Nordic Collaborative Health Register Network for Covid-19 Epidemiology

Grant number: unknown

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Key facts

  • Disease

    COVID-19
  • Funder

    NordForsk
  • Principal Investigator

    Unspecified Morten Andersen
  • Research Location

    Denmark, Norway
  • Lead Research Institution

    University of Copenhagen
  • Research Priority Alignment

    N/A
  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease susceptibility

  • Special Interest Tags

    Data Management and Data Sharing

  • Study Type

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

Coronavirus Disease 2019 (Covid-19) was officially declared a pandemic by WHO on 11 March, and has since spread across the world, affecting the health of millions of people, resulting in a global public health crisis, disrupting healthcare systems, and exerting a huge impact on societies and global economy. Knowledge of this new disease has gathered in parallel with the development of the pandemic. Some risk factors for severe Covid-19 are now apparent, but our knowledge is incomplete. While a medical treatment and a vaccine are extremely important goals, it is equally important to generate new evidence on the characteristics and course of Covid-19. We will establish an internationally unique multi-country database network with data from healthcare registers in Denmark, Norway, Sweden and Scotland with the purpose of conducting epidemiological studies on Covid-19. The access to population-based data covering 25 mill. people will provide opportunities to study and thoroughly characterise disease course in individuals and evaluate the impact of the pandemic on healthcare. We will also evaluate differences across countries and the possible influence of different policies. Focus will be on risk factors for severe Covid-19 (disease leading to hospitalisation, intensive care or fatal outcome), identifying vulnerable populations and characterising the disease course, also pertaining to long-term sequelae among survivors of severe Covid-19. Additionally, we will investigate collateral effects of the pandemic on healthcare and drug utilisation in the general population to assess secondary effects on public health. In our analyses we will combine data science approaches and state-of-the-art epidemiological methods. We will apply machine learning for hypothesis generation and test findings in studies using more conventional epidemiological methods. The generated evidence is expected to have high significance for patients, healthcare professionals and policymakers with an impact on recommendation how to handle Covid-19 in specific patient groups, for protecting vulnerable populations, for recommendations on medication use and for follow-up of patients surviving severe Covid-19 to treat complications. Our studies should thus inform patients, healthcare professionals and policymakers, leading to improved decision-making and providing knowledge that could mitigate some of the effects of the Covid-19 pandemic and protect public health.