Abstract
Introduction
For late-life neurocognitive disorders, as for other late-life chronic diseases, much recent interest has focused on the possible relevance of Developmental Origins of Health and Disease (DOHaD). Programming by undernutrition in utero, followed by overnutrition in adult life may lead to an increased risk, possibly mediated through cardiovascular and metabolic pathways. This study will specifically examine, if lower birth weight is associated with poorer cognitive functioning in late life in a south Indian population.
Methods and analysis
From 1934 onwards, the birth weight, length and head circumference of all babies born in the CSI Holdsworth Memorial Hospital, Mysore, India, were recorded in obstetric notes. Approximately 800 men and women from the Mysore Birth Records Cohort aged above 55 years, and a reliable informant for each, will be asked to participate in a single cross-sectional baseline assessment for cognitive function, mental health and cardiometabolic disorders. Participants will be assessed for hypertension, type-2 diabetes and coronary heart disease, nutritional status, health behaviours and lifestyles, family living arrangements, economic status, social support and social networks. Additional investigations include blood tests (for diabetes, insulin resistance, dyslipidaemia, anaemia, vitamin B12 and folate deficiency, hyperhomocysteinemia, renal impairment, thyroid disease and Apolipoprotein E genotype), anthropometry, ECG, blood pressure, spirometry and body composition (bioimpedance). We will develop an analysis plan, first using traditional univariate and multivariable analytical paradigms with independent, dependent and mediating/confounding/interacting variables to test the main hypotheses.
Ethics and dissemination
This study has been approved by the research ethics committee of CSI Holdsworth Memorial Hospital. The findings will be disseminated locally and at international meetings, and will be published in open access peer reviewed journals.
Keywords: Cognition, Late life, programming, birth weight, fetal growth
Strengths and limitations of this study.
This study is the first lifecourse birth records cohort study in the developing world with cognitive, physical health and mental health outcomes in older adults.
This will add to the scientific understanding of the early life origins of disease, and mediating effect of cardiometabolic disorders in adult and late life on late life cognitive abilities and mental health in an Indian population.
There have been large losses to follow-up since birth, especially between birth and the first adult follow-up, and the implications of these losses have to be thought through in interpreting the results of this study.
Background
Neurocognitive disorders are a major cause of disability and mortality in late life and are associated with high costs for health systems and society.1 It is estimated that in India, 3.7 million people aged over 60 have dementia and this is expected to increase twofold by 2030 and threefold by 2050 because of steady growth in the older population and stable increments in life expectancy.2 In the accompanying health transition, non-communicable diseases have assumed a progressively greater significance in low and middle-income countries (LMICs) like India. Although neurocognitive disorders are the second highest source of burden after tropical diseases, research in India is minimal.1 2
Recent research suggests that the quality of nutrition during intrauterine development, reflected crudely in size at birth, is an important determinant of lifelong function, health and disease risk.3 Birth weight and head circumference at birth are indicators of intrauterine growth and brain development, respectively.4 Larger birth weight, the most widely researched birth size measure, is associated with better cognitive function from infancy through the third decade of life in several populations and countries, independent of social background.5 However, this association between birth weight and cognition diminishes as individuals reach middle age.6 Cohort studies with follow-up through late life have been sparse, making the size of effects in late life unclear.6 7 Head circumference at birth has shown a similar pattern to birth weight, such that associations observed in young cohorts were not replicated in the older adults in follow-up studies.8 Conditions in early postnatal life and childhood may supersede and compensate for fetal vulnerabilities such as small birth size and moderate long-term cognitive abilities.
Adult cardiometabolic disorders are also thought to have origins in fetal life.8 Low birth weight is linked to adult cardiometabolic disorders (type-2 diabetes, coronary heart disease, hypertension and metabolic syndrome).9–12 These disorders are independently associated with impaired cognition and dementia in old age.13 While hypertension, obesity and hyperlipidaemia in midlife predict an increased risk of later dementia, associations become inverted in late life.14–16 Understanding long-term trajectories in Body Mass Index (BMI), blood pressure and cholesterol among those who do and do not develop cognitive impairment can be clarified by conducting prospective lifecourse epidemiological studies.
