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Article(id=1241342454432068528, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241342451043061769, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202309501, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1695657600000, receivedDateStr=2023-09-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773888704163, onlineDateStr=2026-03-19, pubDate=1708790400000, pubDateStr=2024-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773888704163, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773888704163, creator=13701087609, updateTime=1773888704163, updator=13701087609, issue=Issue{id=1241342451043061769, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='4', pageStart='577', pageEnd='768', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773888703356, creator=13701087609, updateTime=1773893026321, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241360582939562716, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241342451043061769, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241360582939562717, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241342451043061769, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=736, endPage=741, ext={EN=ArticleExt(id=1241342454805361589, articleId=1241342454432068528, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Detection rate and influencing factors of osteosarcopenia in the elderly in Shihezi community, columnId=1228016573156360233, journalTitle=Modern Preventive Medicine, columnName=Disease Control and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the detection rate of osteosarcopenia in the elderly in the community of Shihezi city and to analyze its influencing factors.

Methods

From March to July 2023, a stratified random sampling method was used to select the elderly over 60 years old in Shihezi community for on-site questionnaire survey and physical measurement. Multivariate Logistic regression was used to analyze the influencing factors.

Results

Among 1 051 elderly people, the detection rate of osteosarcopenia was 21.9% (230 people). Multivariate analysis showed that BMI (OR=0.78, 95% CI: 0.712-0.857), comorbidities (OR=1.774, 95% CI: 1.106-2.845), fracture history (OR=3.493, 95% CI: 1.748-6.979), duration of menopause (OR=1.080, 95% CI: 1.013-1.152), drinking strong tea OR coffee or carbonated drinks (OR=5.161, 95% CI: 2.731-9.752), sedentary inactivity (OR=1.899, 95% CI: 1.073-3.363), malnutrition (OR=4.601, 95% CI: 2.352-9.000), married (OR=0.041, 95% CI: 0.007-0.247), living alone (OR=3.404, 95% CI: 1.335-8.683), social frailty (OR=5.669, 95% CI: 3.852-10.674), low income (OR=2.248, 95% CI: 1.053-4.798) and employee medical insurance (OR=0.138, 95% CI: 0.037-0.510) and resident medical insurance (OR=0.023, 95% CI: 0.005-0.096) were the main influencing factor for the high-risk population of osteosarcopenia in the community.

Conclusion

The detection rate of sarcopenia and osteoporosis is high in the elderly in the community of Shihezi city. Community health workers should find the high-risk population of osteosarcopenia as soon as possible and take intervention measures.

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目的

了解石河子市社区老年人肌少-骨质疏松症检出率并分析其影响因素。

方法

于2023年3—7月，采用分层随机抽样的方法，选取石河子社区60岁以上老年人进行现场问卷调查和身体测量，运用二元logistic回归进行影响因素分析。

结果

1 051名老年人中，肌少-骨质疏松症检出率为21.9%（230人）。多因素分析结果显示，BMI（OR=0.781；95% CI：0.712~0.857）、合并症（OR=1.774；95% CI：1.106~2.845）、骨折史（OR=3.493；95% CI：1.748~6.979）、女性绝经年限（OR=1.080；95% CI：1.013~1.152）、饮用浓茶咖啡或碳酸饮料（OR=5.161；95% CI：2.731~9.752）、久坐少动（OR=1.899；95% CI：1.073~3.363）、营养不良（OR=4.601；95% CI：2.352~9.000）、已婚（OR=0.041；95% CI：0.007~0.247）、独居（OR=3.404；95% CI：1.335~8.683）、社会衰弱（OR=5.669；95% CI：3.852~10.674）、低收入（OR=2.248；95% CI：1.053~4.798）以及职工医保（OR=0.138；95% CI：0.037~0.510）和居民医保（OR=0.023；95% CI：0.005~0.096）是社区肌少-骨质疏松症高危人群的主要影响因素。

