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中国健康与养老追踪调查:中老年人跌倒的相关因素分析及列线图预测模型的构建(PDF)

《现代预防医学》[ISSN:1003-8507/CN:51-1365/R]

期数:
2022年11期
页码:
2040-2047
栏目:
健康与社会行为
出版日期:
2022-06-15

文章信息/Info

Title:
China Health and elderly care follow-up survey: analysis of related factors of falls in middle-aged and elderly people and construction of nomogram prediction model
作者:
沈炼伟王维
锦州医科大学附属第一医院,辽宁 121000
Author(s):
SHEN Lian-wei WANG Wei
The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning 121000, China
关键词:
中老年人 跌倒 列线图预测模型 中国健康与养老追踪调查
Keywords:
Middle-aged and senior people Fall Prediction model of nomogram China Health and Old-age Tracking Survey
分类号:
R161.7
DOI:
-
文献标识码:
A
摘要:
目的 探讨我国中老年人跌倒的相关因素及构建跌倒的列线图预测模型,为中老年跌倒的防控工作提供指导。 方法 抽样方法为全样本分析,研究数据来源于中国健康与养老追踪调查(China Health and Retirement Longitudinal Study,CHARLS)2015年随访数据,以自2013年以来是否曾跌倒为因变量,纳入年龄、BMI(身体质量指数)、左手肌力、右手肌力、站立时间、步行速度、从椅子上起立时间、性别、吸烟、饮酒、腰围、亚洲人骨质疏松自我筛查工具(OSTA指数)共12个变量探讨中老年人跌倒的相关因素,采用SPSS进行1:1倾向性评分、描述性分析,Rstudio将数据集分为训练集与验证集,训练集进行单因素、多因素回归分析、列线图模型构建及内部验证,验证集进行外部验证。 结果 共纳入中老年人15 584人,其中检出有跌倒史2 339人(15.0%),无跌倒史13 185人(85.0%)。倾向性评分以1:1从无跌倒史的13 185人中选出2 339人作为对照组,有跌倒史患者作为患病组,构建数据集,以7:3将数据集随机分为训练集(3 276人)与验证集(1 402人)。根据单、多因素回归分析结果从训练集选取出年龄、身体质量指数(BMI)、右手肌力、从椅子上起立时间、腰围、骨质疏松OSTA指数等6个变量纳入列线图预测模型,模型ROC曲线下面积为0.704,表明模型具有较好的区分度,校准曲线说明模型具有较高的校准度。内部验证曲线下面积0.708,校准曲线拟合良好。由验证集对跌倒列线图预测模型进行外部验证,外部验证的ROC曲线下面积为0.729,表明模型具有较高的区分度,校准曲线说明模型具有较高的校准度。 结论 列线图模型可根据年龄、身体质量指数(BMI)、右手肌力、从椅子上起立时间、腰围、骨质疏松OSTA指数等因素预计老年人跌倒的概率,有利于跌倒的预防,可在临床与实际生活中上推广及应用。
Abstract:
Objective To explore the related factors of falls in middle-aged and elderly people in China, to construct a nomogram prediction model of falls, and to provide guidance for the prevention and control of falls in middle-aged and elderly people. Methods The research data were from the follow-up survey of health and elderly care in China(China Health And Retirement Longitudinal Study, CHARLS)based on the follow-up data in 2015. Taking whether there had been a fall since 2013 as the dependent variable, 12 variables including age, body mass index(BMI), left hand muscle strength, right hand muscle strength, standing time, walking speed, standing time from chair, gender, smoking, drinking, waist circumference and osteoporosis self-assessment tool for Asians(OSTA)index were included to explore related factors of the falls of middle-aged and elderly people. SPSS was used for 1:1 propensity score and descriptive analysis. Rstudio divided the data set into training set and verification set. The training set was subjected to single factor analysis, multi factor regression analysis, nomogram model construction and internal verification, and the verification set was subjected to external verification. Results 15 584 middle-aged and elderly people were included, and 2 339 people(15.0%)with a history of falls and 13 185 people(84.0%)without a history of falls were detected. The propensity score was 1:1. 2 339 people from 13 185 people without a history of falls were selected as the control group and patients with a history of falls as the disease group. The data set was constructed. The data set was randomly divided into training set(3 276 people)and verification set at 7:3(1 402 persons). According to the results of single and multivariate regression analysis, six variables such as age, body mass index(BMI), right hand muscle strength, standing time from chair, waist circumference and osteoporosis OSTA index were selected from the training set and included in the nomogram prediction model. The area under the ROC curve of the model was 0.704, indicating that the model had a high discrimination, and the calibration curve indicated that the model had a high calibration degree. The area under the internal verification curve was 0.708, and the calibration curve fit well. The fall nomogram prediction model was externally verified by the verification set. The area under the ROC curve of external verification was 0.729, indicating that the model had a high discrimination, and the calibration curve indicated that the model had a high calibration degree. Conclusion Nomogram model can predict the probability of falls in the elderly according to age, body mass index(BMI), right hand muscle strength, standing time from chair, waist circumference, osteoporosis OSTA index and other factors, which is conducive to the prevention of falls, and can be popularized and applied in clinical and practical life.