Much interest has focused on the possible relevance of Barker's fetal and developmental origins of adult disease (DOHaD) hypothesis with two plausible pathways to late-onset cognitive impairment and dementia.17 18
a direct effect of reduced intrauterine nutrition (reflected in smaller birth size) on fetal brain development and leading to decreased cognitive abilities and hence reduced cognitive reserve;17
programming of metabolism by undernutrition in very early life, exacerbated by overnutrition in adult life, leading to increased risk mediated through cardiometabolic disease.13 17 18
Cognition in late life is impacted by the cumulative effect of nutrition, education, social and family environment in early life and midlife.17 18 Adverse lifetime socioeconomic conditions, including lower educational attainment, less complex occupational activities, area of birth and childhood residence are associated with cognitive impairment and dementia.17 18 However, only a few studies have examined the role of mediating factors between socioeconomic deprivation across the life course and cognitive ageing.17 19 Data spanning the lifecourse into old age are hard to come by in LMICs. The Mysore Birth Records Cohort20 in south India represents a unique resource for the study of the lifecourse aetiology of cognitive ageing in a radically different cultural context from those where such studies have previously been conducted.
Mysore Birth Records Cohort
CSI Holdsworth Memorial Hospital (HMH), Mysore, India has preserved obstetric records since 1934 until the present date. These include the birth weight, length and head circumference of all babies born in the hospital, as well as maternal weight and pelvic diameters. During 1993–2001, in a collaborative study with Barker's group at the MRC Lifecourse Epidemiology Unit, University of Southampton, UK, the records were used to trace people born in HMH between 1934 and 1966. The tracing process resulted in 3427 men and women being matched to their birth records.20
Of those born between 1934 and 1966, 1069 participated in a study between 1993 and 1999 to evaluate the relationship between size at birth and adult cardiometabolic disorders.21–27 A subset (n=435) was assessed for cardiac dimensions and arterial compliance 3–4 years later.25 26 Ten years after the first study, 383 participated in a follow-up study for incident diabetes and metabolic syndrome between 2003 and 2004.27
The 3427 cohort members who were retraced and matched to their birth records in 1993–2001 were only a small percentage (14%) of all the births in HMH during 1934–1966, and of these ∼30% (n=1069) took part in the previous studies of the cohort. Compared with the remainder of the original births, the 3427 individuals who were traced were 48 g heavier (95% CI 28 to 68, p<0.001), 0.25 cm longer (95% CI 0.08 to 0.42, p=0.005) and had a 0.092 cm larger head circumference (95% CI −0.004 to 0.188; p=0.06) at birth. Compared with the 3427, the 1069 men and women who were studied were slightly lighter and shorter, but these differences were not statistically significant (birth weight −36 g (95% CI −74 to 2, p=0.07); birth length −0.26 cm (95% CI −0.55 to 0.03, p=0.08).20
Previous studies from the Mysore Birth Records Cohort provided partial support for the Barker hypothesis, with predicted associations between lower birth weight and coronary heart disease,21 insulin resistance22 and lower lung function.24 The prevalence and incidence of type-2 diabetes was higher compared with the west and was associated with short birth length rather than low birth weight.21 27 Associations between cortisol concentration and cardiovascular risk factors were stronger.23 No associations were identified between birth weight and hypertension, left ventricular mass and arterial compliance.25 26
For the purpose of this study, we aim to investigate ∼800 men and women from the Mysore Birth Records Cohort, born before 1959. There was no contact with the cohort between 2005 and 2011. Between 2011 and 2015 we retraced the cohort members and of the 1069, 186 are deceased, 727 are alive and 156 could not be traced. Recruitment for the study has started and the study is expected to be completed by 2017.
Aims and objectives
The full potential of this cohort will be realised through repeated measures of cognitive and other functions in older age, to track cognitive trajectories, and the development of frailty across other physiological and organ systems. The purpose of this study is to establish baseline cognitive function in later life. We will be conducting cognitive assessments for all participants once during this study. However, to begin to explore pathways from early life growth and development to late-life impairment and decline, these assessments will be repeated in future follow-up studies of the cohort.
