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Abstract
Understanding 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.
Original language | English |
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Article number | 16485 |
Number of pages | 15 |
Journal | Scientific Reports |
Volume | 12 |
DOIs | |
Publication status | Published - 1 Oct 2022 |
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