Abstract
BACKGROUND AND OBJECTIVES: Longitudinal studies among older adults often feature elevated dropout rates and multiple chronic conditions. How Taiwanese multimorbid patterns relate to different cognitive domains remains unclear. This study aims to identify sex-specific multimorbid patterns and associate them with cognitive performance while modeling the risk for dropout. RESEARCH DESIGN AND METHODS: A prospective cohort study (2011-19) in Taiwan recruited 449 Taiwanese older adults without dementia. Global and domain-specific cognition were assessed biennially. We used exploratory factor analysis to identify baseline sex-specific multimorbid patterns of 19 self-reported chronic conditions. We utilized a joint model incorporating longitudinal and time-to-dropout data to examine the association between multimorbid patterns and cognitive performance accounting for the informative dropout via the shared random effect. RESULTS: At the end of the study, 324 participants (72.1%) remained in the cohort, with an average annual attrition rate of 5.5%. We found that advanced age, low levels of physical activities, and poor cognition at baseline were associated with increased dropout risks. Besides, 6 multimorbid patterns were identified, labeled Mental, Renal-vascular, and Cancer-urinary patterns in men, and Mental, Cardiometabolic, and Cancer-endocrine patterns in women. For men, as the follow-up time increased, the Mental pattern was associated with poor global cognition and attention; the Renal-vascular pattern was associated with poor executive function. For women, the Mental pattern was associated with poor memory; as follow-up time increased, and Cardiometabolic patterns were related to poor memory. DISCUSSION AND IMPLICATIONS: Sex-specific multimorbid patterns identified in the Taiwanese older population showed differences (notably Renal-vascular pattern in men) from patterns found in Western countries and were differentially associated with cognitive impairment over time. When informative dropout is suspected, appropriate statistical methods should be applied.