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Abu-Amara H, Zhao W, Li Z, Leung YY, Schellenberg GD, Wang LS, Moorjani P, Dey AB, Dey S, Zhou X, Gross AL, Lee J, Kardia SLR, Smith JA. Region-based analysis with functional annotation identifies genes associated with cognitive function in South Asians from India. RESEARCH SQUARE 2024:rs.3.rs-4712660. [PMID: 39149469 PMCID: PMC11326367 DOI: 10.21203/rs.3.rs-4712660/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
The prevalence of dementia among South Asians across India is approximately 7.4% in those 60 years and older, yet little is known about genetic risk factors for dementia in this population. Most known risk loci for Alzheimer's disease (AD) have been identified from studies conducted in European Ancestry (EA) but are unknown in South Asians. Using whole-genome sequence data from 2680 participants from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD), we performed a gene-based analysis of 84 genes previously associated with AD in EA. We investigated associations with the Hindi Mental State Examination (HMSE) score and factor scores for general cognitive function and five cognitive domains. For each gene, we examined missense/loss-of-function (LoF) variants and brain-specific promoter/enhancer variants, separately, both with and without incorporating additional annotation weights (e.g., deleteriousness, conservation scores) using the variant-Set Test for Association using Annotation infoRmation (STAAR). In the missense/LoF analysis without annotation weights and controlling for age, sex, state/territory, and genetic ancestry, three genes had an association with at least one measure of cognitive function (FDR q<0.1). APOE was associated with four measures of cognitive function, PICALM was associated with HMSE score, and TSPOAP1 was associated with executive function. The most strongly associated variants in each gene were rs429358 ( APOE ε4), rs779406084 ( PICALM ), and rs9913145 ( TSPOAP1 ). rs779406084 is a rare missense mutation that is more prevalent in LASI-DAD than in EA (minor allele frequency=0.075% vs. 0.0015%); the other two are common variants. No genes in the brain-specific promoter/enhancer analysis met criteria for significance. Results with and without annotation weights were similar. Missense/LoF variants in some genes previously associated with AD in EA are associated with measures of cognitive function in South Asians from India. Analyzing genome sequence data allows identification of potential novel causal variants enriched in South Asians.
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Nichols E, Petrosyan S, Lee J. Mental Health Impacts of COVID-19: Does Prepandemic Cognition and Dementia Status Matter? J Gerontol A Biol Sci Med Sci 2024; 79:glae028. [PMID: 38267562 PMCID: PMC10972580 DOI: 10.1093/gerona/glae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic disrupted daily life and led to sharp shocks in trends for various health outcomes. Although substantial evidence exists linking the pandemic and mental health outcomes and linking dementia and mental health outcomes, little evidence exists on how cognitive status may alter the impact of COVID-19 on mental health. METHODS We used prepandemic data from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia study and 9 waves of data from the Real-Time Insights of COVID-19 in India study (N = 1 182). We estimated associations between measures of prepandemic cognition (continuous cognition based on 22 cognitive tests, dementia status) and mental health measures during the pandemic (Patient Health Questionnaire [PHQ]-4 [9 time points], PHQ-9 [2 time points], Beck Anxiety Inventory [3 time points]), adjusting for age, gender, rural/urban residence, state, education, and prepandemic mental health. RESULTS Summarizing across time points, PHQ-9 score was marginally or significantly associated with prepandemic cognition (PHQ-9 difference: -0.38 [-0.78 to 0.14] points per SD higher cognition; p = .06), and prepandemic dementia (PHQ-9 difference: 0.61 [0.11-1.13] points for those with dementia compared to no dementia; p = .02). Associations with BAI were null, whereas associations with PHQ-4 varied over time (p value for interaction = .02) and were strongest during the delta wave, when pandemic burden was highest. CONCLUSIONS We present initial evidence that mental health impacts of COVID-19 or other acute stressors may be unequally distributed across strata of cognitive outcomes. In dynamically changing environments, those with cognitive impairment or dementia may be more vulnerable to adverse mental health outcomes.