结论

石河子市社区老年人肌少-骨质疏松症检出率高，社区卫生工作者应尽早发现肌少-骨质疏松症高危人群，并采取干预措施。

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何斌，E-mail：
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=s3+zP2CXfJyrK8hVvWSoIw==, magXml=M/4X4WaO/iLUOltQiWiXzg==, pdfUrl=null, pdf=hbwDn1U1PsFiwTJs93CCJg==, pdfFileSize=916000, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=h2Oww89eejmuxBScQJRZnQ==, mapNumber=null, authorCompany=null, fund=null, authors=

刘柯汝（1998—），女，硕士在读，研究方向：老年护理

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Experimental and Therapeutic Medicine, 2023, 25(4): 189., articleTitle=Research advances in crosstalk between muscle and bone in osteosarcopenia (Review), refAbstract=null), Reference(id=1241342467698651731, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342454432068528, doi=null, pmid=null, pmcid=null, year=2021, volume=21, issue=1, pageStart=69, pageEnd=null, url=null, language=null, rfNumber=[25], rfOrder=33, authorNames=Park KS, Lee GY, Seo YM, journalName=BMC Geriatrics, refType=null, unstructuredReference=Park KS, Lee GY, Seo YM, et al. Disability, frailty and depression in the community-dwelling older adults with osteosarcopenia[J]. 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Comparison of the detection rate ofosteosarcopenia in community elderly with different characteristics (n=1 051)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量人数非肌少-骨质疏松症高危人群肌少-骨质疏松症高危人群统计量Z/χ2P
个人特质
年龄[岁，M（QR）]70（65,77）80（76,84）-14.652<0.001
性别
男性3953425326.534<0.001
女性656479177
BMI〔kg/m2，M（QR）〕25.8（23.7,28.0）23.1（21.3，24.8）-11.109<0.001
民族
汉族90068321718.177<0.001
少数民族15113813
女性绝经年限[年，M（QR）]13（5.0,24.0）31.0（18.0,35.0）-12.195<0.001
aCCI[分，M（QR）]3.0（2.0,4.0）4.0（4.0,5.0）-13.506<0.001
骨折史
无91074716362.595<0.001
有1417467
抑郁
正常83674195264.582<0.001
有抑郁症状21580135
行为特征
营养状况
营养状态良好879773106303.279<0.001
有营养状态不良的风险或营养不良17248124
PASE得分[分，M（QR）]96.0（76.0,111.0）51.0（34.0,76.0）-14.574<0.001
久坐时长（h/d）
≤462155566112.492<0.001
>4430266164
是否饮用浓茶咖啡或碳酸饮料
否75561813721.914<0.001
是29620393
人际网络层
婚姻状况
已婚74761713030.334<0.001
离异或丧偶304204100
居住情况
与家人同住79265913348.727<0.001
独居25916297
社会衰弱
非社会衰弱63659739306.121<0.001
社会衰弱前期25817484
社会衰弱15750107
生活和工作条件
文化程度
小学及以下57740816941.043<0.001
初中及以上47441361
月收入（元/月）
≤3 0004483331156.5460.011
>3 000603488115
政策环境
医保类型
职工医保76258317956.249<0.001
居民医保21519619
商业保险651
新农合30228
无381523
), ArticleFig(id=1241342460455088293, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342454432068528, language=CN, label=表1, caption=

