|Year : 2021 | Volume
| Issue : 2 | Page : 152-158
Prevalence of dementia in India: A systematic review and meta-analysis
Amrita Choudhary1, Jay Kumar Ranjan2, Hari Shanker Asthana3
1 Research Scholar, Department of Psychology, Banaras Hindu University, Varanasi, Uttar Pradesh, India
2 Assistant Professor, Department of Psychology, Banaras Hindu University, Varanasi, Uttar Pradesh, India
3 Professor, Department of Psychology, Banaras Hindu University, Varanasi, Uttar Pradesh, India
|Date of Submission||14-Aug-2020|
|Date of Decision||18-Feb-2021|
|Date of Acceptance||19-Apr-2021|
|Date of Web Publication||14-Jun-2021|
Jay Kumar Ranjan
Department of Psychology, Banaras Hindu University, Varanasi - 221 005, Uttar Pradesh
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: There are rich state-based epidemiological evidences on dementia in India, which shows that neurodegenerative disorders are one of the major public health problems. However, inconsistencies and variability have been observed among the findings of most of the reported studies. Objectives: This meta-analysis was conducted to estimate the overall prevalence of dementia in India. Methods: Databases of several web sources, namely EBSCOhost, PubMed, PsycINFO, and Google Scholar were explored for searching the studies that had previously observed the prevalence of dementia in India. Retrieved articles were systematically selected using specific inclusion and exclusion criteria. The quality of the included studies was assessed through guidelines of strengthening the reporting of observational studies in epidemiology, and the risk of bias was assessed using the guidelines of Quality in Prognosis Studies. Meta-analysis was computed using R software (3.5.3) with “metaphor” package. Results: The present meta-analysis included 20 epidemiological studies consisting of 86,312 persons, out of which a total number of 1193 persons reported to have dementia across several states of India. The results of the meta-analysis indicated the number of elderly that suffer from dementia amounts to 20 per 1000 population (95% confidence interval: 0.02–0.03). The prevalence was higher in older age groups (75 years and above) as compared to those below 75 years of age. However, the prevalence rates were similar for males and females and rural and urban population. Conclusion: Dementia is a common neuropsychiatric condition in the Indian elderly population. Further, evidence-based national survey is needed to estimate the exact prevalence of the disease in the country.
Keywords: Epidemiology, dementia, India, meta-analysis, prevalence
|How to cite this article:|
Choudhary A, Ranjan JK, Asthana HS. Prevalence of dementia in India: A systematic review and meta-analysis. Indian J Public Health 2021;65:152-8
|How to cite this URL:|
Choudhary A, Ranjan JK, Asthana HS. Prevalence of dementia in India: A systematic review and meta-analysis. Indian J Public Health [serial online] 2021 [cited 2023 Mar 26];65:152-8. Available from: https://www.ijph.in/text.asp?2021/65/2/152/318349
| Introduction|| |
India is currently undergoing through a phase of demographic transition wherein the aged population is rapidly growing. The life expectancy in India has almost doubled from 36.98 years in 1950–1960 to 69.27 in 2015–2020. The census conducted in 2011 has shown that the elderly population of India accounted for 103.9 million compared to 5.63 million in 1961. Decadal growth in the elderly population accounted for 35.5% in 2001–2011 compared to only 23.9% in 1951–1961. This growth has been attributed to the changes in mortality rate, development of basic health infrastructure, growth in economy, and increment in literacy rate. Mental health conditions of elderly play a significant role in their morbidity and premature mortality. Dementia is one of the leading contributors of disability in the elderly among all the neuropsychiatric conditions. Therefore, these shifts in the paradigm may affect the prevalence rates of dementia in India. There have been various researches in different parts of India that have reported the incidence rates of dementia.