Primary goal
This study aims to test the hypothesis that lower birth weight and smaller head circumference at birth are associated with poorer scores in tests of cognitive function in people aged 55 years and over, relative to age and education norms for the south Indian population. The mediating effects of childhood growth (proxied by adult head circumference and leg length), and cardiometabolic risk factors (in adult and late life) will be explored. Potential interactions will also be explored; for example, the association between birth weight and late-life cognitive ability may be modified by changes in socioeconomic status in midlife.
Other key goals
- To estimate the prevalence of cognitive impairment and its association with age, gender, education and socioeconomic position. We will investigate risk factors for cognitive impairment and test the following specific hypotheses:
- Cardiovascular risk factors (diabetes, hypertension, large waist circumference, high waist:hip ratio, hypercholesterolaemia, hypertriglyceridaemia, metabolic syndrome, insulin resistance and smoking), coronary heart disease and stroke are associated with an increased risk of cognitive impairment and dementias, after controlling for confounding effects. Associations will be quantified as relative risks and population attributable fractions.
- Micronutrient deficiencies (vitamin B12 and folate), and anaemia are associated with an increased risk of cognitive impairment.
- ApoE4 (Apolipoprotein E4) genotype is associated with an increased risk of cognitive impairment and dementia.
- Markers of socioeconomic adversity across life are associated with an increased risk of cognitive impairment and dementia.
We will study the social aetiology of late-life depression cross-sectionally including associations with relative and absolute poverty, ill health and disability, nutritional status, social support, life events, marital circumstances and living arrangements.
Methods and analysis
Study design
Approximately 800 men and women from the Mysore Birth Records Cohort, and the original 3427 who were traced and matched to their birth records, aged above 55 years, and a key informant will be asked to participate in a cross-sectional comprehensive 1-phase baseline assessment lasting ∼2–3 hours. Recruitment is expected to complete by 2017 and our analyses by 2018.
Interviews and measures
A battery of cognitive tests and instruments developed, validated and normed in India by the 10/66 Dementia Research Group, suitable for people with little or no education, is administered in local languages.28 29 30 Interviews for participants and key informant are carried out in participants' own homes. Physical assessments and investigations are carried out the following day at the research unit. There is good evidence to suggest that adult head circumference and leg length are proxy markers for early life growth and development. We are measuring head circumference and leg length using standardised protocols in the current study. Table 1 lists the assessments and investigations that are conducted in this study.
Table 1.
Battery of Cognitive tests |
|
Instruments for diagnosis of dementia |
|
Health status and physical health assessment |
|
A structured interview† |
|
Socioeconomic assessments | |
Blood tests and genetic assay | Blood tests: Haemoglobin, glucose tolerance test, lipid profile, albumin, total protein, thyroid function tests, vitamin B12, folate, insulin and creatinineGenetic assay: ApoE lipoprotein |
*Instruments administered to informants only.
†Instruments administered to the informants only if the participants have communication difficulties arising from cognitive problems, severe mental illness, deafness or mutism.
ApoE, Apolipoprotein E.
Blood tests and genetic assays: These are conducted according to a standard protocol. Samples for biochemical analyses are sent to the Diabetes Unit, KEM Hospital, Pune, India, whose laboratory participates in the UK National External Quality Assessment Service for insulin assays. The DNA sample was sent to the Centre for Cellular and Molecular Biology, Hyderabad, India, where DNA is extracted for APOE genotyping.
The WHO criteria are employed to diagnose diabetes and hypertension. Metabolic syndrome is diagnosed using International Diabetes Federation criteria.31 Diagnosis of coronary heart disease is derived from an algorithm, drawing information from the Rose Chest Pain Questionnaire,32 Minnesota ECG coding33 and a clinical history of cardiac revascularisation procedures. Diagnosis of coronary heart disease is made, if there is typical angina on Rose Chest Pain Questionnaire, or a history of cardiac revascularisation procedures or the presence of Minnesota codes 1–1 or 1–2 for major Q waves on the ECG.