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Affiliation(s)
- Emma Nichols
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Sarah Petrosyan
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Jinkook Lee
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
- Department of Economics, University of Southern California, Los Angeles, California, USA
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Abu-Amara H, Zhao W, Li Z, Leung YY, Schellenberg GD, Wang LS, Moorjani P, Dey A, Dey S, Zhou X, Gross AL, Lee J, Kardia SL, Smith JA. Region-based analysis with functional annotation identifies genes associated with cognitive function in South Asians from India. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.18.24301482. [PMID: 38293024 PMCID: PMC10827235 DOI: 10.1101/2024.01.18.24301482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The prevalence of dementia among South Asians across India is approximately 7.4% in those 60 years and older, yet little is known about genetic risk factors for dementia in this population. Most known risk loci for Alzheimer's disease (AD) have been identified from studies conducted in European Ancestry (EA) but are unknown in South Asians. Using whole-genome sequence data from 2680 participants from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD), we performed a gene-based analysis of 84 genes previously associated with AD in EA. We investigated associations with the Hindi Mental State Examination (HMSE) score and factor scores for general cognitive function and five cognitive domains. For each gene, we examined missense/loss-of-function (LoF) variants and brain-specific promoter/enhancer variants, separately, both with and without incorporating additional annotation weights (e.g., deleteriousness, conservation scores) using the variant-Set Test for Association using Annotation infoRmation (STAAR). In the missense/LoF analysis without annotation weights and controlling for age, sex, state/territory, and genetic ancestry, three genes had an association with at least one measure of cognitive function (FDR q<0.1). APOE was associated with four measures of cognitive function, PICALM was associated with HMSE score, and TSPOAP1 was associated with executive function. The most strongly associated variants in each gene were rs429358 (APOE ε4), rs779406084 (PICALM), and rs9913145 (TSPOAP1). rs779406084 is a rare missense mutation that is more prevalent in LASI-DAD than in EA (minor allele frequency=0.075% vs. 0.0015%); the other two are common variants. No genes in the brain-specific promoter/enhancer analysis met criteria for significance. Results with and without annotation weights were similar. Missense/LoF variants in some genes previously associated with AD in EA are associated with measures of cognitive function in South Asians from India. Analyzing genome sequence data allows identification of potential novel causal variants enriched in South Asians.
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Affiliation(s)
- Hasan Abu-Amara
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Zheng Li
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, United States of America
| | - Gerard D. Schellenberg
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, United States of America
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, United States of America
| | - Priya Moorjani
- Department of Molecular and Cell Biology, University of California, Berkeley, United States of America
- Center for Computational Biology, University of California, Berkeley, United States of America
| | - A.B. Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Sharmitha Dey
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jinkook Lee
- Department of Economics, University of Southern California, Los Angeles, California, United States of America
| | - Sharon L.R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
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Bhagwasia M, Rao AR, Banerjee J, Bajpai S, Khobragade PY, Raman AV, Talukdar A, Jain A, Rajguru C, Sankhe L, Goswami D, Shanthi GS, Kumar G, Varghese M, Dhar M, Gupta M, Koul PA, Mohanty RR, Chakrabarti SS, Yadati SR, Dey S, Lee J, Dey AB. Defining anthropometric thresholds (mid-arm circumference and calf circumference) in older adults residing in the community: a cross-sectional analysis using data from the population representative Longitudinal Aging Study in India (LASI DAD). BMJ Open 2023; 13:e077530. [PMID: 38151275 PMCID: PMC10753742 DOI: 10.1136/bmjopen-2023-077530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 11/28/2023] [Indexed: 12/29/2023] Open
Abstract
OBJECTIVES To identify factors associated with malnutrition (undernutrition and overnutrition) and determine appropriate cut-off values for mid-arm circumference (MAC) and calf circumference (CC) among community-dwelling Indian older adults. DESIGN Data from the first wave of harmonised diagnostic assessment of dementia for Longitudinal Ageing Study in India (LASI-DAD) were used. Various sociodemographic factors, comorbidities, geriatric syndromes, childhood financial and health status were included. Anthropometric measurements included body mass index (BMI), MAC and CC. SETTING Nationally representative cohort study including 36 Indian states and union territories. PARTICIPANTS 4096 older adults aged >60 years from LASI DAD. OUTCOME MEASURES The outcome variable was BMI, categorised as low (<18.5 kg/m2), normal (18.5-22.9 kg/m2) and high (>23 kg/m2). The cut-off values of MAC and CC were derived using ROC curve with BMI as the gold standard. RESULTS 902 (weighted percentage 20.55%) had low BMI, 1742 (44.25%) had high BMI. Undernutrition was associated with age, wealth-quintile and impaired cognition, while overnutrition was associated with higher education, urban living and comorbidities such as hypertension, diabetes and chronic heart disease. For CC, the optimal lower and upper cut-offs for males were 28.1 cm and >31.5 cm, respectively, while for females, the corresponding values were 26 cm and >29 cm. Similarly, the optimal lower and upper cut-offs for MAC in males were 23.9 cm and >26.9 cm, and for females, they were 22.5 cm and >25 cm. CONCLUSION Our study identifies a high BMI prevalence, especially among females, individuals with higher education, urban residents and those with comorbidities. We establish gender-specific MAC and CC cut-off values with significant implications for healthcare, policy and research. Tailored interventions can address undernutrition and overnutrition in older adults, enhancing standardised nutritional assessment and well-being.
