HIV continues to spread among men who have sex with men in Georgia; time for action
Bibliographic record
Abstract
INTRODUCTION: In order to determine the impact of HIV prevention and care programs, it is essential to look at both HIV incidence and prevalence estimates and trends over time. We estimated the HIV incidence and prevalence and assessed the trend using data from three cross-sectional surveys of men who have sex with men (MSM) in two cities in Georgia. METHODS: Using respondent-driven sampling strategy, a total of 796 eligible MSM (18 years or older men with self-reported oral or anal sex with another man in past 12 months) were recruited in Tbilisi in 2010, 2012 and 2015 and 115 in Batumi 2015 into behavioral surveys and HIV testing. To estimate the HIV incidence, we divided the number MSM tested positive for HIV to the time at risk. We calculated the time at risk as years since age at first anal intercourse to the age at last HIV-negative test or the age at first HIV-positive test, accounted for the interval censorship. We calculated the respondent-driven sampling adjusted estimates for HIV prevalence and assessed the trend in Tbilisi by Chi2 test for trend. For HIV incidence rate, we used Kaplan Meier method to estimate the rates and assessed the subgroup differences by log-rank test. RESULTS: The HIV prevalence was 14.9% in Batumi in 2015; it significantly increased in Tbilisi from 6.2% in 2010 to 14.1% in 2012, and to 19.6% in 2015 (p-value for trend < 0.001). Likewise, the HIV incidence rate in Tbilisi significantly increased form 0.45 per 100 person-years (PY) in 2010 to 0.98 per 100 PY in 2012 (p-value 0.01), and to 1.63 per 100 PY in 2015 (p-value < 0.001). HIV incidence rate was 1.37 per 100 PY in Batumi in 2015. In 2015, young MSM (Tbilisi: 3.71, Batumi: 3.92 per 100 PY, p-value< 0.008), single MSM (Tbilisi: 1.99, per 100 PY, p-value 0.03) and less educated MSM (Batumi: 1.86 per 100 PY, p-value 0.03) had higher HIV incidence than other MSM. CONCLUSION: Our findings suggest the continuous transmission of HIV among MSM in Tbilisi and a high prevalence of HIV among MSM in Batumi and the critical need for scaling up the coverage and accessibility of combination prevention packages including rapid HIV diagnosis and treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".