Outcomes: Cognitive functioning as a continuous measure is obtained by administering the 10/66 battery of cognitive tests.28–30 Cognitive impairment is defined as 1.5 SDs or more below the norm for the age and educational group for individual tests, and scoring below that threshold on two out of three memory tests (immediate and delayed recall and Community Screening Instrument for Dementia (CSI‘D’) memory subscale). Dementia is defined by a score above a cut-off point of predicted probability of Diagnostic Statistical Manual for mental disorders (DSM) IV Dementia Syndrome34 from the logistic regression equation of the 10/66 dementia diagnostic algorithm.28 Depression is diagnosed from the computerised Geriatric Mental State Examination algorithm (GMS-AGECAT).35
Power calculation and data analysis
Using a test at the 5% significance level, our sample size of 800 will provide 80% power to detect an association of 0.099 SDs of a continuous outcome (cognitive score) per SD of a continuous exposure (eg, birth weight). We will first calculate the prevalence of cognitive impairment (outcome of interest) and its association with age and gender.
Cardiometabolic data will be collected objectively using standardised protocols in this study, and are available from previous two studies. We will use these data to understand long-term trajectories in BMI, blood pressure, and cholesterol among those with and without cognitive impairment. Indicators of socioeconomic status have been collected from the cohort members at the time of birth (using data from maternity records), and during adult life using standard methods when they participated in previous studies. During this study, we will conduct an assessment of current socioeconomic position. We will examine for the association between lifecourse socioeconomic position and cognitive function in late life in our analyses.
We will develop an analysis plan, first using traditional univariate and multivariable analytical paradigms with independent, dependent and mediating/confounding/interacting variables to test the main hypotheses. We will then extend these analyses using path analysis and structural equation modelling by use of MPlus statistical software package to explore lifecourse pathways and mediated effects in more detail, allowing for the assumed causal ordering of observed and latent variables (eg, cognitive reserve) across the lifecourse.36 At the end of the recruitment, we plan to compare participants in the current study with non-participants. We will examine for any differences in birth size, age, gender mix, height, BMI, educational attainment, socioeconomic position and cardiometabolic variables collected previously.
Ethics and dissemination
Full informed consent is obtained from the participants. If the participant is illiterate, verbal consent is obtained, witnessed and signed by a relative. If individuals were unable to consent (due to severe cognitive problems), witnessed assent is obtained from their nearest/authorised relative. The findings from this study will be disseminated locally and at international meetings, and will be published in open access peer review journals.
Discussion
India is experiencing a phenomenal increase in adult cardiometabolic disorders, a trend attributed to wide spread intrauterine and infant-growth retardation and recent economic transition leading to unhealthy diets and lifestyles in childhood and adult life. These disorders in adult life increase the risk of cognitive impairment in old age where comorbidity with chronic non-communicable disorders is common. Despite the growing interest in chronic diseases in the elderly, there are few detailed data available on their prevalence, impact and developmental origins in the developing world.
This project constitutes a unique opportunity to begin to chart the epidemiologic transition and its impact on older persons in India. In addition to cognitive impairment, this cross-sectional survey includes ascertainment of mental disorders and other chronic conditions in late life. As well as providing a rich baseline for studying the aetiology of cognitive decline in future follow-up studies, this will allow us to explore the relative, independent and interactive contributions of various chronic conditions to late-life cognitive functioning. We will carry out comparative analyses using the publicly accessible data archive from the 10/66 Dementia Research Group's cross-sectional population-based surveys including over 19 000 older people from 10 developing countries. There have been large losses to follow-up since birth, especially between birth and the first adult follow-up, and the implications of these losses have to be thought through in interpreting the results of this study.
Numbers of older people are growing rapidly in India and the extent to which nutrition in early life and cardiometabolic risk factors across the lifespan are related to late-life cognition is of substantial public health relevance. Understanding the nature of this relationship and identification of critical periods could strengthen the rationale for policies to improve maternal and child nutrition and to develop interventions to prevent or modify chronic non-communicable disorders. Effective dissemination of findings from this research will raise awareness, inform evidence-based policymaking and enhance service development for older people in India.