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Affiliation(s)
- Manjusha Bhagwasia
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Abhijith Rajaram Rao
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Joyita Banerjee
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Swati Bajpai
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Pranali Yogiraj Khobragade
- University of Southern California Dana and David Dornsife College of Letters Arts and Sciences, Los Angeles, California, USA
| | - Aruna V Raman
- Department of Medicine, Medical College, Thiruvananthapuram, India
| | - Arunanshu Talukdar
- Department of Geriatric Medicine, Medical College Kolkata, Kolkata, India
| | - Arvind Jain
- Dr Sampurnanand Medical College, Jodhpur, Rajasthan, India
| | - Chhaya Rajguru
- Department of Community Medicine, JJ Hospital, Mumbai, India
| | - Lalit Sankhe
- Department of Community Medicine, JJ Hospital, Mumbai, India
| | | | | | - Govind Kumar
- Department of Medicine, Indira Gandhi Institute of Medical Science, Patna, Bihar, India
| | - Mathew Varghese
- Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, India
| | - Minakshi Dhar
- Internal Medicine, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
| | - Monica Gupta
- Department of General Medicine, Government Medical College and Hospital, Thiruvananthapuram, India
| | - Parvaiz A Koul
- internal and Pulmonary Medicine, Sheri Kashmir Inst Med Sci, Srinagar, Jammu & Kashmir, India
| | | | | | | | | | - Jinkook Lee
- University of Southern California Dana and David Dornsife College of Letters Arts and Sciences, Los Angeles, California, USA
| | - Aparajit Ballav Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
- Venu Geriatric Center, Venu Eye Institute and Research Centre, New Delhi, India
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Lee J, Meijer E, Langa KM, Ganguli M, Varghese M, Banerjee J, Khobragade P, Angrisani M, Kurup R, Chakrabarti SS, Gambhir IS, Koul PA, Goswami D, Talukdar A, Mohanty RR, Yadati RS, Padmaja M, Sankhe L, Rajguru C, Gupta M, Kumar G, Dhar M, Chatterjee P, Singhal S, Bansal R, Bajpai S, Desai G, Rao AR, Sivakumar PT, Muliyala KP, Bhatankar S, Chattopadhyay A, Govil D, Pedgaonkar S, Sekher TV, Bloom DE, Crimmins EM, Dey AB. Prevalence of dementia in India: National and state estimates from a nationwide study. Alzheimers Dement 2023; 19:2898-2912. [PMID: 36637034 PMCID: PMC10338640 DOI: 10.1002/alz.12928] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 11/29/2022] [Accepted: 12/19/2022] [Indexed: 01/14/2023]
Abstract
INTRODUCTION Prior estimates of dementia prevalence in India were based on samples from selected communities, inadequately representing the national and state populations. METHODS From the Longitudinal Aging Study in India (LASI) we recruited a sample of adults ages 60+ and administered a rich battery of neuropsychological tests and an informant interview in 2018 through 2020. We obtained a clinical consensus rating of dementia status for a subsample (N = 2528), fitted a logistic model for dementia status on this subsample, and then imputed dementia status for all other LASI respondents aged 60+ (N = 28,949). RESULTS The estimated dementia prevalence for adults ages 60+ in India is 7.4%, with significant age and education gradients, sex and urban/rural differences, and cross-state variation. DISCUSSION An estimated 8.8 million Indians older than 60 years have dementia. The burden of dementia cases is unevenly distributed across states and subpopulations and may therefore require different levels of local planning and support. HIGHLIGHTS The estimated dementia prevalence for adults ages 60+ in India is 7.4%. About 8.8 million Indians older than 60 years live with dementia. Dementia is more prevalent among females than males and in rural than urban areas. Significant cross-state variation exists in dementia prevalence.
