Prevalence of dementia in India: National and state estimates from a nationwide study

Lee, Jinkook; Meijer, Erik; Langa, Kenneth M.; Ganguli, Mary; Varghese, Mathew; Banerjee, Joyita; Khobragade, Pranali; Angrisani, Marco; Kurup, Ravi; Chakrabarti, Sankha Shubhra; Gambhir, Indrajeet Singh; Koul, Parvaiz A; Goswami, Debabrata; Talukdar, Arunanshu; Mohanty, Rashmi Ranjan; Yadati, Raju Sathyanarayana; Padmaja, Mekala; Sankhe, Lalit; Rajguru, Chhaya; Gupta, Monica; Kumar, Govind; Dhar, Minakshi; Chatterjee, Prasun; Singhal, Sunny; Bansal, Rishav; Bajpai, Swati; Desai, Gaurav; Rao, Abhijith Rajaram; Sivakumar, Palanimuthu T.; Muliyala, Krishna Prasad; Bhatankar, Swaroop; Chattopadhyay, Aparajita; Govil, Dipti; Pedgaonkar, Sarang; Sekher, T. V.; Bloom, David E.; Crimmins, Eileen M.; Dey, Aparajit Ballav

doi:10.1002/alz.12928

Cited by 64 publications

(32 citation statements)

References 51 publications

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“…We also acknowledge that machine learning is a relatively new approach to estimating dementia prevalence and may generate results that are different to estimates from other statistical methods. Based on the same LASI and LASI-DAD data, a regression-based multiple imputation analysis estimated the dementia prevalence to be 7.4% for adults 60 years or older [39], which is about 1 percentage point lower than this paper's estimate but still higher than prior estimates synthesized from local studies. The reasons for the difference may involve different choices in predictors and statistical models and are worth of further investigation.…”

Section: Discussioncontrasting

confidence: 57%

Estimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach

et al. 2023

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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 have 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/Conclusion. 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 ageing studies that have a similar data structure.

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Section: Discussioncontrasting

confidence: 57%

Estimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach

et al. 2023

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“…Therefore, these broad estimates must be followed up with in-depth studies in order to accurately quantify dementia levels in each population. Previous independent reports that assessed dementia rates in locally-conducted community-based cohort studies show prevalence rates in South Asian nations – 8% in Bangladesh [38], 7.4% in India [39], and 4% in Sri Lanka according to a report from 2003 [40].…”

Section: Resultsmentioning

confidence: 99%

“…Such follow-up, focused studies have shown that dementia rates in some South Asian countries have historically been underestimated due to underdiagnoses and underreporting. For example, a recent report showed that there are a few million more cases of dementia in India than previously estimated [39]. This is likely the case for many other LMICs that don’t have dedicated resources to track and monitor the status of aging-associated conditions like dementia.…”

Section: Resultsmentioning

confidence: 99%

Status, determinants and risk factors of all-cause dementia in South Asia: Findings from a preliminary analysis of global health data

Palliyaguru,

Ligo Teixeira

et al. 2024

Preprint

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South Asia is one of the most populous regions of the world. At present, South Asians make up more than 25 percent of the world population but little effort has been dedicated to studying trends and etiological factors driving aging and aging-related conditions in this part of the world. Even less characterization has been done on brain-related conditions, particularly all-cause dementia in South Asia. To address this, we examined data from the US Census Bureau, World Health Organization Global Dementia Observatory and the Global Burden of Disease (GBD) 2019 Study, and conducted a comparative analysis of population statistics, dementia as a priority health area, and dementia incidence rates, death rates and disability-adjusted life years (DALYs) in 8 South Asian countries, Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. Our analysis reiterated that there are limited resources dedicated to dementia in this region. Sri Lanka and Afghanistan had the highest dementia crude rates and age-standardized rates, respectively. The burden of dementia in some South Asian countries was comparable to estimated global averages, and was largely driven by population aging. Analyses of available data on known biological, behavioral, environmental and disease risk factors, highlighted the role of metabolic risk factors such as high fasting blood glucose. Here, we also underscore the critical need for future follow-up longitudinal studies focusing on brain aging and dementia and describe a roadmap for designing them - taking into account cultural, economic and public health dynamics that may be uniquely applicable to countries in South Asia.

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“…India is now the most populous nation in the world, with a current population of over 1.4 billion people, and tremendous diversity with respect to sociodemographic characteristics, language, culture, geographical region, rural‐urban residence status, and so forth, which, in turn, could play a significant role in altering the risk and prevalence of dementia. A recent nationwide study that there were substantial inter‐state and rural‐urban differences and that sociodemographic factors, such as age, sex, and education, significantly accounted for these disparities 3 …”

Section: Introductionmentioning

confidence: 99%

“…inter-state and rural-urban differences and that sociodemographic factors, such as age, sex, and education, significantly accounted for these disparities. 3 In addition to the above sociodemographic risk factors, lifestylerelated factors could also impact dementia risk, either directly or indirectly, by influencing the well-known cardiovascular risk factors for dementia. This is particularly relevant in the Indian scenario, wherein the burden of cardiovascular morbidity is accumulating at a disturbing rate.…”

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confidence: 99%

Rural‐urban disparities in the diagnosis and treatment of hypertension and diabetes among aging Indians

Rai,

Sahadevan,

Mensegere

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONHypertension and diabetes are modifiable risk factors for dementia. We aimed to assess rural‐urban disparities in the diagnosis and treatment of these conditions among aging Indians.METHODSParticipants (n = 6316) were from two parallel, prospective aging cohorts in rural and urban India. Using self‐report and clinical/biochemical assessments, we subdivided participants with diabetes and hypertension into undiagnosed and untreated groups. Logistic regression and Fairlie decomposition analysis were the statistical methods utilized.RESULTSThere was a significant rural‐urban disparity in undiagnosed hypertension (25.14%), untreated hypertension (11.75%), undiagnosed diabetes (16.94%), and untreated diabetes (11.62%). Further, sociodemographic and lifestyle factors, such as age and tobacco use were the common contributors to the disparities in both undiagnosed hypertension and undiagnosed diabetes, whereas education and body mass index (BMI) were significant contributors to the disparity in untreated hypertension.DISCUSSIONRural Indians face significant healthcare disadvantages as compared to their urban counterparts, which prompts the urgent need for strategies for equitable healthcare.

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Prevalence of dementia in India: National and state estimates from a nationwide study

Cited by 64 publications

References 51 publications

Estimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach

Estimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach

Status, determinants and risk factors of all-cause dementia in South Asia: Findings from a preliminary analysis of global health data

Rural‐urban disparities in the diagnosis and treatment of hypertension and diabetes among aging Indians

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