Acknowledgments
Our sincere thanks to Mr Somashekar, Mr Santosh, Ms Saroja, Mr Praveen, Ms Malathi and Ms Pavithra, research assistants at CSI Holdsworth Memorial Hospital, Mysore who have been involved in tracing the study subjects. We thank Mr Kiran Nagaraj from CSI Holdsworth Memorial Hospital, Mysore for data management of this study.
Footnotes
Contributors: CF is Professor of International Paediatric Epidemiology at the MRC, Lifecourse Epidemiology Unit, University of Southampton and initiated the Mysore Birth Records Cohort study in 1993. SRV and GVK have headed the Mysore Birth Records Cohort study team successively since 1995. JRMC is an Emeritus Professor at the University of Liverpool who developed the Geriatric Mental State Examination and will run the diagnostic algorithms for this study. MP is Professor of Epidemiological Psychiatry who coordinates the 10/66 Dementia Research group, a network of over 100 researchers, mainly from the developing world, who have worked together to promote more good research into dementia in those regions. MV is Professor in Geriatric Psychiatry at the National Institute of Mental Health and Neurosciences, Bangalore, India and a founding member of the 10/66 Dementia Research group in India. GC and DB developed the protocol for genetic and biochemical analyses, respectively. This project is supported by a personal fellowship awarded to MK. The study protocol is the joint effort of MK, SRV, GVK, KK, CO, MV, JRMC, GC, DB, MP and CF. SRV, GVK, KK and MK coordinate the field work and trained the research assistants. CO carried out the power calculation and planned the statistical analyses for this study.
Funding: This study is funded by the Wellcome DBT India Alliance as a personal fellowship to MK. The research is also supported by the Medical Research Council, UK, and Department for International Development, UK, through a core programme grant to the MRC Lifecourse Epidemiology Unit, University of Southampton.
Disclaimer: The funding bodies had no influence on the conduct of the stuy or interpretation of the results.
Competing interests: None declared.
Ethics approval: Wellcome Trust UK-DBT India and CSI Holdsworth Memorial Hospital, Mysore, India.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data sharing statement: The study data are not freely available, but the Mysore Birth Records Cohort team would welcome collaborations with other researchers. For further information contact SCK or MK based at Holdsworth Memorial Hospital, Mysore in India (cshihmh@bsnl.in, muralidoc@gmail.com) or CF, MRC Lifecourse Epidemiology Unit at Southampton in the UK (chdf@mrc.soton.ac.uk).
References
- 1.World Health Organization. Revised global burden of disease (GBD) 2002 estimates. 2004 World Health Report Geneva: World Health Organization, 2004. [Google Scholar]
- 2.Shaji KS, Jotheeswaran AT, Girish N et al. . The Dementia India Report: prevalence, impact, costs and services for dementia. New Delhi: Alzheimers and Related Disorders Society India, 2010. [Google Scholar]
- 3.Barker DJ, Gluckman PD, Godfrey KM et al. . Fetal nutrition and cardiovascular disease in adult life. Lancet 1993;341:938–41. [DOI] [PubMed] [Google Scholar]
- 4.Epstein HT, Epstein EB. The relationship between brain weight and head circumference from birth to age 18 years. Am J Phys Anthropol 1978;48:471–3. 10.1002/ajpa.1330480405 [DOI] [PubMed] [Google Scholar]
- 5.Shenkin SD, Starr JM, Deary IJ. Birth weight and cognitive ability in childhood: a systematic review. Psychol Bull 2004;130: 989–1013. 10.1037/0033-2909.130.6.989 [DOI] [PubMed] [Google Scholar]
- 6.Shenkin SD, Deary IJ, Starr JM. Birth parameters and cognitive ability in older age: a follow-up study of people born 1921–1926. Gerontology 2009;55:92–8. 10.1159/000163444 [DOI] [PubMed] [Google Scholar]
- 7.Gale CR, Walton S, Martyn CN. Foetal and postnatal head growth and risk of cognitive decline in old age. Brain 2003;126:2273–8. 10.1093/brain/awg225 [DOI] [PubMed] [Google Scholar]
- 8.Barker DJ. Fetal origins of coronary heart disease. BMJ 1995;311:171–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Whincup PH, Kaye SJ, Owen CG et al. . Birth weight and risk of type 2 diabetes: a systematic review. JAMA 2008;300:2886–97. 10.1001/jama.2008.886 [DOI] [PubMed] [Google Scholar]
- 10.Huxley R, Owen CG, Whincup PH et al. . Is birth weight a risk factor for ischemic heart disease in later life? Am J Clin Nutr 2007;85:1244–50. [DOI] [PubMed] [Google Scholar]
- 11.Huxley R, Neil A, Collins R. Unravelling the fetal origins hypothesis: is there really an inverse association between birth weight and subsequent blood pressure? Lancet 2002;360:659–65. 10.1016/S0140-6736(02)09834-3 [DOI] [PubMed] [Google Scholar]
- 12.Barker DJ, Osmond C, Kajantie E et al. . Growth and chronic disease: findings in the Helsinki Birth Cohort. Ann Hum Biol 2009;36:445–58. 10.1080/03014460902980295 [DOI] [PubMed] [Google Scholar]
- 13.Craft S. The role of metabolic disorders in Alzheimer disease and vascular dementia: two roads converged. Arch Neurol 2009;66:300–5. 10.1001/archneurol.2009.27 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Stewart R, Masaki K, Xue QL et al. . A 32-year prospective study of change in body weight and incident dementia. The Honolulu-Asia Aging Study. Arch Neurol 2005;62:55–60. 10.1001/archneur.62.1.55 [DOI] [PubMed] [Google Scholar]
- 15.Qiu C, Winblad B, Fratiglioni L. The age-dependent relation of blood pressure to cognitive function and dementia. Lancet Neurol 2005;4:487–99. 10.1016/S1474-4422(05)70141-1 [DOI] [PubMed] [Google Scholar]
- 16.Stewart R, Xue QL, White LR et al. . 26-year change in total cholesterol levels and incident dementia: the Honolulu-Asia Aging Study. Arch Neurol 2007;62:55–60. [DOI] [PubMed] [Google Scholar]
- 17.Whalley LJ, Dick FD, McNeill G. A life-course approach to the aetiology of late-onset dementias. Lancet Neurol 2006;5:87–96. 10.1016/S1474-4422(05)70286-6 [DOI] [PubMed] [Google Scholar]
- 18.Miller DB, O'Callaghan JP. Do early-life insults contribute to the late-life development of Parkinson and Alzheimer diseases? Metab Clin Exp 2008;57(Suppl 2):S44–9. 10.1016/j.metabol.2008.07.011 [DOI] [PubMed] [Google Scholar]
- 19.Al Hazzouri A, Haan MN, Kalbfleisch JD et al. . Life-course socioeconomic position and incidence of dementia and cognitive impairment without dementia in older Mexican Americans: results from the Sacramento area Latino study on aging. Am J Epidemiol 2011;173:1148–58. 10.1093/aje/kwq483 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Krishna M, Kalyanaraman K, Veena SR et al. . Cohort profile: the 1934–66 Mysore birth records cohort in South India. Int J Epidemiol 2015;44:1833–41. 10.1093/ije/dyv176 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Stein CE, Fall CHD, Kumaran K et al. . Fetal growth and coronary heart disease in South India. Lancet 1996;348:1269–73. [DOI] [PubMed] [Google Scholar]
- 22.Fall CHD, Stein CE, Kumaran K et al. . Size at birth, maternal weight, and non-insulin-dependent diabetes (NIDDM) in South Indian adults. Diabet Med 1998;15:220–7. [DOI] [PubMed] [Google Scholar]
- 23.Ward AM, Fall CH, Stein CE et al. . Cortisol and the metabolic syndrome in South Asians. Clin Endocrinol 2003;58:500–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Stein CE, Kumaran K, Fall CH et al. . Relation of fetal growth to adult lung function in south India. Thorax 1997;52:895–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kumaran K, Fall CH, Martyn CN et al. . Left ventricular mass and arterial compliance: relation to coronary heart disease and its risk factors in South Indian adults. Int J Cardiol 2002;83:1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kumaran K, Fall CHD, Martyn CN et al. . Blood pressure, arterial compliance, and left ventricular mass: no relation to small size at birth in south Indian adults . Heart 2000;83:272–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Veena SR, Wills AK, Fisher DJ et al. . Early life factors and type 2 diabetes in South-India: do the associations change with age? J Diabetes 2009;1:218–26. 10.1111/j.1753-0407.2009.00036.x [DOI] [PubMed] [Google Scholar]
