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dc.contributor.authorCezard, Genevieve I.
dc.contributor.authorSullivan, Frank
dc.contributor.authorKeenan, Katherine L.
dc.date.accessioned2022-10-05T15:30:18Z
dc.date.available2022-10-05T15:30:18Z
dc.date.issued2022-10-01
dc.identifier281576356
dc.identifierd03c6a3e-1dfc-4412-a8a6-1e2646a9d50a
dc.identifier000862559400003
dc.identifier85139106775
dc.identifier.citationCezard , G I , Sullivan , F & Keenan , K L 2022 , ' Understanding multimorbidity trajectories in Scotland using sequence analysis ' , Scientific Reports , vol. 12 , 16485 . https://doi.org/10.1038/s41598-022-20546-4en
dc.identifier.issn2045-2322
dc.identifier.otherORCID: /0000-0002-9670-1607/work/120434275
dc.identifier.otherORCID: /0000-0002-6623-4964/work/120434338
dc.identifier.urihttps://hdl.handle.net/10023/26140
dc.descriptionFunding information: This work was supported by the Academy of Medical Sciences, the Wellcome Trust, the Government Department of Business, Energy and Industrial Strategy, the British Heart Foundation Diabetes UK, and the Global Challenges Research Fund [Grant number SBF004\1093 awarded to Katherine Keenan].en
dc.description.abstractUnderstanding how multiple conditions develop over time is of growing interest, but there is currently limited methodological development on the topic, especially in understanding how multimorbidity (the co-existence of at least two chronic conditions) develops longitudinally and in which order diseases occur. We aim to describe how a longitudinal method, sequence analysis, can be used to understand the sequencing of common chronic diseases that lead to multimorbidity and the socio-demographic factors and health outcomes associated with typical disease trajectories. We use the Scottish Longitudinal Study (SLS) linking the Scottish census 2001 to disease registries, hospitalisation and mortality records. SLS participants aged 40–74 years at baseline were followed over a 10-year period (2001–2011) for the onset of three commonly occurring diseases: diabetes, cardiovascular disease (CVD), and cancer. We focused on participants who transitioned to at least two of these conditions over the follow-up period (N = 6300). We use sequence analysis with optimal matching and hierarchical cluster analysis to understand the process of disease sequencing and to distinguish typical multimorbidity trajectories. Socio-demographic differences between specific disease trajectories were evaluated using multinomial logistic regression. Poisson and Cox regressions were used to assess differences in hospitalisation and mortality outcomes between typical trajectories. Individuals who transitioned to multimorbidity over 10 years were more likely to be older and living in more deprived areas than the rest of the population. We found seven typical trajectories: later fast transition to multimorbidity, CVD start with slow transition to multimorbidity, cancer start with slow transition to multimorbidity, diabetes start with slow transition to multimorbidity, fast transition to both diabetes and CVD, fast transition to multimorbidity and death, fast transition to both cancer and CVD. Those who quickly transitioned to multimorbidity and death were the most vulnerable, typically older, less educated, and more likely to live in more deprived areas. They also experienced higher number of hospitalisations and overnight stays while still alive. Sequence analysis can strengthen our understanding of typical disease trajectories when considering a few key diseases. This may have implications for more active clinical review of patients beginning quick transition trajectories.
dc.format.extent15
dc.format.extent4568098
dc.language.isoeng
dc.relation.ispartofScientific Reportsen
dc.subjectRA Public aspects of medicineen
dc.subject3rd-DASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectMCCen
dc.subject.lccRAen
dc.titleUnderstanding multimorbidity trajectories in Scotland using sequence analysisen
dc.typeJournal articleen
dc.contributor.sponsorAcademy of Medical Sciencesen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews. Population and Behavioural Science Divisionen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. School of Geography & Sustainable Developmenten
dc.identifier.doi10.1038/s41598-022-20546-4
dc.description.statusPeer revieweden
dc.identifier.grantnumberSBF004\1093en


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