不同特征的社区老年人肌少-骨质疏松症检出情况比较（n=1 051）

, figureFileSmall=null, figureFileBig=null, tableContent=
变量人数非肌少-骨质疏松症高危人群肌少-骨质疏松症高危人群统计量Z/χ2P
个人特质
年龄[岁，M（QR）]70（65,77）80（76,84）-14.652<0.001
性别
男性3953425326.534<0.001
女性656479177
BMI〔kg/m2，M（QR）〕25.8（23.7,28.0）23.1（21.3，24.8）-11.109<0.001
民族
汉族90068321718.177<0.001
少数民族15113813
女性绝经年限[年，M（QR）]13（5.0,24.0）31.0（18.0,35.0）-12.195<0.001
aCCI[分，M（QR）]3.0（2.0,4.0）4.0（4.0,5.0）-13.506<0.001
骨折史
无91074716362.595<0.001
有1417467
抑郁
正常83674195264.582<0.001
有抑郁症状21580135
行为特征
营养状况
营养状态良好879773106303.279<0.001
有营养状态不良的风险或营养不良17248124
PASE得分[分，M（QR）]96.0（76.0,111.0）51.0（34.0,76.0）-14.574<0.001
久坐时长（h/d）
≤462155566112.492<0.001
>4430266164
是否饮用浓茶咖啡或碳酸饮料
否75561813721.914<0.001
是29620393
人际网络层
婚姻状况
已婚74761713030.334<0.001
离异或丧偶304204100
居住情况
与家人同住79265913348.727<0.001
独居25916297
社会衰弱
非社会衰弱63659739306.121<0.001
社会衰弱前期25817484
社会衰弱15750107
生活和工作条件
文化程度
小学及以下57740816941.043<0.001
初中及以上47441361
月收入（元/月）
≤3 0004483331156.5460.011
>3 000603488115
政策环境
医保类型
职工医保76258317956.249<0.001
居民医保21519619
商业保险651
新农合30228
无381523
), ArticleFig(id=1241342460559945908, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342454432068528, language=EN, label=Table 2, caption=

Influencing factors of high-risk population of osteosarcopenia in community（n=1 051）

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1模型2模型3模型4模型5
OR值P值OR值P值OR值P值OR值P值OR值P值
个人特质
性别0.1420.0110.1750.0410.2930.1760.3300.2280.3540.303
民族0.5990.2201.3550.5271.8260.2342.5090.0811.9520.504
年龄1.0810.0041.0630.0471.0500.1521.0750.0491.0640.126
BMI0.745<0.0010.797<0.0010.795<0.0010.788<0.0010.781<0.001
aCCI1.7910.0011.4990.0471.5940.0381.5330.0601.7740.017
骨折史3.651<0.0012.969<0.0013.0250.0012.994<0.0013.493<0.001
抑郁4.661<0.0012.2960.0021.8070.0551.8150.0541.5310.186
女性绝经年限1.096<0.0011.0920.0011.0850.0051.0790.0111.0800.018
行为特征
是否饮用浓茶咖啡或碳酸饮料2.936<0.0014.406<0.0014.740<0.0015.161<0.001
久坐2.0040.0051.6860.0541.7780.0361.8990.028
PASE得分0.979<0.0010.9890.0140.9890.0140.9880.015
营养状况5.250<0.0014.781<0.0014.723<0.0014.601<0.001
人际网络
婚姻状况0.0550.0010.0560.0010.041<0.001
居住情况3.6000.0063.5860.0063.4040.010
社会衰弱（以非社会衰弱为参考）
社会衰弱7.355<0.0015.919<0.0015.669<0.001
社会衰弱前期3.400<0.0012.8870.0022.6890.004
生活和工作条件
文化程度0.9960.9890.9910.977
收入水平1.9750.0272.2480.036
政策环境
医保政策（以无为对照）
职工医保0.1380.003
居民医保0.023<0.001
商业保险1.3620.830
新农合0.3760.230
), ArticleFig(id=1241342460702552254, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342454432068528, language=CN, label=表2, caption=

