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Multimorbidity measures differentially predicted mortality among older Chinese adults.
Yao, Shan-Shan; Xu, Hui-Wen; Han, Ling; Wang, Kaipeng; Cao, Gui-Ying; Li, Nan; Luo, Yan; Chen, Yu-Ming; Su, He-Xuan; Chen, Zi-Shuo; Huang, Zi-Ting; Hu, Yong-Hua; Xu, Beibei.
Afiliación
  • Yao SS; 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.
  • Xu HW; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
  • Han L; Department of Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Wang K; Graduate School of Social Work, University of Denver, Denver, CO, USA.
  • Cao GY; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
  • Li N; Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, USA.
  • Luo Y; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
  • Chen YM; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
  • Su HX; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
  • Chen ZS; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
  • Huang ZT; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
  • Hu YH; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Medical Informatics Center, Beijing, China.
  • Xu B; Peking University Medical Informatics Center, Beijing, China. Electronic address: xubeibei@bjmu.edu.cn.
J Clin Epidemiol ; 146: 97-105, 2022 06.
Article en En | MEDLINE | ID: mdl-35259446
OBJECTIVES: This study aimed to examine and compare the associations between different multimorbidity measures and mortality among older Chinese adults. STUDY DESIGN AND SETTING: 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. RESULTS: 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). CONCLUSION: 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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Estado de Salud / Multimorbilidad Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Humans / Middle aged País/Región como asunto: Asia Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Estado de Salud / Multimorbilidad Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Humans / Middle aged País/Región como asunto: Asia Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: China