Higher-Order Disease Interactions in Multimorbidity Measurement: Marginal Benefit Over Additive Disease Summation

Current multimorbidity measures often oversimplify complex disease interactions by assuming a merely additive impact of diseases on health outcomes. This oversimplification neglects clinical observations that certain disease combinations can exhibit synergistic effects. Thus, we aimed to incorporate simultaneous higher-order disease interactions into the validated ICD-coded multimorbidity-weighted index, to assess for model improvement.

Health and Retirement Study participants with linked Medicare data contributed ICD-9-CM claims, 1991-2012. Top 20 most prevalent and impactful conditions (based on associations with decline in physical functioning) were assessed through higher-order interactions (2-way, 3-way). We applied the least absolute shrinkage and selection operator and bootstrapping to identify and retain statistically significant disease interactions. We compared model fit in multimorbidity-weighted index with and without disease interactions in linear models.

We analyzed 73 830 observations from 18 212 participants (training set N = 14 570, testing set N = 3 642). Multimorbidity-weighted index without interactions produced an overall R2 = 0.26. Introducing 2-way interactions for the top 10 most prevalent and impactful conditions resulted in a R2 = 0.27, while expanding to top 20 most prevalent and impactful conditions yielded a R2 = 0.26. When adding 3-way interactions, the same top 10 conditions produced a R2 = 0.26, while expanding to top 20 conditions resulted in a R2 = 0.24.

We present novel insights into simultaneous higher-order disease interactions for potential integration into multimorbidity measurement. Incorporating 2-way disease interactions for the top 10 most prevalent and impactful conditions showed a minimal improvement in model fit. A more precise multimorbidity index may incorporate both the main effects of diseases and their significant interactions.