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Affiliation(s)
- Jinkook Lee
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
- Department of Economics, University of Southern California, Los Angeles, California, USA
| | - Erik Meijer
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Kenneth M. Langa
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Center for Clinical Management Research, Veterans Affairs, Ann Arbor, Michigan, USA
- Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA
| | - Mary Ganguli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Mathew Varghese
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Joyita Banerjee
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Pranali Khobragade
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Marco Angrisani
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
- Department of Economics, University of Southern California, Los Angeles, California, USA
| | - Ravi Kurup
- Department of Medicine, Government Medical College, Thiruvananthapuram, India
| | - Sankha Shubhra Chakrabarti
- Department of Geriatric Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India
| | - Indrajeet Singh Gambhir
- Department of Geriatric Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India
| | - Parvaiz A. Koul
- Department of Internal and Pulmonary Medicine, Sher-e-Kashmir Institute of Medical Sciences, Srinagar, India
| | | | | | - Rashmi Ranjan Mohanty
- Department of Medicine, All India Institute of Medical Sciences, Bhubaneshwar, India
| | | | - Mekala Padmaja
- Department of Medicine, Nizam’s Institute of Medical Sciences, Hyderabad, India
| | - Lalit Sankhe
- Department of Community Medicine, Grant Medical College and J.J. Hospital, Mumbai, India
| | - Chhaya Rajguru
- Department of Community Medicine, Grant Medical College and J.J. Hospital, Mumbai, India
| | - Monica Gupta
- Department of General Medicine, Government Medical College and Hospital, Chandigarh, India
| | - Govind Kumar
- Department of Medicine, Indira Gandhi Institute of Medical Science, Patna, India
| | - Minakshi Dhar
- Department of Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Prasun Chatterjee
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Sunny Singhal
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Rishav Bansal
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Swati Bajpai
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Gaurav Desai
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Abhijith R. Rao
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Palanimuthu T. Sivakumar
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Krishna Prasad Muliyala
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | | | | | - Dipti Govil
- International Institute for Population Sciences, Mumbai, India
| | | | - T. V. Sekher
- International Institute for Population Sciences, Mumbai, India
| | - David E. Bloom
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Eileen M. Crimmins
- School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Aparajit Ballav Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
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Gross AL, Li C, Briceno EM, Rentería MA, Jones RN, Langa KM, Manly JJ, Nichols EL, Weir D, Wong R, Berkman L, Lee J, Kobayashi LC. Harmonization of Later-Life Cognitive Function Across National Contexts: Results from the Harmonized Cognitive Assessment Protocols (HCAPs). MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.09.23291217. [PMID: 37398152 PMCID: PMC10312860 DOI: 10.1101/2023.06.09.23291217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Background The Harmonized Cognitive Assessment Protocol (HCAP) is an innovative instrument for cross-national comparisons of later-life cognitive function, yet its suitability across diverse populations is unknown. We aimed to harmonize general and domain-specific cognitive scores from HCAPs across six countries, and evaluate precision and criterion validity of the resulting harmonized scores. Methods We statistically harmonized general and domain-specific cognitive function across the six publicly available HCAP partner studies in the United States, England, India, Mexico, China, and South Africa (N=21,141). We used an item banking approach that leveraged common cognitive test items across studies and tests that were unique to studies, as identified by a multidisciplinary expert panel. We generated harmonized factor scores for general and domain- specific cognitive function using serially estimated graded-response item response theory (IRT) models. We evaluated precision of the factor scores using test information plots and criterion validity using age, gender, and educational attainment. Findings IRT models of cognitive function in each country fit well. We compared measurement reliability of the harmonized general cognitive function factor across each cohort using test information plots; marginal reliability was high (r> 0·90) for 93% of respondents across six countries. In each country, general cognitive function scores were lower with older ages and higher with greater levels of educational attainment. Interpretation We statistically harmonized cognitive function measures across six large, population-based studies of cognitive aging in the US, England, India, Mexico, China, and South Africa. Precision of the estimated scores was excellent. This work provides a foundation for international networks of researchers to make stronger inferences and direct comparisons of cross-national associations of risk factors for cognitive outcomes. Funding National Institute on Aging (R01 AG070953, R01 AG030153, R01 AG051125, U01 AG058499; U24 AG065182; R01AG051158).
