Latent class analysis of multimorbidity patterns and associated factors among older adult fallers in the hospital

Falls among older adults in hospitals remain a frequent occurrence, and multimorbidity is significantly associated with this adverse event. However, research on multimorbidity patterns specifically among older hospitalized patients who experienced falls remains limited. This study aimed to identify multimorbidity classes and their associated factors in this population. Data were extracted from the nursing management information system of a tertiary hospital in Hangzhou, China, covering fall incidents recorded between January 2015 and August 2025. Information on patient characteristics, fall event features, and outcomes was collected. Latent class analysis was performed using 12 chronic conditions to delineate multimorbidity classes among older adult fallers in the hospital. Multinomial logistic regression was used to identify factors associated with each multimorbidity class. Among the 705 older adult fallers, cancer was the most prevalent chronic condition. Four distinct multimorbidity patterns were identified: the Stroke group (11.9%), the Cancer group (22.8%), the Multisystem multimorbidity group (22.4%), and the Relatively healthy group (42.8%). Logistic regression analysis revealed that age, ward, accompaniment status, pre-fall status, staff years of service, risk assessment score, and the number of fall-risk-increasing drugs were significantly associated with latent multimorbidity classes among older hospitalized fallers ( p < 0.05). Multimorbidity among older adult fallers in the hospital was common, underscoring the urgent need to incorporate multimorbidity into current fall prevention guidelines. Distinct multimorbidity classes can be identified among older hospitalized fallers, each characterized by specific comorbidity patterns and associated with different factors. Based on these findings, clinical practitioners can develop targeted nursing intervention strategies to reduce fall risk in this population.