参考文献/References

[1] Moyer VA, Force UT. Prevention of falls in community-dwelling older adults: U.S. Preventive Services Task Force recommendation statement[J]. Annals of Internal Medicine, 2012, 157(3): 197-204.
[2] Hopewell S, Adedire O, Copsey BJ, et al. Multifactorial and multiple component interventions for preventing falls in older people living in the community[M]. New Jersey: John Wiley & Sons, 2016.
[3] 胡惠菊,韩静,唐启群,等.天津、唐山养老机构老年人跌倒现况及其影响因素研究[J].现代预防医学,2021,48(11):2018-2021, 2045.
[4] 石蕊,武丽,张涛,等.湖北省农村老年人跌倒发生情况及影响因素分析[J].华中科技大学学报:医学版,2012,41(4):500-503.
[5] 胡依,郭芮绮,闵淑慧,等.1990—2019年中国老年人群跌倒疾病负担分析[J].现代预防医学,2021,48(9):1542-1545, 1630.
[6] 刘翠鲜,沈志祥.老年跌倒的特点与预防策略[J].中国老年学杂志,2013,33(2):459-461.
[7] Stevens JA, Corso PS, Finkelstein EA, et al. The costs of fatal and non-fatal falls among older adults[J]. Injury Prevention: Journal of the International Society for Child and Adolescent Injury Prevention, 2006, 12(5): 290-295.
[8] 黄海涛,于敬芬,吴震,等.老年人焦虑与跌倒风险的Meta分析[J].现代预防医学,2021,48(14):2594-2598.
[9] 林桂永,梁创银,梁伟仪.老年人跌倒预防措施研究进展[J].医学理论与实践,2021,34(1):34-37.
[10] Van Gameren M, Bossen D, Bosmans JE, et al. The(cost-)effectiveness of an implemented fall prevention intervention on falls and fall-related injuries among community-dwelling older adults with an increased risk of falls: protocol for the in balance randomized controlled trial[J]. BMC Geriatrics, 2021, 21(1): 381.
[11] 刘晓红,吴淼,牛茜.老年人跌倒危害因素分析[J].北京医学,2021,43(6):533-534, 538.
[12] Sibley KM, Touchette AJ, Singer JC, et al. To what extent do older adult community exercise programs in Winnipeg, Canada address balance and include effective fall prevention exercise? A descriptive self-report study[J]. BMC Geriatrics, 2019, 19(1): 201.
[13] Zhao YH, Strauss J, Yang GH, et al. China health and retirement longitudinal study, 2011-2012 national baseline users’ guide[M]. Beijing: Peking University, 2013.
[14] 刘佳,彭红叶,阎早芳,等.中国健康与养老追踪调查:中老年残疾人抑郁症状及相关因素[J].中华行为医学与脑科学杂志,2021,30(3):273-278.
[15] Van SM, Moons KG, De Groot JA, et al. Sample size for binary logistic prediction models: Beyond events per variable criteria[J]. Statistical Methods in Medical Research, 2019, 28(8): 2455-2474.
[16] Garfein AJ, Regula HA. Robust aging among the young-old, old-old, and oldest-old[J]. Journals of Gerontology Series B-Psychological Sciences and Social Sciences, 1995, 50(2): S77-87.
[17] 吕燕宇,姜红如,张兵,等.中国九省(区)农民超重肥胖的变化趋势及人口经济因素的影响[J].中国食物与营养,2020,26(1):73-76.