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Briceño EM, Rentería MA, Gross AL, Jones RN, Gonzalez C, Wong R, Weir DR, Langa KM, Manly JJ. A cultural neuropsychological approach to harmonization of cognitive data across culturally and linguistically diverse older adult populations. Neuropsychology 2023; 37:247-257. [PMID: 35482625 PMCID: PMC9639608 DOI: 10.1037/neu0000816] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVE To describe a cultural neuropsychological approach to prestatistical harmonization of cognitive data across the United States (U.S.) and Mexico with the Harmonized Cognitive Assessment Protocol (HCAP). METHOD We performed a comprehensive review of the administration, scoring, and coding procedures for each cognitive test item administered across the English and Spanish versions of the HCAP in the Health and Retirement Study (HRS) in the U.S. and the Ancillary Study on Cognitive Aging in Mexico (Mex-Cog). For items that were potentially equivalent across studies, we compared each cognitive test item for linguistic and cultural equivalence and classified items as confident or tentative linking items, based on the degree of confidence in their comparability across cohorts and language groups. We evaluated these classifications using differential item functioning techniques. RESULTS We evaluated 132 test items among 21 cognitive instruments in the HCAP across the HRS and Mex-Cog. We identified 72 confident linking items, 46 tentative linking items, and 14 items that were not comparable across cohorts. Measurement invariance analysis revealed that 64% of the confident linking items and 83% of the tentative linking items showed statistical evidence of measurement differences across cohorts. CONCLUSIONS Prestatistical harmonization of cognitive data, performed by a multidisciplinary and multilingual team including cultural neuropsychologists, can identify differences in cognitive construct measurement across languages and cultures that may not be identified by statistical procedures alone. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Emily M. Briceño
- Department of Physical Medicine & Rehabilitation, University of Michigan Medical School, Ann Arbor, MI, 48108
| | - Miguel Arce Rentería
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University College of Physicians and Surgeons, New York City, NY, USA
| | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Baltimore, MD, USA
| | - Richard N. Jones
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Providence RI, USA
| | | | - Rebeca Wong
- Sealy Center on Aging, University of Texas Medical Branch at Galveston, Galveston, Texas, USA
| | - David R. Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Kenneth M. Langa
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI
- Institute for Social Research, University of Michigan, Ann Arbor, MI
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor MI
- Veterans Affairs Ann Arbor Center for Clinical Management Research, Ann Arbor, MI
| | - Jennifer J. Manly
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University College of Physicians and Surgeons, New York City, NY, USA
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Nichols E, Ng DK, Hayat S, Langa KM, Lee J, Steptoe A, Deal JA, Gross AL. Differences in the measurement of cognition for the assessment of dementia across geographic contexts: Recommendations for cross-national research. Alzheimers Dement 2023; 19:1009-1019. [PMID: 35841625 PMCID: PMC9891734 DOI: 10.1002/alz.12740] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/23/2022] [Accepted: 06/14/2022] [Indexed: 02/03/2023]
Abstract
INTRODUCTION Most cognitive assessments have been developed in high-income countries but are used in diverse contexts. Differences in culture and context may affect the performance of cognitive items. METHODS We used the Harmonized Cognitive Assessment Protocol (HCAP) surveys in the United States, Mexico, India, England, and South Africa (combined N = 11,364) to quantify associations across countries between cognitive items and cognitive impairment status using age- and sex-adjusted logistic regression. RESULTS Associations were stronger in the United States (median odds ratio [OR] across items = 0.17) and England (median OR = 0.19), compared to South Africa (median OR = 0.23), India (median OR = 0.29), and Mexico (median OR = 0.28). Items assessing memory (e.g., delayed recall tasks) had the most consistent associations of the largest magnitudes across contexts. DISCUSSION Transporting cognitive items among countries and cultures warrants caution. Our results can guide the design of future instruments by identifying items that performed well either in individual contexts or across the range of contexts considered. HIGHLIGHTS Little quantitative evidence exists to guide the design of cognitive assessments in cross-national studies. The performance of cognitive items for the measurement of dementia varied across countries. Items with lower variation across countries (e.g., delayed word recall) should be used in future cross-national assessments. Across countries, there was variability in the performance of language assessments, with the exception of the animal naming task. Results can be used to design future cross-national or location-specific cognitive assessments.
