Comparing MMSE and RUDAS in detecting cognitive impairment: A population-based study from Punjab, North India

doi:10.21203/rs.3.rs-3242017/v1

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Comparing MMSE and RUDAS in detecting cognitive impairment: A population-based study from Punjab, North India

https://doi.org/10.21203/rs.3.rs-3242017/v1

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Background

The Mini Mental State Examination (MMSE) and Rowland Universal Dementia Assessment Scale (RUDAS) are commonly used tools for screening cognitive impairment (CI) in epidemiological research globally. However, discrepancies have been noted in their ability to detect CI. Hence, present study was carried out to understand the test performance of MMSE and RUDAS among adults of a homogeneous group of rural Punjab, North India.

Methods

A total of 956 adults aged 18–75 years of both sexes, were recruited from the Jat Sikh community in rural Punjab. Sociodemographic (age, sex, marital status, employment status, annual income, family size) and lifestyle variables (smoking behavior, alcohol intake, and diet pattern) were collected using pretested and modified interview schedules. CI screening was performed using MMSE and RUDAS tools. Statistical analysis was conducted using SPSS software version 22.

Results

The overall prevalence of CI was found to be 40.3% and 27.8% using MMSE & RUDAS respectively. MMSE and RUDAS demonstrated a moderate correlation (Spearman correlation coefficient = 0.43) and a moderate agreement (Kappa value = 0.42). The highest concordance (79.1%) between the two tools in detecting CI was observed among elderly participants ( > = 60 years). Furthermore, in sociodemographic and lifestyle categories, both tools showed relatively higher concordance, with no discrepancies among the elderly group.

Conclusions

MMSE and RUDAS have shown a moderate correlation and agreement among the studied group. However, relatively higher concordance was observed among elderly individuals. Hence, both the screening tools should primarily be used for the elderly group. Whereas, modifications maybe needed to administer them on the younger group.

MMSE

RUDAS

Cognitive impairment

Dementia

homogeneous population

Rural Punjab

Dementia affects more than 50 million people worldwide and causes a huge burden and a challenge for healthcare professionals and society as whole [1]. Dementia is accounted for the seventh leading cause of death worldwide [1]. Similarly, the prevalence of dementia among older adults is 8.8 million across Indian states. Also, a high prevalence was observed among females than males and in rural than urban areas [2]. World population is ageing rapidly due to the demographic transition and advancements in health infrastructure. There is a tremendous upsurge of 36% in the elderly population during the last two decades i.e., from 2001 to 2011 & 2011 to 2021 [3]. Impaired cognitive abilities can be resulted in the pathological extremities like dementia which may result in several comorbid conditions, dependency among elderlies and their families [4]. Alongside, their quality of life, activities of daily living (ADL) and instrumental activities of daily living (IADL) also gets worse [4].

One of the major challenges faced by the healthcare-professionals in contemporary times is to find appropriate and validated measures of cognitive impairment in a community setting. Though the number of screening tools have developed for the assessment and diagnosis of cognitive impairment among clinical and research settings, MMSE and RUDAS are still an extensively validated tools found to be used in the epidemiogical researches. However, there are large discrepancies in the estimates of the prevalence of cognitive impairment due to methodological and administration issues [5].

Mini mental state examination (MMSE) is one of the most common screening tools, despite its limitations among populations having low level of literacy and socio-economy across various culturally and linguistically diverse population groups. Rowland Universal Dementia Assessment Scale (RUDAS) a screening tool for cognitive impairment was specifically developed to minimize the impact of cultural and linguistic factors on the test performance [6–8]. RUDAS tool was validated in culturally and linguistically diverse populations (CLAD) in Australia where it was initially designed and internationally as well [6, 9, 10]. Similar studies have also been done across different population groups worldwide, but they were conducted on a single region and having considerably less sample size also on clinically established patients of dementia, thus critiqued with lack of representation and reliability [11–13]. Studies particularly comparing the MMSE and RUDAS in the detection of cognitive impairment are almost negligible worldwide [14].

Multiple studies highlight socio-demographic and lifestyle factors contributing to cognitive impairment, including age, sex, literacy, marital status, employment, income, exercise, mental health, social engagement, chronic diseases, smoking, alcohol, and diet [15, 16]. Certain contentious results have also been documented, potentially stemming from differences in study locations, variations in research methods, a range of ages considered, and the use of diverse assessment criteria. Literacy level being the most important factor having a strong influence on the test performance, though unexpected results such as individuals with more literacy level performing poorer than their counterparts with low literacy level have also been observed [17, 18]. It is also necessary to understand the discrepancies in the potential risk factors affecting cognitive function on administering varied assessment scales on the same sample [14].

Hence, to bridge the existing gap, to the best of our knowledge this study is the first to compare the test performance of the two cognitive screening tools namely MMSE and RUDAS in terms of CI prevalence and also to test their concordance in various sociodemographic and lifestyle variables categories among rural homogeneous population group of Mansa district of Punjab, India.

Study design and recruitment of participants

The study design used for the present study was population-based, cross-sectional, which was carried on a total of 956 individuals both the sexes (59.5% males and 40.5% females) belonging to the age 18–76 years (median age-50 years) were recruited from Jat Sikh community residing in rural areas of Mansa district, Punjab, India. All the participants were recruited by randomly visiting door to door among Jat Sikh community of Mansa district, Punjab, India. Individuals with history of cardiovascular diseases, cancers, a long-term medication and other comorbid conditions that may affect cognitive status, were excluded from the study. Also, pregnant women and lactating mothers were also excluded.

Ethical clearance

The study's protocol received approval from the ethics committee of Department of Anthropology, University of Delhi. Prior to data collection, each participant provided voluntary informed written consent in Punjabi. (Reference No. Anth/2022-23/823)

Socio-demographic and lifestyle data collection

Data pertaining to sociodemographic variables like age, sex, education status, marital status, current employment status, family size, annual household income, current smoking, alcohol intake and diet pattern were collected by using pretested and modified interview schedules. Further, all the above collected characteristics were categorized for different statistical analyses age is reported as median age; sex: males & females, educational status: illiterate, 1st -5th, 6th -8th, 9th -12th, graduates & postgraduates; marital status: married, unmarried/widowed/separated; current employment status: employed, unemployed, housewives, agriculture; median was calculated for total number of family members which came out to be 5 then family size was categorized into two categories < 5, >=5; same in the case of annual family income i.e. <200000, >=200000 INR; smoking status and alcohol intake in the last 3 months: yes and no; diet pattern: vegetarian and non-vegetarian.

Assessment of cognitive status

All the recruited participants were screened for cognitive status by using mini mental state examination (MMSE) and Rowland universal dementia assessment scale (RUDAS) which are two extensively used tools in epidemiological studies worldwide [8–19]. All the participants were screened strictly by face-to-face interview as per the protocol and guidelines by trained investigators.

The MMSE is a 30-point scale, comprised of 10 domains of cognitive skills namely orientation, registration, attention & calculation, recall, language tests, repetition, follow command, reading, writing and copying. As per MMSE cut off, individuals who have scored more than or equal to 24 points indicates a better cognitive function while, individuals who have scored less than 24 points were considered to fall in cognitive impairment category. Similarly, RUDAS is also a 30-point scale constitutes 8 domains of cognitive skills namely memory, visuospatial orientation, praxis, visuconstructional drawing, judgement, memory recall and language. As per RUDAS cut off, individuals who have score 23 and above points were considered to have a normal cognitive status while those who have scored less than 23 points were considered to fall in cognitive impairment category.

Statistical analysis

All the statistical tests were performed by using IBM’s SPSS software version 22. Mean ± standard deviation and median IQR at 25th and 75th percentiles were calculated for continuous variables while, percentage distribution as n (%) was calculated for the categorical variables. Non-parametric tests i.e., Mann-witney and Kruskal-Walllis analysis was performed to test the differences in the distribution of MMSE and RUDAS total scores among all the sociodemographic and lifestyle characteristics. Kappa value was calculated to see the agreement between both the scales (MMSE and RUDAS). The Kolmogorov-Smirnov test was used to determine whether the data was distributed normally or not. The categorical variables have been documented with their corresponding frequencies and percentages. The chi-square test has been utilized to assess variations in proportions. Relative standard deviation was calculated as SD/Mean*100.

General overview of the studied sample

Out of the total sample of 956 participants proportion of females (59.5%) was higher than males (40.5). Median age of the overall sample was 52 years. 60% of the studied sample had formal education (from primary level upto postgraduation). Majority of the participants were married (84.6%). Almost 50% of the females were housewives and 28% of the participants were farmers. Almost 60% of the participants were having income higher than its median. People who have smoked and consumed alcohol in the last three months were 5.1% and 26.1% respectively. More than half of the studied sample (64.5%) was vegetarian.

Agreement between MMSE and RUDAS in detecting the prevalence of cognitive impairment

And as per the cut offs, the prevalence of CI in the studied population by MMSE and RUDAS was estimated to be 40.3% and 27.8% respectively. Individuals who were screened positive for CI from both the tools were 199/956 (20.81%), individuals who were screened positive only for CI by MMSE but normal as per RUDAS were 186/956 (19.45%), individuals who were screened positive for CI by RUDAS but normal as per MMSE 67/956 (7%) and 504/956 (52.71%) were tested to have normal cognition from both the tools (Table 1). MMSE and RUDAS both were found to be in concordance in detecting CI, 74.8% in overall sample while, it was 71.5% among participants aged below 60 years and 79.1% among participants aged >=60 years (Table 1). The highest concordance between both the tools was observed among elderly participants and further, clarifications are required in detecting CI among the younger group (individuals of age <60 years) in the present study. Moreover, Kappa value reflected an agreement for detecting CI using MMSE versus RUDAS was 0.42 with p<0.001 for the overall sample, 0.408 with p<0.001 for individuals aged below 60 years, and 0.40 with p<0.001 for participants aged 60 years and above (Table 1). Similarly, scattered plots have also indicated a good correlation between scores of MMSE and RUDAS among 60 years and above age group (R²=0.35) (Fig. 1).

Agreement between MMSE and RUDAS in various sociodemographic and lifestyle categories.

Concordance as well as discrepancies between MMSE and RUDAS in detecting CI among various socio-demographic and lifestyle categories were observed in the present study. In an overall sample, female participants, illiterates, unmarried/widowed/separated, unemployed and follows vegetarian diet pattern were found in significantly higher proportions in CI category identified by both the tools while, significantly higher proportion of individual who smoked were in CI category only by MMSE (Table 2). Further, the overall sample was stratified into two age groups <60 years (n=671) and >=60 years (n=285). Among below 60 years group, female sex, illiteracy, unmarried/widowed/separated status were found to be in significantly higher proportions in the category of CI from the both the tools while, unemployed individuals, those having family members >=5, who smoked and follows vegetarian diet were found to be in significantly higher proportions in category of CI only from MMSE and not RUDAS (Supplementary table 1). However, among above 60 years age group significantly higher proportion of individuals who were illiterate, having family members >=5, had annual income <200000 INR and follows vegetarian diet pattern were found to be in CI category from both the tools (Supplementary table 2).

The present population-based, cross-sectional study was carried out to compare the test performance of two widely used & validated tools in epidemiological researches across nations, namely mini mental state examination (MMSE) & Rowland U Dementia Assessment Scale (RUDAS) for screening cognitive impairment (CI) among adults (n = 956) of homogeneous population group from different villages of Mansa district of Punjab, North India.

MMSE is the most extensively used screening tool for cognitive impairment in various clinical and population-based research settings worldwide [20]. MMSE has been reported to be biased for years of formal education but still one of the most used screening tools for dementia in numerous studies globally [21]. Though RUDAS has not been used extensively but it has been reported to be a validated screening tool in culturally diverse population groups and it is free from bias on the basis of education and gender [22]. Both the scales ranges from 0–30 points that allows a direct comparison between them. However, limited literature is available on comparison of the test performance of these two tools in a homogeneous group.

In the present study the prevalence of CI was found to be 40.3% and 27.8% by using MMSE & RUDAS respectively. However, prevalence among the participants aged > = 60 years was found to be 54.7% and 40.4% by MMSE & RUDAS respectively. A disparity of over 10% was noted in the prevalence rate estimated by the two tools. MMSE was found to be having a moderate correlation with RUDAS and moderate agreement in detecting CI. Moreover, concerning the proportions of individuals who screened positive for CI, the highest agreement (79.1%) in detecting CI between the two tools was observed within the 60 years and above age group, compared to the overall sample (74.8%) and the under 60 years age group (71.5%).

Furthermore, both the tools exhibited the ceiling effect (28–30 points) among the highest educated group (postgraduates) in the overall sample and below 60 years group however, no such effect was observed among 60 years and above age group which also suggest that both the tools are giving better performance among elderly individuals. However, in other recent studies, only MMSE has been reported to exhibit the ceiling effect or a constrained dynamic performance range for healthy people, which raises the possibility that people in the early stages of dementia will function inside the normal range [23]. In addition, the RSD% (relative standard deviation percentage) for MMSE and RUDAS were 19.1% and 17.1% respectively that suggest that scores of both MMSE and RUDAS were distributed in the sample across a broader range of scores with less ceiling effect.

In addition, the test performance of both the tools in different sociodemographic and lifestyle categories was compared. Among below 60 age group both the tools shown similar performance for sex, literacy status & marital status while both tools behaved differently for employment status, family size, annual income, smoking behavior and diet pattern. Whereas, among elderly participants both the tools were behaving similarly for all the studied variables. Which revealed that there were no discrepancies between both the tools among 60 years and above age group than below 60 age group. Hence, it also suggests that both the tools were performing in relatively higher concordance and no discrepancy in detecting CI among elderly group in the studied sample. Various other related studies which were conducted on patients suffering from neurological and mental health disorders also suggested a good correlation and concordance in the test performance of MMSE and RUDAS among the patients of dementia and Alzheimer’s disease [13, 24, 25].

Even within a seemingly homogeneous population, notable discrepancies persist between MMSE and RUDAS in assessing CI. The findings underscore the potential effectiveness and compatibility of MMSE and RUDAS for identifying CI in older individuals. Recognizing limitations in assessing CI among younger groups underscores the need to tailor methods of cognitive assessment. This may involves adding age-appropriate domains, tech considerations, and cultural elements to enhance accuracy.

CI: Cognitive impairment; MMSE: Mini-Mental State Examination; RUDAS: Rowland Dementia Assessment Scale; AD: Alzheimer’s disease; RSD: Relative standard deviation; SD: Standard deviation; IQR: Interquartile range

Acknowledgements

None.

Authors’ contributions

K.N.S. and R.K.D. has designed the idea for the manuscript, N.Y. & V.C. analysed the data, and wrote the first draft of the manuscript. N.K.D., N.Y. & V.C. has designed and coordinated the study. N.Y. collected and managed the data. N.K.D., K.N.S., N.Y. and V.C. has revised the manuscript. All the authors read and approved the final manuscript as submitted.

Funding

The present work was supported by the University of Delhi, Institution of Eminence (IoE) [Grant No. /IoE/2021/12/FRP] and the University grant commission (UGC) [reference no. 190520154540].

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Consent for publication

Not applicable.

Competing interests

Authors declare that they have no competing interests.

Author details

¹Department of Anthropology, University of Delhi, Delhi-110007.²The Institute of Human Behavior and Allied Sciences, Delhi, Delhi, India.