[18] 陈志平,王金娣,许家亮,等.OSTA指数对江苏镇江地区健康中老年人群骨质疏松的筛选价值[J].中国骨质疏松杂志,2021,27(10):1491-1494, 1522.
[19] Jutberger H, Lorentzon M, Barrett-Connor E, et al. Smoking predicts incident fractures in elderly men: Mr OS Sweden[J]. Journal of Bone and Mineral Research: the Official Journal of the American Society for Bone and Mineral Research, 2010, 25(5): 1010-1016.
[20] 杨梅,肖静,蔡辉.多元分析中的多重共线性及其处理方法[J].中国卫生统计,2012,29(4):620-624.
[21] 朱秀芬,林华.中老年人群跌倒风险与年龄的相关性研究[J].中国骨质疏松杂志,2012,18(8):734-737.
[22] 王晓睿.重庆市沙坪坝区老年人跌倒风险与年龄的相关性[J].中国老年学杂志,2013,33(16):3933-3934.
[23] 王志灼,谷莉,周谋望.中国老年人跌倒风险因素识别及评估工具应用的研究进展[J].中国康复医学杂志,2021,36(11):1440-1444.
[24] 朱文娟,吴善玉.社区老年人跌倒恐惧的现状及其影响因素[J].中国老年学杂志,2011,31(7):1225-1226.
[25] Teasdale N, Simoneau M, Corbeil P, et al. Obesity alters balance and movement control[J]. Current Obesity Reports, 2013, 2(3): 235-240.
[26] 张阳,王波,张英媛,等.超重、肥胖对中老年人动静态平衡能力影响的研究[J].吉林体育学院学报,2021,37(1):58-63.
[27] 姜宜君,郑乔木,邹敏,等.跌倒损伤入院老年人单次与多次跌倒特征及危险因素的比较研究[J].中国护理管理,2021,21(6):861-865.
[28] 吴跃迪,刘腊梅,王珍珠,等.老年人衰弱和跌倒相关性研究进展[J].全科护理,2021,19(24):3377-3380.
[29] Skelton D, Kennedy J, Rutherford OM. Explosive power and asymmetry in leg muscle function in frequent fallers and non-fallers aged over 65[J]. Age and Ageing, 2002, 31(2): 119-125.
[30] Asakawa Y, Ikezoe T, Hazaki K, et al. Relationship between falls and knee extension strength in the elderly[J]. The Journal of Physical Therapy Science, 2001, 8(2): 45-48.
[31] 祝令庆,张建国,张建玉.负荷伸膝运动对中老年人下肢肌力及平衡能力的影响[J].沈阳体育学院学报,2008,27(3):49-51.
[32] 李宗涛,赖勤,孙晋海,等.下肢蹲起蹬伸肌力衰退导致老年女性跌倒风险的研究[J].中国运动医学杂志,2017,36(8):700-705, 679.
[33] 宣言,赵琳,刘冬梅,等.单独或联合握力和OSTA指数在诊断骨量下降/骨质疏松症中的作用[J].中华骨质疏松和骨矿盐疾病杂志,2018,11(1):85-93.
[34] 李毅中,庄华烽,林长堃,等.年龄和维生素D状态对绝经后女性骨质疏松患者握力的影响[J].中国骨质疏松杂志,2017,23(3):310-312, 420.
[35] 龙玉其.医疗服务体系的满意度评价——基于2 557个城乡不同收入家庭的调查[J].社会保障研究,2011(1):92-96.
[36] 刘增超,袁建芬,胡磊.无锡市新区社区卫生医疗服务满意度综合评价[J].公共卫生与预防医学,2015,26(4):58-60.
[37] 宋学文,白璧辉,谢兴文,等.中老年人群体质形态、肌力、坐立平衡与骨质疏松相关性研究[J].中国骨质疏松杂志,2020,26(5):625-630.
[38] Iasonos A, Schrag D, Raj GV, et al. How to build and interpret a nomogram for cancer prognosis[J]. Journal of Clinical Oncology, 2008, 26(8): 1364-1370.
[39] 谷鸿秋,周支瑞,章仲恒,等.临床预测模型:基本概念、应用场景及研究思路[J].中国循证心血管医学杂志,2018,10(12):1454-1456, 1462.
[40] 王俊峰,章仲恒,周支瑞,等.临床预测模型:模型的验证[J].中国循证心血管医学杂志,2019,11(2):141-144.
[41] 文玲子,王俊峰,谷鸿秋.临床预测模型:新预测因子的预测增量值[J].中国循证心血管医学杂志,2020,12(6):655-659.

备注/Memo

备注/Memo:
作者简介:沈炼伟(1995—),男,硕士在读,研究方向:肌骨康复的研究
通讯作者:王维,E-mail:dubutianxia315@163.com
更新日期/Last Update: 2022-06-14