The Burden of Non-Communicable Diseases (NCDs) among Prisoners in India: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The increasing prevalence and subsequent mortality due to non-communicable diseases (NCDs) among Indian prisoners are often ignored by policymakers. This systematic review and meta-analysis aim to analyze the rising burden of Noncommunicable Diseases in Indian prisons and estimate the pooled prevalence of depression among Indian prisoners. METHODS: A total 9 studies were chosen in accordance with PRISMA guidelines that investigated the burden of NCDs in Indian prisons and were published between January 2010 and August 2022. Statistical analysis was performed in STATA Version 16 software, and the funnel plot was used to identify publication bias. RESULTS: A total of 167 articles were identified, and 9 were included in this analysis. The pooled prevalence of depression among prisoners was 48.78% (95% CI, 27.24-70.55%). According to the review, prisoners showed a significant prevalence of moderate to severe depression, dental caries, poor periodontal condition, and suicide ideation. This study is the first to analyze NCDs prevalence among Indian prisoners. Poor mental and dental health standards and the virtual absence of healthcare facilities necessitate governmental actions to boost inmates' health. It is essential to develop preventative interventions for this extremely isolated and vulnerable group in addition to diagnosing and treating noncommunicable diseases. CONCLUSIONS: Our study findings will enable decision-makers to structure and develop appropriate preventative and curative programs for inmates' general wellbeing.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| 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 | 0.002 |
| 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".