HIV prevalence and continuum of care among incarcerated people in Iran from 2010 to 2017
Bibliographic record
Abstract
BACKGROUND: Incarcerated people are at an increased risk of contracting HIV and transmitting it to the community post-release. In Iran, HIV epidemics inside prisons were first detected in the early 1990s. We assessed the HIV prevalence and its correlates, as well as the continuum of care among incarcerated people in Iran from 2010 to 2017. METHODS: We used data collected in three national bio-behavioral surveillance surveys among incarcerated individuals in 2010 (n = 4,536), 2013 (n = 5,490), and 2017 (n = 5,785) through a multistage cluster sampling approach. HIV was tested by the ELISA method in 2010 and 2013 surveys and rapid tests in 2017. Data on demographic characteristics, risky behaviors, HIV testing, and treatment were collected via face-to-face interviews. HIV prevalence estimates along with 95% confidence intervals (CI) were reported. Using data from the 2017 round, multivariable logistic regression models were built to assess the correlates of HIV sero-positivity and conduct HIV cascade of care analysis. RESULTS: The HIV prevalence was 2.1% (95% CI: 1.2%, 3.6%) in 2010, 1.7% (95% CI: 1.3%, 2.1%) in 2013, and 0.8% (95% CI: 0.6%, 1.1%) in 2017 (trend P value < 0.001). Among people with a history of injection drug use, HIV prevalence was 8.1% (95% CI: 4.6%, 13.8%) in 2010, 6.3% (95% CI: 4.8%, 8.3%) in 2013, and 3.9% (95% CI: 2.7%, 5.7%) in 2017. In 2017, 64% (32 out of 50) of incarcerated people living with HIV were aware of their HIV status, of whom 45% (9 out of 20) were on antiretroviral therapy, and of whom 44% (4 out of 9) were virally suppressed (< 1000 copies/ml). CONCLUSIONS: While HIV prevalence has decreased among incarcerated people in Iran, their engagement in the HIV continuum of care is suboptimal. Further investments in programs to link incarcerated people to HIV care and retain them in treatment are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".