社区肌少-骨质疏松症高危人群影响因素分析（n=1 051）

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1模型2模型3模型4模型5
OR值P值OR值P值OR值P值OR值P值OR值P值
个人特质
性别0.1420.0110.1750.0410.2930.1760.3300.2280.3540.303
民族0.5990.2201.3550.5271.8260.2342.5090.0811.9520.504
年龄1.0810.0041.0630.0471.0500.1521.0750.0491.0640.126
BMI0.745<0.0010.797<0.0010.795<0.0010.788<0.0010.781<0.001
aCCI1.7910.0011.4990.0471.5940.0381.5330.0601.7740.017
骨折史3.651<0.0012.969<0.0013.0250.0012.994<0.0013.493<0.001
抑郁4.661<0.0012.2960.0021.8070.0551.8150.0541.5310.186
女性绝经年限1.096<0.0011.0920.0011.0850.0051.0790.0111.0800.018
行为特征
是否饮用浓茶咖啡或碳酸饮料2.936<0.0014.406<0.0014.740<0.0015.161<0.001
久坐2.0040.0051.6860.0541.7780.0361.8990.028
PASE得分0.979<0.0010.9890.0140.9890.0140.9880.015
营养状况5.250<0.0014.781<0.0014.723<0.0014.601<0.001
人际网络
婚姻状况0.0550.0010.0560.0010.041<0.001
居住情况3.6000.0063.5860.0063.4040.010
社会衰弱（以非社会衰弱为参考）
社会衰弱7.355<0.0015.919<0.0015.669<0.001
社会衰弱前期3.400<0.0012.8870.0022.6890.004
生活和工作条件
文化程度0.9960.9890.9910.977
收入水平1.9750.0272.2480.036
政策环境
医保政策（以无为对照）
职工医保0.1380.003
居民医保0.023<0.001
商业保险1.3620.830
新农合0.3760.230
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石河子市社区老年人肌少-骨质疏松症检出率及影响因素分析
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刘柯汝 1 , 王玉环 1 , 陶晶 2 , 王若仙 1 , 魏杉杉 1 , 何斌 2
现代预防医学 | 疾病预防控制 2024,51(4): 736-741
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现代预防医学 | 疾病预防控制 2024, 51(4): 736-741
石河子市社区老年人肌少-骨质疏松症检出率及影响因素分析
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刘柯汝1, 王玉环1, 陶晶2, 王若仙1, 魏杉杉1, 何斌2
作者信息
  • 1.石河子大学医学院，新疆 石河子 832000
  • 2. 石河子大学医学院第三附属医院

    • 刘柯汝（1998—），女，硕士在读，研究方向：老年护理

通讯作者:

何斌，E-mail：
Detection rate and influencing factors of osteosarcopenia in the elderly in Shihezi community
Ke-ru LIU1, Yu-huan WANG1, Jing TAO2, Ruo-xian WANG1, Shan-shan WEI1, Bin HE2
Affiliations
  • Shihezi University School of Medicine, Shihezi, Xinjiang 832002,China
出版时间: 2024-02-25 doi: 10.20043/j.cnki.MPM.202309501
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摘要
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目的

了解石河子市社区老年人肌少-骨质疏松症检出率并分析其影响因素。

方法

于2023年3—7月，采用分层随机抽样的方法，选取石河子社区60岁以上老年人进行现场问卷调查和身体测量，运用二元logistic回归进行影响因素分析。

结果

1 051名老年人中，肌少-骨质疏松症检出率为21.9%（230人）。多因素分析结果显示，BMI（OR=0.781；95% CI：0.712~0.857）、合并症（OR=1.774；95% CI：1.106~2.845）、骨折史（OR=3.493；95% CI：1.748~6.979）、女性绝经年限（OR=1.080；95% CI：1.013~1.152）、饮用浓茶咖啡或碳酸饮料（OR=5.161；95% CI：2.731~9.752）、久坐少动（OR=1.899；95% CI：1.073~3.363）、营养不良（OR=4.601；95% CI：2.352~9.000）、已婚（OR=0.041；95% CI：0.007~0.247）、独居（OR=3.404；95% CI：1.335~8.683）、社会衰弱（OR=5.669；95% CI：3.852~10.674）、低收入（OR=2.248；95% CI：1.053~4.798）以及职工医保（OR=0.138；95% CI：0.037~0.510）和居民医保（OR=0.023；95% CI：0.005~0.096）是社区肌少-骨质疏松症高危人群的主要影响因素。

结论

石河子市社区老年人肌少-骨质疏松症检出率高，社区卫生工作者应尽早发现肌少-骨质疏松症高危人群，并采取干预措施。

肌少-骨质疏松症  /  检出率  /  社区  /  影响因素  /  健康生态学模型
Objective

To investigate the detection rate of osteosarcopenia in the elderly in the community of Shihezi city and to analyze its influencing factors.

