Multimorbidity measures differentially predicted mortality among older Chinese adults

医学 人口学 多发病率 统计的 比例危险模型 老年学 统计 共病 内科学 数学 社会学
作者
Shanshan Yao,Huiwen Xu,Ling Han,Kaipeng Wang,Guiying Cao,Nan Li,Yan Luo,Yu-Ming Chen,Hexuan Su,Zishuo Chen,Zi-Ting Huang,Yonghua Hu,Beibei Xu
出处
期刊:Journal of Clinical Epidemiology [Elsevier BV]
卷期号:146: 97-105 被引量:21
标识
DOI:10.1016/j.jclinepi.2022.03.002
摘要

This study aimed to examine and compare the associations between different multimorbidity measures and mortality among older Chinese adults.Using the Chinese Longitudinal Healthy Longevity Survey 2002-2018, data on fourteen chronic conditions from 13,144 participants aged ≥65 years were collected. Multimorbidity measures included condition counts, multimorbidity patterns (examined by exploratory factor analysis), and multimorbidity trajectories (examined by a group-based trajectory model). Mortality risk associated with different multimorbidity measures was each analyzed using Cox regression. C-statistic, the Integrated Discrimination Improvement (IDI), and the Net Reclassification Index (NRI) were used to compare the performance of different multimorbidity measures.Participants with multimorbidity, regardless of measurements, had a higher risk of death compared with people without multimorbidity. Compared with the mortality prediction model using age and sex, C-statistics showed added discrimination (over 0.77, all P < .05) for models with multimorbidity measures. Multimorbidity trajectory showed integrated discrimination and net reclassification improvement for mortality prediction compared to condition count (IDI = 0.042, NRI = 0.033) and multimorbidity pattern (IDI = 0.041, NRI = 0.069).Adding multimorbidity measures significantly improved the performance of a mortality prediction model using age and sex as predictors. Trajectory-based measures of multimorbidity performed better than count- and pattern-based measures for mortality prediction.
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
舒心新儿应助元谷雪采纳,获得10
刚刚
qiuy发布了新的文献求助10
刚刚
刚刚
kll完成签到,获得积分10
刚刚
一蓑烟雨任平生完成签到,获得积分10
刚刚
木木完成签到,获得积分10
1秒前
zzt发布了新的文献求助10
1秒前
端庄的火龙果完成签到,获得积分10
2秒前
任性雁风完成签到 ,获得积分10
2秒前
Li完成签到,获得积分10
2秒前
渡安完成签到 ,获得积分10
2秒前
2秒前
3秒前
LX完成签到,获得积分10
3秒前
4秒前
4秒前
艾雪完成签到,获得积分10
4秒前
范伟完成签到,获得积分10
4秒前
研友_LN7x6n完成签到,获得积分0
4秒前
冷酷的冰岚完成签到 ,获得积分10
4秒前
GSW发布了新的文献求助10
4秒前
聪明惊蛰完成签到,获得积分10
5秒前
NexusExplorer应助积极天玉采纳,获得10
5秒前
天行健完成签到,获得积分10
6秒前
晴空完成签到,获得积分10
6秒前
彭于晏应助摸水的鱼采纳,获得10
6秒前
科研通AI6.2应助摸水的鱼采纳,获得10
6秒前
坚定的咖啡完成签到,获得积分10
6秒前
眯眯眼的以蕊完成签到,获得积分10
7秒前
尚影芷发布了新的文献求助10
7秒前
7秒前
于是完成签到 ,获得积分10
7秒前
kk发布了新的文献求助10
7秒前
CodeCraft应助tusyuki采纳,获得10
8秒前
鲤鱼小蕾完成签到 ,获得积分10
8秒前
欢喜的元枫完成签到,获得积分10
8秒前
Fluoxetine完成签到,获得积分10
8秒前
后知不觉完成签到,获得积分10
8秒前
8秒前
香蕉觅云应助qiuy采纳,获得10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
核安全综合知识2024版 500
Photothermal Science and Techniques 500
Digital Displacement Hydrostatic Transmission for Rotorcraft and Distributed Propulsion 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7706198
求助须知:如何正确求助?哪些是违规求助? 9263689
关于积分的说明 20044915
捐赠科研通 7282091
什么是DOI,文献DOI怎么找? 3295470
关于科研通互助平台的介绍 2450669
邀请新用户注册赠送积分活动 2302468