THE Mini-Mental State Examination (MMSE), first introduced

Transcription

1 Journal of Gerontology: PSYCHOLOGICAL SCIENCES 998, Vol. B, No. 6, P9-P6 Copyright 998 by The Gemntological Society of America A Normative, Community-Based Study of Mini-Mental State in Elderly Adults: The Effect of Age and Educational Level Junichi Ishizaki, Kenichi Meguro, Hideo Ambo, 2 Masumi Shimada, Satoshi Yamaguchi, Chika Hayasaka, Hiroshi Komatsu, Yasuyoshi Sekita, and Atsushi Yamadori 'Section of Neuropsychology, Division of Disability Science, Tohoku University Graduate School of Medicine, Sendai, Japan. department of Cultural Anthropology, Faculty of Arts and Letters, Tohoku University. Tohoku University Graduate School of Economics. We investigated community-based data of the Mini-Mental State Examination (MMSE) scores of elderly residents along with the effects of age and educational level. MMSE was planned for all residents over 6 years of age in a town in northern Japan. The number of elders who took the MMSE was 2,266 (90%). The score significantly declined with age and lower educational level, although no effect of sex was apparent. For the MMSE subitems, all the values except for that of naming showed effects of both age and educational level. Those screened by MMSE who fell in the range of cognitive impairment (< 24) accounted for 2.8% and those with severe cognitive impairment (< 8) constituted 6.0%. Despite the differences in language and culture, the mean scores are remarkably similar between Japan and other countries. This is the first normative, community-based study of MMSE among elderly adults in Japan. THE Mini-Mental State Examination (MMSE), first introduced by Folstein, Folstein, and McHugh (97), is used widely for assessing cognitive mental status in both clinical practice and research. As a clinical instrument, the MMSE is used to detect changes in mental status, to follow the course of an illness, and to monitor response to treatment (Tombaugh & Mclntyre, 992). As a research tool, it is widely used to screen cognitive disorders in epidemiologic studies of community and institutionalized populations (Bassett & Folstein, 99; Folstein, Anthony, Parhad, Luffy, & Gruenberg, 98). The MMSE provides a structured approach to mental status testing that screens intellectual impairment and allows comparison of performance across time and among patients or studies (Abraham et al., 994; Galasko et al., 990). It can be used in different cultures and has been translated into several different languages (Katzman et al., 988; Park & Ha, 988; Salmon et al., 989). It includes eleven subitems that assess the abilities of orientation (time and place), registration, attention, recall, naming, repetition, command, reading, writing, and copying. The validity of the Japanese version of the MMSE has been confirmed and it has also been used as a measure of cognitive impairment. Mori, Mitani, and Yamadori (98) reported that the Japanese version has adequate sensitivity and specificity for detecting cognitive deficits in neurological patients. They also reported it to be highly reliable and useful for international comparison. Some previous reports noted the prevalence of cognitive impairment associated with age and educational level (Callahan et al., 996; Crum, Anthony, Bassett, & Folstein, 99; Jorm, Scott, Henderson, & Kay, 988; Launer, Dinkgreve, Jonker, Hooijer, & Lindeboom, 99; Magaziner, Bassett, & Hebel, 987; O'Connor, Pollitt, Treasure, Brook, & Reiss, 989). Bleecker, Bolla-Wilson, Kawas, and Agnew (988) reported age-specific MMSE values in 94 healthy men and women aged 40 to 89 years, with 7 to 2 years of schooling. Total score was correlated with age but not with education. O'Connor and colleagues (989) suggested that education and social class influenced scores on all sections within the MMSE with the exception of the score of registration. Crum and colleagues (99) noted the distribution of 8,06 individuals by age and education at five sites in the United States. In their report, there were two demonstrable normative influences on MMSE scores, namely age and education. In Japan, however, there have been no reports of MMSE scores by age and education based on a large sample. In this article we present community-based data of MMSE scores of elderly adults with the effects of age and educational level. We also present the effects of age and educational level on the MMSE subitems. This is the first normative, community-based information on the MMSE in Japan. PARTICIPANTS AND METHODS Tajiri Town Based on the Tohoku University and Tajiri Project of Stroke, Dementia, and Bed Confinement Prevention (Tajiri Project), the survey was conducted from May to August in the town of Tajiri, located in a typical agricultural area in the northern part of Japan. The total population in 99 was 4,707. According to the national census in 99, Tajiri was ranked rd in population size and 0th in "the rate of aging" (the ratio of elders aged 6 years and older to the total population) among the 60 towns in Miyagi Prefecture. P9