Prevalence of dementia in South Asia was 1.9% in the year 2005 and estimated to reach 3.6 million by 2020 and 7.5 million by 2040. Various epidemiological evidences have been reported a varying prevalence rate of Dementia in India, ranging from 2 per 1000 to 35 per 1000 person.,
Even though this neurodegenerative condition is a crucial public health problem and leads to psychological as well as economic burden on the caregivers and nation, there is no true nationwide estimate available to understand the prevailing trends of dementia in India. This study will provide an overall estimation of dementia in India, which may be helpful for health workers and policymakers in defining the need and planning service delivery models.
| Materials and Methods|| |
Identification of studies
Ebscohost, PubMed, PsychINFO, and Google Scholar were searched up to June 2020. Keywords related to dementia (namely Alzheimer's disease, vascular dementia, cognitive impairment, neurodegenerative disorder), terms related to the design of the study (i.e., epidemiology, prevalence, observational study, cross-sectional study, cohort study, and disease frequency), and “India” were combined using Boolean operators for retrieving articles. Initially, first two authors independently examined the titles of the articles and their abstracts based on predefined inclusion and exclusion criteria. After that, full texts of suitable articles were retrieved for further rigorous inspection and evaluation. Additional articles were identified through manual search of bibliographies, references, and cross-references of the previously identified published studies. Apart from the aforementioned, online accessible Indian journals such as Indian Journal of Psychiatry and Indian Journal of Gerontology, Neurology India was searched manually. Full texts of articles were examined rigorously as per preset inclusion and exclusion criteria by the two authors independently.
Inclusion criteria and exclusion criteria
Manuscripts written and published in the English language by June 2020 reporting both genders in the general population with rural, urban, or mixed background and having quantitative data on either number or frequency were included. On the other hand, review articles, single-case studies, hospital-based studies, and studies on a particular religion, or ethnicity were excluded.
In addition, the guidelines of PRISMA were considered while identifying the articles for review and meta-analysis. A total of 725 articles were identified (PubMed – 198, PsychINFO – 86, Google Scholar – 42, and Ebsocohost – 399) as potentially relevant; among them, 681 were excluded as the titles/abstracts of these articles were not relevant as per defined inclusion and exclusion criteria. Full text of the remaining 44 articles was retrieved for a further comprehensive evaluation, and after discussion and deliberation of findings and methodology, 20 studies were kept for meta-analysis.
Risk of bias assessment
The Quality in Prognosis Studies Tool (QUIPS) was used to assess the risk of bias of the included articles. As the studies were observational in nature; therefore, two particular domains of the tool, namely (a) study participation and (b) outcome measurement were assessed for possible assessment of risk of bias. The appraisal from QUIPS provides a subjective assessment of risk of bias, for example, low, moderate, or high of included studies. The appraisal of the articles indicated that the risk of selection bias was low in 19 studies,,,,,,,,,,,,,,,,,, and moderate in one study. The risk of outcome measurement bias was low in 13 studies,,,,,,,,,,,, moderate in 4 studies,,,, and high in 3 studies.,,
The qualities of the articles included in the study were assessed on the strengthening the reporting of observational studies in epidemiology (STROBE) checklist. The STROBE is a checklist comprising 22 different parameters of quality assessment of published articles, for example, study design, variables, sample size, and statistical methods. Strobe checklist is recommended for explanation and elaboration of epidemiological studies, which are observational in nature. The range of strobe scores across studies was 18–22.
Data extraction and selection procedure
Articles were included on the basis of the inclusion criteria, and further reviewed thoroughly. A custom-made data extraction form was used to mine relevant information from the each article that comprises name of the author, publication year, location, sample size, diagnostic criteria, and the number of persons screened of having dementia, etc. The data extracted are incorporated in [Table 1].
