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Article(id=1322168601264636903, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322168583854080893, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.08.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750089600000, receivedDateStr=2025-06-17, revisedDate=1775145600000, revisedDateStr=2026-04-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159159246, onlineDateStr=2026-10-28, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159159246, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159159246, creator=13701087609, updateTime=1793159159246, updator=13701087609, issue=Issue{id=1322168583854080893, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='8', pageStart='1241', pageEnd='1424', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793159155095, creator='13701087609', updateTime=1793159155095, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1298, endPage=1306, ext={EN=ArticleExt(id=1322168601465963496, articleId=1322168601264636903, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction of cognitive impairment identification model for older adults in China from the perspective of health ecology, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the occurrence rate of cognitive impairment among older adults in China and its influencing factors based on the health ecological model,and to construct the identification model to provide data support and theoretical basis for the early and accurate identification of cognitive impairment in older adults.

Methods

A total of 4 204 older adults aged 60 and above from the 2018 data of the China health and retirement longitudinal study(CHARLS) were selected as the research subjects.They were randomly divided into training set and validation set at a ratio of 7∶3.LASSO regression was used to screen variables.Cognitive impairment identification model was constructed based on Logistic regression.The performance of the model was evaluated using the area under the receiver operating characteristic curve(AUC),sensitivity,and specificity.

Results

The incidence of cognitive impairment among older adults aged 60 and above in China was 19.60%.The results of the Logistic regression analysis showed that personal traits(IADL),interpersonal networks(being married,frequency of meeting with children,social participation level),work and life conditions(retirement,heating facilities of housing),and policy environment(endowment insurance,medical insurance) were protective factors of cognitive impairment in older adults(P<0.05),and personal traits(age,disability,depression),behavioral characteristics(smoking),and policy environment(central region,western region) were risk factors of cognitive impairment in older adults(P<0.05).The AUC of the training set and validation set of the elderly cognitive impairment identification model constructed were 0.81 and 0.78, respectively.

Conclusions

The incidence of cognitive impairment among older adults was relatively high.The influencing factors included five aspects:personal traits,behavioral characteristics,interpersonal networks,working and living conditions,and policy environment.The constructed cognitive impairment identification model for the elderly was helpful for achieving early and accurate identification of cognitive impairment among people aged 60 and above.

, authors=Meixian LI, Zonghua WANG, Xiaodi ZHANG, Haiwei FU, authorsList=Meixian LI, Zonghua WANG, Xiaodi ZHANG, Haiwei FU, authorCompany=null, correspAuthors=Zonghua WANG, authorNote=null, correspAuthorsNote=
WANG Zonghua， E-mail：
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目的

基于健康生态学模型探讨我国老年人认知障碍发生情况及其影响因素，并构建识别模型，为老年人认知障碍的早期精准识别提供数据支撑和理论依据。

方法

选取中国健康与养老追踪调查2018年数据中4 204名年龄≥60岁的老年人作为研究对象，依照7∶3的比例随机分为训练集与验证集。采用LASSO回归筛选变量，基于Logistic回归构建认知障碍识别模型，使用受试者工作特征曲线下面积（AUC）、灵敏度、特异度评价模型性能。

结果

我国60岁以上老年人认知障碍发生率为19.60%。Logistic回归分析结果显示，个人特质中的工具性日常生活活动能力（IADL），人际网络中的已婚、与子女见面频率、社会参与水平，工作和生活条件中的退休、住房供暖设施，政策环境中的养老保险、医疗保险是老年人认知障碍发生的保护因素（P<0.05）；个人特质中的年龄、残疾、抑郁，行为特征中的吸烟，政策环境中的中部地区、西部地区是老年人认知障碍发生的危险因素（P<0.05）。构建的老年人认知障碍识别模型训练集和验证集的AUC分别为0.81，0.78。

结论

老年人认知障碍发生率较高，影响因素涉及个人特质、行为特点、人际网络、工作和生活条件、政策环境5个层面。构建的老年人认知障碍识别模型有助于实现老年人认知障碍的早期精准识别。

, authors=黎美娴, 王宗华*, *, 张小迪, 付海炜, authorsList=黎美娴, 王宗华, 张小迪, 付海炜, authorCompany=null, correspAuthors=王宗华, authorNote=