Methods

From March to July 2023, a stratified random sampling method was used to select the elderly over 60 years old in Shihezi community for on-site questionnaire survey and physical measurement. Multivariate Logistic regression was used to analyze the influencing factors.

Results

Among 1 051 elderly people, the detection rate of osteosarcopenia was 21.9% (230 people). Multivariate analysis showed that BMI (OR=0.78, 95% CI: 0.712-0.857), comorbidities (OR=1.774, 95% CI: 1.106-2.845), fracture history (OR=3.493, 95% CI: 1.748-6.979), duration of menopause (OR=1.080, 95% CI: 1.013-1.152), drinking strong tea OR coffee or carbonated drinks (OR=5.161, 95% CI: 2.731-9.752), sedentary inactivity (OR=1.899, 95% CI: 1.073-3.363), malnutrition (OR=4.601, 95% CI: 2.352-9.000), married (OR=0.041, 95% CI: 0.007-0.247), living alone (OR=3.404, 95% CI: 1.335-8.683), social frailty (OR=5.669, 95% CI: 3.852-10.674), low income (OR=2.248, 95% CI: 1.053-4.798) and employee medical insurance (OR=0.138, 95% CI: 0.037-0.510) and resident medical insurance (OR=0.023, 95% CI: 0.005-0.096) were the main influencing factor for the high-risk population of osteosarcopenia in the community.

Conclusion

The detection rate of sarcopenia and osteoporosis is high in the elderly in the community of Shihezi city. Community health workers should find the high-risk population of osteosarcopenia as soon as possible and take intervention measures.