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Affiliation(s)
- Emma Nichols
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Derek K. Ng
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Shablina Hayat
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Kenneth M. Langa
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA
- VA Center for Clinical Management Research, University of Michigan, Ann Arbor, Michigan, USA
| | - Jinkook Lee
- Department of Economics, University of Southern California, Los Angeles, California, USA
- Center for Economic and Social Research, University of California, Los Angeles, California, USA
| | - Andrew Steptoe
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Jennifer A. Deal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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Jin H, Crimmins E, Langa KM, Dey A, Lee J. Estimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach. Neuroepidemiology 2023; 57:43-50. [PMID: 36617419 PMCID: PMC10038923 DOI: 10.1159/000528904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/16/2022] [Indexed: 01/07/2023] Open
Abstract
INTRODUCTION Accurate estimation of dementia prevalence is essential for making effective public and social care policy to support individuals and families suffering from the disease. The purpose of this paper is to estimate the prevalence of dementia in India using a semi-supervised machine learning approach based on a large nationally representative sample. METHODS The sample of this study is adults 60 years or older in the wave 1 (2017-2019) of the Longitudinal Aging Study in India (LASI). A subsample in LASI received extensive cognitive assessment and clinical consensus ratings and therefore has diagnoses of dementia. A semi-supervised machine learning model was developed to predict the status of dementia for LASI participants without diagnoses. After obtaining the predictions, sampling weights and age standardization to the World Health Organization (WHO) standard population were applied to generate the estimate for prevalence of dementia in India. RESULTS The prevalence of dementia for those aged 60 years and older in India was 8.44% (95% CI: 7.89%-9.01%). The age-standardized prevalence was estimated to be 8.94% (95% CI: 8.36%-9.55%). The prevalence of dementia was greater for those who were older, were females, received no education, and lived in rural areas. DISCUSSION The prevalence of dementia in India may be higher than prior estimates derived from local studies. These prevalence estimates provide the information necessary for making long-term planning of public and social care policy. The semi-supervised machine learning approach adopted in this paper may also be useful for other large population aging studies that have a similar data structure.
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Affiliation(s)
- Haomiao Jin
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Eileen Crimmins
- School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Kenneth M. Langa
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Center for Clinical Management Research, Veterans Affairs, Ann Arbor, MI, USA
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - A.B. Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Jinkook Lee
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
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Schaner S, Theys N, Angrisani M, Banerjee J, Khobragade PY, Petrosyan S, Agarwal A, Chien S, Weerman B, Chakrawarty A, Chatterjee P, Madaan N, Bloom D, Lee J, Dey AB. Adherence to COVID-19 protective behaviours in India from May to December 2020: evidence from a nationally representative longitudinal survey. BMJ Open 2022; 12:e058065. [PMID: 35105601 PMCID: PMC8808317 DOI: 10.1136/bmjopen-2021-058065] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVES Since the onset of the COVID-19 pandemic, behavioural interventions to reduce disease transmission have been central to public health policy worldwide. Sustaining individual protective behaviour is especially important in low-income and middle-income settings, where health systems have fewer resources and access to vaccination is limited. This study seeks to assess time trends in COVID-19 protective behaviour in India. DESIGN Nationally representative, panel-based, longitudinal study. SETTING We conducted a panel survey of Indian households to understand how the adoption of COVID-19 protective behaviours has changed over time. Our data span peaks and valleys of disease transmission over May-December 2020. PARTICIPANTS Respondents included 3719 adults from 1766 Indian households enrolled in the Harmonised Diagnostic Assessment of Dementia for the Longitudinal Ageing Study in India. ANALYSIS We used ordinary least squares regression analysis to quantify time trends in protective behaviours. RESULTS We find a 30.6 percentage point (95% CI (26.7 to 34.5); p<0.01) decline in protective behaviours related to social distancing over the observation period. Mask wearing and handwashing, in contrast, decreased by only 4.3 percentage points (95% CI (0.97 to 7.6); p<0.05) from a high base. Our conclusions are unchanged after adjusting for recorded COVID-19 caseload and nationwide COVID-19 containment policy; we also observe significant declines across socioeconomic strata spanning age, gender, education and urbanicity. CONCLUSION We argue that these changes reflect, at least in part, 'COVID-19 fatigue,' where adherence to social distancing becomes more difficult over time irrespective of the surrounding disease environment.
