Identifying disease patterns in high-cost individuals with multimorbidity in primary care

Abstract

Background: High-cost individuals with multimorbidity account for a disproportionately large share of healthcare costs and are at most risk of poor quality of care and health outcomes. Aim: This paper compares high-cost with lower-cost individuals with multimorbidity and assesses whether these populations can be clustered based on similar disease patterns. Design and Setting: Cross-sectional study based on 2019/20 electronic medical records from adults registered to primary care practices (n=41) in a London borough. Method: Multimorbidity is defined as having two or more long-term conditions (LTCs). Primary care costs reflected consultations, which were costed based on provider and consultation types. High-cost was defined as the top 20% individuals in the cost distribution. Descriptive analyses identified combinations of 32 LTCs and their contribution to costs. Latent class analysis explored clustering patterns. Results: Of 386,238 individuals, 101,498 (26%) had multimorbidity. The high-cost group (n=20,304) incurred 53% of total costs and had 6,833 unique disease combinations, about three times the diversity of lower-cost (n=81,194). The trio of Anxiety, Chronic Pain and Depression represented the highest share of costs (5.12%). High-cost individuals were best grouped into five clusters, but no cluster was dominated by a single LTC combination. In 3/5 clusters, mental health conditions were the most prevalent. Conclusion: High-cost individuals with multimorbidity have extensive heterogeneity in LTCs, with no single LTC combination dominating their primary care costs. The frequent presence of mental health conditions in this population supports the need to enhance coordination of mental and physical healthcare to improve outcomes and reduce costs.