Tea-tribe communities in Assam face a substantial and under-recognised burden of stroke, shaped by socioeconomic marginalisation, limited access to formal healthcare and distinct cultural practices.

We estimated the prevalence of physician-diagnosed stroke, described associated functional and psychological disabilities, characterised gaps in diagnosis and care, and developed a logistic regression-based classification score for prevalent stroke among tea-tribe adults in Cachar district, Assam, Northeast India.

We conducted a community-based cross-sectional door-to-door survey (July 2024–August 2025) across five tea estates among permanent tea-tribe residents aged ≥18 years. Trained community health workers collected data on sociodemographics, vascular risk factors, healthcare utilisation and poststroke functional and psychological outcomes using a structured questionnaire, the Modified Rankin Scale and a symptom inventory. Stroke was identified through community symptom screening and confirmed by teleconsultation with a senior neurologist, supported by neuroimaging where available. A multivariable logistic regression model with stepwise selection identified factors associated with prevalent stroke and regression coefficients were converted into an additive point-based classification score. Internal validation used 1000 bootstrap samples and a 70:30 train–test split. The model performance was assessed using the area under the receiver operating characteristic curve and Brier score.

Among 3818 participants, 164 had a history of stroke (prevalence 4.3%), with a higher prevalence in older adults, men, unemployed or semiskilled or skilled workers and households below the poverty line. Hypertension was the strongest risk factor and its combination with daily tea containing added salt was associated with markedly higher odds of stroke. Alcohol consumption, smoking and previous heart disease were also associated with stroke. Stroke survivors had high functional and psychological burdens and fewer than half reached a health facility within 4.5 hours or underwent CT/MRI. The final model showed good discrimination (area under the curve 0.84) and calibration (Brier score 0.037). The derived classification score (0–19 points) identified a highly associated group (scores >13) with a stroke prevalence of 14.5%.

The tea-tribe community in Assam faces a high stroke burden, substantial disability and major gaps in timely diagnosis and care. A simple, internally validated classification score based on demographic, socioeconomic and vascular factors may help target community screening and prevention but requires external validation and cautious interpretation given the cross-sectional design and self-reported exposures.

This study examines the prevalence of and risk factors associated with cognitive impairment among Indian stroke survivors aged ≥60 using data from the Longitudinal Ageing Study in India (LASI).

Cross-sectional comparative analysis.

Secondary analysis of Wave 1 (2017–2019) data from the nationally representative LASI.

A total of 844 individuals aged ≥60 years with a self-reported history of stroke and 2532 age-matched and gender-matched non-stroke controls selected at a 3:1 ratio from the LASI dataset.

The outcome measure was the prevalence of cognitive impairment among older adults with a history of stroke, assessed using a composite cognitive index (score ≤14) derived from five cognitive domains. We also aimed to identify socio-demographic, socioeconomic, behavioural and health-related factors associated with post-stroke cognitive impairment using multivariable logistic regression models.

Stroke survivors had a higher prevalence of cognitive impairment (15.3%) compared with non-stroke individuals (11.6%) and significantly lower cognitive function (composite score: 22.23 vs 23.78, p

Stroke survivors in India face increased risk of cognitive impairment, with associated risk factors including age, female gender, inactivity, depression and low body mass index, while higher education and expenditure status were associated with a reduced risk. Early detection and targeted interventions, including screening, lifestyle changes and personalised rehabilitation, are vital for preserving cognitive health after stroke.