Prevalence of Elder Abuse and Sub-types in South Asia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Elder abuse is a critical public health and human rights issue, particularly in South Asia, where patriarchal norms, family-centered caregiving and inadequate institutional support exacerbate the issue. Despite global focus, regional data on elder abuse in South Asian countries are limited. This systematic review and meta-analysis estimated the prevalence of community-based elder abuse and its subtypes among older adults (≥60 years) in South Asia and explored the gender differences in abuse experiences. A comprehensive search of six databases and gray literature identified 33 relevant studies from Bangladesh, India, Nepal, Pakistan, and Sri Lanka. Data were extracted using Covidence, and study quality was assessed with the modified Newcastle-Ottawa Scale. Meta-analyses were conducted using random-effects models, with heterogeneity evaluated via the I² statistic. Most studies were from India (n = 21) and Nepal (n = 7), with an overall elder abuse prevalence of 31.8%, ranging from 25.8% in India to 49.9% in Nepal. Women experienced higher abuse rates (33.0%) than men (24.3%). Caregiver neglect and psychological abuse (around 20% each) were the most common, disproportionately affecting women. Physical abuse (3.9%), financial exploitation (8.0%), and sexual abuse (0.7%) were less frequent, with women experiencing higher abuse in all categories except sexual abuse. This study underscores the significant prevalence of elder abuse in South Asia, with women being disproportionately affected, emphasizing the urgency for culturally appropriate interventions. Targeted policies and community-driven initiatives are essential to combat elder abuse, enhance eldercare, and safeguard vulnerable populations in the region.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.029 |
| Bibliometrics | 0.009 | 0.009 |
| 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".