A comparison of the Mini-Mental State Examination (MMSE) with the Montreal Cognitive Assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study

被引:548
作者
Jia, Xiaofang [1 ]
Wang, Zhihong [1 ]
Huang, Feifei [1 ]
Su, Chang [1 ]
Du, Wenwen [1 ]
Jiang, Hongru [1 ]
Wang, Huijun [1 ]
Wang, Jiaqi [2 ]
Wang, Fangjun [3 ]
Su, Weiwu [4 ]
Xiao, Huifang [5 ]
Wang, Yanxin [6 ]
Zhang, Bing [1 ]
机构
[1] Chinese Ctr Dis Control & Prevent, Natl Inst Nutr & Hlth, Beijing 100050, Peoples R China
[2] Hebei Med Univ, Sch Publ Hlth, Shijiazhuang 050017, Hebei, Peoples R China
[3] Yongkang Ctr Dis Control & Prevent, Yongkang 321300, Peoples R China
[4] Yuelu Dist Ctr Dis Control & Prevent, Changsha 410013, Peoples R China
[5] Changde Ctr Dis Control & Prevent, Changde 415000, Peoples R China
[6] Shaanxi Prov Ctr Dis Control & Prevent, Xian 710054, Peoples R China
关键词
Mild cognitive impairment; MMSE; MoCA; Correlation; Agreement; Risk factors; BAHASA MALAYSIA; RISK-FACTORS; PREVALENCE; DEMENTIA; VERSION; MCI; VALIDATION; DEPRESSION; MANAGEMENT; SUBTYPES;
D O I
10.1186/s12888-021-03495-6
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Background The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are the most commonly used scales to detect mild cognitive impairment (MCI) in population-based epidemiologic studies. However, their comparison on which is best suited to assess cognition is scarce in samples from multiple regions of China. Methods We conducted a cross-sectional analysis of 4923 adults aged >= 55 years from the Community-based Cohort Study on Nervous System Diseases. Objective cognition was assessed by Chinese versions of MMSE and MoCA, and total score and subscores of cognitive domains were calculated for each. Education-specific cutoffs of total score were used to diagnose MCI. Demographic and health-related characteristics were collected by questionnaires. Correlation and agreement for MCI between MMSE and MoCA were analyzed; group differences in cognition were evaluated; and multiple logistic regression model was used to clarify risk factors for MCI. Results The overall MCI prevalence was 28.6% for MMSE and 36.2% for MoCA. MMSE had good correlation with MoCA (Spearman correlation coefficient = 0.8374, p < 0.0001) and moderate agreement for detecting MCI with Kappa value of 0.5973 (p < 0.0001). Ceiling effect for MCI was less frequent using MoCA versus MMSE according to the distribution of total score. Percentage of relative standard deviation, the measure of inter-individual variance, for MoCA (26.9%) was greater than for MMSE (19.0%) overall (p < 0.0001). Increasing age (MMSE: OR = 2.073 for >= 75 years; MoCA: OR = 1.869 for >= 75 years), female (OR = 1.280 for MMSE; OR = 1.163 for MoCA), living in county town (OR = 1.386 and 1.862 for MMSE and MoCA, respectively) or village (OR = 2.579 and 2.721 for MMSE and MoCA, respectively), smoking (OR = 1.373 and 1.288 for MMSE and MoCA, respectively), hypertension (MMSE: OR = 1.278; MoCA: OR = 1.208) and depression (MMSE: OR = 1.465; MoCA: OR = 1.350) were independently associated with greater likelihood of MCI compared to corresponding reference group in both scales (all p < 0.05). Conclusions MoCA is a better measure of cognitive function due to lack of ceiling effect and with good detection of cognitive heterogeneity. MCI prevalence is higher using MoCA compared to MMSE. Both tools identify concordantly modifiable factors for MCI, which provide important evidence for establishing intervention measures.
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