The research team, led by Sneha Sarah Mani of the Longitudinal Aging Study in India collaboration together with Yuan S. Zhang, Lindsay C. Kobayashi, and Alden L. Gross, drew on an unusually rich data infrastructure. The Longitudinal Aging Study in India, or LASI, is a nationally representative survey of more than 70,000 Indian adults aged 45 and above, conducted in 2017 to 2019. Within it, a specialized sub-study called LASI-DAD administered an in-depth neuropsychological battery to 4,096 participants aged 60 and older. The battery included the Hindi Mental State Examination, word list learning and recall, digit span tests, constructional praxis, and animal naming, all adapted and validated for India’s many languages and for respondents with little or no formal schooling. Family members were also interviewed to corroborate each participant’s cognitive status.

Using an algorithm based on the DSM-5 criteria for mild and major neurocognitive disorders, the researchers classified each participant as cognitively healthy, as having mild cognitive impairment, or as having dementia. The picture that emerged was sobering: nearly a quarter of older Indian adults had some form of cognitive impairment, with 17.4 percent showing mild cognitive impairment and 7.4 percent living with dementia. Among men, the prevalence of mild impairment was actually higher, at 18.5 percent, than among women, at 16.3 percent. But women carried a heavier dementia burden, at 8.0 percent versus 6.8 percent for men, and that gap widened sharply after age 70, when 11.8 percent of women had dementia compared with 9.3 percent of men.

To convert these snapshots into life expectancy estimates, the team applied the Sullivan method, a classic demographic technique from 1971 that apportions the years in a standard life table across different health states according to the prevalence of each condition at every age. Mortality rates came from the Sample Registration System, a continuous vital-events monitoring panel that India has operated since 1971, while caste-specific mortality had to be estimated separately from household death reports in the National Family Health Survey, because official life tables are not published by caste. The result is a measure called cognitively healthy life expectancy, or CHLE: the average number of remaining years a person at a given age can expect to live free of cognitive impairment, given current mortality and prevalence patterns.

The headline numbers are striking. At age 60, Indian women can expect 14.3 years of cognitively healthy life and men 13.5 years. Both genders spend roughly 75 percent of their remaining years free of cognitive impairment, with men spending slightly more time in the mild impairment state, 3.4 years versus 3.1 for women, and women spending more time with dementia, 1.6 years versus 1.2. By age 70, both men and women have about 8.5 to 9 cognitively healthy years left, and roughly 70 percent of their remaining life is lived with an unimpaired mind. These proportions are comparable to, or worse than, those seen in wealthy countries such as the United States and England, despite India’s much shorter total life expectancy at these ages.

The regional and social fault lines, however, are where the study becomes truly revealing. Urban women at age 60 could expect 15.8 cognitively healthy years, about 2.2 more than rural women, while urban men could expect 15.3 years, a full 3.1 years more than their rural counterparts. Urban men spent 80 percent of their remaining life cognitively healthy, compared with just 72 percent for rural men. Caste gradients proved even steeper. Women from Scheduled Castes and Scheduled Tribes, historically marginalized communities, could expect only 11.6 cognitively healthy years at age 60, 3.7 fewer than higher-caste women, and they spent 31 percent of their remaining life with impairment versus 26 percent for higher-caste women. Among men, the gap was wider still: higher-caste men could expect more than five additional years of good cognitive health compared with Scheduled Caste and Tribe men, who faced roughly four times the disadvantage that Other Backward Class men did relative to the top group.

Perhaps the most technically innovative part of the study is its decomposition analysis. A gap in cognitively healthy life expectancy between two groups can arise in two ways: one group may die earlier, cutting short the years available for healthy cognition, or it may live just as long but spend more of those years impaired. Using stepwise replacement methods that swap age-specific mortality and prevalence parameters between populations, the researchers disentangled these pathways. The answer was consistent across most comparisons: mortality, not cognitive impairment prevalence, is the dominant driver of inequality. For the 3.1-year gap between urban and rural men, lower urban mortality contributed 1.6 years and lower urban impairment prevalence 1.5 years, but for caste gaps, mortality dominated overwhelmingly, contributing 2.5 of the 3.7-year gap among women and 3 of the 5.3-year gap among men.

The gender story is more nuanced. The overall gap between women and men at age 60 was small, just 0.86 years, because two opposing forces nearly cancel out. Women’s lower mortality pushes their cognitively healthy expectancy up by 0.68 years, but their higher dementia prevalence at older ages pulls it down. Only among higher-caste groups did women fare worse than men in absolute terms, with a net deficit of 0.68 years driven almost entirely by high levels of dementia among women over 70, which cost them 1.64 years and overwhelmed their survival advantage. The researchers suggest several mechanisms: women survive longer into the ages when dementia is most common, men with impairment face higher competing mortality, and men carry a heavier burden of vascular risk factors such as tobacco and alcohol use. Distinguishing among these explanations, they caution, will require longitudinal follow-up.

