Prevalence of depression among elderly patients in India: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Depression is a growing mental health concern among the elderly, particularly in low- and middle-income countries like India, where the aging population is rapidly increasing. This systematic review aims to estimate the pooled prevalence of depression among elderly individuals in India using available population-based studies. Methods: A comprehensive literature search was performed across PubMed, Scopus, Google Scholar, and Indian research databases for studies published up to April 2025. Studies were included if they assessed depression prevalence in Indian elderly populations (≥60 years) using standardized diagnostic tools such as the Geriatric Depression Scale (GDS) or PHQ-9. Data extraction and quality appraisal were done independently by two reviewers. Meta-analysis was conducted using an inverse-variance weighted fixed-effect model. Results: A total of 512 studies were identified, and after screening and eligibility checks, 10 studies involving 9,050 elderly participants were included in the meta-analysis. The reported prevalence of depression in the studies included ranged from 27.5% to 40.2%. The pooled prevalence was estimated at 32.9% (95% CI: 31.4% – 34.4%). Moderate heterogeneity was observed (I² = 39.7%), reflecting variation in geographic regions and assessment tools. Conclusion: Depression among elderly individuals in India is highly prevalent, affecting nearly one-third of the population studied. These findings emphasize the urgent need for early detection, community-based screening, and culturally sensitive mental health interventions in geriatric care policies to reduce the burden of depression in aging populations.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".