Disease patterns in high-cost individuals with multimorbidity: a retrospective cross-sectional study in primary care

Area of Science:

• Health economics
• Primary care research
• Multimorbidity research

Background:

• High-cost individuals with multimorbidity represent a significant burden on healthcare systems.
• These patients are at increased risk for poor quality of care and adverse health outcomes.

Purpose of the Study:

• To compare high-cost and lower-cost individuals with multimorbidity.
• To identify distinct disease patterns and clusters within these populations.

Main Methods:

• Cross-sectional study using electronic medical records from 41 London primary care practices (2019/2020).
• Multimorbidity defined as ≥2 long-term conditions (LTCs).
• Latent class analysis used to explore disease clustering in high-cost patients.

Main Results:

• 26% of 386,238 adults had multimorbidity; the top 20% high-cost group incurred 53% of total costs.
• High-cost patients exhibited three times the disease combination diversity compared to lower-cost patients.
• Anxiety, chronic pain, and depression were the costliest trio (5%); mental health conditions were prevalent in three of five identified patient clusters.

Conclusions:

• High-cost individuals with multimorbidity display significant heterogeneity in LTCs, without a single dominant disease combination.
• The prevalence of mental health conditions underscores the need for integrated mental and physical healthcare.
• Improved care coordination can potentially enhance outcomes and reduce healthcare expenditures.