Comparison of count-based and clustering definitions of multimorbidity and their association with prevalence of multimorbidity, health profiles, and mortality: A cohort study of UK Biobank participants.
Multimorbidity prevalence varied hugely, from 1.0% to 35.3%, by definition used
Comparison of count-based and clustering definitions of multimorbidity and their association with prevalence of multimorbidity, health profiles, and mortality: A cohort study of UK Biobank participants.
Multimorbidity, the presence of several chronic conditions, is linked to higher mortality and healthcare use and thus poses a major challenge for aging populations.
We aimed to evaluate the extent to which these methodological choices influence prevalence and association with health profiles and mortality.
Using UK Biobank baseline data (n = 474,397), collected between 2006 and 2010, we compared six count-based definitions of multimorbidity based on different condition lists (extended, most prevalent, or body systems) and thresholds (≥2 versus ≥3 conditions).
prevalence hinges entirely on how multimorbidity is defined, not on true disease burden
Count-based definitions using lists with more conditions yielded higher prevalence.
Higher thresholds identified more severe health profiles on all measured health outcomes, blood-based measures, but not higher mortality risks.
Odds ratios for 3-year mortality ranged from 1.44 [1.26; 1.64] to 4.60 [3.73; 5.62] for men and 1.35 [1.07; 1.69] to 3.83 [2.78; 5.14] for women.
For 10-year mortality, they ranged from 1.42 [1.34; 1.50] to 3.86 [3.46; 4.30] in men and 1.29 [1.21; 1.39] to 3.33 [2.93; 3.77] for women, with clustering identifying groups with low prevalence and high mortality risks.
Operational definitions of multimorbidity substantially influence prevalence estimates, while associations with mortality appear more robust across count-based approaches.
Clustering analyses provide complementary insights into heterogeneity within multimorbid populations.
Future translational studies are warranted to determine how multimorbidity definitions can be optimized to ultimately improve clinical management and health outcomes in practice.