Computer software R (3.5.3) Software R was developed by R Core Team - University of Auckland, New Zealand. was used for conducting the meta-analysis. The level of significance of the analysis was 5% (two-tailed tests). Three packages, i.e., “metafor,” “meta,” and “xlsx” were used for conducting meta-analysis. The data of the manuscript were extracted from MS Excel Sheet and uploaded in R console using the command library “xlsx.” Further, the “escalc()” function was used for the calculation of effect size. The package “metaphor” uses “xi” and “ni” as generic input commands for event and sample size, respectively. In general, the escalc() and rma() functions use Freeman–Tukey transformation, sometimes also called the “Freeman-Tukey double arcsine transformation." Subsequently, effect size with random-effects method was computed using function rma(). The function “predict” was used to calculate the true summary proportion. The “transf()” function directs to transform summary effect size back into proportion, and function “metaprop” was used to calculate an overall prevalence of dementia combing converted proportions from different studies. The “sm” argument in the metaprop() function directs the transformation method to convert the original proportions into prevalence. Further, “forest” function was used to generate the forest plot. Finally, a funnel plot was plotted to estimate the publication bias. Egger's unweighted regression test was also performed to verify the publication bias, which was obtained by executing the regtest() function.
| Results|| |
The prevalence of dementia in India is computed from the epidemiological studies, which are shown in [Table 2]. The present meta-analysis is based on 20 epidemiological studies consisting of 86,312 persons, out of which a total number of 1193 persons were reported to have dementia. This amount of dementia is approximately 20 per 1000 persons (0.02; 95% confidence interval [CI]: 0.02–0.03) in India [Figure 1]. Out of the 20 articles, 15 were further analyzed to calculate the prevalence of dementia among males and females. The results indicated that the prevalence rates were similar for both males (20 per 1000; 95% CI: 0.01–0.03) and females (20 per 1000; 95% CI: 0.01–0.03). Based on the available raw data, age-specific prevalence of dementia was also estimated. To include the maximum number of articles in the analysis, the age bands were divided into two categories, i.e., below 75 years of age, 75 years and above, as majority of the articles had reported on the basis of this age band. The analysis revealed that the prevalence of dementia was higher in persons aged 75 years and above (80 per 1000; 95% CI: 0.04–0.12) as compared to those who were below 75 years (20 per 1000; 95% CI: 0.01–0.02). Further, the domicile-specific prevalence of dementia was also calculated. Out of the 20 articles included in the meta-analysis, 12 were based on urban population and 11 on the rural population. The results indicated that the prevalence of dementia was higher in rural elderly (30 per 1000; 95% CI: 0.01–0.05) as compared to the elderly population residing in urban areas (20 per 1000; 95% CI: 0.01–0.03).
|Table 2: Sociodemographic distribution of prevalence rates of dementia in India|
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The Q-test and its P value serve as a test of significance to address heterogeneity wherein the null hypothesis is that all studies share the same true effect. The results of the heterogeneity analysis based on the random-effects model indicated the presence of heterogeneity (Q22 = 1487.22, P < 0.0001) among the studies. The forest plot [Figure 1] is also reflecting heterogeneity among the studies. I2 = 99% indicates a high amount of variances across the findings.
The funnel plot is a graphical indicator of heterogeneity and publication bias. Specifically, if studies with small and/or nonsignificant outcomes remain unpublished (therefore are less likely to be included in a meta-analysis), it may lead to an asymmetric funnel plot.,, The present funnel plot does indicate the presence of publication bias [Figure 2].
The presence of the publication bias was also verified through Egger's unweighted regression test. This test for the funnel plot asymmetry (z = 3.23, P = 0.001) was found to be significant.
| Discussion|| |
The results of the present meta-analysis of 20 studies revealed that the prevalence of dementia in India is 20 per thousand. The prevalence of dementia in India is low in comparison to the studies reported from other parts of the globe. Prevalence rates of dementia in western countries have been found to be higher than in Indian subcontinents and other Asian countries., For instance, the prevalence of dementia in America has been reported to be 13.9%, while in European countries, it ranges from 5.9% to 9.4%.
The reason behind the lower prevalence rate of dementia in India may be multifarious. First, findings of many previous studies are solely based on self-reported questionnaires, which is a source of several possible biases. Second, all the self-reported measures might have different sensitivities and specificities in detecting dementia and its subtypes; hence, findings may be varied across different studies. Third, negative stigma and discrimination are strongly associated with different mental illnesses in India; consequently, many participants may have a tendency to underreport the symptoms of dementia. Fourth, many participants may have considered it as a part of normal aging due to lack of knowledge and awareness about the common symptoms of dementia. For example, a number of studies have identified the common names given to the typical characteristics of dementia, such as “Chinnan” in Malayalam language (meaning childishness), “nervafrakese” (tired brain) in Konkani language, and “weak brain” in Hindi. Fifth, institutionalization is an important probable risk factor for dementia and other mental health consequences. Institutionalization of elderly is still less in India in comparison to western countries, as majority of the Indian elderly live with their children and only about one-fifth either live alone or with the spouse. Elderly living in the home environment remain stimulated and engaged in several activities such as raising grandchildren, financial as well as domestic household management and perform various spiritual and religious activities. Therefore, dwelling type could be one of the reasons behind the difference in prevalence rates in different countries. Finally, environmental and climatic conditions may be another probable reason for the variation in prevalence rate across the globe. A recent meta-regressive analysis has reported that atmospheric variabilities, for example, rainfall, temperature, humidity, and air pressure are major determinants for the variation of prevalence of common mental disorders across South Asia.
Dementia also increases with an increase in age like the other neurodegenerative diseases. The rate of increment of dementia is steep after 65 years of age. The result of the present meta-analysis indicates that prevalence rates increase drastically after 75 years of age. The findings of the present study are similar to the studies conducted on the findings of census data of the USA, wherein 44% of the population of age band of 75–84 years have symptoms of dementia in comparison to only 16% of the population of 65–74 years age band have dementia.
The findings of the present meta-analysis also indicate a similar prevalence rate of dementia across both the genders. However, previous studies have found that significantly larger percentage of females have dementia in comparison to males,, and researchers have resonated the findings on the basis hormonal secretions., Contradiction in the findings can be attributed to various psychosocial factors such as alcoholism, smoking, and occupational involvement. Alcoholism is a public health problem and has been related to cognitive impairment.,, Literature suggests that men outnumber women on alcohol consumption in India. Similarly, retirement from the job is also a major life crisis, experienced by the elderly., This transition phase brings about significant changes in individuals' life cycle, which adversely affects the cognitive capacity of the aged population., According to the Census of India 2011, the female workforce constitutes only 23.3% as compared to 76.7% of male workers. It would be reasonable to infer that retirement-related life crisis experienced much more by males than females. Therefore, the prevalence of dementia is much higher among women in western countries as compared to India.
Urban population have less prevalence rate of dementia in comparison to rural population. The findings can be attributed to multiple factors, such as higher proportion of urban participants receiving pension benefits, health insurance, and easy access to health-care facilities in urban areas as compared to their rural counterparts.,, Further, urbanization always proffers more educational and occupational opportunities, as compared to equivalents residing in rural areas.
Although the present study is the first attempt to estimate the prevalence rates of dementia in India, it has certain limitations and potential publication biases. The studies included in the analysis incorporate different diagnostic criteria, which may have been a source of variability among the findings. Second, diagnosing dementia is sometimes difficult and uncertain during the early stages. In addition, the time span of the included studies was very broad (1996–2017); therefore, the applicable standardized and adjusted crude rates of dementia could not be estimated. This calls for a need to incorporate dementia as a crucial mental health problem while conducting nationwide survey, i.e., National Mental Health Survey of India 2014–2016.
Dementia and its types should be acknowledged as a geriatric priority. The dementia care services in India are fragmented and patchy. A country that accommodates such a large number of elders has only six residential care facilities, 10-day care centers, domiciliary care services, a hundred memory clinics, and ten dementia help lines. Future researches should focus in identifying the potential risk factors and the various health agencies should commence the preventive measures to delay the onset of dementia.
| Conclusion|| |
The results of the present meta-analysis indicate that the prevalence of dementia in India is 20 per thousand. Due to the heterogeneity in individual studies based on their methodology, there is a need of incorporating dementia in the National Mental Health Survey of India.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Division UNP. World Population Prospects; 2019.
India CO. Population census. In: Office of the Registrar General & Census Commissioner I. New Delhi, India: Ministry of Home Affairs, Government of India, Editor; 2011.
Khan F. Dementia in India: It's high time to address the need! Arch Ment Health 2011;12:64.
Patel V, Prince M. Ageing and mental health in a developing country: Who cares? Qualitative studies from Goa, India. Psychol Med 2001;31:29-38.
Murray CJ, Lopez AD, Organization WH. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020: Summary. Harvard: Harvard University Press. World Health Organization; 1996.
Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, Ganguli M, et al.
Global prevalence of dementia: A Delphi consensus study. Lancet 2005;366:2112-7.
Ganguli M, Chandra V, Kamboh MI, Johnston JM, Dodge HH, Thelma BK, et al.
Apolipoprotein E polymorphism and Alzheimer disease: The Indo-US Cross-National Dementia Study. Arch Neurol 2000;57:824-30.
Rajkumar S, Kumar S, Thara R. Prevalence of dementia in a rural setting: A report from India. Int J Geriatr Psychiatry 1997;12:702-7.
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Ann Intern Med 2009;151:264-9.
Hayden JA, van der Windt DA, Cartwright JL, Côté P, Bombardier C. Assessing bias in studies of prognostic factors. Ann Intern Med 2013;158:280-6.
Shaji S, Promodu K, Abraham T, Roy KJ, Verghese A. An epidemiological study of dementia in a rural community in Kerala, India. Br J Psychiatry 1996;168:745-9.
Rajkumar S, Kumar S. Prevalence of dementia in the community: A rural-urban comparison from Madras, India. Aust J Ageing 1996;15:57-61.
Chandra V, Ganguli M, Pandav R, Johnston J, Belle S, DeKosky ST. Prevalence of Alzheimer's disease and other dementias in rural India: The Indo-US study. Neurology 1998;51:1000-8.
Chandra V, Pandav R, Dodge HH, Johnston JM, Belle SH, DeKosky ST, et al.
Incidence of Alzheimer's disease in a rural community in India: The Indo-US study. Neurology 2001;57:985-9.
Vas CJ, Pinto C, Panikker D, Noronha S, Deshpande N, Kulkarni L, et al.
Prevalence of dementia in an urban Indian population. Int Psychogeriatr 2001;13:439-50.
Shaji S, Bose S, Verghese A. Prevalence of dementia in an urban population in Kerala, India. Br J Psychiatry 2005;186:136-40.
Das SK, Biswas A, Roy J, Bose P, Roy T, Banerjee TK, et al.
Prevalence of major neurological disorders among geriatric population in the metropolitan city of Kolkata. J Assoc Physicians India 2008;56:175-81.
Mathuranath PS, Cherian PJ, Mathew R, Kumar S, George A, Alexander A, et al.
Dementia in Kerala, South India: Prevalence and influence of age, education and gender. Int J Geriatr Psychiatry 2010;25:290-7.
Llibre Rodriguez JJ, Ferri CP, Acosta D, Guerra M, Huang Y, Jacob KS, et al.
Prevalence of dementia in Latin America, India, and China: A population-based cross-sectional survey. Lancet 2008;372:464-74.
Banerjee TK, Mukherjee CS, Dutt A, Shekhar A, Hazra A. Cognitive dysfunction in an urban Indian population – Some observations. Neuroepidemiology 2008;31:109-14.
Raina SK, Razdan S, Pandita KK. Prevalence of dementia in ethnic Dogra population of Jammu district, North India: A comparison survey. Neurol Asia 2010;15: 65-9.
