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dc.contributor.authorDoetsch, Julia Nadine
dc.contributor.authorKajantie, Eero Olavi
dc.contributor.authorDias, Vasco
dc.contributor.authorIndredavik, Marit Sæbø
dc.contributor.authorDevold, Randi Kallar
dc.contributor.authorTeixeira, Raquel
dc.contributor.authorReittu, Jarkko
dc.contributor.authorBarros, Henrique
dc.date.accessioned2023-11-20T15:26:35Z
dc.date.available2023-11-20T15:26:35Z
dc.date.created2023-08-23T11:22:14Z
dc.date.issued2023
dc.identifier.citationInternational Journal of Population Data Science (IJPDS). 2023, 8 (1), .en_US
dc.identifier.urihttps://hdl.handle.net/11250/3103711
dc.description.abstractThe initial public health response to the COVID-19 pandemic aimed to prevent exponential dissemination and circumvent drastic collapses of healthcare systems [1]. Containment measures and required isolation promoted sedentary behaviours and stressful responses, which, as major determinants of chronic diseases, exacerbated prevalent co-morbidities. Patients with underlying chronic health conditions, older age, and less favourable social contexts have a threefold disadvantage: developing the disease with a higher risk, suffering a more severe course, and experiencing a fatal outcome [2]. Hence, the COVID-19 pandemic has a syndemic dimension [3], aggregating epidemics in a population, with social and complex biological interactions, which aggravate the burden of disease and challenge population-level forecasting. Therefore, a better understanding of the association between physical and mental chronic diseases, socioeconomic status, and risk of COVID-19 adverse outcomes could have a transformative effect on controlling long-term consequences. Although multiple tools and data collection methods have been used to stimulate research on COVID-19, these population data, collected either routinely (e.g., electronic health records, prescription claims), or through population-based observational cohorts, are collected in separate data systems, so that yet too few COVID-19 trials use medical databases that have been previously linked. Hence, a pressing demand to refine treatment requires a joint call: Record linkage – defined as the merging of data from an individual or an incident, not existing in a distinct record, into a combined dataset [4].en_US
dc.language.isoengen_US
dc.publisherSwansea Universityen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleRecord linkage as a vital key player for the COVID-19 syndemic – The call for legal harmonization to overcome research challengesen_US
dc.title.alternativeRecord linkage as a vital key player for the COVID-19 syndemic – The call for legal harmonization to overcome research challengesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber0en_US
dc.source.volume8en_US
dc.source.journalInternational Journal of Population Data Science (IJPDS)en_US
dc.source.issue1en_US
dc.identifier.doi10.23889/ijpds.v8i1.2131
dc.identifier.cristin2168971
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal