|Year : 2014 | Volume
| Issue : 2 | Page : 135-143
Prevalence and correlates of cognitive impairment in a north Indian elderly population
Paramita Sengupta1, Anoop I Benjamin1, Yashpal Singh1, Ashoo Grover2
1 Christian Medical College, Ludhiana, Punjab, India
2 Division of NCD, Indian Council of Medical Research, New Delhi, India
|Date of Web Publication||22-May-2017|
Professor, Department of Community Medicine, Christian Medical College, Ludhiana 141008, Punjab
Background: Cognitive impairment, an age-related condition, is often considered a precursor to more serious diseases such as depression/dementia/Alzheimer’s disease. Alzheimer’s disease, which is characterized by cognitive impairment, could have a devastating impact on low- and middle-income countries whose populations are ageing rapidly. The disease has, so far, largely remained neglected by researchers and national health services in India. In view of the growing elderly population and diverse sociocultural and geographical milieu of India, epidemiological data for the condition are desirable for different populations. Moreover, there is a dearth of population-based epidemiological studies on cognitive impairment in the Punjab state of India.
Methods: Three thousand and thirty-eight consenting elderly adults aged over 60 years, of both sexes, residing in Ludhiana, Punjab state, India were examined for cognitive functioning, using a modified Hindi Mental State Examination, and a score of ≤25 was considered to be indicative of cognitive impairment. Data analysis included calculations of proportions; odds ratio and their 95% confidence intervals (CIs) were also calculated. The chi-square test and multiple logistic regression model were used to determine the association of cognitive impairment with various sociodemographic parameters.
Results: The prevalence of cognitive impairment in the study population was 8.8% (95% CI = 8.06 to 9.54). Increasing age, unmarried/widowed status, illiteracy, unemployment and poverty were found to be independently associated with cognitive impairment.
Conclusions: The prevalence of cognitive impairment in this north Indian population of elderly individuals is higher than that found in northern India. With emerging focus on geriatric health services, cognitive impairment, which is a precursor of Alzheimer’s disease/dementias, needs to be included in priority care within the national primary health-care framework.
Keywords: Alzheimer’s disease, cognitive impairment, elderly, Hindi Mini-Mental State Examination, Mini-Mental State Examination, prevalence
|How to cite this article:|
Sengupta P, Benjamin AI, Singh Y, Grover A. Prevalence and correlates of cognitive impairment in a north Indian elderly population. WHO South-East Asia J Public Health 2014;3:135-43
|How to cite this URL:|
Sengupta P, Benjamin AI, Singh Y, Grover A. Prevalence and correlates of cognitive impairment in a north Indian elderly population. WHO South-East Asia J Public Health [serial online] 2014 [cited 2019 Aug 23];3:135-43. Available from: http://www.who-seajph.org/text.asp?2014/3/2/135/206729
| Introduction|| |
It has been estimated that in India, the population of those aged over 60 years will have increased from its level of 7.7% in 2001 to 12.30% by 2025, and there will be nearly 150 million elderly individuals. Cognitive disability or dementia is a relatively common disorder among the elderly. Most people with cognitive disability live in low- or middle-income countries (60% in 2001, estimated to rise to 71% by 2040); the rate of increase in cognitive disability over the decades is around 300% for India, whereas it is estimated to be only 100% in high-income countries. A neurologically degenerative disorder is the underlying cause in the majority of cases of significant cognitive decline.
The importance of dementia, a syndrome of global cognitive impairment, has gained widespread recognition in the past two decades. The most common cause, Alzheimer’s disease, may be the single greatest source of dysfunction among persons aged over 85 years. A working group of the National Institute on Aging and the Alzheimer’s Association came to a consensus that there is a phase of Alzheimer’s disease when individuals experience a gradually progressive cognitive decline that results from deposition of abnormal substances in the brain. When the cognitive impairment is sufficiently great, such that there is interference with daily functioning, the patient is diagnosed to have dementia due to Alzheimer’s disease. Dementia and Alzheimer’s disease are age related. The frequency of dementia doubles every 5 years after the age of 60 years.
In the year 2000, India had 3.5 million patients with Alzheimer’s disease/dementia as compared with 4.5 million in the United States of America (USA). Alzheimer’s disease, the most common form of dementia,, is an acquired cognitive and behavioural impairment of sufficient seventy to markedly interfere with social and occupational functioning. The presence of different biomarkers may suggest the onset of Alzheimer’s disease but does not lead to a definite diagnosis. The condition itself is difficult to diagnose and requires assessment by an experienced clinician. The burden of detailed assessment can be lessened by cognitive screening in community-based studies. Those who screen positive can be further evaluated. A study of individuals with mild cognitive impairment (MCI) has shown that those who have memory impairment as a prominent feature in their cognitive profile (i.e. amnestic MCI) have the highest probability of developing Alzheimer’s disease in the future. Hence, although cognitive impairment by itself cannot be labelled as Alzheimer’s disease, considering that it is the single most consistent and characteristic symptom of the disease, it may be considered as indicative of or a proxy for Alzheimer’s disease in population-based studies.
In India, the prevalence of cognitive impairment in the elderly measured using the Hindi Mental State Examination (HMSE) or modified Mini-Mental State Examination (MMSE) was reported to vary from 3.5% in Himachal Pradesh to 5.1% in Uttar Pradesh and 6.5% in Kashmir. A study in Kerala, south India, found the prevalence of cognitive impairment to be 11.5% in those aged >65 years. On further assessment by the criteria of the Diagnostic and statistical manual of mental disorders (DSM-IV) for the cognitively impaired, the prevalence of Alzheimer’s disease was found to be 15.5/1000 (95% confidence interval [CI] = 9.6/1000 to 20.0/1000) and the male:female ratio for the number of cases was 1:1.3. A study in Ballabgarh, Haryana found the overall prevalence of Alzheimer’s disease to be 0.62% in those aged >55 years and 1.07% in those aged >65 years.
India, the second most populous country in the world after China, is a mosaic of diverse peoples, cultures and sociodemographic and regional characteristics. A definite diagnosis of Alzheimer’s disease or most other diseases that cause dementia can only be made post mortem, after necropsy. Elderly individuals with cognitive impairment may experience loss of self-confidence and of prestige and importance within the household, and increased financial dependence on the family, and may be left out of decision-making in the family. Patients in the earliest stages of Alzheimer’s disease and other dementias may potentially profit most from disease-modifying drugs, which underlines the importance of a correct clinical diagnosis early in the course of the disease. Hence, studies in different populations and regions of the country are necessary to obtain epidemiological information regarding cognitive impairment, which is a precursor to Alzheimer’s disease/ dementia. In view of the paucity of epidemiological data on cognitive impairment/Alzheimer’s disease in Punjab state, this study was carried out to ascertain the prevalence of cognitive impairment in the elderly and to gain an insight into some sociodemographic factors that may have a bearing on the prevalence of the disease in this population.
| Methods|| |
Study design and duration
This cross-sectional study was carried out between 1 June and 30 November 2011.
The study population comprised all the elderly residents aged over 60 years, of both sexes, residing in the field practice area of the Department of Community Medicine, Christian Medical College, Ludhiana, Punjab, India, which covers a population of approximately 45 000 (30 000 urban and 15 000 rural). The department has a well-established health management information system that uses the “family folder” methodology for constant community monitoring, wherein each household has a family folder, in which the demographic and health profile of the family is maintained and updated through regular house-to-house visits in a beat visit fashion, by a health team consisting of a multipurpose health worker (female), medical interns and nursing students.
Sample size estimation
With those aged >60 years constituting 7.2% of the population of Punjab state, according to Sample Registration System (SRS) estimates for 2003, there were expected to be about 3240 elderly individuals in the population of approximately 45 000 under study. If the prevalence of cognitive impairment in the elderly population under study is presumed to be similar to the prevalence of Alzheimer’s disease in those aged >65 years in the neighbouring state of Haryana, as found in the study in Ballabgarh i.e. 1.07%, the sample size required at 95% confidence limit and 10% allowable error would be 2955 (EpiInfo version 6, Statcalc, calculation for sample size estimation for population surveys). All the elderly individuals in this population of 45 000 were studied. Though no sampling was done, the sample size was estimated only to find out the minimum number of elderly individuals requiring to be studied in the absence of epidemiological information on cognitive impairment/Alzheimer’s disease among the elderly in this area. A total of 3054 elderly individuals were found in the population under study through house-to-house visiting, of which 16 met the exclusion criteria, and the final study sample consisted of 3038 respondents.
To meet the core clinical criteria for MCI, it is necessary to rule out other systemic or brain diseases that could account for the decline in cognition (e.g. vascular, traumatic or medical causes). The goal of such an evaluation is to increase the likelihood that the underlying disease is a neurodegenerative disorder with characteristics consistent with Alzheimer’s disease. Elderly individuals with a history of neurological disorders (stroke, Parkinson’s disease, severe head injury or brain neoplasm) were excluded. Those who were blind, or hearing and/or speech impaired; those with diagnosed psychiatric illness (schizophrenia, mental retardation); and those who were too ill at the time of the study, were also excluded, as it would have been difficult to obtain reliable information from them. Sixteen individuals met the exclusion criteria. The response rate was 100%, as the communities studied had belonged to the field practice areas of the authors’ department for the past five decades and thus sufficient rapport existed between the health workers and the families through regular home visits by these workers.
Ethical approval and consent
Before commencement, the study protocol was approved by the institutional ethics committee. Informed consent was obtained from the respondents, and from the caregiver in the event of the subject being unable to give consent, by obtaining their signatures or, in the case of illiterate respondents, by their thumb impressions.
Data collection and instruments
House-to-house visits were carried out among the study population, and all those in the household in the eligible age group were included in the study. A pre-tested questionnaire was used to obtain sociodemographic information from consenting respondents about their residence, sex, age, marital status, type of family, literacy, present employment status, family income and the respondent's own income (independent income such as from regular paid employment, pension, assets like property/agricultural land, etc.), to assess his/her financial dependence on the family, and participation of the elderly respondent in family decision-making. Respondents who had received formal schooling for up to at least the fifth class (primary schooling; age 9–10 years) were considered literate.
The respondents were assessed for cognitive function by a team of investigators, which included a neurologist. The MMSE is the most common screening tool for the evaluation of cognitive impairment/Alzheimer’s disease dementia. It is particularly important to assess the performance of the MMSE in research protocols to establish the incidence or prevalence of cognitive impairment and to examine correlates of mental statusľ The MMSE consists of simple questions and tasks with a maximum score of 30. Scores below 25 are considered to be congruent with a diagnosis of dementia. In a study in 3974 community-dwelling individuals aged 65–84 years, logistic regression analysis was used to examine the independent and joint effects of sociodemographic variables on borderline (MMSE score 22–25) and poor (MMSE score ≤21) functioning, relative to adequate functioning (MMSE sore 26–30). The effect of age and education on MMSE performance was found to be relatively stable, even after adjusting for age- and education-related health conditions and sensory impairments that also influenced the level of cognitive functioning.
Low- and middle-income countries have a large number of elderly people who are illiterate. Ganguly’s HMSE, a tool developed by the Indo–US Cross-National Dementia Epidemiology Study, is simple, rapidly administered and addressed to people who are illiterate. This tool is intended only to screen individuals and populations for cognitive impairment and not for diagnostic purposes. It is composed of 22 items that examine various cognitive capacities, with a total score of 30, if all items are answered, as in the MMSE. The HMSE has already been validated. In a study on 100 illiterate elderly subjects (50 without dementia and 50 with Alzheimer’s disease), Tsolaki et al. found the HMSE to be 94% sensitive and 98% specific; hence, the test was deemed to be fit to be used as a screening test for illiterate elderly people. This HMSE, while retaining the original MMSE scoring and cut-off, also took care of the rural illiterate Indian population by carefully substituting some words with relevant local words, so that it could be used for both urban and rural residents. The HMSE was used for the present study, after being suitably modified by replacing some words with others that were more appropriate for the Punjabi population. It was then translated into the local language Punjabi, and back-translated into Hindi and English by two independent bilingual translators, to ensure the integrity of the translation. The interview was conducted by trained health workers in the language (Hindi/Punjabi) with which the subject was familiar.
Operational definition of cognitive impairment
Applying the HMSE, suitably adapted for this population, those with MMSE scores ≤25 were considered “cognitively impaired” in the present study.,,,, These cognitively impaired individuals were further classified as severe (MMSE score ≤10), moderate (MMSE score 11–21) or mild (MMSE score 22–25).
The data were analysed using EpiInfo version 6 software. The prevalence of cognitive impairment, and its severity according to the sociodemographic parameters under study, was calculated in terms of proportions. The chi-square test was applied, and odds ratios along with their 95% CIs were calculated, where appropriate. Multiple logistic regression analysis was carried out using SPSS software version 21.
| Results|| |
[Table 1] shows the sociodemographic characteristics of the study population. Rural/urban and male/female ratios were 1790/1248 and 1384/1654, respectively, with the majority of the respondents being in the age group 61–65 years.
|Table 1: Sociodemographic characteristics of the study population (N = 3038)|
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The prevalence of cognitive impairment in the study population was 8.8% (95% CI = 8.06 to 9.54). The proportion of individuals with cognitive impairment was higher among women, individuals who were older, unmarried/widowed, illiterate, unemployed, and poorer (P<0.05). No statistically significant differences were found in the prevalence of cognitive impairment in relation to residence (urban/rural) and type of family (nuclear/joint) (see [Table 2]). Gender differences in the prevalence of cognitive impairment among the 978 widowed respondents (data not shown in table) also showed that the widows had a higher prevalence (16.1%) than the widowers (8.8%), and the differences were statistically significant (OR 1.99, 95% CI 1.23–3.26, P=0.003).
Multiple logistic regression analysis was used to assess the association of sociodemographic factors with cognitive impairment, controlling for each of the factors significantly associated with cognitive impairment at the bivariate level (as per [Table 2]). Based on the adjusted analysis, age above 70 years, unmarried/widowed status, illiteracy, unemployment and monthly per capita income of ≤1000 rupees, were found to be independently associated with cognitive impairment (see [Table 3]). Sex was no longer statistically significantly associated with cognitive impairment after controlling for confounders.
|Table 3: Association of sociodemographic factors with cognitive impairment (multiple logistic regression)|
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Among the 268 respondents with cognitive impairment, 163 (60.8%) had mild impairment, 63 (23.5%) had moderate and 42 (15.7%) had severe impairment. [Table 4] shows the trends in severity of cognitive impairment within each sociodemographic category. For all sociodemographic characteristics except for age, the majority of respondents had mild cognitive impairment, followed by moderate and severe impairment. The proportion of individuals with severe cognitive impairment (versus mild and moderate) was the highest among individuals aged >80 years (N=19, 38.8%). The odds of having severe cognitive impairment were 4.5 times greater among individuals aged >70 years, in comparison with those aged 61–70 years (odds ratio [OR]=4.50, 95% CI=2.05 to 10.09, P<0.001). The odds of having severe cognitive impairment among those aged over 80 years were 5.4 times greater than the other elderly aged 61–80 years (OR=5.40, 95% CI=2.48 to 11.78, P<0.001), and 6.6 times greater than the younger elderly aged 61–65 years (OR=6.57, 95% CI 2.40 to 18.47, P<0.001). The odds of severe cognitive impairment were significantly higher among rural subjects (OR=2.18, 95% 0=1.06 to 4.50, P=0.021) and in those who were unmarried/ widowed (OR=2.59, 95% CI=1.20 to 5.66, p=0.008); 93.9% of those with mild cognitive impairment, and all those with moderate or severe cognitive impairment, were unemployed (P=0.035). No statistically significant differences were observed in the severity of cognitive impairment in relation to sex, family type, literacy or income.
|Table 4: Level of impairment among individuals with cognitive impairment (N = 268)|
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As shown in [Table 5], individuals who were cognitively impaired were observed to be more financially dependent on the family (N = 191/268, 71.3%) than those with no cognitive impairment (N = 1718/2770, 62.0%, P=0.003), with no statistically significant difference among the different severity levels of the impairment (P=0.827). Individuals with cognitive impairment had less participation in family decision-making (N = 214/268, 79.8%) than those with no cognitive impairment (N = 2625/2770, 94.8%) (P<O.001), with the lowest involvement (N= 4/42, 9.5%) among those with severe cognitive impairment (P<0.001).
|Table 5: Financial dependence and participation in family decision-making|
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| Discussion|| |
The loss of cognitive abilities is one of the most feared outcomes of ageing. Half of the population of those aged 85 years and older suffers from some form of cognitive impairment, making the fear a reality for all too many. A definite diagnosis of Alzheimer’s disease or most other diseases that cause dementia can only be made post mortem, after necropsy. The most common form of dementia is Alzheimer’s disease, accounting for 60–80% of dementia cases. Vascular dementia, which occurs after a stroke, is the second most common form accounting for about 10% of cases. In general, cognitive function is influenced by a variety of factors, mainly female sex, older age, low education level, and a history of stroke., Those with a history of stroke were excluded from this study; hence, vascular dementia was excluded from the study. A study in Rotterdam found the prevalence of Alzheimer’s disease to be 4.5%, and it was the main subdiagnosis (72%) of all types of dementia.
The prevalence of cognitive impairment in the study population was 8.8%. Other studies have reported varied cognitive impairment, 3.5% in Himachal Pradesh, 5.1% in Uttar Pradesh, 6.5% in Kashmir, and 11.5% in Kerala, South India. Advanced age, widowed status, illiteracy, unemployment and lower monthly per capita income (≤1000 rupees) were found to be significant predictors of cognitive impairment in the present study. Sharma et al. also reported that the oldest, illiterate and widowed individuals had a greater probability of cognitive impairment. In a similar study using the MMSE in West Bengal, age, sex, marital status, individual earning, family earning, family size and number of living children were found to be important predictors of cognitive impairment.
Residence (urban/rural) and type of family (joint/nuclear) did not appear to significantly influence the prevalence of cognitive impairment. However, severe cognitive impairment was found to be significantly higher among the rural respondents. In the study in West Bengal, severe cognitive impairment was also relatively high in the rural elderly population. In a study in Kerala, Shaji et al. reported a higher prevalence of Alzheimer’s disease in the rural population (17/1000) than in the urban population (15.5/1000). Sharma et al. also found an increased prevalence of cognitive impairment in the rural population, though it did not appear to be significant in multivariate analysis.
A significant association was found in this study between increasing age and cognitive impairment. In a sample of age-stratified individuals aged over 55 years, Chandra et al also reported that greater age was associated with both Alzheimer's disease and overall dementia. Other researchers have also found increasing age to be associated with cognitive impairment.,,,,,
In this study, cognitive impairment was found to be higher among females than males at the bivariate level; however, this was no longer the case at the multivariate level. Similarly, Chandra et al. also found no association between sex and cognitive impairment, though females were found to have lower HMSE scores. Sharma et al. did not observe any statistically significant association of sex, residence or income with cognitive decline. Other studies have reported cognitive impairment to be more prevalent in women.,,
The study found widowed/unmarried status to be a strong predictor for cognitive impairment, particularly severe cognitive impairment.
A substantial and independent association between marital status in mid-life and cognitive function later in life was reported by Hakansson et al. in a large population-based cohort study. They reported that people without a partner had twice the risk of developing cognitive impairment and Alzheimer’s disease when compared with people living with a partner, even after controlling for other factors.
The study also found a higher likelihood of cognitive impairment in widowed women as compared with widowed men. A larger proportion of older women than men who have Alzheimer's disease is believed to be explained by the fact that women live longer. It has been suggested that lack of estrogen, or other hormonal changes in postmenopausal women, either by themselves or in association with other factors, account for the increased risk, and estrogen replacement therapy in postmenopausal women has been shown to reduce the risk of Alzheimer’s disease.
Illiteracy, poverty and unemployment were also identified as factors significantly associated with cognitive impairment in the study population. Low literacy is often linked to poverty or lower socioeconomic status, which is also associated with poorer health, poorer access to health care and increased risk of cognitive impairment. Some researchers believe that a higher level of education provides a “cognitive reserve” that enables individuals to better compensate for changes in the brain that could result in Alzheimer’s or another dementia. A lower educational level has been consistently reported to be a risk factor for cognitive impairment.,,,,, However, Chandra et al. did not establish an association between Alzheimer’s disease and literacy.
The presence of cognitive impairment was associated with the elderly being financially dependent upon the family, irrespective of the severity of cognitive impairment, and less involved in the family decision-making process. In the socioeconomic context in Indian society, this can have a major impact in lowering a person’s self-image, when a senior person who once was in a position of importance and authority in the family finds himself/herself reduced to dependency and ignored by the family in important family matters.
| Conclusions|| |
With the ageing of Indian society, maintaining cognitive health in late life is a public health priority. This study shows the prevalence of cognitive impairment in the population of Ludhiana, Punjab aged>60 years to be 8.8%, with 60.8% having mild, 23.5% moderate and 15.7% severe impairment. Those suffering from cognitive impairment were less likely to be gainfully employed, have an independent source of income, or be involved in family decision-making. Higher age, unmarried/ widowed status, illiteracy, poverty and unemployment were found to be predictors of cognitive impairment.
Individuals with Alzheimer’s disease lose, on average, 3.3 (95% CI = 2.9 to 3.7) MMSE points each year; hence, early detection of cognitive impairment in the elderly population is crucial for timely intervention. Screening of the elderly for cognitive impairment/Alzheimer’s dementia needs to be included in the national primary health-care services at grass-roots level. The “ 10 warning signs of Alzheimer’s disease” approach has been shown in this population to be a useful tool in the hands of primary health-care workers for population-based screening for Alzheimer’s disease in the elderly. The provision and financing of measures to meet the long-term care needs of people with Alzheimer’s or other dementias is an urgent priority. There is also a need for a multicentric prevalence survey of cognitive impairment in India, using neuropsychological assessment tools and biomarkers, encompassing rural/urban, regional, religious, ethnic, cultural, environmental and socioeconomic diversity. Finally, there is a great need to develop packages and programmes of care for people with dementia/Alzheimer’s disease and their caregivers, which are capable of being scaled up across the country’s mixed health-care delivery system.
Limitations of the study
Given the time and expertise needed for testing of memory and mental status, identification of cognitive impairment in the elderly in a community setting may not be very accurate. The data in this study came from a cross-sectional survey and, therefore, it is not possible to make causal inferences from the associations found. Even after translation and standardization of the MMSE instrument, there may be false positive results because of higher cut-off scores, though it is better to overestimate cognitive impairment. Moreover, the MMSE is a screening tool only. However, despite these limitations, the study provides epidemiological data for a population for which, as yet, there is no information about the condition, using methods and instruments that are comparable to those used in other studies carried out in other populations of the country.
Source of Support: Indian Council of Medical Research, New Delhi, ad hoc research grant (IRIS ID 2010S11070).
Conflict of Interest: None declared.
Contributorship: All authors contributed to the conception and design of the study. PS, AIB and YS contributed to data collection, analysis and interpretation; PS and AIB contributed to manuscript preparation; and the final manuscript was read and approved by all authors.
| References|| |
Bose A, Shankardass MK. Growing old in India: voices reveal, statistics speak. New Delhi: BR Publishing Corporation, 2000. pp. 244–246.
Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, Ganguly M, et al. Global prevalence of dementia: a Delphi consensus study. Lancet. 2005;366:2112–17.
Ritchie K, Lovestone S. The dementias. Lancet. 2002;360:1759–66.
Larson EB, Kukull WA, Katzman RL. Cognitive impairment: dementia and Alzheimer’s disease. Annu Rev Public Health. 1992;13:431–19.
Albert MS, DeKosky ST, Dicksond D, Duboise B, Feldman HH, Fox NC et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:270–9.
Development in mental health scenario: need to stop exclusion – dare to care. ICMR Bulletin. 2001;31:1–9.
Upadhyay P, Panjwani D, Yadav AK. Neuropathology staging and treatment strategies of Alzheimer’s disease: an update. International Journal of Nutrition, Pharmacology, Neurological Diseases. 2014;4:28–42.
Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR, Ofstedal MB, et al. Prevalence of dementia in the United States: the ageing demographics and memory study. Neuroepidemiology. 2007;29:125–32.
Ott A, Breteler MMB, van Harskamp F, Claus JJ, van der Cammen TJ, Grobbee DE, et al. Prevalence of Alzheimer’s disease and vascular dementia: association with education. The Rotterdam Study. BMJ. 1995;310:970–3.
Fields JA, Ferman TJ, Boeve BF, Smith GE. Neuropsychological assessment of patients with dementing illness. Nat. Rev. Neurol. 2011;7:677–87.
Ganguli M, Ratcliff G, Chandra V, Sharma S, Gilby J, Pandav R, et al. A Hindi version of the MMSE: the development of a cognitive screening instrument for a largely illiterate rural elderly population in India. International Journal of Geriatric Psychiatry. 1995;10:367–77.