黎美娴，硕士研究生在读

, correspAuthorsNote=
王宗华，E-mail：
WANG Zonghua， E-mail：
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city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学护理学院，重庆 400038)])], figs=[ArticleFig(id=1322530844737127388, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168601264636903, language=EN, label=Figure 1, caption=LASSO coefficient path diagram, figureFileSmall=GK/K/TgUCtf2haBfEG9BEA==, figureFileBig=VPp8rvtsrQthFU0TiJK+cA==, tableContent=null), ArticleFig(id=1322530844867150814, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168601264636903, language=CN, label=图1, caption=LASSO系数路径图, figureFileSmall=GK/K/TgUCtf2haBfEG9BEA==, figureFileBig=VPp8rvtsrQthFU0TiJK+cA==, tableContent=null), ArticleFig(id=1322530845559210975, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168601264636903, language=EN, label=Figure 2, caption=Cross-validation curve, figureFileSmall=wAbgUpaoKDdGvSzg5veZSQ==, figureFileBig=G53yimB9h5/Dezczqw03vw==, tableContent=null), ArticleFig(id=1322530845630514144, 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caption=老年人认知障碍的健康生态学模型, figureFileSmall=MfQ4Ym90k0BEZTdp7nP+Qw==, figureFileBig=2gCE7Ddvgd5JR+3VSKsDuw==, tableContent=null), ArticleFig(id=1322530847803163624, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168601264636903, language=EN, label=Table 1, caption=

Potential influencing factors of cognitive impairment based on the HEM

, figureFileSmall=null, figureFileBig=null, tableContent=
层面变量
个人特质年龄，性别，自评健康状况，慢性病共病情况，残疾，抑郁症状，基础性日常生活活动能力（BADL），工具性日常生活活动能力（IADL），身体功能（PF）
行为特点吸烟，饮酒，锻炼，睡眠时长
人际网络婚姻状况，居住地，与子女见面频率，与子女联系频率，社会参与
工作和生活条件文化程度，退休，住房自来水，住房洗澡设施，住房供暖设施，家庭年总收入，家庭年人均消费
政策环境养老保险，医疗保险，地区分布
), ArticleFig(id=1322530848113542121, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168601264636903, language=CN, label=表1, caption=

基于健康生态学模型的认知障碍可能影响因素

, figureFileSmall=null, figureFileBig=null, tableContent=
层面变量
个人特质年龄，性别，自评健康状况，慢性病共病情况，残疾，抑郁症状，基础性日常生活活动能力（BADL），工具性日常生活活动能力（IADL），身体功能（PF）
行为特点吸烟，饮酒，锻炼，睡眠时长
人际网络婚姻状况，居住地，与子女见面频率，与子女联系频率，社会参与
工作和生活条件文化程度，退休，住房自来水，住房洗澡设施，住房供暖设施，家庭年总收入，家庭年人均消费
政策环境养老保险，医疗保险，地区分布
), ArticleFig(id=1322530848197428202, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168601264636903, language=EN, label=Table 2, caption=

Comparison of the occurrence of cognitive impairment for older adults with different characteristics（n=4 204）

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类认知障碍（n=824）非认知障碍（n=3 380）统计值P
年龄（例）60~69岁4992 355χ²=30.13<0.001
70~79岁275911
≥80岁50114
性别（例）女3661 403χ²=2.300.129
男4581 977
婚姻状况（例）其他163453χ²=21.56<0.001
已婚6612 927
居住地（例）城市2161 586χ²=116.02<0.001
农村6081 794
自评健康状况（例）很差85142χ²=91.93<0.001
较差220640
一般3941 787
较好68426
很好57385
慢性病共病情况（例）无104504χ²=2.810.245
1种或2种3601 442
3种及以上3601 434
文化程度（例）文盲1381 259χ²=288.61<0.001
小学1841 117
中学及以上5021 004
退休（例）否7462 338χ²=154.69<0.001
是781 042
残疾（例）否3612 179χ²=118.21<0.001
是4631 201
抑郁症状（例）无3652 396χ²=207.81<0.001
有459984
住房自来水（例）无181530χ²=18.63<0.001
有6432 850
住房洗澡设施（例）无3841 105χ²=56.04<0.001
有4402 275
住房供暖设施（例）无7812 789χ²=77.84<0.001
有43591
吸烟（例）否5412 389χ²=7.920.005
是283991
饮酒（例）什么都不喝104504χ²=2.810.245
每个月少于1次3601 442
每个月超过1次3601 434
锻炼（例）否91211χ²=22.90<0.001
是7333169
养老保险（例）无115248χ²=36.79<0.001
有7093 132
医疗保险（例）无2940χ²=22.39<0.001
有7953 340
地区分布（例）东部1691 153χ²=73.04<0.001
中部287985
西部318960
东北部50282
每周与子女见面频率（次）3.00(1.75,5.00)4.00(3.00,6.00)Z=-10.52<0.001
每周与子女联系频率（次）5.00(4.00,6.00)5.00(5.00,6.00)Z=-9.12<0.001
社会参与（分）0.00(0.00,2.00)1.00(0.00,3.00)Z=-11.50<0.001
家庭年总收入（万元）3.94(3.75,4.15)4.06(3.85,4.31)Z=-9.55<0.001
家庭年人均消费（万元）3.93(3.57,4.38)4.34(3.83,4.78)Z=-14.26<0.001
BADL（分）17.00(16.00,18.00)18.00(16.00,18.00)Z=-5.40<0.001
IADL（分）18.00(15.00,18.00)18.00(18.00,18.00)Z=-17.21<0.001
PF（分）19.00(16.00,21.00)20.00(18.00,21.00)Z=-9.35<0.001
每日睡眠时长（h）6.00(4.50,8.00)6.00(5.00,7.50)Z=-3.33<0.001
), ArticleFig(id=1322530848507806699, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168601264636903, language=CN, label=表2, caption=

