Prevalence of Cognitive Impairment and Its Determinants among Older Adults in Urban South India – A Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is a growing public health challenge for older adults in nations like India due to demographic changes and chronic diseases, significantly impacting daily function. Despite its importance, there is limited research in quantifying the burden of cognitive impairment. AIMS: This study aims to assess the prevalence of cognitive impairment among the elderly in an urban area and to determine the associated factors. SUBJECTS AND METHODS: A community-based cross-sectional study was conducted between July 2024 and February 2025 in an urban area of Chengalpattu district, Tamil Nadu. 300 participants aged 60 years and above were selected using two-stage random sampling. Data were collected using a sociodemographic questionnaire, Montreal Cognitive Assessment (MoCA), Katz index of independence of activities of daily living (ADL) and DASS-21 scale. Logistic regression analysis was used to identify factors associated with cognitive impairment (MoCA score ≤24). RESULTS: The prevalence of cognitive impairment was 36.67%. Mild Cognitive Impairment (MCI) was present in 11.67% of participants. Multivariate analysis revealed that engaging in leisure activities was protective against cognitive impairment (adjusted odds ratio [AOR] 0.332). Factors significantly associated with higher odds of cognitive impairment included gait disturbances (AOR 2.872), dependence in ADL (AOR 5.983) and depression (AOR 7.393). CONCLUSIONS: Cognitive impairment is highly prevalent among the elderly in this urban South Indian community. Promoting leisure activities and addressing modifiable factors such as depression, functional dependence and gait disturbances are important strategies for mitigating cognitive decline in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".