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Table 1 Agreement of MMSE and RUDAS scores in detecting CI among studied sample.

Overall (956)

Kappa

p-value

<60 years (671)

Kappa

p-value

>=60 years (285)

Kappa

p-value

MMSE

Normal

RUDAS

Normal

504 (73.0)

186 (27.0%)

0.421

<0.001

399 (76.7)

121 (23.3)

0.408

<0.001

105 (61.8)

65 (38.2)

0.387

<0.001

67 (25.2)

199 (74.8)

43 (28.5)

108 (71.5)

24 (20.9)

91 (79.1)

Table 2 Distribution and chi-square between MMSE and RUDAS outcomes among socio-demographic variables in overall sample.

		Overall (956)	MMSE score		p-value	RUDAS score		p-value	Screened positive for CI			p-value
		Overall (956)	MMSE score			RUDAS score			MMSE	p-value	RUDAS
		N (%)	Mean ± SD	Median (IQR)		Mean ± SD	Median (IQR)		N (%)		N (%)
Median Age (IQR)		50 (38-60)	23.5 ± 4.5	24 (21-27)	-	23.9 ± 4.1	25 (22-27)	-	385 (40.3)	-	266 (27.8)	-
Sex	Males	387 (40.5)	24 ± 4.3	25 (21-27)	P_mean-0.062 P_median-0.010*	24 ± 3.8	25 (23-27)	P_mean-0.017* P_median-0.134	139 (36.1)	0.024*	93 (35.0)	0.031*
Sex	Females	569 (59.5)	23 ± 4.7	24 (20-27)	P_mean-0.062 P_median-0.010*	23 ± 4.3	25 (22-27)	P_mean-0.017* P_median-0.134	246 (63.9)	0.024*	173 (65.0)	0.031*
Literacy Status	Illiterates	391 (41.0)	20 ± 4.7	21 (18-24)	P_mean-<0.001* p_median-<0.001*	21.9 ± 4.4	23 (20-25)	P_mean-<0.001* P_median-<0.001*	255 (66.6)	<0.001*	183 (69.1)	<0.001*
	1-5^th	149 (15.6)	24.1 ± 3.8	25 (22-27)		24.1 ± 3.6	25 (23-26)		55 (14.4)		35 (13.2)
	6^th-8^th	123 (12.9)	25.03 ± 3.4	25 (23-28)		25.1 ± 3.6	26 (24-28)		32 (8.4)		17 (6.4)
	9^th-12^th	233 (24.4)	26.1 ± 3.05	27 (24-29)		25.9 ± 2.7	26 (25-28)		34 (8.9)		28 (10.6)
	Graduated	42 (4.4)	26.5 ± 2.6	26 (25-29)		26.1 ± 2.5	26 (24-28)		5 (1.3)		2 (0.8)
	Post-Graduated	15 (1.6)	27.6 ± 2.8	29 (25-30)		27.2 ± 2.5	28 (24-30)		2 (0.5)		0 (0.0)
Marital status	Married	809 (84.6)	23.6 ± 4.5	24 (21-27)	p_mean-0.051 p_median-0.07*	24.5 ± 4.1	25 (22-27)	P_mean-0.001* P_median-<0.001*	313 (81.3)	0.019*	212 (79.7)	0.009*
Marital status	Unmarried/widowed/Separated	147 (15.4)	22.8 ± 4.9	24 (19-27)	p_mean-0.051 p_median-0.07*	22.9 ± 4.3	24 (20-26)	P_mean-0.001* P_median-<0.001*	72 (18.7)	0.019*	54 (20.3)	0.009*
Current employment status	Employed	141 (14.8)	24.5 ± 4.04	25 (22-28)	p_mean-<0.034* p_median-<0.001*	24.7 ± 3.7	26 (23-28)	P_mean-0.156 P_median-<0.001*	50 (13.1)	0.04*	30 (11.3)	0.035*
	Unemployed	39 (4.1)	21.1 ± 5.06	21 (18-25)		21.6 ± 4.5	23 (19-25)		23 (6.0)		16 (6.0)