不同特征老年人认知障碍发生情况比较（n=4 204）

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类认知障碍（n=824）非认知障碍（n=3 380）统计值P
年龄（例）60~69岁4992 355χ²=30.13<0.001
70~79岁275911
≥80岁50114
性别（例）女3661 403χ²=2.300.129
男4581 977
婚姻状况（例）其他163453χ²=21.56<0.001
已婚6612 927
居住地（例）城市2161 586χ²=116.02<0.001
农村6081 794
自评健康状况（例）很差85142χ²=91.93<0.001
较差220640
一般3941 787
较好68426
很好57385
慢性病共病情况（例）无104504χ²=2.810.245
1种或2种3601 442
3种及以上3601 434
文化程度（例）文盲1381 259χ²=288.61<0.001
小学1841 117
中学及以上5021 004
退休（例）否7462 338χ²=154.69<0.001
是781 042
残疾（例）否3612 179χ²=118.21<0.001
是4631 201
抑郁症状（例）无3652 396χ²=207.81<0.001
有459984
住房自来水（例）无181530χ²=18.63<0.001
有6432 850
住房洗澡设施（例）无3841 105χ²=56.04<0.001
有4402 275
住房供暖设施（例）无7812 789χ²=77.84<0.001
有43591
吸烟（例）否5412 389χ²=7.920.005
是283991
饮酒（例）什么都不喝104504χ²=2.810.245
每个月少于1次3601 442
每个月超过1次3601 434
锻炼（例）否91211χ²=22.90<0.001
是7333169
养老保险（例）无115248χ²=36.79<0.001
有7093 132
医疗保险（例）无2940χ²=22.39<0.001
有7953 340
地区分布（例）东部1691 153χ²=73.04<0.001
中部287985
西部318960
东北部50282
每周与子女见面频率（次）3.00(1.75,5.00)4.00(3.00,6.00)Z=-10.52<0.001
每周与子女联系频率（次）5.00(4.00,6.00)5.00(5.00,6.00)Z=-9.12<0.001
社会参与（分）0.00(0.00,2.00)1.00(0.00,3.00)Z=-11.50<0.001
家庭年总收入（万元）3.94(3.75,4.15)4.06(3.85,4.31)Z=-9.55<0.001
家庭年人均消费（万元）3.93(3.57,4.38)4.34(3.83,4.78)Z=-14.26<0.001
BADL（分）17.00(16.00,18.00)18.00(16.00,18.00)Z=-5.40<0.001
IADL（分）18.00(15.00,18.00)18.00(18.00,18.00)Z=-17.21<0.001
PF（分）19.00(16.00,21.00)20.00(18.00,21.00)Z=-9.35<0.001
每日睡眠时长（h）6.00(4.50,8.00)6.00(5.00,7.50)Z=-3.33<0.001
), ArticleFig(id=1322530848608469996, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168601264636903, language=EN, label=Table 3, caption=

Logistic regression analysis of factors affecting cognitive impairment among older adults in the training set（n=2 942）

