Your browser doesn't support javascript.
loading
[Prediction of 6-year risk of activities of daily living disability in elderly aged 65 years and older in China].
Zhou, J H; Lyu, Y B; Wei, Y; Wang, J N; Ye, L L; Wu, B; Liu, Y; Qiu, Y D; Zheng, X L; Guo, Y B; Ju, A P; Xue, K; Zhang, X C; Zhao, F; Qu, Y L; Chen, C; Liu, Y C; Mao, C; Shi, X M.
Affiliation
  • Zhou JH; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Lyu YB; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Wei Y; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Wang JN; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Ye LL; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Wu B; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Liu Y; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Qiu YD; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Zheng XL; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Guo YB; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Ju AP; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Xue K; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Zhang XC; Division of Non-communicable Disease and Aging Health Management, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Zhao F; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Qu YL; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Chen C; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Liu YC; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Mao C; Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China.
  • Shi XM; China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
Zhonghua Yi Xue Za Zhi ; 102(2): 94-100, 2022 Jan 11.
Article in Zh | MEDLINE | ID: mdl-35012296
Objective: To construct an easy-to-use risk prediction tool for 6-year risk of activities of daily living(ADL) disability among Chinese elderly aged 65 and above. Methods: A total of 34 349 elderly aged 65 and above were recruited from the Chinese Longitudinal Healthy Longevity Survey. Demographic characteristics, lifestyle and chronic diseases of the elderly were collected through face-to-face interviews. The functional status of the elderly was evaluated by the instrumental activities of daily living(IADL) scale. The mental health status of the elderly was evaluated by the Mini-Mental State Examination. The height, weight, blood pressure and other information of the subjects were obtained through physical examination and body mass index(BMI) was calculated. The ADL status was evaluated by Katz Scale at baseline and follow-up surveys. Taking ADL status as the dependent variable and the key predictors were selected from Lasso regression as the independent variables, a Cox proportional risk regression model was constructed and visualized by the nomogram tool. Area under the receiver operating characteristic curve(AUC) and calibration curve were used to evaluate the discrimination and calibration of the model. A total of 200 bootstrap resamples were used for internal validation of the model. Sensitivity analysis was used to evaluate the robustness of the model. Results: The M(Q1, Q3) of subjects' age as 86(75, 94) years old, of which 9 774(46.0%) were males. A total of 112 606 person-years were followed up, 4 578 cases of ADL disability occurred and the incidence density was 40.7/1 000 person-years. Cox proportional risk regression model analysis showed that older age, higher BMI, female, hypertension and history of cerebrovascular disease were associated with higher risk of ADL disability [HR(95%CI) were 1.06(1.05-1.06), 1.05(1.04-1.06), 1.17(1.10-1.25),1.07(1.01-1.13) and 1.41(1.23-1.62), respectively.]; Ethnic minorities, walking 1 km continuously, taking public transportation alone and doing housework almost every day were associated with lower risk of ADL disability [HR(95%CI): 0.71(0.62-0.80), 0.72(0.65-0.80), 0.74(0.68-0.82) and 0.69(0.64-0.74), respectively]. The AUC value of the model was 0.853, and the calibration curve showed that the predicted probability was highly consistent with the observed probability. After excluding non-intervening factors(age, sex and ethnicity), the AUC value of the model for predicting the risk of ADL disability was 0.779. The AUC values of 65-74 years old and 75 years old and above were 0.634 and 0.765, respectively. The AUC values of the model based on walking 1 km continuous and taking public transport alone in IADL and the model based on comprehensive score of IADL were 0.853 and 0.851, respectively. Conclusion: The risk prediction model of ADL disability established in this study has good performance and robustness.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Activities of Daily Living / Disabled Persons Type of study: Etiology_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Limits: Aged / Female / Humans / Male Country/Region as subject: Asia Language: Zh Journal: Zhonghua Yi Xue Za Zhi Year: 2022 Type: Article Affiliation country: China

Full text: 1 Database: MEDLINE Main subject: Activities of Daily Living / Disabled Persons Type of study: Etiology_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Limits: Aged / Female / Humans / Male Country/Region as subject: Asia Language: Zh Journal: Zhonghua Yi Xue Za Zhi Year: 2022 Type: Article Affiliation country: China