Dement Geriatr Cogn Disord 2015;39:176–185 DOI: 10.1159/000368827 Accepted: October 5, 2014 Published online: January 6, 2015
© 2015 S. Karger AG, Basel 1420–8008/14/0394–0176$39.50/0 www.karger.com/dem
Original Research Article
Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore Tze Pin Ng a Lei Feng a Wee Shiong Lim b Mei Sian Chong b Tih Shih Lee c Keng Bee Yap d Tung Tsoi a Tau Ming Liew e Qi Gao a Simon Collinson f Nagaendran Kandiah g Philip Yap e a
Gerontology Research Programme, Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, b Geriatric Medicine Department, Tan Tock Seng Hospital, Ministry of Health, c Duke-NUS Graduate Medical School, d Department of Geriatric Medicine, Alexandra Hospital, e Geriatric Medicine Department, Khoo Teck Puat Hospital, Ministry of Health, f Department of Psychology, National University of Singapore, and g National Neuroscience Centre, Tan Tock Seng Hospital, Singapore
Key Words Cognitive screening · Mild neurocognitive disorder · Variation in test performance and scores · Montreal Cognitive Assessment Abstract Background: The Montreal Cognitive Assessment (MoCA) was developed as a screening instrument for mild cognitive impairment (MCI). We evaluated the MoCA’s test performance by educational groups among older Singaporean Chinese adults. Method: The MoCA and Mini-Mental State Examination (MMSE) were evaluated in two independent studies (clinic-based sample and community-based sample) of MCI and normal cognition (NC) controls, using receiver operating characteristic curve analyses: area under the curve (AUC), sensitivity (Sn), and specificity (Sp). Results: The MoCA modestly discriminated MCI from NC in both study samples (AUC = 0.63 and 0.65): Sn = 0.64 and Sp = 0.36 at a cut-off of 28/29 in the clinic-based sample, and Sn = 0.65 and Sp = 0.55 at a cut-off of 22/23 in the community-based sample. The MoCA’s test performance was least satisfactory in the highest (>6 years) education group: AUC = 0.50 (p = 0.98), Sn = 0.54, and Sp = 0.51 at a cut-off of 27/28. Overall, the MoCA’s test performance was not better than that of the MMSE. In multivariate analyses controlling for age and gender, MCI diagnosis was associated with a 5 on the modified Geriatric Depression Scale, were excluded from the study. For the purpose of comparison with prior studies among Chinese populations, a small number of non-Chinese subjects were also excluded from the study. The GRP sample was a clinic- and population-based sample of Chinese patients with a diagnosis of MCI from the memory clinics at Khoo Teck Puat Hospital (principal investigator: P. Yap) and community-living participants in the population-based Singapore Longitudinal Ageing Studies (SLAS; principal investigator: T.P. Ng) who were diagnosed with MCI. Patients with MCI at the memory clinics at Khoo Teck Puat Hospital had a CDR of 0.5 (‘questionable dementia’), and the diagnosis was similarly made based on a comprehensive clinical assessment including clinical examination, neuroimaging, blood tests, and bedside cognitive tests: the Frontal Assessment Battery and Clock Drawing Task, or standardized neuropsychological assessments of attention, memory, language, visual-spatial and constructional abilities, and executive function (in 25% of the patients). Community-living MCI subjects in the SLAS cohort were identified from initial screening with the MMSE (score ≤26). Their CDR values were subsequently evaluated (0.5) [29], and a full neuropsychological assessment with a comprehensive test battery was conducted (Digit Span, Rey Auditory Verbal Learning Test, Story Memory, Brief Visuospatial Memory Test-Revised, Boston Naming Test, Color Trails Tests 1 and 2, and Block Design) as well as a clinical diagnostic workup [including clinical examination, relevant blood measurements, and brain imaging (MRI)]. Individuals were diagnosed with MCI after a consensus review of the clinical diagnostic assessment results by a three-member panel of geriatricians and psychiatrists, based on Petersen’s [28] criteria (memory complaint usually corroborated by an informant, objective memory impairment for age, essentially preserved general cognitive function, largely intact functional activities, and not demented). Normal cognition (NC) subjects in the GRP sample comprised Chinese age- and sex-matched SLAS participants who had a CDR of 0 and no history of significant head injury, stroke, or evidence of cerebrovascular disease (Hachinski score >4), any other neurological disease, systemic illness, or medical conditions that may affect cognitive functioning and activities of daily living. They were free of clinically important depressive symptoms (a score of ≥4 on the 15-item Geriatric Depression Scale) or other psychiatric and substance-related disorders which affect cognitive functioning. They had not used long-acting benzodiazepines or barbiturates within the past 2 years. In both study samples, the MoCA was administered independently of the clinical assessment. We used a version of the MoCA that had been modified for Singaporeans by a senior psychologist and a specialist neurologist providing dementia care, with test items adapted in consultation with the original MoCA developers [8]. The translation and back-translation of the Chinese and Malay versions of the MoCA were undertaken by bilingual psychologists, and equivalent versions of the MoCA in English, Chinese, and Malay were established. Statistical Analyses Receiver operating characteristic curve analysis was used to evaluate the performance of the MMSE and MoCA in discriminating MCI from NC subjects. AUCs, Sn, and Sp were reported for the whole sample and three subgroups, depending on length of education: no education, 1–6 years of education, and >6 years of education. The differences between the educational groups were analyzed using independent t tests for continuous dependent variables and the χ2 test for categorical dependent variables. The relative effect sizes of age, gender, education, and MCI on variations in MoCA scores was estimated using analysis of variance in general linear modelling. The IBM SPSS Statistics 20 software for Windows (IBM SPSS Inc., Chicago, Ill., USA) was used for all data analyses.
Results
The demographic characteristics of all MCI and NC subjects in the NNI and the GRP study populations are shown in table 1. Notably, the NNI study participants were younger and received more years of formal education, with correspondingly higher overall mean MoCA and MMSE scores than the participants in the GRP sample. In each of the two samples, the mean MoCA scores of the MCI subjects were significantly lower than those of the NC subjects:
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Dement Geriatr Cogn Disord 2015;39:176–185 © 2015 S. Karger AG, Basel www.karger.com/dem
DOI: 10.1159/000368827
Ng et al.: Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
Table 1. MoCA and MMSE scores of MCI and NC subjects as well as test performance by study samples
GRP study
Participants Females, % Mean age ± SD, years Mean education ± SD, years Mean MoCA score ± SD Mean MMSE score ± SD MoCA AUC ± SE Cut-off Sensitivity Specificity MMSE AUC ± SE Cut-off Sensitivity Specificity
NNI study
MCI
NC
p
MCI
NC
p
62 56.5 70.8 ± 6.8 4.6 ± 4.4 20.3 ± 4.8 25.5 ± 3.1
176 59.1 69.5 ± 7.4 5.1 ± 3.8 22.4 ± 3.9 27.1 ± 2.7
0.72 0.21 0.42 0.001 0.001
45 42.2 62.0 ± 9.3 10.6 ± 4.5 27.0 ± 3.0 28.1 ± 2.2
53 60.4 58.7 ± 7.0 12.1 ± 3.2 28.5 ± 1.5 29.2 ± 0.9
0.07 0.06 0.07 0.002 0.001
0.63 ± 0.04 22/23 0.65 0.55
0.003a
0.65 ± 0.06 28/29 0.64 0.36
0.008a
0.67 ± 0.04 27/28 0.71 0.56
0.001a
0.64 ± 0.06 29/30 0.767 0.33
0.016a
SE = Standard error. a Null hypothesis: true area = 0.5.
NNI population sample
1.0
1.0
0.8
0.8 Sensitivity
Sensitivity
SLAS population sample
0.6 0.4 0.2
MoCA MMSE
0.4 0.2
0
Fig. 1. Receiver operating characteristic curve of MMSE and MoCA by study samples.
0.6
0 0
0.2 0.4 0.6 0.8 1 – specificity
1.0
0
0.2 0.4 0.6 0.8 1 – specificity
1.0
they were approximately 2 points lower in the GRP sample and 1.5 points lower in the NNI sample. However, the overall AUCs for discriminating MCI from NC subjects were modest in both the NNI (0.63) and the GRP samples (0.65), with Sn and Sp below 0.70 in the NNI sample (Sn = 0.64 and Sp = 0.36 at an optimum cut-off of 28/29) and in the GRP sample (Sn = 0.65 and Sp = 0.55 at an optimum cut-off of 22/23). These test performance parameters were not better than those for the MMSE (fig. 1). We pooled the data from the two samples and evaluated the test performance parameters of the MoCA and MMSE by educational groups (table 2). The MoCA’s test performance was least satisfactory in the group with the longest education (>6 years). At a higher optimum cut-off (27/28), it had the lowest Sn (0.54) and Sp (0.51), and its AUC of 0.50 was not statistically significantly different (p = 0.98) from the null value of 0.50. On the other hand, the test performance of the MMSE was poorest in the group with no education (AUC = 0.56, p = 0.38).
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Dement Geriatr Cogn Disord 2015;39:176–185 © 2015 S. Karger AG, Basel www.karger.com/dem
DOI: 10.1159/000368827
Ng et al.: Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
Table 2. Test performance of the MoCA and MMSE for detecting MCI by educational level No education
Participants Mean MoCA score ± SD Mean MMSE score ± SD MOCA AUC ± SE Cut-off Sensitivity Specificity MMSE AUC ± SE Cut-off Sensitivity Specificity
1 – 6 years of education
>6 years of education
MCI
NC
p
MCI
NC
p
MCI
NC
p
27 18.5 ± 5.4 24.2 ± 3.8
55 21.0 ± 4.5 25.1 ± 2.9
0.028 0.27
34 21.9 ± 4.5 26.2 ± 2.1
86 22.8 ± 3.8 27.6 ± 2.2
0.28 0.002
46 26.8 ± 2.7 28.3 ± 2.0
88 26.5 ± 3.2 29.1 ± 1.1
0.68 0.002
0.64 ± 0.07 21/22 0.63 0.55
0.035a
0.65 ± 0.06 22/23 0.59 0.57
0.025a
0.50 ± 0.05 27/28 0.54 0.51
0.98a
0.56 ± 0.07 25/26 0.59 0.51
0.38a
0.70 ± 0.05 27/28 0.68 0.63
0.01a
0.62 ± 0.05 29/30 0.70 0.45
0.02a
SE = Standard error. a Null hypothesis: true area = 0.5.
>6 years of education
1–6 years of education
1.0
0.8
0.8
0.8
0.6 0.4 0.2
Sensitivity
1.0
Sensitivity
Sensitivity
No education 1.0
0.6 0.4
0.2 0.4 0.6 0.8 1 – specificity
1.0
MoCA MMSE
0
0 0
0.4 0.2
0.2
0
0.6
0
0.2 0.4 0.6 0.8 1 – specificity
1.0
0
0.2 0.4 0.6 0.8 1 – specificity
1.0
Fig. 2. Receiver operating characteristic curve of MMSE and MoCA by different education groups.
The MoCA’s test performance was not much better than the MMSE’s test performance, particularly in the groups with education (1–6 years and >6 years) (fig. 2). We examined the relative contributions of education, and MCI diagnosis, in addition to age and gender, to variances of the MoCA’s test scores as well as the MMSE’s test scores in multiple regression models (table 3). As expected, age, education, and MCI diagnosis were independent predictors of both the MoCA and the MMSE scores. However, gender was not an independent predictor. Still, there were notable differences in the magnitudes of the individual contributions, especially between education and MCI diagnosis, as shown in their regression coefficients and partial η2 values. Whereas MCI diagnosis was associated with a 6 years)
–4.95 ± 0.62
–7.974
0.0001
0.162
–3.20 ± 0.36
–8.912
0.0001
0.194
1 – 6 years of education –3.31 ± 0.50 (vs. >6 years)
–6.564
0.0001
0.115
–1.20 ± 0.29
–4.131
0.0001
0.049
–0.80 ± 0.44
–1.807
0.07
0.010
–1.03 ± 0.26
–4.012
0.0001
0.045
Male gender (vs. female)
MCI (vs. NC)
regression t coefficient ± SE
p
η2
0.36
0.003
0.92
Discussion
Since 2005, a large majority of research publications have reported high accuracy, Sn, and Sp of the MoCA at various cut-offs for detecting MCI. Compared to these earlier studies, we found that the MoCA showed a less satisfactory test performance when screening for MCI in our population of older Chinese Singaporean adults. Indeed, it appeared to perform no better than the MMSE in discriminating MCI from NC subjects. We observed this consistently in two independent studies conducted on different samples. The AUC measures of diagnostic accuracy were based on adequate sample sizes in both study samples (SLAS and NNI). Given the estimated AUC of the MoCA and the ratio of NC to MCI subjects in the respective studies, they were close to the estimated sample sizes required for 80% power to test a null hypothesis value of 0.50 at p < 0.05: 202 (53 MCI and 149 NC subjects) in the SLAS sample and 115 (52 MCI and 63 NC subjects) in the NNI study sample. Another important point is that the two studies generated differing MoCA cut-off values (22/23 vs. 28/29) in accordance with the 1–6 years of education group in the community sample and the >6 years of education group in the clinic sample. This is reflective of the widely varying cut-offs reported for the MoCA in different studies conducted on heterogeneous population groups that differ by education, language, and culture around the world. Our findings are in accord with several emerging studies in Chinese populations that indicate relatively lower levels of accuracy, Sn, and Sp than those previously reported [19, 20, 22]. Yu et al. [19], conducting a study in Beijing involving a large heterogeneous populationbased sample, reported a modest accuracy (AUC = 0.71) with Sn = 0.69 and Sp = 0.64 at a reduced cut-off of 22. Zhou et al. [22] evaluated the MoCA among rural-living elderly subjects in two villages in the outskirts of Beijing and reported an AUC of 0.72 (at a cut-off of 20/21) with Sn = 0.75 and Sp = 0.62. Both studies showed that the MoCA performed no better than the MMSE. Wu et al. [20] evaluated the use of the MoCA for detecting vascular cognitive impairment among ischemic stroke patients: the AUC was 0.79, but with Sn = 0.65 and Sp = 0.79 (at a cut-off of 22/23). Furthermore, these studies showed likewise that the MoCA’s test performance appeared to vary between different educational groups, but in unpredictable fashions. In the study by Zhou et al. [22], the MoCA’s performance was especially poor among those with 0–6 years of education, and relatively better among those with >6 years of education. Wu et al. [20]
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DOI: 10.1159/000368827
Ng et al.: Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
No education
No education 40
15 NC
20
0 15 MCI
10
10 0 40 30 MCI
Frequency
Frequency
5
NC
30
10
20
5
10
0
0 5
10
15 20 25 MoCA score
30
10
35
1–6 years of education
30
1–6 years of education
NC
20
0 15 MCI
10
10 0 40 30 MCI
Frequency
5
NC
30
10 Frequency
20 25 MMSE score
40
15
20
5
10
0
0 5
10
15 20 25 MoCA score
30
10
35
>6 years of education
15
20 25 MMSE score
30
35
>6 years of education 40
15 NC
20
0 15 MCI
10 5
10 0 40 30 MCI
Frequency
5
NC
30
10 Frequency
15
20 10
0
0 5
10
15 20 25 MoCA score
30
35
10
15
20 25 MMSE score
30
35
Fig. 3. Frequency distribution of the MoCA scores by MCI status and education groups.
reported the opposite, the MoCA’s test performance was better among those with less education and relatively poorer among those with more education. This latter finding resembles our observation that the MoCA’s test performance was poorer among those with the highest education. In our study, the >6 years of education group (mean: 11.4 years) included subjects with secondary education or high school to university degrees (7–19
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Ng et al.: Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
years). The duration of their education appeared to have a widely heterogeneous effect on the subjects’ performance in the MoCA. The MoCA showed a ceiling effect in more highly educated individuals, and this most likely contributed in a major fashion to the poor test performance among them. We further analyzed individual test domains in the MoCA which performed poorly in differentiating between MCI and NC subjects (data not shown). The MoCA domains of naming, attention, language, abstraction, and orientation failed to show significant differences between MCI and NC subjects, and they were the most likely contributors to the poor overall MoCA performance, especially in the >6 years of education group. Interestingly, these domains were the same as those identified in the Chinese study by Wu et al. [20], who discriminated vascular cognitive impairment without dementia according to the MoCA’s test performance. As has consistently been reported in an increasing number of studies, education strongly influences the MoCA’s test scores. Estimates of the effect of education on MoCA scores, based on crude comparisons of available published data, vary widely from a 2.6- to a 5-point difference in MoCA scores between the groups with the shortest education compared to the next higher educational group [14, 17, 22] and likely reflect the heterogeneous effect of education on the MoCA’s test scores across different populations. Previous studies provide scant information to assess the relative effect of education vis-à-vis MCI diagnosis on the MoCA’s test scores. In this study, multivariate analysis controlling for gender and age showed that fewer years of education were associated with an adjusted 3–5 points lower MoCA score, whereas MCI diagnosis was associated with a barely 1 point lower MoCA score. Education thus had an overwhelmingly stronger effect than MCI diagnosis on the MoCA’s test score variations, at least among older Chinese Singaporean persons. In contrast to the dementia syndrome, MCI is associated with more subtle cognitive impairment and is harder to diagnose. Since it is a clinical diagnosis, it is possible that cognitive problems do not stem from a common neuropathology. Consequently, it is more difficult to distinguish the low cognitive functioning of MCI due to underlying brain pathology (AD or cerebrovascular disease) from a more profound background low cognitive functioning associated with low education. In this situation, the MoCA is not sensitive to the presence of MCI, and its use, at least in this and possibly other Chinese or Asian populations, is likely to pose serious problems for accurate case detection due to high rates of false-positive and false-negative cases. Although the participants in this study were Chinese, they included English- and Chinesespeaking test subjects. Whereas the original English version of the MoCA was used without alteration, the adaptations made in the Chinese version of the MoCA included replacing the English alphabet with numbers, the picture of a rhinoceros with that of an elephant, and the less familiar name of a flower (‘daisy’) with a more familiar flower (‘chrysanthemum’). Further work should be done to evaluate the equivalence (and differences) of the overall test and item performance of the English and Chinese versions of the MoCA among Chinese subjects in Singapore. It is well established that Chinese speakers outperform their English-speaking counterparts in regard to Digit Span memory and other number-based tasks [30, 31]. Our results suggest that further adaptations to, and evaluations of, the MoCA are necessary to address the wide heterogeneity of MoCA scores and test performance across languages and education levels in Chinese populations. In conclusion, the MoCA showed modest accuracy and is not better than the MMSE for detecting MCI, at least in this and other Chinese populations. Education exerts a considerably stronger influence than MCI diagnosis on variations in MoCA scores. Given the heterogeneity of MoCA scores and test performance among populations that vary according to years of education, language, and culture, more research is needed to develop cognitive screening tools that are universally appropriate for all population groups.
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Ng et al.: Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
Acknowledgements The authors thank the following voluntary welfare organizations for their support of the SLAS: Geylang East Home for the Aged, Presbyterian Community Services, Thye Hua Kwan Moral Society (Moral Neighbourhood Links), Yuhua Neighbourhood Link, Henderson Senior Citizens’ Home, NTUC Eldercare Co-op Ltd, Thong Kheng Seniors Activity Centre (Queenstown Centre), and Redhill Moral Seniors Activity Centre. The study was supported by research grant funding from the Biomedical Research Council, Agency for Science, Technology and Research (03/1/21/17/214), and the National Medical Research Council (08/1/21/19/567).
Disclosure Statement The authors declare no conflicts of interest in relation to the study.
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Original Research Article
Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore Tze Pin Ng a Lei Feng a Wee Shiong Lim b Mei Sian Chong b Tih Shih Lee c Keng Bee Yap d Tung Tsoi a Tau Ming Liew e Qi Gao a Simon Collinson f Nagaendran Kandiah g Philip Yap e a
Gerontology Research Programme, Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, b Geriatric Medicine Department, Tan Tock Seng Hospital, Ministry of Health, c Duke-NUS Graduate Medical School, d Department of Geriatric Medicine, Alexandra Hospital, e Geriatric Medicine Department, Khoo Teck Puat Hospital, Ministry of Health, f Department of Psychology, National University of Singapore, and g National Neuroscience Centre, Tan Tock Seng Hospital, Singapore
Key Words Cognitive screening · Mild neurocognitive disorder · Variation in test performance and scores · Montreal Cognitive Assessment Abstract Background: The Montreal Cognitive Assessment (MoCA) was developed as a screening instrument for mild cognitive impairment (MCI). We evaluated the MoCA’s test performance by educational groups among older Singaporean Chinese adults. Method: The MoCA and Mini-Mental State Examination (MMSE) were evaluated in two independent studies (clinic-based sample and community-based sample) of MCI and normal cognition (NC) controls, using receiver operating characteristic curve analyses: area under the curve (AUC), sensitivity (Sn), and specificity (Sp). Results: The MoCA modestly discriminated MCI from NC in both study samples (AUC = 0.63 and 0.65): Sn = 0.64 and Sp = 0.36 at a cut-off of 28/29 in the clinic-based sample, and Sn = 0.65 and Sp = 0.55 at a cut-off of 22/23 in the community-based sample. The MoCA’s test performance was least satisfactory in the highest (>6 years) education group: AUC = 0.50 (p = 0.98), Sn = 0.54, and Sp = 0.51 at a cut-off of 27/28. Overall, the MoCA’s test performance was not better than that of the MMSE. In multivariate analyses controlling for age and gender, MCI diagnosis was associated with a 5 on the modified Geriatric Depression Scale, were excluded from the study. For the purpose of comparison with prior studies among Chinese populations, a small number of non-Chinese subjects were also excluded from the study. The GRP sample was a clinic- and population-based sample of Chinese patients with a diagnosis of MCI from the memory clinics at Khoo Teck Puat Hospital (principal investigator: P. Yap) and community-living participants in the population-based Singapore Longitudinal Ageing Studies (SLAS; principal investigator: T.P. Ng) who were diagnosed with MCI. Patients with MCI at the memory clinics at Khoo Teck Puat Hospital had a CDR of 0.5 (‘questionable dementia’), and the diagnosis was similarly made based on a comprehensive clinical assessment including clinical examination, neuroimaging, blood tests, and bedside cognitive tests: the Frontal Assessment Battery and Clock Drawing Task, or standardized neuropsychological assessments of attention, memory, language, visual-spatial and constructional abilities, and executive function (in 25% of the patients). Community-living MCI subjects in the SLAS cohort were identified from initial screening with the MMSE (score ≤26). Their CDR values were subsequently evaluated (0.5) [29], and a full neuropsychological assessment with a comprehensive test battery was conducted (Digit Span, Rey Auditory Verbal Learning Test, Story Memory, Brief Visuospatial Memory Test-Revised, Boston Naming Test, Color Trails Tests 1 and 2, and Block Design) as well as a clinical diagnostic workup [including clinical examination, relevant blood measurements, and brain imaging (MRI)]. Individuals were diagnosed with MCI after a consensus review of the clinical diagnostic assessment results by a three-member panel of geriatricians and psychiatrists, based on Petersen’s [28] criteria (memory complaint usually corroborated by an informant, objective memory impairment for age, essentially preserved general cognitive function, largely intact functional activities, and not demented). Normal cognition (NC) subjects in the GRP sample comprised Chinese age- and sex-matched SLAS participants who had a CDR of 0 and no history of significant head injury, stroke, or evidence of cerebrovascular disease (Hachinski score >4), any other neurological disease, systemic illness, or medical conditions that may affect cognitive functioning and activities of daily living. They were free of clinically important depressive symptoms (a score of ≥4 on the 15-item Geriatric Depression Scale) or other psychiatric and substance-related disorders which affect cognitive functioning. They had not used long-acting benzodiazepines or barbiturates within the past 2 years. In both study samples, the MoCA was administered independently of the clinical assessment. We used a version of the MoCA that had been modified for Singaporeans by a senior psychologist and a specialist neurologist providing dementia care, with test items adapted in consultation with the original MoCA developers [8]. The translation and back-translation of the Chinese and Malay versions of the MoCA were undertaken by bilingual psychologists, and equivalent versions of the MoCA in English, Chinese, and Malay were established. Statistical Analyses Receiver operating characteristic curve analysis was used to evaluate the performance of the MMSE and MoCA in discriminating MCI from NC subjects. AUCs, Sn, and Sp were reported for the whole sample and three subgroups, depending on length of education: no education, 1–6 years of education, and >6 years of education. The differences between the educational groups were analyzed using independent t tests for continuous dependent variables and the χ2 test for categorical dependent variables. The relative effect sizes of age, gender, education, and MCI on variations in MoCA scores was estimated using analysis of variance in general linear modelling. The IBM SPSS Statistics 20 software for Windows (IBM SPSS Inc., Chicago, Ill., USA) was used for all data analyses.
Results
The demographic characteristics of all MCI and NC subjects in the NNI and the GRP study populations are shown in table 1. Notably, the NNI study participants were younger and received more years of formal education, with correspondingly higher overall mean MoCA and MMSE scores than the participants in the GRP sample. In each of the two samples, the mean MoCA scores of the MCI subjects were significantly lower than those of the NC subjects:
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Dement Geriatr Cogn Disord 2015;39:176–185 © 2015 S. Karger AG, Basel www.karger.com/dem
DOI: 10.1159/000368827
Ng et al.: Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
Table 1. MoCA and MMSE scores of MCI and NC subjects as well as test performance by study samples
GRP study
Participants Females, % Mean age ± SD, years Mean education ± SD, years Mean MoCA score ± SD Mean MMSE score ± SD MoCA AUC ± SE Cut-off Sensitivity Specificity MMSE AUC ± SE Cut-off Sensitivity Specificity
NNI study
MCI
NC
p
MCI
NC
p
62 56.5 70.8 ± 6.8 4.6 ± 4.4 20.3 ± 4.8 25.5 ± 3.1
176 59.1 69.5 ± 7.4 5.1 ± 3.8 22.4 ± 3.9 27.1 ± 2.7
0.72 0.21 0.42 0.001 0.001
45 42.2 62.0 ± 9.3 10.6 ± 4.5 27.0 ± 3.0 28.1 ± 2.2
53 60.4 58.7 ± 7.0 12.1 ± 3.2 28.5 ± 1.5 29.2 ± 0.9
0.07 0.06 0.07 0.002 0.001
0.63 ± 0.04 22/23 0.65 0.55
0.003a
0.65 ± 0.06 28/29 0.64 0.36
0.008a
0.67 ± 0.04 27/28 0.71 0.56
0.001a
0.64 ± 0.06 29/30 0.767 0.33
0.016a
SE = Standard error. a Null hypothesis: true area = 0.5.
NNI population sample
1.0
1.0
0.8
0.8 Sensitivity
Sensitivity
SLAS population sample
0.6 0.4 0.2
MoCA MMSE
0.4 0.2
0
Fig. 1. Receiver operating characteristic curve of MMSE and MoCA by study samples.
0.6
0 0
0.2 0.4 0.6 0.8 1 – specificity
1.0
0
0.2 0.4 0.6 0.8 1 – specificity
1.0
they were approximately 2 points lower in the GRP sample and 1.5 points lower in the NNI sample. However, the overall AUCs for discriminating MCI from NC subjects were modest in both the NNI (0.63) and the GRP samples (0.65), with Sn and Sp below 0.70 in the NNI sample (Sn = 0.64 and Sp = 0.36 at an optimum cut-off of 28/29) and in the GRP sample (Sn = 0.65 and Sp = 0.55 at an optimum cut-off of 22/23). These test performance parameters were not better than those for the MMSE (fig. 1). We pooled the data from the two samples and evaluated the test performance parameters of the MoCA and MMSE by educational groups (table 2). The MoCA’s test performance was least satisfactory in the group with the longest education (>6 years). At a higher optimum cut-off (27/28), it had the lowest Sn (0.54) and Sp (0.51), and its AUC of 0.50 was not statistically significantly different (p = 0.98) from the null value of 0.50. On the other hand, the test performance of the MMSE was poorest in the group with no education (AUC = 0.56, p = 0.38).
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DOI: 10.1159/000368827
Ng et al.: Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
Table 2. Test performance of the MoCA and MMSE for detecting MCI by educational level No education
Participants Mean MoCA score ± SD Mean MMSE score ± SD MOCA AUC ± SE Cut-off Sensitivity Specificity MMSE AUC ± SE Cut-off Sensitivity Specificity
1 – 6 years of education
>6 years of education
MCI
NC
p
MCI
NC
p
MCI
NC
p
27 18.5 ± 5.4 24.2 ± 3.8
55 21.0 ± 4.5 25.1 ± 2.9
0.028 0.27
34 21.9 ± 4.5 26.2 ± 2.1
86 22.8 ± 3.8 27.6 ± 2.2
0.28 0.002
46 26.8 ± 2.7 28.3 ± 2.0
88 26.5 ± 3.2 29.1 ± 1.1
0.68 0.002
0.64 ± 0.07 21/22 0.63 0.55
0.035a
0.65 ± 0.06 22/23 0.59 0.57
0.025a
0.50 ± 0.05 27/28 0.54 0.51
0.98a
0.56 ± 0.07 25/26 0.59 0.51
0.38a
0.70 ± 0.05 27/28 0.68 0.63
0.01a
0.62 ± 0.05 29/30 0.70 0.45
0.02a
SE = Standard error. a Null hypothesis: true area = 0.5.
>6 years of education
1–6 years of education
1.0
0.8
0.8
0.8
0.6 0.4 0.2
Sensitivity
1.0
Sensitivity
Sensitivity
No education 1.0
0.6 0.4
0.2 0.4 0.6 0.8 1 – specificity
1.0
MoCA MMSE
0
0 0
0.4 0.2
0.2
0
0.6
0
0.2 0.4 0.6 0.8 1 – specificity
1.0
0
0.2 0.4 0.6 0.8 1 – specificity
1.0
Fig. 2. Receiver operating characteristic curve of MMSE and MoCA by different education groups.
The MoCA’s test performance was not much better than the MMSE’s test performance, particularly in the groups with education (1–6 years and >6 years) (fig. 2). We examined the relative contributions of education, and MCI diagnosis, in addition to age and gender, to variances of the MoCA’s test scores as well as the MMSE’s test scores in multiple regression models (table 3). As expected, age, education, and MCI diagnosis were independent predictors of both the MoCA and the MMSE scores. However, gender was not an independent predictor. Still, there were notable differences in the magnitudes of the individual contributions, especially between education and MCI diagnosis, as shown in their regression coefficients and partial η2 values. Whereas MCI diagnosis was associated with a 6 years)
–4.95 ± 0.62
–7.974
0.0001
0.162
–3.20 ± 0.36
–8.912
0.0001
0.194
1 – 6 years of education –3.31 ± 0.50 (vs. >6 years)
–6.564
0.0001
0.115
–1.20 ± 0.29
–4.131
0.0001
0.049
–0.80 ± 0.44
–1.807
0.07
0.010
–1.03 ± 0.26
–4.012
0.0001
0.045
Male gender (vs. female)
MCI (vs. NC)
regression t coefficient ± SE
p
η2
0.36
0.003
0.92
Discussion
Since 2005, a large majority of research publications have reported high accuracy, Sn, and Sp of the MoCA at various cut-offs for detecting MCI. Compared to these earlier studies, we found that the MoCA showed a less satisfactory test performance when screening for MCI in our population of older Chinese Singaporean adults. Indeed, it appeared to perform no better than the MMSE in discriminating MCI from NC subjects. We observed this consistently in two independent studies conducted on different samples. The AUC measures of diagnostic accuracy were based on adequate sample sizes in both study samples (SLAS and NNI). Given the estimated AUC of the MoCA and the ratio of NC to MCI subjects in the respective studies, they were close to the estimated sample sizes required for 80% power to test a null hypothesis value of 0.50 at p < 0.05: 202 (53 MCI and 149 NC subjects) in the SLAS sample and 115 (52 MCI and 63 NC subjects) in the NNI study sample. Another important point is that the two studies generated differing MoCA cut-off values (22/23 vs. 28/29) in accordance with the 1–6 years of education group in the community sample and the >6 years of education group in the clinic sample. This is reflective of the widely varying cut-offs reported for the MoCA in different studies conducted on heterogeneous population groups that differ by education, language, and culture around the world. Our findings are in accord with several emerging studies in Chinese populations that indicate relatively lower levels of accuracy, Sn, and Sp than those previously reported [19, 20, 22]. Yu et al. [19], conducting a study in Beijing involving a large heterogeneous populationbased sample, reported a modest accuracy (AUC = 0.71) with Sn = 0.69 and Sp = 0.64 at a reduced cut-off of 22. Zhou et al. [22] evaluated the MoCA among rural-living elderly subjects in two villages in the outskirts of Beijing and reported an AUC of 0.72 (at a cut-off of 20/21) with Sn = 0.75 and Sp = 0.62. Both studies showed that the MoCA performed no better than the MMSE. Wu et al. [20] evaluated the use of the MoCA for detecting vascular cognitive impairment among ischemic stroke patients: the AUC was 0.79, but with Sn = 0.65 and Sp = 0.79 (at a cut-off of 22/23). Furthermore, these studies showed likewise that the MoCA’s test performance appeared to vary between different educational groups, but in unpredictable fashions. In the study by Zhou et al. [22], the MoCA’s performance was especially poor among those with 0–6 years of education, and relatively better among those with >6 years of education. Wu et al. [20]
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No education
No education 40
15 NC
20
0 15 MCI
10
10 0 40 30 MCI
Frequency
Frequency
5
NC
30
10
20
5
10
0
0 5
10
15 20 25 MoCA score
30
10
35
1–6 years of education
30
1–6 years of education
NC
20
0 15 MCI
10
10 0 40 30 MCI
Frequency
5
NC
30
10 Frequency
20 25 MMSE score
40
15
20
5
10
0
0 5
10
15 20 25 MoCA score
30
10
35
>6 years of education
15
20 25 MMSE score
30
35
>6 years of education 40
15 NC
20
0 15 MCI
10 5
10 0 40 30 MCI
Frequency
5
NC
30
10 Frequency
15
20 10
0
0 5
10
15 20 25 MoCA score
30
35
10
15
20 25 MMSE score
30
35
Fig. 3. Frequency distribution of the MoCA scores by MCI status and education groups.
