Patterns of multimorbidity in older adults with multiple myeloma: An analysis of SEER-Medicare - Atinuke G. Oyinbo; Mayra Tisminetzky; Jonggyu Baek; Maira A. Castaneda-Avila; Clark DuMontier; Muthalagu Ramanathan; Kate L. Lapane; Mara M. Epstein (PLoS ONE, 2025)
Objective Multimorbidity influences the management of symptoms, treatment, and health outcomes for older adults with multiple myeloma. Our study characterized patient profiles defined by discrete chronic condition patterns and examined the distribution of their sociodemographic factors in older adults at time of diagnosis with multiple myeloma. Methods Using the Surveillance, Epidemiology and End Results (SEER) Program database linked with Medicare insurance claims (2007–2017), we examined 11,926 individuals diagnosed with multiple myeloma at age ≥ 65 years. Hierarchical cluster analysis was conducted to identify prevalent patterns of multimorbidity from thirty-three chronic conditions, and sociodemographic factors associated with the identified patterns were examined. Results The median age of the cohort was 77 years and 48% were women. Three patient groups with distinct patterns of multimorbidity were identified: minimal multimorbidity featuring minor burden of comorbid conditions (54.3%), psychiatric and musculoskeletal multimorbidity (21.5%), and a high multimorbidity group featuring cardiometabolic and multiple system disorders (20.7%). The remaining 3.5% did not have multimorbidity. Patients in the cardiometabolic and multisystem multimorbidity pattern were the oldest, more likely to reside in geographic areas with high poverty levels and comprised more men and Hispanic individuals than the other two patterns. The psychiatric and musculoskeletal multimorbidity pattern comprised the largest proportion of women and lowest proportion of non-Hispanic Black individuals. Those with relatively minimal multimorbidity burden were the most likely to be married. Conclusions Identifying patterns of multimorbidity in older adults newly diagnosed with multiple myeloma provides insight into the burden and clustering tendency of chronic conditions within similar patient groups. Our findings can inform tailored disease management and treatment programs that take these unique multimorbidity profiles into account.