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类回归系数标准误Z值POR值[95%CI]
年龄60~69岁
70~79岁0.230.121.960.0501.26[1.01,1.60]
≥80岁0.820.273.040.0022.27[1.34,3.84]
残疾否
是0.280.112.610.0091.33[1.07,1.64]
抑郁症状无
有0.590.115.35<0.0011.80[1.45,2.23]
IADL-0.240.02-10.09<0.0010.79[0.75,0.83]
吸烟否
是0.380.113.36<0.0011.46[1.17,1.82]
婚姻状况其他
已婚-0.320.14-2.250.0240.72[0.55,0.96]
居住地城市
农村0.230.131.800.0711.25[0.98,1.61]
每周与子女见面频率-0.100.02-4.14<0.0010.91[0.87,0.95]
社会参与-0.200.03-6.46<0.0010.82[0.77,0.87]
退休否
是-0.650.17-3.74<0.0010.52[0.37,0.73]
住房供暖设施无
有-0.870.22-3.89<0.0010.42[0.27,0.65]
家庭年人均消费-0.230.15-1.570.1170.79[0.59,1.06]
养老保险无
有-0.730.16-4.57<0.0010.48[0.35,0.66]
医疗保险无
有-0.890.34-2.590.0100.41[0.21,0.80]
地区分布东部
中部0.670.144.69<0.0011.96[1.48,2.59]
西部0.680.144.79<0.0011.97[1.49,2.59]
东北部0.320.231.390.1631.38[0.88,2.15]
), ArticleFig(id=1322530848679773165, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168601264636903, language=CN, label=表3, caption=

训练集老年人认知障碍影响因素的Logistic回归分析（n=2 942）

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类回归系数标准误Z值POR值[95%CI]
年龄60~69岁
70~79岁0.230.121.960.0501.26[1.01,1.60]
≥80岁0.820.273.040.0022.27[1.34,3.84]
残疾否
是0.280.112.610.0091.33[1.07,1.64]
抑郁症状无
有0.590.115.35<0.0011.80[1.45,2.23]
IADL-0.240.02-10.09<0.0010.79[0.75,0.83]
吸烟否
是0.380.113.36<0.0011.46[1.17,1.82]
婚姻状况其他
已婚-0.320.14-2.250.0240.72[0.55,0.96]
居住地城市
农村0.230.131.800.0711.25[0.98,1.61]
每周与子女见面频率-0.100.02-4.14<0.0010.91[0.87,0.95]
社会参与-0.200.03-6.46<0.0010.82[0.77,0.87]
退休否
是-0.650.17-3.74<0.0010.52[0.37,0.73]
住房供暖设施无
有-0.870.22-3.89<0.0010.42[0.27,0.65]
家庭年人均消费-0.230.15-1.570.1170.79[0.59,1.06]
养老保险无
有-0.730.16-4.57<0.0010.48[0.35,0.66]
医疗保险无
有-0.890.34-2.590.0100.41[0.21,0.80]
地区分布东部
中部0.670.144.69<0.0011.96[1.48,2.59]
西部0.680.144.79<0.0011.97[1.49,2.59]
东北部0.320.231.390.1631.38[0.88,2.15]
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健康生态学视角下我国老年人认知障碍识别模型的构建
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黎美娴 , 王宗华 *, * , 张小迪 , 付海炜
护理研究 | 科研论著 2026,40(8): 1298-1306
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护理研究 |科研论著 2026 , 40 (8) : 1298 -1306
健康生态学视角下我国老年人认知障碍识别模型的构建
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黎美娴, 王宗华*, * , 张小迪, 付海炜
作者信息
  • 1陆军军医大学护理学院，重庆 400038
通讯作者:
王宗华，E-mail：
WANG Zonghua， E-mail：
作者简介:

黎美娴，硕士研究生在读

Construction of cognitive impairment identification model for older adults in China from the perspective of health ecology
Meixian LI, Zonghua WANG , Xiaodi ZHANG, Haiwei FU
Affiliations
  • 1School of Nursing， Army Medical University, Chongqing 400038 China
    • Corresponding Author:
    WANG Zonghua， E-mail：
出版时间: 2026-04-25 doi: 10.12102/j.issn.1009-6493.2026.08.008
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目的

基于健康生态学模型探讨我国老年人认知障碍发生情况及其影响因素，并构建识别模型，为老年人认知障碍的早期精准识别提供数据支撑和理论依据。

方法

选取中国健康与养老追踪调查2018年数据中4 204名年龄≥60岁的老年人作为研究对象，依照7∶3的比例随机分为训练集与验证集。采用LASSO回归筛选变量，基于Logistic回归构建认知障碍识别模型，使用受试者工作特征曲线下面积（AUC）、灵敏度、特异度评价模型性能。

结果

我国60岁以上老年人认知障碍发生率为19.60%。Logistic回归分析结果显示，个人特质中的工具性日常生活活动能力（IADL），人际网络中的已婚、与子女见面频率、社会参与水平，工作和生活条件中的退休、住房供暖设施，政策环境中的养老保险、医疗保险是老年人认知障碍发生的保护因素（P<0.05）；个人特质中的年龄、残疾、抑郁，行为特征中的吸烟，政策环境中的中部地区、西部地区是老年人认知障碍发生的危险因素（P<0.05）。构建的老年人认知障碍识别模型训练集和验证集的AUC分别为0.81，0.78。

结论

老年人认知障碍发生率较高，影响因素涉及个人特质、行为特点、人际网络、工作和生活条件、政策环境5个层面。构建的老年人认知障碍识别模型有助于实现老年人认知障碍的早期精准识别。

老年人  /  认知障碍  /  健康生态学  /  影响因素
Objective

To explore the occurrence rate of cognitive impairment among older adults in China and its influencing factors based on the health ecological model,and to construct the identification model to provide data support and theoretical basis for the early and accurate identification of cognitive impairment in older adults.

