Objective: Multimorbidity is a complex phenomenon with an almost endless number of possible disease combinations with unclear implications. One important aspect in analyzing the clustering of diseases is to distinguish between random coexistence and statistical dependency. We developed a model to account for random coexistence based on stochastic distribution. We analyzed if the number of diseases of the patients influences the occurrence rates of chronic conditions.
Methods: We analyzed claims data of 121,389 persons aged 65+ using a list of 46 chronic conditions. Expected prevalences were simulated by drawing without replacement from all observed diseases using observed overall prevalences as initial probability weights. To determine if a disease occurs more or less frequently than expected by chance we calculated observed-minus-expected deltas for each disease. We defined clinical relevance as |delta| ≥ 5.0%. 18 conditions were excluded because of a prevalence < 5.0%.
Results: We found that (1) two chronic conditions (e.g. hypertension) were more frequent than expected in patients with a low number of comorbidities; (2) four conditions (e.g. renal insufficiency) were more frequent in patients with many comorbidities; (3) six conditions (e.g. cancer) were less frequent with many comorbidities; and (4) 16 conditions had an average course of prevalences.
Conclusion: A growing extent of multimorbidity goes along with a rapid growth of prevalences. This is for the largest part merely a stochastic effect. If we account for this effect we find that only few diseases deviate from the expected prevalence curves. Causes for these deviations are discussed. Our approach also has methodological implications: Naive analyses of multimorbidity might easily be affected by bias, because the prevalence of all chronic conditions necessarily increases with a growing extent of multimorbidity. We should therefore always examine and discuss the stochastic interrelations between the chronic conditions we analyze.