A Systematic and Meta-Analysis Study on the Prevalence of Tuberculosisand Relative Risk Factors for Prisoners in Iran
Bibliographic record
Abstract
Introduction: Tuberculosis (TB) is a serious infectious disease that affects human health globally. The incidence of TB in prisons is usually much higher than the general population in different countries. The aim of this study was to evaluate the incidence of TB among prisoners in Iran, estimating the relative risk factors by performing a systematic and meta-analysis study on the related articles. Methodology: Our systematic and meta-analysis study was performed according to the PRISMA guidelines. Two authors systematically searched Scopus, Iran doc, Cochrane, Pubmed, Medline, Embase, Iran medex, Magiran, SID, Google Scholar, and EBSCO. The quality assessment of articles was performed by using the Newcastle-Ottawa Scale. After article quality assessment, a fixed or random model, as appropriate, was used to pool the results in a meta-analysis. Heterogeneity between the studies was assessed using I-square and Q-test. Forest plots demonstrating the point and pooled estimates were drawn. Results: Overall, data from 19562 prisoners indicated 63 cases of TB. The prevalence of TB in prisoners was reported to range from 0.025% to 52% in eight studies. The highest prevalence of tuberculosis was related to the study of Rasht, 517 in 100,000, and the lowest rate was related to the study of Sought Khorasan, 25 in 100,000. The ES of the random effect model is 0.003 (95% CI, 0.001-0.005) and p-value <0.0001. The Higgins’ I2 of all studies is 86.55%, and the p-value of the Cochrane Q statistics is <0.001, indicating that there is heterogeneity. Based on the Egger regression plot (t=2.18, p = 0.08, CI 95%: -0.001, 0.005), no publication bias existed. Conclusion: According to the analysis findings, the frequency of tuberculosis among the prison in Iran was low. The highest prevalence obtained in our systematic study was 517 in 100,000 in Rasht, which was near the world statistics in the systematic review of world studies. Due to significant limitations in this study, it is not possible to indicate the exact prevalence of TB in prisons in Iran and compare this with the general population. However, more studies are needed to assess the related risk factors for designing health intervention plans to decrease the incidence rate of TB among prisoners.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.056 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".