2 P60 ISHIZAKIETAL Participants We first targeted all 2,6 elderly residents older than 6 years of age in the town. A total of 2,266 respondents (90%) completed the interview. Reasons for interview incompletion were refusal (29 people), prolonged travel (28 people), hospitalization (72 people), death (4 people), change of residence (9 people), and serious illness (i.e., apparent dementia, 9 people). Nineteen people were also excluded because of insufficient description. Table shows the demographics of the study population. Age is described for -year bands, whereas educational level is noted according to four classes based on the old Japanese educational system: no schooling; six years of schooling, i.e., elementary school; eight years of schooling, i.e., completion of junior high school; and ten years or more of schooling, i.e., high school or college. The medical histories of participants based on their selfreports were as follows: a history of hypertension was present in.% of the subjects, heart disease in 2.2%, stroke in.6%, liver disease in 9.4%, kidney disease in.9%, stomach disease in 0.0%, lung disease in 7.7%, diabetes mellitus in 8.4%, and rheumatism in.9%. There may be disagreement in details between self-reports and the medical records. However, the reliability of self-reports of stroke is rather high and should be taken into account because stroke is a risk factor for cognitive impairment and dementia (Callahan et al., 996; Censori et al., 996). Actually, the total MMSE scores of subjects with a history of stroke were lower than scores for other subjects (data not shown). Therefore, the subjects with a medical history of stroke were not included in our analysis in order to exclude the influence of stroke on cognitive impairment. We also excluded the subjects with severe vision and/or hearing impairment. MMSE and Other Tests as Interview All data were gathered with standardized interviewing Sex Men Women Table. Demographics of Study Population Educational class No schooling 6 years of schooling 8 years of schooling 0 or more years of schooling Total N Note: 6 years schooling: elementary school; 8 years of schooling: junior high school; 0 or more years of schooling: high school or college. These are based on the old Japanese educational system. methods. Each participant was given a brief explanation about the purpose and contents of the interview in order to get the highest possible test score through high motivation. They underwent a comprehensive interview to examine a number of factors including symptoms associated with medical disorders, ADLs (activities of daily living), care given by family, utilization of social services, and demographic factors. Each subject took the MMSE and other cognitive and behavioral tests, and was interviewed to determine social and demographic characteristics. The MMSE was performed at the participants' homes by trained examiners who were public health nurses and local welfare commissioners, under the direction of neurologists and psychologists. Participants were asked to perform both the serial sevens (sequential subtraction of 7 from 00) and also asked to say "FU-JI-NO-YA-MA" (a five-syllable Japanese word, i.e., Mt. Fuji) backward. The question that led to the highest total MMSE score was used. The MMSE score was calculated by summing up the correct responses to all MMSE subitems to yield a total score. Test scores fell between 0, the lowest possible score, and 0, the highest score. Analysis The data did not show a normal distribution. Therefore, the nonparametric Kruskal-Wallis one-way analysis of variance (ANOVA) and Mann-Whitney U tests were used to examine the effects of age, sex, and educational level on total MMSE scores. The results are presented using the median values with 2th and 7th percentiles. To examine the effect of each independent variable (sex, age, and education) on the MMSE scores, the quantification theory of the first type by Hayashi (92) and multiple regression analysis were used. The method of Hayashi was available mainly for categorical data consisting of a nominal or an ordinal scale, and sometimes used with multiple regression analysis (Sun, Zhao, & Yan, 99). The method is in essence the same as multiple regression analysis as far as mathematical structure, the only exception being that the categorical data are employed as explanatory variables instead of continuous variables. Various kinds of multivariate analysis are based on the assumed linearity of relationship between those variables which are more difficult to justify in categorical cases. The chi-square test was also used to detect differences in various MMSE subitem scores for each age band and educational level. The Japanese version of SPSS (The Statistical Package for the Social Sciences) IV, which includes the quantification theory and multiple regression analysis, was used for the analysis. RESULTS There was a relationship between age and educational level: educational level decreased with age. We examined the effect of age for each educational level group and found that there were significant relationships between age and the score of the MMSE in all educational groups. Table 2 shows the median scores of the MMSE by age and educational level.

3 COMMUNITY-BASED STUDY OF MMS P6 The Kruskal-Wallis one-way analysis of variance (ANOVA) was used to determine the effects of age and educational level, and the Mann-Whitney U test was used to examine the effect of sex. There was a significant relationship between the MMSE score and sex (z = 6.27, p < 0.000), age (x 2 = 67., p < 0.000), and years of schooling (x 2 = 24.9, p< 0.000). To examine the effect of each independent variable (sex, age, and educational level), we used the quantification theory of the first type of Hayashi (92) and found the multiple correlation coefficient to be 0.490, sufficiently high for this kind of data set containing three categories. The partial correlation coefficients were 0.09 for sex (negligible), 0.09 for age, and 0.40 for educational level. We also performed multiple regression analysis. The multiple regression coefficient was 0.490, and the partial regression coefficients were for sex, for age, and 0.8 for educational level, respectively, and the effects of age and educational level were significant (p < 0.000). This allowed us to determine the individual effect of age and educational level on the MMSE score. Figure illustrates the median, lower, and upper quartile scores of the MMSE of each age group. The median values were 28, 27, 26, 2, and 2 for those aged,,,, and 8 years old and older, respectively. Table 2. MMSE Scores by Age and Educational Level Educational Class No schooling 6 years 8 years 0 or more years 2 (2) 27(7) 28 (60) 29(7) 2 (7) 26(88) 27 (268) 29 (99) Age Group 9 (2) 2(0) 27(47) 28 (8) 8 (9) 24 (48) 26 (20) 26 (60) (2) 2(7) 2 () 26(6) Note: The median scores on the MMSE and the number of participants (in the parenthesis). 0 r 2 o to UJ W 0 - " A Upper Quartile Median Lower Quartile 8- (n=684) 72) 27) 47) (n=4) *p = 0.006, "p < 0.000, #p = 0. Figure. MMSE score for each age group. ** i t The scores declined with age and were lowest for the oldest age group. Figure 2 illustrates the MMSE scores according to the four educational classes. The median MMSE scores were 9, 2, 27 and 29 for no schooling, six years of schooling, eight years of schooling and ten or more years of schooling, respectively. The scores decreased with lower educational level. Table shows the effect of age on the various MMSE 0 2 o> 20 o u U </> 2 0 Upper Quartile Median Lower Quartile no school 6y 8y 0y + 72) 692) 90) (n=70). nnnn, Educational classes p = Figure 2. MMSE score for each educational class. Subitems OR-Time OR-Place Registration Attention Recall Name Repeat Command Read Write Copy Table. Effect of Age on MMSE Subitems Maximum score 2 748) («= 629) ) ) ) P Notes: Each value is noted as the % of subjects who could answer completely (maximum scores). The \ 2 test was used to detect differences in various MMSE subitem scores for each age band. The significant effect of age was noted for all the subitems. The eleven subitems were as follows:. OR-Time: Orientation of time (year, season, month, day, date); 2. OR-Place: Orientation of place (prefecture, town, name of building, floor, region);. Registration: Repeat the name of three objects; 4. Attention: Serial subtraction of 7 from 00, after five times; Alternative: Say a word backward;. Recall: Recall the names of three objects learned in Question ; 6. Name: Name two subjects; 7. Repeat: Repeat a phrase; 8. Command: Follow a three-stage command; 9. Read: Read and obey a command; 0. Write: Write a sentence;. Copy: Copy a figure (double-pentagon).

4 P62 ISHIZAKIETAL subitems. Age highly affected all the subitems except naming. Exactly the same pattern was found for the effect of educational level on each subitem (data not shown). DISCUSSION We have presented the MMSE scores of 2,266 elderly adults in a community in Japan. We found age and educational level to be associated with the MMSE scores, as in other studies. Crum and colleagues (99) reported the distribution of MMSE scores by age and level of education in 8,06 individuals surveyed at five sites in the United States, as a normative sample. The participants included both cognitively normal and abnormal individuals, as in our study. Despite the differences in language and culture between the United States and Japan, the median scores on the MMSE in the two countries are remarkably similar. The MMSE scores were lower for the older age groups and for those with fewer years of schooling. Park and Ha (988) reported the prevalence rates of cognitive impairment among elders aged 6 years or older in a Korean rural community. The prevalence of severe impairment as shown by MMSE scores less than 8 (Weissman et al., 98) was 8% among men and 9% among women. Compared with other reports, the prevalence of cognitive impairment among elderly adults was much higher in Korea than in two Western countries,.4% in the United States and 4.8% in Australia (Kay et al., 98). In the present study, the prevalence of severe cognitive impairment was 6.0%. Salmon and colleagues (989) compared MMSE performance in Finland and China. The samples were 2,87 elderly Shanghai residents, aged 6 to 74 years, and 2 Finns of the same age group. When the Shanghai subset of 79 subjects who had no formal education was eliminated from the analysis, the distribution of total scores was almost identical in the two populations. The mean scores on the MMSE total scores by age in Salmon's report and those in our study matched almost exactly. Folstein and colleagues (98) reported that in the adult population of Eastern Baltimore, a low score (< 24) on the MMSE by 92 people aged 6 and older was observed in 20.8% of the sampled population. Using the same cutoff points (2/24), Kay and colleagues (98) reported a rate of 9.7% in a community sample of 274 people aged 70 and older, which is close to the 2.8% in our data. Yu and colleagues (989) showed a somewhat higher rate, 26.8%, in a large-scale study of elderly subjects 6 years of age and older in Shanghai. O'Connor and colleagues (989) reported the distribution of the lower MMSE scores (< 24) of,86 community dwelling elders aged 7 years and older in the United Kingdom. The proportions of respondents were 2%, 9%, and 4% for those years old, years old, and 8 years old and over, respectively. In our study, the proportions were.2%, 7.%, 22.0%, 7.%, and.7% for those years old, years old, years old, years old, and 8 years old and older, respectively. We also showed that educational level influenced total scores on the MMSE and that the scores were more variable for those with low education than for those with high education. How might such differences arise on a cognitive screening test such as the MMSE? Several explanations for this difference in variability of educational level have been previously discussed (Callahan et al., 996; Crum et al., 99; Jorm et al., 988; Launer et al., 99). For example, poor education is a risk factor for brain pathology and the MMSE is an indicator of this brain pathology. A second possibility is that low education has an effect on clinically indicated functional cognitive impairment itself. Finally, low education might influence the MMSE directly, in which case, the test would be a biased indicator of cognitive function. These possibilities, however, cannot be clearly distinguished from psychometric data such as those in our report. The recall of three objects has been reported as a specific item on the MMSE which is sensitive to age (Anthony, LeResche, Niaz, von Korff, & Folstein, 982). Bleecker and colleagues (988) showed that three specific items were significantly associated with age: recall of three objects; spelling the word "world" backward, which they used instead of serial 7s; and repetition of the phrase. In community-dwelling elders, O'Connor and colleagues (989) reported that age had a great effect on variations in all subsections. They divided eleven subitems into five categories (subsections); education was found to contribute to all categories with the exception of registration. In our study, there were strong effects on all subitems with age and educational level, except for that of naming. Although our data cannot be compared directly with the normal data of Bleecker and colleagues (988), the rate of decline in their study was higher in those aged years, whereas the percentage of correct responses of most subitems similarly decreased with age. In this study, the attention subitem (serial 7s or the word spelled backward) had particularly lower scores. This task requires continuous attention and mental operation, cognitive functions that may decline in the early period of aging. It is interesting to consider which types of cognitive function are impaired in the early period and which are spared until the late period. We examined 2,266 residents aged 6 years and older and found age and educational level, but not sex, to be associated with the MMSE. The median score was 9 on the MMSE for the no schooling group, and 2 for the oldest group aged 8 years and older. Therefore in cases with poor education, there is a limit to the use of cognitive screening tests such as MMSE as a measure of cognitive impairment, and it may be necessary to evaluate in more detail in the oldest age group to assess the clinical abnormal range. The cognitively impaired subjects who scored 2 or below (Bassett & Folstein, 99) were 2.8% of the population 6 years of age and older, similar to findings of other studies. Those who scored under 22 on the MMSE, i.e., poor performance (Launer et al., 99), were.% of the elderly population, and those who scored under 8, i.e., severe impairment (Weissman et al., 98), were 6.0%. Such people may suffer from dementia. More detailed cognitive assessment and clinical evaluation of the group is necessary as a follow-up to the present study. In summary, we have presented large-scale, community-based data of total and subitem scores on the MMSE in a Japanese population. The results will be useful for those who wish to compare an individual patient's MMSE score with a cross-cultural population reference group.

5 COMMUNITY-BASED STUDY OF MMS P6 ACKNOWLEDGMENTS This study was supported, in part, by the Ministry of Health and Welfare, Public Welfare Work on Elderly, , and by the Uehiro Foundation on Ethics and Education. We wish to thank the officials of Tajiri, especially E. Kimura and T. Mikami. We also thank Drs. T. Saita and M. Ito for valuable comments. Address correspondence to Dr. Kenichi Meguro, Section of Neuropsychology, Division of Disability Science, Tohoku University Graduate School of Medicine, 2-, Seiryo-machi, Aoba-ku, Sendai, Japan. REFERENCES Abraham, I. L., Manning, C. A., Snustad, D. G., Brashear, H. R., Newman, M. C, & Wofford, A. B. (994). Cognitive screening of nursing home residents: Factor structures of the Mini-Mental State Examination. Journal of the American Geriatrics Society, 42, Anthony, J. C, LeResche, L., Niaz, U., Von Korff, M. R., & Folstein, M. F. (982). Limits of the Mini-Mental State as a screening test for dementia and delirium among hospital patients. 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