Saldanha D, Mani MR, Srivastava K, Goyal S, Bhattacharya D. An epidemiological study of dementia under the aegis of mental health program, Maharashtra, Pune chapter. Indian J Psychiatry 2010;52:131-9.
] [Full text]
Sagar T. Prevalence and Correlates of Dementia – A Community Based Study in Rural Kerala. Kerala - India: SCTIMST; 2010.
Poddar K, Kant S, Singh A, Singh TB. An epidemiological study of dementia among the habitants of eastern Uttar Pradesh, India. Ann Indian Acad Neurol 2011;14:164-8.
] [Full text]
Raina SK, Raina S, Chander V, Grover A, Singh S, Bhardwaj A. Is dementia differentially distributed? A study on the prevalence of dementia in migrant, urban, rural, and tribal elderly population of himalayan region in northern India. N Am J Med Sci 2014;6:172-7.
Gambhir IS, Khurana V, Kishore D, Sinha AK, Mohapatra SC. A clinico-epidemiological study of cognitive function status of community-dwelling elderly. Indian J Psychiatry 2014;56:365-70.
] [Full text]
Banerjee TK, Dutta S, Das S, Ghosal M, Ray BK, Biswas A, et al.
Epidemiology of dementia and its burden in the city of Kolkata, India. Int J Geriatr Psychiatry 2017;32:605-14.
Seby K, Chaudhury S, Chakraborty R. Prevalence of psychiatric and physical morbidity in an urban geriatric population. Indian J Psychiatry 2011;53:121-7.
] [Full text]
von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al.
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. J Clin Epidemiol 2008;61:344-9.
Ganguli HC. Epidemiological findings on prevalence of mental disorders in India. Indian J Psychiatry 2000;42:14-20.
] [Full text]
Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Statis Software 2010;36: 1-48.
Hedges LV, Vevea JL. Fixed-and random-effects models in meta-analysis. Psychological Methods 1998;3:486.
Richard J, Light R, Pillemer DB. Summing Up. Cambridge: Harvard University Press; 1984.
Sterne JA, Egger M. Funnel plots for detecting bias in meta-analysis: Guidelines on choice of axis. J Clin Epidemiol 2001;54:1046-55.
Rothstein HR, Sutton AJ, Borenstein M. Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments. USA: John Wiley & Sons; 2006.
Li K, Wei S, Liu Z, Hu L, Lin J, Tan S, et al.
The Prevalence of Alzheimer's Disease in China: A Systematic Review and Meta-analysis. Iran J Public Health 2018;47:1615-26.
Kim YJ, Han JW, So YS, Seo JY, Kim KY, Kim KW. Prevalence and trends of dementia in Korea: A systematic review and meta-analysis. J Korean Med Sci 2014;29:903-12.
Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR, Ofstedal MB, et al.
Prevalence of dementia in the United States: The aging, demographics, and memory study. Neuroepidemiology 2007;29:125-32.
Berr C, Wancata J, Ritchie K. Prevalence of dementia in the elderly in Europe. Eur Neuropsychopharmacol 2005;15:463-71.
Prince MJ. The 10/66 dementia research group – 10 years on. Indian J Psychiatry 2009;51 Suppl 1:S8-15.
Shaji KS, Smitha K, Lal KP, Prince MJ. Caregivers of people with Alzheimer's disease: A qualitative study from the Indian 10/66 Dementia Research Network. Int J Geriatr Psychiatry 2003;18:1-6.
Cohen L. Toward an anthropology of senility: Anger, weakness, and Alzheimer's in Banaras, India. Med Anthropol Q 1995;9:314-34.
Samuel R, McLachlan CS, Mahadevan U, Isaac V. Cognitive impairment and reduced quality of life among old-age groups in Southern Urban India: Home-based community residents, free and paid old-age home residents. QJM 2016;109:653-9.