	Housewife	462 (48.6)	23.2 ± 4.8	24 (20-27)		23.6 ± 4.4	25 (22-27)		196 (51.4)		142 (53.6)
	Agriculture	273 (28.7)	24.02 ± 4.1	25 (21-27)		24.4 ± 3.3	25 (23-27)		100 (26.2)		66 (24.9)
	Retired/Dependent	35 (3.7)	23.4 ± 4.9	24 (21-26)		22.6 ± 4.8	24 (22-25)		12 (3.1)		11 (4.2)
Family size	<5	387 (41.0)	23.2 ± 4.6	24 (20-27)	p_mean-0.279 p_median-0.037*	23.8 ± 4.1	25 (22-27)	p_mean-0.338 p_median-0.495	173 (45.8)	0.05	114 (43)	0.430
Family size	>=5	557 (59.0)	23.8 ± 4.5	25 (21-27)	p_mean-0.279 p_median-0.037*	23.9 ± 4.1	25 (22-27)	p_mean-0.338 p_median-0.495	205 (54.2)	0.05	151 (57)	0.430
Annual income	≥200000	521 (58.0)	23.8 ± 4.4	25 (21-27)	p_mean-0.118 p_median-0.136	24.1± 4.07	25 (22-27)	P_mean-0.278 P_median-0.06	200 (56.0)	0.34	137 (55.2)	0.309
Annual income	<200000	378 (42.0)	23.3 ± 4.7	24 (20-27)	p_mean-0.118 p_median-0.136	23.6 ± 4.2	25 (22-26)	P_mean-0.278 P_median-0.06	157 (44.0)	0.34	111 (44.8)	0.309
Smoking status	No	892 (94.9)	23.6 ± 4.5	24 (21-27)	p_mean-0.116 p_median-0.01*	24.01 ± 4.1	25 (22-27)	P_mean-0.209 P_median-0.06	354 (93.2)	0.04*	243 (93.5)	0.217
Smoking status	Yes	48 (5.1)	21.8 ± 4.9	23 (17-26)	p_mean-0.116 p_median-0.01*	22.8 ± 4.4	24 (20-26)	P_mean-0.209 P_median-0.06	26 (6.8)	0.04*	17 (6.5)	0.217
Alcohol intake	No	704 (73.9)	23.3 ± 4.7	24 (20-27)	p_mean-0.042* p_median-0.101	23.8 ± 4.3	25 (22-27)	P_mean-0.108 P_median-0.333	291 (75.8)	0.270	202 (75.9)	0.366
Alcohol intake	Yes	249 (26.1)	24.04 ± 4.2	25 (21-27)	p_mean-0.042* p_median-0.101	24.2 ± 3.7	25 (22-27)	P_mean-0.108 P_median-0.333	93 (24.2)	0.270	64 (24.1)	0.366
Diet pattern	Veg	534 (64.5)	22.7 ± 4.7	24 (19-26)	p_mean-<0.015* p_median-<0.001*	23.7 ± 4.3	25 (22-26)	P_mean-0.030* P_median-0.01*	254 (73.6)	<0.001*	160 (70.8)	0.020*
Diet pattern	Non-veg	294 (35.5)	24.4 ± 4.3	25 (22-28)	p_mean-<0.015* p_median-<0.001*	24.5 ± 3.7	25 (23-27)	P_mean-0.030* P_median-0.01*	91 (26.4)	<0.001*	66 (29.2)	0.020*

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Comparing MMSE and RUDAS in detecting cognitive impairment: A population-based study from Punjab, North India

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Abstract

Background

Methods

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Background

Methods

Study design and recruitment of participants

Ethical clearance

Socio-demographic and lifestyle data collection

Assessment of cognitive status

Statistical analysis

Results

Discussion

Conclusions

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