Osteosarcopenia  /  Detection rate  /  community  /  Influencing factors  /  Health ecology model
刘柯汝, 王玉环, 陶晶, 王若仙, 魏杉杉, 何斌. 石河子市社区老年人肌少-骨质疏松症检出率及影响因素分析. 现代预防医学, 2024 , 51 (4) : 736 -741 . DOI: 10.20043/j.cnki.MPM.202309501
Ke-ru LIU, Yu-huan WANG, Jing TAO, Ruo-xian WANG, Shan-shan WEI, Bin HE. Detection rate and influencing factors of osteosarcopenia in the elderly in Shihezi community[J]. Modern Preventive Medicine, 2024 , 51 (4) : 736 -741 . DOI: 10.20043/j.cnki.MPM.202309501
正文
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至2022年底，石河子市60岁以上人口数达14.6万，占总人口的24.6%[1]，老龄化程度逐年加深。肌少-骨质疏松症（Osteosarcopenia, OS）是指骨量减少或骨质疏松症与肌少症同时存在，是一种以骨量低下，骨组织微结构退化、肌量减少、肌力下降以及肌肉功能障碍为特征的老年退行性疾病，全球患病率在5%~40%之间[2]。研究显示，OS不仅增加残疾和过早死亡风险，也加重个人、家庭和社会的负担[3]。国内现有对OS的研究主要集中于微观发病机制[4]，对人群总体患病情况尚不明确，对OS的影响因素也缺乏多层面的系统探索。目前对肌肉骨骼系统疾病提倡“肌骨共治、注重预防”[5]，社区是开展疾病防治工作的优势场所，以社区为单位，对高危人群进行早期筛查，可有效降低OS发病率及危害性。因此，本研究对石河子市社区60岁以上居民进行OS高危人群筛查，同时基于健康生态学模型，从不同层面分析石河子市社区OS高危人群的影响因素，为OS的防治提供科学依据。
1　对象与方法
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1.1　对象
研究经石河子大学医学院第三附属医院学术伦理委员会批准（KJX2022-052-02），于2023年3—7月采用分层随机抽样的方法，在考虑人口密度的基础上，根据新疆2022年城镇居民人均可支配收入38 410（元）[1]，将石河子市社区划分为高收入（月收入>3 000元/人）、中等及以下（月收入≤3 000元/人）两层，使用随机数字表法选取城区20个社区满足纳入与排除标准的老年人作为调查对象。
样本量计算：根据现况调查样本量估算公式[6]：N=tα2PQ/d2，N：估计样本量，P：总体率，经文献回顾[7]，肌少-骨质疏松症全球患病率约为21%，检验水准α取0.05，统计量tα=1.96，Q=1-P，允许误差d=0.05。经计算：N=tα2PQ/d2 = (1.96)2×0.21×(1-0.21)/(0.05)2≈256，考虑10%的样本流失率，确定最小样本量为281例。本研究最终样本量1 051例符合要求。
研究对象纳入标准为：（1）年龄≥60岁；（2）在本社区居住时间不少于6个月；（3）对本研究知情同意且自愿配合调查者。排除标准：（1）有严重的听力、视力及语言障碍；（2）患有严重的精神疾病不能配合调查者。
1.2　方法
1.2.1　因变量定义与测量
本研究将是否为肌少-骨质疏松症高危人群作为因变量。（1）定义 根据《肌少-骨质疏松症专家共识》[8]中指出肌少-骨质疏松症是指同时满足肌少症及骨质疏松症的测量标准：肌少症筛查阳性并伴有握力测试阳性（男性<28 kg，女性<18 kg）可诊断为“可能肌少症”，该方法适用于社区等基层医疗卫生服务机构；腰椎或股骨任何一个部位骨密度T值≤-2.5或发生过脆性骨折可诊断为骨质疏松症，对于未接受过骨密度检测的社区老年人推荐使用骨质疏松风险一分钟测试题和亚洲人骨质疏松自我筛查工具对其进行高危人群筛查。（2）测量工具 改良版老年肌少症筛查问卷（SARC-Cal F）[9]，该问卷共6个条目，共计20分，得分≥11分判断为肌少症筛查阳性；使用CAMRY电子握力计（型号EH101）测量握力；骨质疏松风险一分钟测试题[10]，含10个题目，只要其中1项回答为“是”，即为阳性；亚洲人骨质疏自我筛查工具（OSTA），计算公式为[体质量（kg）-年龄（岁）]×0.2。
1.2.2　自变量定义与测量
依据健康生态学模型[11]和相关研究[12]选取5个层面影响因素，分别为：个人特质，包括年龄、性别、民族、BMI、合并症、抑郁和骨折史；行为特征，包括营养状况、活动情况、久坐时长、是否饮用浓茶咖啡或碳酸饮料；人际网络，包括婚姻状况、居住情况和社会衰弱；生活和工作条件，包括文化程度、月收入；政策环境，包括医保政策。
