Different operational definitions of multimorbidity yielded prevalence estimates ranging from 1.0% to 35.3%, with clustering identifying specific groups with low prevalence but high mortality risks.
Cohort (n=474,397)
How do count-based versus clustering definitions of multimorbidity affect prevalence estimates and associations with health profiles and mortality?
Operational definitions of multimorbidity substantially influence prevalence estimates, though associations with mortality remain relatively robust across count-based approaches.
Background Multimorbidity, the presence of several chronic conditions, is linked to higher mortality and healthcare use and thus poses a major challenge for aging populations. While most studies rely on simple counts of conditions, clustering approaches have been proposed to describe patterns of co-occurring diseases. We aimed to evaluate the extent to which these methodological choices influence prevalence and association with health profiles and mortality. Methods and findings 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). We also applied a clustering analysis to characterize subtypes of multimorbidity among participants with at least two chronic conditions. We compared prevalence and associations with concurrent health outcomes (polypharmacy, self-rated health, frailty, falls, surgery, chronic pain), blood-based measures (C-reactive protein, Cystatin-C, HDL, LDL Cholesterol, IGF-1), and 3- and 10-year mortality risks. Analyses were undertaken separately in men and women using multivariable regression models adjusted for sociodemographic characteristics and body mass index. Multimorbidity prevalence ranged from 1.0% (cluster-based) to 35.3% (count-based). 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. Associations with blood-based measures were more pronounced using clustering, with the highest differences from the standard definition distributed across clusters. 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. Findings should be interpreted in light of the selected nature of the UK Biobank cohort and the cross-sectional assessment of several health indicators. Conclusion 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.
Silva et al. (Fri,) conducted a cohort in Multimorbidity (n=474,397). Count-based and clustering definitions of multimorbidity was evaluated on Prevalence of multimorbidity and 3- and 10-year mortality risks. Different operational definitions of multimorbidity yielded prevalence estimates ranging from 1.0% to 35.3%, with clustering identifying specific groups with low prevalence but high mortality risks.
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