Methods

A total of 4 204 older adults aged 60 and above from the 2018 data of the China health and retirement longitudinal study(CHARLS) were selected as the research subjects.They were randomly divided into training set and validation set at a ratio of 7∶3.LASSO regression was used to screen variables.Cognitive impairment identification model was constructed based on Logistic regression.The performance of the model was evaluated using the area under the receiver operating characteristic curve(AUC),sensitivity,and specificity.

Results

The incidence of cognitive impairment among older adults aged 60 and above in China was 19.60%.The results of the Logistic regression analysis showed that personal traits(IADL),interpersonal networks(being married,frequency of meeting with children,social participation level),work and life conditions(retirement,heating facilities of housing),and policy environment(endowment insurance,medical insurance) were protective factors of cognitive impairment in older adults(P<0.05),and personal traits(age,disability,depression),behavioral characteristics(smoking),and policy environment(central region,western region) were risk factors of cognitive impairment in older adults(P<0.05).The AUC of the training set and validation set of the elderly cognitive impairment identification model constructed were 0.81 and 0.78, respectively.

Conclusions

The incidence of cognitive impairment among older adults was relatively high.The influencing factors included five aspects:personal traits,behavioral characteristics,interpersonal networks,working and living conditions,and policy environment.The constructed cognitive impairment identification model for the elderly was helpful for achieving early and accurate identification of cognitive impairment among people aged 60 and above.