测量工具 （1）一般资料调查表。（2）年龄校正Charlson合并症指数(aCCI)[13]：该量表为量化多种合并症信息、加权年龄评分后的综合指标，使用简便，是目前使用最广泛的共病指数量表。（3）微型营养评价法（Mini Nutritional Assessment, MNA）[14]：该量表共18个项目，总分30分，评分≥24分为营养状况良好,17分≤MNA<24分为存在营养不良风险，MNA<17分为营养不良，该量表的Cronbach α系数为0.906。本研究中该量表的Cronbach α系数为0.806。（4）老年人体力活动量表（Physical Activity Scale for the Elderly, PASE）[15]：该量表包括3个维度，26个问题，总分为400分，得分越高说明体力活动强度越大，该量表的重测信度为0.90。本研究中该量表的重测信度为0.83。（5）社会衰弱筛查工具（Help、Participation、Loneliness、Financial、Talk Scale，HALFT）[16]：包括5个条目，回答“是”计0分，“否”计1分，总分为0~5分，0分为非社会衰弱，1~2分为社会衰弱前期，≥3分表示社会衰弱，该量表的Cronbach α系数为0.725。本研究中该量表的Cronbach α系数为0.88。（6）简版老年抑郁量表（Geriatric Depression Scale, GDS-15）[17]：量表共15个条目，总分0~15分，≤4分为正常，≥5分为有抑郁症状，该量表的Cronbach α系数为0.79。本研究中该量表的Cronbach α系数为0.80。
1.2.3　研究方法
本次研究采用现场问卷调查与身体测量相结合的方法。由培训合格调查员对符合纳入标准的老年人进行面对面调查。使用超声波身高体重测量仪（型号：SY133）测量研究对象的身高和体重，并计算BMI；小腿围测量使用无弹性卷尺沿髌骨下缘12 cm腓肠肌位置测量周径，连续测量2次，取平均值；握力测试时，利用优势手握住仪器把柄，反复测量3次，取最大值作为统计分析指标，数值精确到0.1 kg。每次调查结束后随机抽查5%的问卷进行复核，发现不符或缺失进行追访补齐。本研究共发放问卷1 080份，剔除不合格问卷后，最终获得有效样本1 051例，有效率为97.3%。
1.3　统计学处理
使用EpiData 3.1软件进行双人录入并核对，采用SPSS 26.0进行统计分析。计量资料经Shapiro-Wilk检验均不符合正态分布，使用中位数（四分位数间距）表示，组间比较采用Wilcoxon秩和检验；计数资料以例数、构成比（%）表示，组间比较采用χ2检验；社区肌少-骨质疏松症高危人群影响因素采用二元logistic回归。根据健康生态学模型的5个维度，分别建立5个模型：模型1包含第1层的指标，模型2包含第1、2层的指标，模型3包含第1、2、3层的指标，模型4包含第1、2、3、4层的指标，模型5包含5个层的所有指标。检验水准α=0.05。
2　结果
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2.1　社区老年人的基本情况
1 051名老年人中，男性395例（37.6%），女性656例（32.4%），肌少-骨质疏松症高危人群230例（21.9%）非肌少-骨质疏松症高危人群821例（78.1%）。
2.2　不同特征的社区老年人肌少-骨质疏松症检出情况比较
根据筛查结果，将社区老年人分为肌少-骨质疏松症高危人群和非肌少-骨质疏松症高危人群两组。单因素分析结果显示，不同年龄、性别、民族、骨折史、婚姻状况、文化程度、居住情况、医保类型、饮用浓茶咖啡或碳酸饮料、久坐时长、社会衰弱、抑郁、BMI、女性绝经年限、合并症，活动情况对社区肌少-骨质疏松症高危人群产生影响（P<0.01）。不同特征的社区老年人肌少-骨质疏松症检出情况比较见表1。
2.3　社区肌少-骨质疏松症高危人群影响因素分析
将是否为肌少-骨质疏松症高危人群作为因变量，以表1中差异有统计学意义的指标为自变量进行二元logistic回归分析。结果显示，模型1~5的似然比卡方检验（LR χ2）值分别为511.096、611.586、674.728、680.085、717.873（P<0.001），拟合优度检验值（Pseudo R2）分别为0.592、0.678、0.728、0.733、0.761，模型准确预测率分别为88.8%、90.5%、91.4%、91.5%、91.5%。模型5的结果显示，BMI、合并症、骨折史、女性绝经年限、饮用浓茶咖啡或碳酸饮料、久坐时长、营养状况、老年人活动情况、婚姻状况、居住情况、社会衰弱、收入水平以及医保政策是肌少-骨质疏松症高危人群的主要影响因素（P<0.05）。见表2。