Fund UN. 'Caring for Our Elders: Early Responses' – India Ageing Report – 2017. New Delhi, India: UNFPA; 2017.
Alam M, James K, Sathyanarayana K. Report on the Status of Elderly in Select States of India, 2011. New Delhi: United Nations Population Fund; 2012.
Sharma P, Asthana H, Gambhir I, Ranjan JK. Death Anxiety among elderly people: Role of gender, spirituality and mental health. Indian J Gerontol 2019;33:240-54.
Ranjan JK, Choudhary A, Asthana HS. Atmospheric variability and prevalence of common psychiatric disorders in South Asia: A meta-regressive analysis. Indian J Community Health 2019;31:289-300.
Cova I, Travi N, Maggiore L, Cucumo V, Mariani C, Pomati S. What are the caregivers' needs on dementia care? An integrated qualitative and quantitative assessment. Neurol Sci 2018;39:1085-91.
Gao S, Hendrie HC, Hall KS, Hui S. The relationships between age, sex, and the incidence of dementia and Alzheimer disease: A meta-analysis. Arch Gen Psychiatry 1998;55:809-15.
Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010-2050) estimated using the 2010 census. Neurology 2013;80:1778-83.
Fratiglioni L, Grut M, Forsell Y, Viitanen M, Grafström M, Holmén K, et al.
Prevalence of Alzheimer's disease and other dementias in an elderly urban population: Relationship with age, sex, and education. Neurology 1991;41:1886-92.
Moffat SD, Zonderman AB, Metter EJ, Kawas C, Blackman MR, Harman SM, et al.
Free testosterone and risk for Alzheimer disease in older men. Neurology 2004;62:188-93.
Henderson VW, Hogervorst E. Testosterone and Alzheimer Disease: Is it Men's Turn Now? USA: AAN Enterprises; 2004.
Mohan D, Iype T, Varghese S, Usha A, Mohan M. A cross-sectional study to assess prevalence and factors associated with mild cognitive impairment among older adults in an urban area of Kerala, South India. BMJ Open 2019;9:e025473.
Xu G, Liu X, Yin Q, Zhu W, Zhang R, Fan X. Alcohol consumption and transition of mild cognitive impairment to dementia. Psychiatry Clin Neurosci 2009;63:43-9.
Ngandu T. Lifestyle-Related Risk Factors in Dementia and Mild Cognitive Impairment: A Population-Based Study. Stockholm, Sweden: Karolinska University Press; 2006.
Macbride A. Retirement as a life crisis: Myth or reality? Can Psychiatr Assoc J 1976;21:547-56.
Adam S, Bonsang E, Germain S, Perelman S. Retirement and Cognitive Reserve: A Stochastic Frontier Approach Applied to Survey Data. Belgium: CREPP; 2007.
Celidoni M, Dal Bianco C, Weber G. Retirement and cognitive decline. A longitudinal analysis using SHARE data. J Health Econ 2017;56:113-25.
Singh PK, Jasilionis D, Oksuzyan A. Gender difference in cognitive health among older Indian adults: A cross-sectional multilevel analysis. SSM Popul Health 2018;5:180-7.
Roy K, Chaudhuri A. Influence of socioeconomic status, wealth and financial empowerment on gender differences in health and healthcare utilization in later life: Evidence from India. Soc Sci Med 2008;66:1951-62.
Xu H, Dupre ME, Gu D, Wu B. The impact of residential status on cognitive decline among older adults in China: Results from a longitudinal study. BMC Geriatr 2017;17:107.
Gururaj GV, Benegal V, Rao GN, Pathak K, Singh LK, Mehta RY, et al
. Collaborators Group. National Mental Health Survey of India, 2015-16: Prevalence, Patterns and Outcomes Bengaluru. India: National Institute of Mental Health and Neuro Sciences; 2016. p. 129.
[Figure 1], [Figure 2]
[Table 1], [Table 2]
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