older adults  /  cognitive impairment  /  health ecology  /  influencing factors
黎美娴, 王宗华, 张小迪, 付海炜. 健康生态学视角下我国老年人认知障碍识别模型的构建. 护理研究, 2026 , 40 (8) : 1298 -1306 . DOI: 10.12102/j.issn.1009-6493.2026.08.008
Meixian LI, Zonghua WANG, Xiaodi ZHANG, Haiwei FU. Construction of cognitive impairment identification model for older adults in China from the perspective of health ecology[J]. Chinese Nursing Research, 2026 , 40 (8) : 1298 -1306 . DOI: 10.12102/j.issn.1009-6493.2026.08.008
正文
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随着我国人口老龄化进程加剧，预计到2040年，60岁及以上老年人口将占到总人口的28%[1]，认知障碍（cognitive impairment,CI）问题日趋严重。研究表明，我国老年群体中认知障碍患病率达22.0%[2]，主要表现为个性改变及记忆力、执行力、注意力等认知功能损害[3]，严重影响老年人日常活动、心理健康及整体生活质量，增加痴呆进展风险，给个人、家庭乃至社会带来沉重负担。《应对老年期痴呆国家行动计划（2024—2030年）》强调，需加强老年人认知功能的早期筛查和识别，并鼓励对高风险老年群体实施早期干预，要求到2030年认知功能初筛阳性人群干预指导率≥80%。认知障碍的病因复杂、影响因素众多，受年龄、文化程度、吸烟、饮酒、社交活跃度、抑郁等[4]多因素影响。既往研究多停留于对单层次因素的解释[5-6]，忽略了工作、生活条件等环境因素[7]，且基于理论指导的多层次、多维度视角的综合研究较为缺乏。目前，国内虽已有一些研究构建了老年人认知障碍的识别模型[8-9]，但是这些研究多为样本量较少的单中心研究或模型适用人群局限于特定区域，不利于认知障碍的早期精准识别。健康生态学模型（health ecological model,HEM）将健康影响因素划分为个人特质、行为特点、人际网络、工作和生活条件、政策环境5个层面，该模型强调健康的形成并非单一因素的作用，而是个体内在因素和外部环境因素相互渗透、相互作用的结果[7,10]。因此，本研究以中国健康与养老追踪调查（China health and retirement longitudinal study,CHARLS）数据为依托，基于健康生态学模型，从5个层面多层次、全方位、系统地探讨我国老年人认知障碍的影响因素，构建一种简便易行、准确度高的认知障碍识别模型，实现对老年人认知障碍发生的早筛查、早识别、早干预，为延缓认知障碍向痴呆进展、推动健康老龄化提供依据。
1　对象与方法
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1.1　对象
数据来源于2018年CHARLS调查，共19 816人参与调查。纳入标准：年龄≥60岁。排除标准：未回答简易精神状态检查（Mini-Mental State Examination,MMSE）量表问题；关键变量信息缺失。最终确定4 204名老年人纳入研究。本研究前期已获得CHARLS数据使用授权，主要住户调查（包括人体测量学）经北京大学生物医学伦理委员会批准（编号：IRB00001052-11015）。
1.2　研究指标及工具
1.2.1　因变量
本研究以是否发生认知障碍作为因变量。在2018年的CHARLS调查中，研究人员采用了全版MMSE对调查对象的认知功能进行评估，量表共30题，答对计1分，答错、不知道或拒答计0分，总分为0~30分，得分越高说明认知功能越好。不同文化程度人群采用不同的评定标准：文盲≤17分、小学≤20分、中学及以上≤24分判定为认知障碍。
1.2.2　自变量
根据健康生态学模型的5个层面内容并结合文献综述，梳理数据库中认知障碍发生的可能影响因素，进一步通过专家咨询、小组讨论以及2轮课题组论证后确定本研究纳入的变量，见表1。
1.2.2.1　残疾评估
通过问卷中5项健康问题对残疾状况进行界定，若存在以下其中1项则被认定为残疾：躯体残疾、大脑受损或智力缺陷、失明或半失明、聋或半聋、哑或严重口吃。
1.2.2.2　抑郁症状评估
采用流调中心抑郁评定量表（Center for Epidemiological Studies Depression Scale 10,CES-D10）[11]进行评估，该量表包含10个条目，主要反映调查对象在过去1周经历的抑郁症状。各条目根据症状出现频率计分，“很少或者从未有”“不太多”“有时或者半数时间”“大多数时间”依次赋值0、1、2、3分，其中条目5和条目8反向计分。总分为0~30分，得分越高表示抑郁症状越严重，≥10分为有抑郁症状，0~9分为无抑郁症状。
1.2.2.3　身体基本活动能力评估
从BADL、IADL及PF 3个部分进行评估。1）BADL包括自主穿衣、洗澡、进食、上下床、如厕、控制大小便6个指标。2）IADL包括自主做家务、准备餐食、购物、打电话、服药、管理财务6个指标[12]。3）PF参照健康调查简表（SF-36）[13]中的9个指标：跑步或慢跑1 km；走1 km；走100 m；久坐后站起来；连续爬几层楼；弯腰、屈膝或蹲下；手臂沿肩向上伸展；提10 kg重物；从桌上拿起1枚小硬币。条目按照Likert 4级评分进行赋值，“没有困难”计3分，“有困难但仍可以完成”计2分，“有困难，需要帮助”计1分，“无法完成”计0分，BADL和IADL总分为0~18分，PF总分为0~27分，得分越高，表示个体的身体基本活动能力越强。
