Social Determinant Trends of COVID-19: An Analysis Using Knowledge Graphs from Published Evidence and Online Trends.

COVID-19 risk factors Infodemiology Knowledge Graphs Natural Language Processing Population Trends Social Determinants of Health

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
27 May 2021
Historique:
entrez: 27 5 2021
pubmed: 28 5 2021
medline: 1 6 2021
Statut: ppublish

Résumé

This paper presents the results of a new approach to discover related health and social factors during the COVID-19 pandemic. The approach leverages a knowledge graph of related concepts mined from a corpus of published evidence (PubMed) prior to the pandemic. Population trends from online searches were used to identify social determinants of health (SDoH) concepts that trended high at the outset of the pandemic from a list of SDoH topics from the World Health Organization (WHO). The trending concepts were then mapped to the knowledge graph and a subsequent analysis of the derived insights, spanning two years, was conducted. This paper suggests an approach to derive new related health and social factors that may have either played a role in, or been affected by, the onset of the global COVID-19 pandemic. In particular, our results show how, from a list of SDoH topics, Food Security, Unemployment trended the highest at the start of the pandemic. Further work is needed to continue to ascertain the validity of the derived relations in a population health context and to improve mining insights from published evidence.

Identifiants

pubmed: 34042675
pii: SHTI210271
doi: 10.3233/SHTI210271
doi:

Types de publication

Journal Article

Langues

eng

Pagination

744-748

Auteurs

Martin Gleize (M)

IBM Research Europe.

Natasha Mulligan (N)

IBM Research Europe.

Alessandro Di Bari (A)

IBM Watson Health.

Joao H Bettencourt-Silva (JH)

IBM Research Europe.

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Classifications MeSH