall, 86% of COVID-19 hospitalised cases occurred within the 45% most deprived areas.

    Interpretation Studying a broad range of socioeconomic indicators using census data and hospitalisation data as a readily available and large resource can provide real-time indirect information on populations with a high incidence of COVID-19.

    • healthcare disparities
    • cohort studies
    • communicable diseases
    • COVID-19
    • deprivation

    This article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

    https://bmj.com/coronavirus/usage

    https://doi.org/10.1136/jech-2020-216068

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      Footnotes

      • Collaborators AP-HP/Universities/Inserm COVID-19 research collaboration: Pierre-Yves Ancel (APHP Paris University Center: Local CDW coordinator), Alain Bauchet (AP-HP Saclay University Beeker Nathanael AP-HP Paris University Center: Data scientist), Vincent Benoit (WIND Department APHP Greater Paris University Hospital: Data engineer), Mélodie Bernaux (Strategy and transformation department, APHP Greater Paris University Hospital: Medical coordination of data analysis), Ali Bellamine (WIND Department APHP Greater Paris University Hospital: Data engineer, data scientist), Romain Bey (WIND Department APHP Greater Paris University Hospital: Data engineer, data scientist, regulatory assessment), Aurélie Bourmaud (APHP Paris University North: Local CDW coordinator), Stéphane Bréant (WIND Department APHP Greater Paris University Hospital: Coordination of clinical research informatics), Fabrice Carrat (APHP Sorbonne University Caucheteux Charlotte Université Paris-Saclay, Inria, CEA: Data integration and analysis), Julien Champ (INRIA Sophia-Antipolis – ZENITH team, LIRMM, Montpellier, France: Data integration and analysis), Sylvie Cormont (WIND Department APHP Greater Paris University Hospital: Data standardisation), Christel Daniel (WIND Department APHP Greater Paris University Hospital UMRS1142 INSERM: Medical director of data standardisation and clinical research inform), Julien Dubiel (WIND Department APHP Greater Paris University Hospital: Data engineer), Catherine Ducloas (APHP Paris Seine Saint Denis Universitary Hospital: Local CDW coordinator), Loic Esteve (SED/SIERRA, Inria Centre de Paris: Data engineer, data scientist), Marie Frank (APHP Saclay University: Local CDW coordinator), Nicolas Garcelon (Imagine Institute: Data engineer, data scientist), Alexandre Gramfort (Université Paris-Saclay, Inria, CEA: Data engineer, data scientist), Nicolas Griffon (WIND Department APHP Greater Paris University Hospital, UMRS1142 INSERM: Data standardisation), Olivier Grisel (Université Paris-Saclay, Inria, CEA: Data engineer, data scientist), Martin Guilbaud (WIND Department APHP Greater Paris University Hospital: Data engineer), Claire Hassen-Khodja (Direction of the Clinical Research and Innovation, AP-HP: Medical coordination of data-driven research), François Hemery (APHP Henri Mondor University Hospital: Local CDW coordinator), Martin Hilka (WIND Department APHP Greater Paris University Hospital: Director of Big data platform), Jerome Lambert (APHP Paris University North: Local CDW coordinator), Richard Layese (APHP Henri Mondor University Hospital), Judith Leblanc (Clincial Research Unit, Saint Antoine Hospital, APHP Greater Paris University Hospital: Data scientist), Léo Lebouter (WIND Department APHP Greater Paris University Hospital: Data engineer), Guillaume Lemaitre (Université Paris-Saclay, Inria, CEA: Data engineer, data scientist), Damien Leprovost (Clevy.io: Data engineer, data scientist), Ivan Lerner (Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital: Data engineer, data scientist), Sallah Kankoe Levi (APHP Paris University North), Aurélien Maire (WIND Department APHP Greater Paris University Hospital: Data engineer), Marie-France Mamzer (President of the AP-HP IRB), Patricia Martel (APHP Saclay University: Data scientist), Arthur Mensch (ENS, PSL University: Data engineer, data scientist), Thomas Moreau (Université Paris-Saclay, Inria, CEA: Data engineer, data scientist), Antoine Neuraz (Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital: Data engineer, data scientist), Nina Orlova (WIND Department APHP Greater Paris University Hospital: Data engineer), Nicolas Paris (WIND Department APHP Greater Paris University Hospital: Data engineer, data scientist), Hélène Ravera (WIND Department APHP Greater Paris University Hospital: Data engineer), Antoine Rozes (APHP Sorbonne University), Elisa Salamanca (WIND Department APHP Greater Paris University Hospital: Director of the Data & Innovation department), Arnaud Sandrin (WIND Department APHP Greater Paris University Hospital: Director of the National Rare Diseases Database), Patricia Serre (WIND Department APHP Greater Paris University Hospital: Data engineer, data standardisation), Xavier Tannier (Sorbonne University: Data engineer, data scientist), Jean-Marc Treluyer (APHP Paris University Center: Local CDW coordinator), Damien Van Gysel (APHP Paris University North: Local CDW coordinator), Gael Varoquaux (Université Paris-Saclay, Inria, CEA, Montréal Neurological Institute, McGill University: Data engineer, data scientist), Jill Jen Vie (SequeL, Inria Lille: Data engineer, data scientist), Maxime Wack (Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital: Data engineer, data scientist), Perceval Wajsburt (Sorbonne University: Data engineer, data scientist), Demian Wassermann (Université Paris-Saclay, Inria, CEA: Data engineer, data scientist), Eric Zapletal (Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital: Data engineer), Collégiales of AP-HP: anesthésie-réanimation, médecine intensive réanimation, infectiologie, virologie, nutrition.

      • Contributors A-SJ designed the study. HC, BR and AB developed the framework for GeoCancer, the pipeline that was reused to integrate the geodata of patients with COVID-19 in this study. HC and BR performed the geolocalisation of patients’ residential address and the data aggregation. A-SJ, HC and BR had full access to the aggregated data used for this study and take responsibility for the integrity of the data. A-SJ did the analyses and takes responsibility for the accuracy of the data analysis. AVS extracted the clinical data of patients with COVID-19 and participated in their analysis. A-SJ drafted the paper with the help of BR, AB and SK. Data were collected from Assistance Publique - Hôpitaux de Paris. All authors critically revised the manuscript for important intellectual content and gave final approval for the version to be published.

      • Funding HC received funding from Canceropole Ile-de-France to develop the geolocalisation method used for this study. A-SJ received funding for this project from the COVID-19 sponsorship (mécénat 'Collecte Crise COVID-19') of AP-HP Centre Université de Paris. A-SJ and AVS received funding for this project from Fondation de France as part of the alliance 'Tous unis contre le virus' (Fondation de France, AP-HP, Institut Pasteur).

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      • Competing interests None declared.

      • Provenance and peer review Not commissioned; externally peer reviewed.

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