1.2.2.4　社会参与评估
通过问题“过去1个月是否进行了以下社交活动？”对11种社交活动进行评价：跟朋友交往；棋牌活动；向他人提供帮助；舞蹈、健身等运动；社团活动；志愿者活动；照护病人；参加培训课程；炒股；上网；其他社交活动。各项活动调查对象选择“是”计1分，“否”计0分。随后询问调查对象过去1个月参与上述活动频率并赋值，“不经常”计1分，“差不多每周”计2分，“差不多每天”计3分。各类活动与其频率的乘积之和为社会参与得分，总分为0~33分，得分越高，社会参与水平越高[14]。
1.2.2.5　家庭年总收入/家庭年人均消费评估
采用过去1年家庭总收入/家庭年人均消费金额，取10的对数表示。
1.3　统计学方法
采用Stata 16.0、SPSS 25.0和R 4.4.2软件对数据进行清洗、整理和分析。缺失值使用Python 3.8.0进行多重插补，并运用合成少数类过采样技术（SMOTE）实现数据均衡。经正态性检验，定量资料数据呈偏态分布，故采用中位数（四分位数）[M（P25,P75)]进行统计描述，通过Mann-Whitney U检验进行组间差异比较。定性资料采用频数、百分比（%）表示，组间差异比较采用χ2检验或Fisher精确概率法。将数据按7∶3比例随机分为训练集（n=2 942）和验证集（n=1 262），用R 4.2.2软件的“glmnet”包进行LASSO回归以筛选变量。使用“rms”包基于筛选出的变量进行多因素Logistic回归分析，去除方差膨胀系数（VIF）小于5的变量后进一步构建识别模型。使用Bootstrap重复取样（1 000次）法对该模型进行内部验证以获取最优参数。模型性能评估采用精确率、灵敏度、特异度以及受试者工作特征（ROC）曲线下面积（AUC）、校准曲线等多项指标。采用双侧检验，检验水准α=0.05。
2　结果
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2.1　老年人认知障碍发生情况及单因素分析
研究对象以60~69岁的老年人为主，占67.89%，其中男2 435人（57.92%），女1 769人（42.08%）；824名老年人存在认知障碍，认知障碍检出率为19.60%。单因素分析结果见表2。
2.2　老年人认知障碍识别模型的构建
将研究变量纳入LASSO回归进行分析，筛选出具有非零系数的变量，通过十折交叉验证选择交叉验证误差为λmin+1个标准误（lambda.1se）时的λ值（右侧垂直虚线）为模型最优值，对应变量数目为20个，见图1、图2。将LASSO回归筛选出的变量进行多因素Logistic回归分析。运用双向逐步回归法构建基于Logistic回归的老年人认知障碍识别模型，分析结果显示，个人特质中IADL强，人际网络中已婚、与子女见面频率高、社会参与水平高，工作和生活条件中退休、住房有供暖设施，政策环境中有养老保险、有医疗保险等是老年人认知障碍发生的保护因素（P<0.05）；个人特质中年龄80岁及以上、残疾、有抑郁症状，行为特征中吸烟，政策环境中的中部地区、西部地区等是认知障碍发生的危险因素（P<0.05）。Logistic回归分析结果见表3。内部验证结果显示最佳阈值点取0.196，在训练集和验证集的精确率分别为0.72和0.68，灵敏度分别为0.71和0.67，特异度分别为0.73和0.70，AUC分别为0.81（0.79，0.82）和0.78（0.75，0.81）。模型具有较好的区分度、拟合度和识别性能，训练集和验证集的ROC曲线见图3；Hosmer-Lemeshow检验结果分别为P=0.056和P=0.103，校准曲线见图4。
2.3　建立老年人认知障碍的健康生态学模型
依据多因素Logistic回归分析结果构建我国老年人认知障碍的健康生态学模型。第1层为个人特质层，包括年龄、残疾、抑郁症状以及IADL；第2层为行为特点层，包括吸烟；第3层为人际网络层，包括婚姻状况、居住地、与子女见面频率以及社会参与；第4层为生活和工作条件层，包括退休、住房供暖设施和家庭年人均消费；第5层为政策环境层，包括养老保险、医疗保险以及地区分布，见图5。
3　讨论
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3.1　老年人认知障碍识别模型具有准确性和实用性
本研究结果显示，我国60岁以上老年人认知障碍发生率为19.60%，与施正丽等[15]研究结果（20.05%）相似。本研究构建的认知障碍识别模型表现出良好的区分度与校准性能（Hosmer-Lemeshow检验P>0.05）。娄方丽等[16]运用Logistic回归模型构建的中老年人认知障碍的心血管指标联合识别模型有较好的识别性能（AUC=0.686），但该模型构建过程中纳入了较多的血液指标，限制了其外推性。Song等[17]建立多种机器学习模型对中国老年人轻度认知障碍进行识别，各模型的AUC为0.71~0.77，具有良好的区分度，但模型复杂度可能在基层医疗单位的推广应用存在一定挑战。相较而言，本研究通过LASSO回归筛选变量并结合多因素Logistic回归分析，在训练集保持较高区分度（AUC=0.81）的同时简化了模型结构，具有简单易行、准确无创等优势，更契合基层医疗资源有限的实际需求。但需要指出的是，验证集的区分度（AUC=0.78）略低于训练集，分析可能的原因是存在轻微过拟合，需下一步通过外部数据验证和优化。