reported the opposite, the MoCA’s test performance was better among those with less education and relatively poorer among those with more education. This latter finding resembles our observation that the MoCA’s test performance was poorer among those with the highest education. In our study, the >6 years of education group (mean: 11.4 years) included subjects with secondary education or high school to university degrees (7–19
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Ng et al.: Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
years). The duration of their education appeared to have a widely heterogeneous effect on the subjects’ performance in the MoCA. The MoCA showed a ceiling effect in more highly educated individuals, and this most likely contributed in a major fashion to the poor test performance among them. We further analyzed individual test domains in the MoCA which performed poorly in differentiating between MCI and NC subjects (data not shown). The MoCA domains of naming, attention, language, abstraction, and orientation failed to show significant differences between MCI and NC subjects, and they were the most likely contributors to the poor overall MoCA performance, especially in the >6 years of education group. Interestingly, these domains were the same as those identified in the Chinese study by Wu et al. [20], who discriminated vascular cognitive impairment without dementia according to the MoCA’s test performance. As has consistently been reported in an increasing number of studies, education strongly influences the MoCA’s test scores. Estimates of the effect of education on MoCA scores, based on crude comparisons of available published data, vary widely from a 2.6- to a 5-point difference in MoCA scores between the groups with the shortest education compared to the next higher educational group [14, 17, 22] and likely reflect the heterogeneous effect of education on the MoCA’s test scores across different populations. Previous studies provide scant information to assess the relative effect of education vis-à-vis MCI diagnosis on the MoCA’s test scores. In this study, multivariate analysis controlling for gender and age showed that fewer years of education were associated with an adjusted 3–5 points lower MoCA score, whereas MCI diagnosis was associated with a barely 1 point lower MoCA score. Education thus had an overwhelmingly stronger effect than MCI diagnosis on the MoCA’s test score variations, at least among older Chinese Singaporean persons. In contrast to the dementia syndrome, MCI is associated with more subtle cognitive impairment and is harder to diagnose. Since it is a clinical diagnosis, it is possible that cognitive problems do not stem from a common neuropathology. Consequently, it is more difficult to distinguish the low cognitive functioning of MCI due to underlying brain pathology (AD or cerebrovascular disease) from a more profound background low cognitive functioning associated with low education. In this situation, the MoCA is not sensitive to the presence of MCI, and its use, at least in this and possibly other Chinese or Asian populations, is likely to pose serious problems for accurate case detection due to high rates of false-positive and false-negative cases. Although the participants in this study were Chinese, they included English- and Chinesespeaking test subjects. Whereas the original English version of the MoCA was used without alteration, the adaptations made in the Chinese version of the MoCA included replacing the English alphabet with numbers, the picture of a rhinoceros with that of an elephant, and the less familiar name of a flower (‘daisy’) with a more familiar flower (‘chrysanthemum’). Further work should be done to evaluate the equivalence (and differences) of the overall test and item performance of the English and Chinese versions of the MoCA among Chinese subjects in Singapore. It is well established that Chinese speakers outperform their English-speaking counterparts in regard to Digit Span memory and other number-based tasks [30, 31]. Our results suggest that further adaptations to, and evaluations of, the MoCA are necessary to address the wide heterogeneity of MoCA scores and test performance across languages and education levels in Chinese populations. In conclusion, the MoCA showed modest accuracy and is not better than the MMSE for detecting MCI, at least in this and other Chinese populations. Education exerts a considerably stronger influence than MCI diagnosis on variations in MoCA scores. Given the heterogeneity of MoCA scores and test performance among populations that vary according to years of education, language, and culture, more research is needed to develop cognitive screening tools that are universally appropriate for all population groups.
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Ng et al.: Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
Acknowledgements The authors thank the following voluntary welfare organizations for their support of the SLAS: Geylang East Home for the Aged, Presbyterian Community Services, Thye Hua Kwan Moral Society (Moral Neighbourhood Links), Yuhua Neighbourhood Link, Henderson Senior Citizens’ Home, NTUC Eldercare Co-op Ltd, Thong Kheng Seniors Activity Centre (Queenstown Centre), and Redhill Moral Seniors Activity Centre. The study was supported by research grant funding from the Biomedical Research Council, Agency for Science, Technology and Research (03/1/21/17/214), and the National Medical Research Council (08/1/21/19/567).
Disclosure Statement The authors declare no conflicts of interest in relation to the study.
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