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Affiliation(s)
- Simone Schaner
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
- Department of Economics, University of Southern California, Los Angeles, California, USA
| | - Natalie Theys
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Marco Angrisani
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
- Department of Economics, University of Southern California, Los Angeles, California, USA
| | - Joyita Banerjee
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Pranali Yogiraj Khobragade
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Sarah Petrosyan
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Arunika Agarwal
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA
| | - Sandy Chien
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Bas Weerman
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Avinash Chakrawarty
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Prasun Chatterjee
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Nirupam Madaan
- Department of Hospital Administration, All India Institute of Medical Sciences, New Delhi, India
| | - David Bloom
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA
| | - Jinkook Lee
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
- Department of Economics, University of Southern California, Los Angeles, California, USA
| | - Aparajit Ballav Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
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11
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Bloom DE, Sekher TV, Lee J. Longitudinal Aging Study in India (LASI): new data resources for addressing aging in India. NATURE AGING 2021; 1:1070-1072. [PMID: 37117520 DOI: 10.1038/s43587-021-00155-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Affiliation(s)
- David E Bloom
- Harvard T. H. Chan School of Public Health, Boston, MA, USA.
| | - T V Sekher
- International Institute for Population Sciences, Mumbai, India
| | - Jinkook Lee
- University of Southern California, Los Angeles, CA, USA
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Philip S, Gajera G, Nirisha PL, Sivakumar PT, Barikar MC, Panday P, Patley R, Chander R, Sinha P, Basavarajappa C, Manjunatha N, Kumar CN, Math SB. Future-Proofing Geriatric Mental Health Care Services in India: Training and Policy Directions. Indian J Psychol Med 2021; 43:S134-S141. [PMID: 34732966 PMCID: PMC8543623 DOI: 10.1177/02537176211032342] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND A burgeoning rise in the elderly subpopulation is being noted in India. This rise has already earned the designation as a shining nation by the United Nations/World Health Organization. This growth will take the elderly subpopulation to 324 million persons above 60 years by 2050. The mental health needs of this subpopulation are varied and nuanced compared to that of the general population. The risk and prevalence of mental health morbidity are also greater. There is an urgent need to gear up the health care systems. CONSIDERATIONS Various aspects of the ongoing demographic transitions are considered alongside available health information. Legislative mandates and laws are also reviewed to provide context to qualify the action points and recommendations. Recommendations, India must act urgently to strengthen health care delivery systems in a concurrent multipronged approach. These efforts should focus on developing trained Human Resources and adequate infrastructural resources. Review mechanisms to inform on required updates on best practices and evidence-based medicine must also be incorporated and developed to perform periodically.
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Affiliation(s)
- Sharad Philip
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Gopi Gajera
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - P Lakshmi Nirisha
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Palanimuthu Thangaraju Sivakumar
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Malathesh C Barikar
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Praveen Panday
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Rahul Patley
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Rakesh Chander
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Preeti Sinha
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Chethan Basavarajappa
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Narayana Manjunatha
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Channaveerachari Naveen Kumar
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Suresh Bada Math
- Dept. of Psychiatry, NIMHANS, Bengaluru, Karnataka, India. 2Geriatric Clinic and Services, Dept. of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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13
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Lee J, Ganguli M, Weerman A, Chien S, Lee DY, Varghese M, Dey AB. Online Clinical Consensus Diagnosis of Dementia: Development and Validation. J Am Geriatr Soc 2021; 68 Suppl 3:S54-S59. [PMID: 32815604 DOI: 10.1111/jgs.16736] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 12/19/2019] [Accepted: 01/23/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To introduce cost-effective expert clinical diagnoses of dementia into population-based research using an online platform and to demonstrate their validity against in-person clinical assessment and diagnosis. DESIGN The online platform provides standardized data necessary for clinicians to rate participants on the Clinical Dementia Rating (CDR® ). Using this platform, clinicians diagnosed 60 patients at a range of CDR levels at two clinical sites. The online consensus diagnosis was compared with in-person clinical consensus diagnosis. SETTING All India Institute of Medical Sciences (AIIMS), Delhi, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India. PARTICIPANTS Thirty patients each at AIIMS and NIMHANS with equal numbers of patients previously independently rated in person by experts as CDR is 0 (cognitively normal), CDR is 0.5 (mild cognitive impairment), and CDR is 1 or greater (dementia). MEASUREMENTS Multiple clinicians independently rate each participant on each CDR domain using standardized data and expert clinical judgment. The overall summary CDR is calculated by algorithm. When there are discrepancies among clinician ratings, clinicians discuss the case through a virtual consensus conference and arrive at a consensus overall rating. RESULTS Online clinical consensus diagnosis based on standardized interview data provides consistent clinical diagnosis with in-person clinical assessment and consensus diagnosis (κ coefficient = 0.76). CONCLUSION A web-based clinical consensus platform built on the Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India interview data is a cost-effective way to obtain reliable expert clinical judgments. A similar approach can be used for other epidemiological studies of dementia. J Am Geriatr Soc 68:S54-S59, 2020.
