Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
J Clin Epidemiol. 2022 Jun;146:97-105. doi: 10.1016/j.jclinepi.2022.03.002. Epub 2022 Mar 5.
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.
本研究旨在考察和比较不同多重疾病测量方法与中国老年人群死亡率之间的关系。
利用中国健康长寿追踪调查 2002-2018 年的数据,共纳入了 13144 名年龄≥65 岁的参与者的 14 种慢性疾病数据。多重疾病的测量方法包括疾病数量、多重疾病模式(通过探索性因素分析进行检查)和多重疾病轨迹(通过基于群组的轨迹模型进行检查)。使用 Cox 回归分析了不同多重疾病测量方法与死亡率之间的关联。使用 C 统计量、综合判别改善(IDI)和净重新分类指数(NRI)来比较不同多重疾病测量方法的性能。
患有多重疾病的参与者,无论采用何种测量方法,其死亡风险均高于无多重疾病的参与者。与仅使用年龄和性别构建的死亡率预测模型相比,使用多重疾病测量方法构建的模型的 C 统计量显示出了显著的判别能力提升(均>0.77,均 P<0.05)。与疾病数量和多重疾病模式相比,多重疾病轨迹在死亡率预测方面具有更好的综合判别和净重新分类改善能力(IDI=0.042,NRI=0.033;IDI=0.041,NRI=0.069)。
添加多重疾病测量方法可显著提高使用年龄和性别作为预测因子的死亡率预测模型的性能。基于轨迹的多重疾病测量方法在死亡率预测方面优于基于数量和模式的多重疾病测量方法。