- 28.Prince M, Ferri CP, Acosta D et al. . The protocols for the 10/66 dementia research group population-based research programme. BMC Public Health 2007;7:165 10.1186/1471-2458-7-165 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Prince M, Acosta D, Chiu H et al. . 10/66 Dementia Research Group. Dementia diagnosis in developing countries: a cross-cultural validation study Lancet 2003;361:909–17. 10.1016/S0140-6736(03)12772-9 [DOI] [PubMed] [Google Scholar]
- 30.Krishna M, Beulah E, Jones S et al. . Cognitive function and disability in late life: an ecological validation of the 10/66 battery of cognitive tests among community-dwelling older adults in South India. Int J Geriatr Psychiatry 2016;31:879–91. 10.1002/gps.4404 10.1002/gps.4404 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.IDF REF International Diabetes Federation. IDF diabetes atlas. 6th edn Brussels, Belgium: International Diabetes Federation, 2013. http://www.idf.org/diabetesatlas [Google Scholar]
- 32.Rose GA. The diagnosis of ischaemic heart pain and intermittent claudication in field surveys. Bull World Health Organ 1962;27:645–58. [PMC free article] [PubMed] [Google Scholar]
- 33.Macfarlane PW. Minnesota coding and the prevalence of ECG abnormalities. Heart 2000;84:582–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th edn, text rev Washington DC: American Psychiatric Association, 2000. [Google Scholar]
- 35.Copeland JR, Dewey ME, Griffith-Jones HM. A computerised psychiatric diagnostic system and case nomenclature for elderly subjects: GMS and AGECAT. Psychol Med 1986;16:89–99. [DOI] [PubMed] [Google Scholar]
- 36.De Stavola BL, Nitsch D, dos Santos Silva I et al. . Statistical issues in life course epidemiology. Am J Epidemiol 2006;163:84–96. 10.1093/aje/kwj003 [DOI] [PubMed] [Google Scholar]
- 37.Hall KS, Hendrie HH, Brittain HM et al. . The development of a dementia screening interview in two distinct languages. Int J Methods Psychiatric Res 1993;3:1–28. [Google Scholar]
- 38.Ganguli M, Chandra V, Gilbey JE. Cognitive test performance in a community-based non demented elderly sample in rural India: the Indo-US cross national dementia epidemiology study. Int Psychogeriatr 1996;8:507–24. [DOI] [PubMed] [Google Scholar]
- 39.Dewey ME, Copeland JR. Diagnosis of dementia from the history and aetiology schedule. Int J Geriatr Psychiatry 2001;16:912–17. [DOI] [PubMed] [Google Scholar]
- 40.Broe GA, Akhtar AJ, Andrews GR et al. . Neurological disorders in the elderly at home. J Neurol Neurosurg Psychiatry 1976;39:361–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Kaufer DI, Cummings JL, Ketchel P et al. . Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. J Neuropsychiatry Clin Neurosci 2000;12:233–9. 10.1176/jnp.12.2.233 [DOI] [PubMed] [Google Scholar]
- 42.Duke University Centre for the study of aging and human development. Multidimensional functional assessment: The OARS Methodology. Durham, NC: Duke University, 1978. [Google Scholar]
- 43.Rehm J, Ustun TB, Saxena S. On the development and psychometric testing of the WHO screening instrument to assess disablement in the general population. Int J Methods Psychiatric Res 2000;8:110–22. [Google Scholar]
- 44.Kuppuswamy B. Manual of socioeconomic status (Urban). Delhi: Manasayan, 1981. [Google Scholar]
- 45.New Delhi: Government of India. Household—population and housing characteristics. Minstry of Health and Family Welfare. NFHS—II, 2006:21–51. [Google Scholar]