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Affiliation(s)
- Jinkook Lee
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA.,Department of Economics, University of Southern California, and RAND Corporation, Santa Monica, California, USA
| | - Mary Ganguli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Albert Weerman
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Sandy Chien
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Dong Young Lee
- Department of Psychiatry, Seoul National University, Seoul, South Korea
| | - Mathew Varghese
- Department of Psychiatry, National Institute of Mental Health and Neuroscience, Bengaluru, India
| | - Aparajit B Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
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Hu P, Lee J, Beaumaster S, Kim JK, Dey S, Weir D, Crimmins EM. Cognitive Function and Cardiometabolic-Inflammatory Risk Factors Among Older Indians and Americans. J Am Geriatr Soc 2021; 68 Suppl 3:S36-S44. [PMID: 32815598 DOI: 10.1111/jgs.16734] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 05/05/2020] [Accepted: 05/08/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To investigate how cardiometabolic-inflammatory risk factors are related to cognition among older adults in India and the United States. DESIGN The Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD) and the Harmonized Cognitive Assessment Protocol of the Health and Retirement Study (HRS-HCAP) in the United States conducted an in-depth assessment of cognition, using protocols designed for international comparison. SETTING Cognitive tests were conducted in hospital or household settings in India and in household settings in the United States. PARTICIPANTS Respondents aged 60 years and older from LASI-DAD (N = 1,865) and respondents aged 65 years and older from HRS-HCAP (N = 2,111) who provided venous blood specimen. MEASUREMENTS We used total composite scores from the common cognitive tests administered. Cardiovascular risk was indicated by systolic and diastolic blood pressure, pulse rate, pro-B-type natriuretic peptide (proBNP), and homocysteine. Metabolic risk was measured by body mass index, glycosylated hemoglobin (HbA1c), high-density lipoprotein cholesterol, and lipoprotein (a) (only in India). Inflammatory risk was indicted by white blood cell count, C-reactive protein, albumin, and uric acid (only in India). RESULTS The distribution of both total cognition scores and of cardiometabolic risk factors differed significantly between India and the United States. In both countries, lower cognition was associated with older age, lower education, elevated homocysteine, elevated proBNP, and lower albumin levels. The associations between HbA1c levels and cognitive measures were statistically significant in both countries, but in the opposite direction, with a coefficient of 1.5 (P < .001) in India and -2.4 (P < .001) in the United States for one percentage increase in absolute HbA1c value. CONCLUSION Cardiometabolic-inflammatory biomarkers are associated with cognitive functional levels in each country, but the relationships may vary across countries. J Am Geriatr Soc 68:S36-S44, 2020.
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Affiliation(s)
- Peifeng Hu
- Division of Geriatric Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Jinkook Lee
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA.,Department of Economics, University of Southern California and RAND Corporation, Santa Monica, California, USA
| | - Sidney Beaumaster
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Jung Ki Kim
- Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Sharmistha Dey
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - David Weir
- Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA
| | - Eileen M Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
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15
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Lee J, Dey AB. Introduction to LASI-DAD: The Longitudinal Aging Study in India-Diagnostic Assessment of Dementia. J Am Geriatr Soc 2020; 68 Suppl 3:S3-S4. [PMID: 32815600 DOI: 10.1111/jgs.16740] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 03/31/2020] [Indexed: 01/14/2023]
Affiliation(s)
- Jinkook Lee
- Department of Economics and Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA.,RAND Corporation, Santa Monica, California, USA
| | - Aparajit B Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
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16
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Kenny RA. New Insights and Knowledge on Cognition and Dementia from Population‐Based Cohort of Older Adults in India. J Am Geriatr Soc 2020; 68 Suppl 3:S1-S2. [DOI: 10.1111/jgs.16733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 11/30/2022]
Affiliation(s)
- Rose Anne Kenny
- Department of Medical GerontologyTrinity College Dublin Ireland
- Mercer's Institute for Successful AgeingSt James Hospital Dublin Ireland
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