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<p>Non-communicable diseases (NCDs) represent a significant health burden in Europe, accounting for the vast majority of deaths and consuming a substantial portion of healthcare resources. Despite the largely preventable nature of NCDs through modifiable behavioral risk factors, current prevention efforts remain limited. This project aims to establish a comprehensive data infrastructure to enhance understanding and prevention of NCDs. Leveraging Denmark’s extensive national data resources, this study plans to integrate diverse datasets, including health registers, genomic data, environmental exposures, and national health surveys, covering a total study population up to 8.7 million individuals, including all living and deceased in Denmark since 1975. By integrating these datasets, the project will establish the Danish Chronic Disease Cohort, a platform designed to uncover complex risk patterns associated with non-communicable disease (NCD) development. Future machine learning applications will analyze the range of health determinants, supporting the development of interpretable predictive models to guide targeted prevention and policy initiatives across Europe.</p>

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The Danish Chronic Disease Cohort: using digital footprints to identify chronic disease patterns

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月21日

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  • Ann Taber,
  • Ricco N. H. Flyckt,
  • Margrethe H. B. Henriksen,
  • Kaire Innos,
  • Wenche Nystad,
  • Lars J. Kjerpeseth,
  • Brit L. Sandgren,
  • Claus Varnum,
  • Malene R. V. Pedersen,
  • Christopher Johansen,
  • Torben F. Hansen,
  • Claus L. Brasen

摘要

Non-communicable diseases (NCDs) represent a significant health burden in Europe, accounting for the vast majority of deaths and consuming a substantial portion of healthcare resources. Despite the largely preventable nature of NCDs through modifiable behavioral risk factors, current prevention efforts remain limited. This project aims to establish a comprehensive data infrastructure to enhance understanding and prevention of NCDs. Leveraging Denmark’s extensive national data resources, this study plans to integrate diverse datasets, including health registers, genomic data, environmental exposures, and national health surveys, covering a total study population up to 8.7 million individuals, including all living and deceased in Denmark since 1975. By integrating these datasets, the project will establish the Danish Chronic Disease Cohort, a platform designed to uncover complex risk patterns associated with non-communicable disease (NCD) development. Future machine learning applications will analyze the range of health determinants, supporting the development of interpretable predictive models to guide targeted prevention and policy initiatives across Europe.