3.2　个人特质是老年人发生认知障碍的基础影响因素
从健康生态学模型各层面关系来看，个人特质是先天内在属性和基础影响因素，其中年龄是核心危险因素[4]，80岁及以上老年人认知障碍发生风险高于60~69岁老年人，与既往研究结果[15]相似。分析原因可能是随着年龄增长，大脑海马体和皮质区域呈进行性萎缩，导致神经元功能减退从而发生认知障碍[18]。
残疾状况是评估老年人健康水平的基本指标[19]。本研究发现残疾是认知障碍发生的危险因素，这与既往研究结果[20-21]一致。Zhang等[20]研究发现，身体功能障碍的老年人面临认知障碍的风险增加，同时认知障碍也加速了身体残疾的进展。原因可能是患有残疾的老年人身体功能受损导致个体机能退化，日常生活基本活动的依赖增强，从而使人际关系网络局限到家庭、社会参与减少，增加认知障碍发生风险[21]。
抑郁症状是认知障碍发生的危险因素，与魏霞霞等[22]研究结果一致。有抑郁症状的人更容易发生认知障碍，抑郁等负面情绪产生增加，使脑细胞长期处于不良的非活跃状态，从而导致认知功能下降[23]。研究表明，抑郁与认知障碍二者之间可能存在共同的影响因素及病理生理机制[24-25]，可在一定程度上解释抑郁与认知障碍之间的关系。有研究认为，炎症生物标志物如C反应蛋白、白介素-6、肿瘤坏死因子等可能通过神经毒性作用，导致认知功能下降[26]，并诱发抑郁。有研究认为，慢性低水平炎症对认知功能的损害机制可能涉及氧化应激损伤、谷氨酸递质释放失衡引发的兴奋性毒性以及5-羟色胺转运蛋白活性异常等[27]。
IADL是独立生活能力的体现，其损害是认知障碍向痴呆转化的预示[4]。IADL是认知障碍的保护因素，可能通过刺激大脑细胞而使大脑功能处于活跃状态[28]。也有研究发现，IADL功能保留对认知储备有补偿作用[29]。分析可能原因是IADL通过维持认知储备、延缓神经退行性病变从而减少认知障碍的发生。
3.3　行为特点、人际网络是老年人发生认知障碍的关键可调控因素
吸烟作为认知障碍的危险因素，与多项研究结果[8,15,30]一致，吸烟可能通过作用于胆碱能系统，诱导氧化应激和炎症反应导致神经病理改变从而增加认知障碍的发生风险[31]。且有研究表明，当前吸烟者往往比过去吸烟者或非吸烟者更有可能出现比预期更差的认知功能[32]。已婚、与子女见面频率以及社会参与的保护作用体现了人际网络中社会纽带对认知衰退的缓冲机制。神经生物学研究表明，亲子互动以及社会参与和大脑完整性和认知的整体标志物密切相关，可通过激活默认模式网络增强认知弹性[33-34]。Song等[32]提出，社会活动会增加认知储备并减弱神经病理学的影响。Chen等[6]研究发现，高社交活跃度尤其是参与团体活动可延迟痴呆发病近5年。分析原因可能为社会支持和社会参与可通过影响生活方式、行为习惯等降低认知障碍发生风险。
3.4　工作和生活条件、政策环境是老年人发生认知障碍的重要影响因素
本研究结果显示，退休状态与有供暖设施对认知功能具有保护作用，提示工作和生活条件与政策保障的重要性。退休后的老年人在一定程度上可能通过增加社交活动而改善认知，也有可能是工作相关认知负荷的解除对老年人脑功能的代偿性修复[32]。另一方面，环境温度对脑血管自动调节功能有一定影响，长期寒冷刺激可能引发神经元受损而导致认知功能下降[35]。养老保险与医疗保险的保护作用验证了社会保障制度的意义，分析原因可能是参保老年人相较于未参保老年人往往社会经济地位更高，接触的医疗诊疗、预防保健服务更便利。地区差异则反映区域经济发展水平影响老年人认知障碍的发生，经济发展不平衡是影响健康公平的重要因素[7]，处于经济高水平发展地区的老年人更易获得更丰富的医疗资源。
4　小结
收起
综上所述，我国老年人认知障碍发生率较高，影响因素涉及健康生态学模型的5个层面，本研究为我国老年人认知障碍发生的识别提供了参考，多层次、多维度地提出科学筛查和干预措施。医疗机构和社区管理部门应加强认知障碍风险评估和干预措施，增强健康管理并提高生活保障、促进家庭支持和社会参与，特别关注60岁及以上残疾、抑郁老年人，优先推进惠民政策的普及，如医保覆盖以及针对中西部地区的认知筛查计划等。本研究的局限性：一是基于CHARLS 2018年的横断面数据，不能揭示因果时序关系；二是部分变量依赖调查对象的主观报告，可能会有回忆偏倚等问题；三是CHARLS数据的限制以及健康生态学模型的复杂性可能导致本研究在选取变量时对影响因素纳入不足，且缺乏预试验设计、再调查实施的研究步骤。下一步将采用纵向追踪设计，探讨变量间因果关系，并遵循理论建构、试验设计、调查实施研究路径开展实证研究。

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doi: 10.12102/j.issn.1009-6493.2026.08.008
  • 接收时间：2025-06-17
  • 首发时间：2026-10-28
  • 出版时间：2026-04-25
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  • 收稿日期：2025-06-17
  • 修回日期：2026-04-03
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    1陆军军医大学护理学院，重庆 400038

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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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