Prevalence of hepatitis C virus infection among prisoners in Iran: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) is one of the major public health problems both in developed and developing countries. Prison represents a high-risk environment for prisoners, in that it is characterized by high-risk behaviors such as injecting drug use (IDU), tattooing, unprotected sexual intercourses, or sharing syringes. The aim of this study was to quantitatively evaluate the prevalence of HCV among Iranian prisoners conducting a systematic review and meta-analysis. METHODS: We searched different scholarly databases including Embase, PubMed/MEDLINE, ISI/Web of Sciences, the Cochrane library, Scopus, CINAHL, and PsycINFO as well as Iranian bibliographic thesauri (namely, Barakatns, MagIran, and SID) up to December 2017. The Newcastle Ottawa Scale (NOS) was used to assess the quality of the studies included. HCV prevalence rate with its 95% confidence interval (CI) was estimated using the DerSimonian-Laird random-effects model, with Freeman-Tukey double arcsine transformation. Egger's regression test was used to evaluate publication bias. RESULTS: = 99.3% (p = 0.00). All studies used an ELISA test for the evaluation of HCV antibodies. The findings of this study showed that the highest prevalence rate (53%) was among prisoners who inject drugs. CONCLUSION: The findings of our study showed that the prevalence of HCV among Iranian prisoners is dramatically high. Managing this issue in Iran's prisons requires careful attention to the availability of health facilities and instruments, such as screening, and harm reduction policies, such as giving sterile syringes and needles to prisoners. An integrated program of training for prisoners, prison personnel and medical staff is also needed to improve the level of health condition in prisons.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".