Background:It is still uncertain how multimorbidity patterns affect transitions between fall states among middle-aged and older Chinese.Methods:Data were obtained from China Health and Retirement Longitudinal Study(CH...Background:It is still uncertain how multimorbidity patterns affect transitions between fall states among middle-aged and older Chinese.Methods:Data were obtained from China Health and Retirement Longitudinal Study(CHARLS)2011–2018.We utilized latent class analysis to categorize baseline multimorbidity patterns,Markov multi-state model to explore the impact of multimorbidity characterized by condition counts and multimorbidity patterns on subsequent fall transitions,and Cox proportional hazard models to assess hazard ratios of each transition.Results:A total of 14,244 participants aged 45 years and older were enrolled at baseline.Among these participants,11,956(83.9%)did not have a fall history in the last 2 years,1,054(7.4%)had mild falls,and 1,234(8.7%)had severe falls.Using a multi-state model,10,967 transitions were observed during a total follow-up of 57,094 person-times,6,527 of which had worsening transitions and 4,440 had improving transitions.Among 6,711 multimorbid participants,osteocardiovascular(20.5%),pulmonary-digestive-rheumatic(30.5%),metabolic-cardiovascular(22.9%),and neuropsychiatric-sensory(26.1%)patterns were classified.Multimorbid participants had significantly higher risks of transitions compared with other participants.Among 4 multimorbidity patterns,osteocardiovascular pattern had higher transition risks than other 3 patterns.Conclusions:Multimorbidity,especially the“osteo-cardiovascular pattern”identified in this study,was associated with higher risks of fall transitions among middle-aged and older Chinese.Generally,the effect of multimorbidity is more significant in older adults than in middle-aged adults.Findings from this study provide facts and evidence for fall prevention,and offer implications for clinicians to target on vulnerable population,and for public health policymakers to allocate healthcare resources.展开更多
基金funded by the National Key R&D Program of China(2023YFB4603200 and 2023YFC3606400)National Natural Science Foundation of China(72374013)Capital’s Funds for Health Improvement and Research(CFH 2024-1G-3014).
文摘Background:It is still uncertain how multimorbidity patterns affect transitions between fall states among middle-aged and older Chinese.Methods:Data were obtained from China Health and Retirement Longitudinal Study(CHARLS)2011–2018.We utilized latent class analysis to categorize baseline multimorbidity patterns,Markov multi-state model to explore the impact of multimorbidity characterized by condition counts and multimorbidity patterns on subsequent fall transitions,and Cox proportional hazard models to assess hazard ratios of each transition.Results:A total of 14,244 participants aged 45 years and older were enrolled at baseline.Among these participants,11,956(83.9%)did not have a fall history in the last 2 years,1,054(7.4%)had mild falls,and 1,234(8.7%)had severe falls.Using a multi-state model,10,967 transitions were observed during a total follow-up of 57,094 person-times,6,527 of which had worsening transitions and 4,440 had improving transitions.Among 6,711 multimorbid participants,osteocardiovascular(20.5%),pulmonary-digestive-rheumatic(30.5%),metabolic-cardiovascular(22.9%),and neuropsychiatric-sensory(26.1%)patterns were classified.Multimorbid participants had significantly higher risks of transitions compared with other participants.Among 4 multimorbidity patterns,osteocardiovascular pattern had higher transition risks than other 3 patterns.Conclusions:Multimorbidity,especially the“osteo-cardiovascular pattern”identified in this study,was associated with higher risks of fall transitions among middle-aged and older Chinese.Generally,the effect of multimorbidity is more significant in older adults than in middle-aged adults.Findings from this study provide facts and evidence for fall prevention,and offer implications for clinicians to target on vulnerable population,and for public health policymakers to allocate healthcare resources.