Folstein MF, Folstein SE, McHugh PR. Mini mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–98.
Sharma D, Mazta SR, Parashar A. Prevalence of cognitive impairment and related factors among elderly: a population-based study. Journal of Dr. NTR University of Health Sciences. 2013;2:171–6.
Poddar K, Kant S, Singh A, Sing TB. An epidemiological study of dementia among the habitants of eastern Uttar Pradesh, India. Ann Indian Acad Neurol. 2011;14:164–8.
Raina S, Razdan S, Pandita KK, Raina S. Prevalence of dementia among Kashmiri migrants. Ann Indian Acad Neurol. 2008;11:106–8.
Shaji S, Bose S, Verghese A. Prevalence of dementia in an urban population in Kerala, India. Br J Psychiatry. 2005;
American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-IV. 4th edition. Washigton: American Psychiatric Association, 1994.
Chandra V, Ganguli M, Pandav R, Johnston J, Belle S, DeKosky ST. Prevalence of Alzheimer’s disease and other dementias in rural India. Neurology. 1998;51:1000–8.
van der Flier WM, Scheltens P. Use of laboratory and imaging investigations in dementia. J Neurol Neurosurg Psychiatry. 2005;76(Suppl V):v45–v52 doi: 10.1136/jnnp.2005.082149.
Park K. Park’s textbook of preventive and social medicine. 21st edition. Jabalpur: Banarsidas Bhanot,2011. P. 547.
Magaziner J, Bassett SS, Hebel JR. Predicting performance of the mini-mental state examination. J Am Geriatr Soc. 1987;35:996–1000.
Launer LJ, Dinkgreve MA, Jonker C, Hooijer C, Lindeboom J. Are age and education independent correlates of the mini-mental state exam performance of community-dwelling elderly? J Gerontol. 1993;48:271–7.
Ganguly M, Chandra V, Gilby JE, Ratcliff G, Sharma SD, Pandav R, et al. Cognitive test performance in a community-based nondemented elderly sample in rural India: the Indo-US Cross-national Dementia Epidemiology Study. Int Psychogeriat. 1996;8:507–24.
Tsolaki M, Iakovidou V, Navrozidou H, Aminta M, Pantazi T, Kazis A. Hindi mental state examination (HMSE) as a screening test for illiterate demented patients. Int J Geriatr Psychiatry. 2000;15:662–4.
Association for Research in Nervous and Mental Disease. Neurobiology of primary dementia. Ist edition. Edited by Marshal F. Folstein. Washington DC: American Psychiatric Press, Inc., 1998. PP. 236.
Alzheimer’s Association. Types of dementia. Washington DC: Alzheimer’s Association, 2014. - www.alz.org/dementia/types-of-dementia.asp - accessed 20 August 2014.
Anderson TM, Sachdev PS, Brodaty H, Trollor JN, Andrews G. Effects of socio-demographic and health variables on Mini-Mental State Exam scores in older Australians. Am J Geriatr Psychiatry. 2007;15:467–76.
van Hooren SA, Valentijn AM, Bosma H, Ponds RW, van Boxtel MP, Jolles J. Cognitive functioning in healthy older adults aged 64—81: a cohort study into the effects of age, sex, and education. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2007;14:40–54.
Hakansson K, Rovio S, Helkala E-L, Vilska A-R, Winblad B, Soininen H, et al. Association between mid-life marital status and cognitive function in later life: population based cohort study. BMJ. 2009;339:b2462.
Paganini-Hill A and Henderson VW. Estrogen replacement therapy and risk of Alzheimer’s disease. Arch Intern Med. 1996;156:2213–17.
Roe CM, Xiong C, Miller JP, Morris JC. Education and Alzheimer disease without dementia: support for the cognitive reserve hypothesis. Neurology. 2007;68:223–8.
Han L, Cole M, Bellavance F, McCusker J, Primeau F. Tracking cognitive decline in Alzheimer’s disease using the mini-mental state examination: a meta-analysis. International Psychogeriatrics. 2000;12:231–47.
Benjamin AI, Sengupta P, Singh Y, Grover A. Identifying Alzheimer’s disease through the “10 Warning Signs” approach: a useful tool for population-based screening in the elderly. Asian J Med Res. 2012;192:40–4..
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]