Developing and validating a Chinese multimorbidity-weighted index for middle-aged and older community-dwelling individuals

纵向研究 日常生活活动 医学 老年学 阿卡克信息准则 索引(排版) 巴氏指数 人口学 物理疗法 统计 数学 计算机科学 万维网 病理 社会学
作者
Weihua Hu,Yuyang Liu,Conghui Yang,Zhou Tong,Chun Yang,Ying‐Si Lai,Jing Liao,Yuantao Hao
出处
期刊:Age and Ageing [Oxford University Press]
卷期号:51 (2) 被引量:14
标识
DOI:10.1093/ageing/afab274
摘要

Abstract Objective To develop and validate an index to quantify the multimorbidity burden in Chinese middle-aged and older community-dwelling individuals. Methods We included 20,035 individuals aged 45 and older from the China Health and Retirement Longitudinal Study (CHARLS) and 19,297 individuals aged 65 and older from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Health outcomes of physical functioning (PF), basic and instrumental activities of daily living (ADL and IADL) and mortality were obtained. Based on self-reported disease status, we calculated five commonly used western multimorbidity indexes for CHARLS baseline participants. The one that predicted the health outcomes the best was selected and then modified through a linear mixed model using the repeated individual data in CHARLS. The performance of the modified index was internally and externally evaluated with CHARLS and CLHLS data. Results The multimorbidity-weighted index (MWI) performed the best among the five indexes. In the modified Chinese multimorbidity-weighted index (CMWI), the weights of the diseases varied greatly (range 0.2–5.1). The top three diseases with the highest impact were stroke, memory-related diseases and cancer, corresponding to weights of 5.1, 4.3 and 3.4, respectively. Compared with the MWI, the CMWI showed better model fits for PF and IADL with larger R2 and smaller Akaike information criterion, and comparable prediction performances for ADL, IADL and mortality (e.g. the same predictive accuracy of 0.80 for ADL disability). Conclusion The CMWI is an adequate index to quantify the multimorbidity burden for Chinese middle-aged and older community-dwelling individuals. It can be directly computed via disease status examined in regular community health check-ups to facilitate health management.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
寒冷的奇异果完成签到,获得积分10
1秒前
hziyu发布了新的文献求助10
2秒前
2秒前
野性的南蕾完成签到,获得积分10
2秒前
毛毛哦啊发布了新的文献求助10
2秒前
zzzzzk发布了新的文献求助10
2秒前
2秒前
lalala发布了新的文献求助10
3秒前
三里墩头应助oldlee采纳,获得20
3秒前
3秒前
iNk应助西安小小朱采纳,获得10
3秒前
CodeCraft应助西安小小朱采纳,获得10
3秒前
无花果应助爱学习的小迟采纳,获得10
4秒前
哭泣的映寒完成签到 ,获得积分10
4秒前
xls完成签到,获得积分10
4秒前
4秒前
故意的傲玉应助圈圈采纳,获得10
4秒前
5秒前
522完成签到,获得积分10
5秒前
5秒前
kbj发布了新的文献求助10
5秒前
6秒前
老西瓜发布了新的文献求助10
6秒前
人各有痣完成签到,获得积分10
6秒前
后知后觉发布了新的文献求助10
6秒前
xiaoxiao发布了新的文献求助30
6秒前
6秒前
7秒前
7秒前
英姑应助哈哈呀采纳,获得10
8秒前
8秒前
hurry完成签到,获得积分10
8秒前
Hungrylunch应助陈玉婷采纳,获得20
8秒前
领导范儿应助hu970采纳,获得10
9秒前
new_vision发布了新的文献求助10
9秒前
拼搏翠桃完成签到,获得积分10
10秒前
糖糖科研顺利呀完成签到 ,获得积分10
10秒前
10秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527304
求助须知:如何正确求助?哪些是违规求助? 3107454
关于积分的说明 9285518
捐赠科研通 2805269
什么是DOI,文献DOI怎么找? 1539827
邀请新用户注册赠送积分活动 716708
科研通“疑难数据库(出版商)”最低求助积分说明 709672