What explains the deep social gradients? Education looms large. Schooling is one of the most consistently protective factors against dementia, building cognitive reserve that buffers the brain against pathology, yet access to education in India has historically been rationed along precisely the lines the study examines: men over women, urban over rural, and higher castes over Scheduled Castes and Tribes. Although affirmative action policies and schooling expansion since the 1970s have narrowed some gaps, children from marginalized communities still lag in school completion today. Beyond education, the same social lines pattern early-life nutritional deprivation, occupational exposures in agricultural and informal manual labor, indoor and outdoor air pollution, and unequal access to diagnosis and management of hypertension and diabetes, all of which feed into both cognitive decline and premature death.

The policy implications are pointed. Because excess mortality, rather than dementia prevalence, drives most of the inequality in cognitively healthy life expectancy, the authors argue that interventions should prioritize the underlying causes of early death among disadvantaged groups: expanding healthcare access, controlling chronic disease through existing primary-care platforms such as Ayushman Arogya Mandirs and the National Program for Health Care of the Elderly, and addressing the social determinants that shape health across the entire life course. At the same time, reducing dementia risk remains vital, since evidence suggests prevention both adds healthy years and compresses the period of illness for those who do develop the condition. With global projections attributing a large share of the coming surge in dementia cases to South Asia, and with the Lancet Commission noting that a greater proportion of dementia risk is modifiable in low- and middle-income countries than in rich ones, the stakes could hardly be higher. The study has limitations, including the Sullivan method’s assumption of stable rates and a classification algorithm that likely underestimates impairment, but its central message stands: as India’s demographic tide turns, ensuring that the nation’s added years are thinking years will depend on closing the survival gaps that have divided its population for generations.

Subject of Research: Cognitively healthy life expectancy and dementia burden among older adults in India by gender, urban-rural residence, and caste

Article Title: Cognitively Healthy Life Expectancy of Older Women and Men in India

Article References: Cognitively Healthy Life Expectancy of Older Women and Men in India. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: India, cognitively healthy life expectancy, dementia, mild cognitive impairment, population aging, LASI-DAD, Sullivan method, health inequality, caste, rural-urban disparities, gender differences, mortality decomposition

The research team, led by Lantian Zhang of the Institute of Geographic Sciences and Natural Resources Research with collaborators including Mark W. Rosenberg of Queen’s University, assembled an unusually ambitious dataset covering 1,376 counties across China, of which 379 are classified as Kashin-Beck disease endemic. Rather than treating the disease itself as the focus, the researchers used these endemic counties as a natural laboratory for studying healthy ageing under conditions of ecological fragility and long-term socioeconomic disadvantage. Their outcome measure was old-age healthy life expectancy at age 60, a metric endorsed by the World Health Organization that combines mortality data with the prevalence of good health to estimate how many years a 60-year-old can expect to live in reasonable health.

Calculating this measure for more than a thousand counties across fifteen years was itself a technical feat. The team drew on census microdata from 2005, 2010, and 2020 provided through the Peking University Research Data Center, applying the Sullivan method to construct life tables from age 60 onward. Self-rated health, in which individuals assess their own overall condition, served as the health indicator, with people classified as healthy or basically healthy counted among the healthy states. Because no nationally representative mortality survey was conducted in 2005, the researchers estimated that year’s mortality using the 2000 national survey, refined through small-area estimation models that incorporated county-level covariates such as education rates, ethnic composition, income, and population density, then scaled the results to match provincial statistics.

On the explanatory side, the researchers built a multidimensional sustainability indicator system aligned with the United Nations Sustainable Development Goals, spanning the classic three pillars of sustainable development. The ecological dimension included forest coverage, vegetation indices measured by satellite, soil conservation, air pollutants such as fine particulate matter and ozone, and carbon emissions. The economic dimension captured income, employment, industrial structure, and financial resources. The social dimension encompassed safe drinking water, sanitation, housing, road density, education, and critically, the density of health facilities, health technicians, and hospital beds. Each indicator was standardized, and dimension-specific indices were constructed using a hybrid weighting scheme that combined expert-driven analytic hierarchy process weights with data-driven entropy weights in a 70:30 ratio, a balance the team tested for robustness against alternative weightings.

The analytical engine of the study was a hierarchical Bayesian framework implemented through Integrated Nested Laplace Approximation, a computational approach that has revolutionized spatial statistics by making complex models tractable. The researchers started with a baseline model decomposing healthy life expectancy into structured and unstructured spatial effects, a common temporal trend, and region-specific deviations from that trend. They then layered on increasingly sophisticated specifications, culminating in a spatiotemporal varying coefficient model in which the regression coefficients themselves shift across both space and time, and a further extension allowing county-by-time interactions. Model selection relied on the deviance information criterion, the Watanabe-Akaike information criterion, and cross-validation, while a spatiotemporal variance partitioning index quantified how much of the variation in healthy life expectancy each sustainability dimension explained.

The headline finding is stark: social sustainability matters most, and it matters most precisely where societies are weakest. Across all counties, a one-unit increase in the standardized social sustainability index was associated with an 11.66-year difference in healthy life expectancy at 60, with a 95 percent credible interval of 10.85 to 12.48 years. Ecological and economic sustainability showed positive but far smaller associations of roughly 2.3 and 2.2 years respectively. Yet the averages conceal dramatic heterogeneity. In Kashin-Beck endemic counties, the social sustainability coefficient rose to 13.56 years, while economic sustainability showed no detectable association at all. In non-endemic counties, the pattern reversed: economic sustainability was positively linked to healthy ageing, and social effects were somewhat weaker. On the Qinghai-Tibet Plateau, endemic counties showed the strongest ecological associations anywhere in the study, at 6.15 years, suggesting that in harsh high-altitude environments the natural surroundings themselves become a meaningful determinant of healthy old age.

Perhaps the most striking result is temporal. When the researchers decomposed the variation in healthy life expectancy, temporal dynamics of the sustainability indicators explained more than 90 percent of the variance in both endemic and non-endemic areas, dwarfing spatial effects, with model residuals accounting for less than 5 percent. This dominance of change over time reflects the sweeping nationwide improvements in living conditions and healthcare that China experienced between 2005 and 2020, including the 2009 New Healthcare Reform that expanded insurance coverage and strengthened primary care. In the Kashin-Beck affected areas specifically, decades of government intervention, including selenium supplementation, population relocation, and ecological compensation, appear to have paid dividends as the cohorts affected by earlier epidemics reached old age. The study also documented a narrowing of regional health inequalities, with non-endemic counties averaging 15.96 healthy years at age 60 compared with 15.15 years in endemic counties, a significant but not overwhelming gap.

The geography of these associations shifted in revealing ways. In 2005, the strongest links between social sustainability and healthy ageing were concentrated on the Qinghai-Tibet Plateau, where geographic inaccessibility and weak infrastructure made clinics, sanitation, and schools the binding constraints on wellbeing. By 2020, those strongest associations had migrated toward more economically developed regions, a redistribution the authors interpret through the lens of evolving policy: as basic healthcare shortages were alleviated in poorer areas, the marginal importance of social conditions migrated to wealthier regions grappling with chronic disease burdens that demand long-term, integrated health and social care. Meanwhile, the influence of ecological sustainability grew steadily over the fifteen-year window, hinting that as other determinants improve, environmental quality becomes an increasingly relevant pillar of healthy ageing.

For policymakers, the message is that one size does not fit all. In regions with fragile socioeconomic foundations, investments in social infrastructure, healthcare accessibility, sanitation, and education offer the greatest returns for healthy ageing, while sustained economic development and environmental governance matter over the longer horizon. The authors are candid about limitations: self-rated health captures a subjective dimension of wellbeing, the Sullivan method relies on cross-sectional data and cannot disentangle birth cohort effects, and as an ecological analysis the study describes regional patterns rather than individual-level mechanisms. Still, by demonstrating that the sustainability-health relationship is neither fixed in place nor uniform across the landscape, the study delivers a methodological lesson as important as its substantive one. Static, aspatial analyses of healthy ageing, it suggests, are not merely incomplete; they can obscure the very dynamics that determine whether the world’s fastest-ageing regions, many of them its most environmentally fragile, can convert rising life expectancy into genuinely healthy longevity.

Subject of Research: Spatiotemporal associations between ecological, economic, and social sustainability and old-age healthy life expectancy in ecologically fragile regions of China

Article Title: Spatiotemporal effects of sustainability dimensions on old-age healthy life expectancy in ecologically fragile regions

Article References: Spatiotemporal effects of sustainability dimensions on old-age healthy life expectancy in ecologically fragile regions. (n.d.). https://doi.org/10.1016/j.indic.2026.101546

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101546

Keywords: healthy life expectancy, population ageing, sustainability indicators, Kashin-Beck disease, Bayesian spatiotemporal modeling, Qinghai-Tibet Plateau, Loess Plateau, social sustainability, Sullivan method, spatial heterogeneity, Sustainable Development Goals, China