Health Policy & Community Health: Faculty Publications

Multimorbidity Patterns and Mortality Risk in a National Sample of US Adults Identified Using Latent Class Analysis

Document Type

Article

Publication Date

7-24-2026

Publication Title

Diseases

DOI

10.3390/diseases14080270

Abstract

Background/Objectives: Multimorbidity is increasingly recognized as a major contributor to mortality worldwide, yet its underlying patterns and prognostic implications remain poorly understood in the United States. This study identified distinct multimorbidity patterns and examined their association with all-cause mortality in a nationally representative sample of U.S. adults. Methods: We conducted a retrospective cohort study using data from the 2004 National Health Interview Survey linked to the National Death Index through 2019 (n = 28,598). Latent class analysis identified unobserved multimorbidity classes based on patterns of co-occurring physician-diagnosed chronic conditions, and Cox proportional hazards models were fitted to estimate mortality risk while accounting for complex survey design. Six distinct multimorbidity classes were identified, reflecting cardiometabolic, respiratory, cardiovascular, and inflammatory disease profiles. Results: Compared with the Low Multimorbidity group, all other classes were associated with increased mortality risk. In fully adjusted analyses, the Severe Cardiopulmonary–Metabolic (HR 3.71, 95% CI 2.99–4.60) and Advanced Cardiovascular (HR 2.81, 95% CI 2.52–3.14) classes showed the highest risks. Intermediate risks were observed in the Cardiometabolic–Arthritis (HR 2.45, 95% CI 2.13–2.81) and Respiratory–Musculoskeletal (HR 1.39, 95% CI 1.22–1.58) classes, while the Inflammatory Pain–Airway class showed a more modest increase. Subgroup analyses suggested stronger relative effects among younger adults and women in the most severe classes. Conclusions: The study findings highlight the heterogeneous nature of multimorbidity and suggest that specific disease clusters carry substantially different mortality risks. Recognizing these patterns may improve risk stratification and support more targeted, patient-centered care strategies.

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