3　讨论
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3.1　石河子市社区老年人肌少-骨质疏松症高危人群筛查情况
研究显示，石河子市社区老年人肌少-骨质疏松症高危人群检出率为21.9%，高于2015年Wang等[18]对长沙市社区老年人的调查结果（12.7%）。上世纪五、六十年代，来自全国各地的大批屯垦戍边青年为建设新疆扎根于石河子市，构成了本地区庞大的老年人口基数，这些人群年轻时工作环境较为艰苦，且多从事重体力劳动，影响晚年肌肉骨骼系统功能[19]。
3.2　基于健康生态学模型的社区肌少-骨质疏松症高危人群影响因素
健康生态学模型五个层面联合分析能最大程度地提高各变量对社区肌少-骨质疏松症高危人群的预测率。结果显示，BMI、合并症、骨折史、女性绝经年限、饮用浓茶咖啡或碳酸饮料、久坐少动、营养不良、离异或丧偶、独居、社会衰弱、低收入以及职工/居民医保对社区肌少-骨质疏松症高危人群有直接的预测作用。
3.2.1　个人特质
（1）BMI BMI较高可能是该疾病的保护因素。BMI下降通常也预示肌肉质量的下降，骨诱导因子分泌不足，影响骨生成[20]。（2）骨折史 既往发生过骨折的人群骨质疏松的程度更加严重，且在骨折后由于长期制动，活动量下降，肌肉可能发生萎缩[21]。
3.2.2　行为特征
（1）饮用浓茶咖啡或碳酸饮料 长期饮用浓茶和咖啡会影响肠道对钙剂的吸收，同时含糖饮料会加速细胞老化，影响肌力[20]。（2）营养不良 老年人胃肠蠕动减慢，容易出现食欲减退和营养不良，蛋白质等营养物质的缺乏导致肌肉细胞逐渐萎缩，加快肌骨老化进程[23]。（3）久坐少动 老年人受到衰老和疾病的影响出现活动量下降，加之本地区冬季漫长寒冷，老年人因恐惧跌倒不敢外出，久坐时间增加，久坐时下肢长期处于放松状态，导致肌骨结构和功能退化，增加患病风险[24]。因此，社区应定期邀请上级医院医生开展健康讲座，指导老年人进行科学饮食，同时教会其室内锻炼的方法，提高活动的依从性。
3.2.3　人际网络
离异或丧偶、独居的老年人社会交往意愿下降，造成活动量减少，容易引发抑郁等不良情绪，使老年人步态减慢，损害运动功能[25]，同时，社会参与的减少也会进一步引发社会衰弱，增加肌无力的发生风险[26]。建议子女多关心独居老年人，为其提供精神慰藉，同时鼓励老年人参与社区娱乐活动，提升老年人的幸福感和获得感。
3.2.4　生活和工作条件、政策环境
月收入≤3 000元以及非职工/居民医保的老年人肌肉骨骼健康状况较差。可能是因为此类老年人在营养及饮食健康方面可选择空间比经济状况较好者低，且由于医保报销比例较低，对肌骨系统健康的重视程度不足，增加了患病风险。因此，保健人员要加强对疾病的知识宣传，提高人群对疾病的重视程度。同时，建议子女在经济范围内对低收入老年人提供经济支持。
综上所述，石河子社区老年人肌少-骨质疏松症检出率较高，社区医护人员应定期评估老年人的肌肉骨骼健康状况，对高危人群进行饮食及运动指导。社区医疗机构定期开展专科培训，以提升社区医护人员对疾病的认识，采用社区-医院联动的方式构建社区老年人肌少-骨质疏松症防治体系，为健康老龄化助力。
基金
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  • 新疆生产建设兵团科技创新人才计划(2022CB010-04)
文献
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2024年第51卷第4期
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doi: 10.20043/j.cnki.MPM.202309501
  • 接收时间：2023-09-26
  • 首发时间：2026-03-19
  • 出版时间：2024-02-25
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  • 收稿日期：2023-09-26
基金
新疆生产建设兵团科技创新人才计划(2022CB010-04)
作者信息
    1.石河子大学医学院，新疆 石河子 832000
    2. 石河子大学医学院第三附属医院

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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
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species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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