Risk factors of HIV and variation in access to clean needles among people who inject drugs in Pakistan
Bibliographic record
Abstract
We identified key risk factors for HIV among people who inject drugs (PWID) in Pakistan and explored access to free clean needles. Multivariable logistic regression was used to investigate associations between HIV prevalence and demographic, behavioral, and socio-economic characteristics of PWID. Data came from the Government of Pakistan's Integrated Biological and Behavioral Surveillance (IBBS) Round 5 (2016-17; 14 cities). A secondary analysis investigated associations with reported access to clean needles. Unweighted HIV prevalence among 4,062 PWID (99% male) was 21.0%. Longer injecting duration (Odds ratio [OR] 1.06 [95% confidence interval: 1.02-1.10]; per year), higher injecting frequency (OR 1.67 [1.30-2.13]; per unit increase), and injecting heroin (OR 1.90 [1.11-3.25]) were positively associated with HIV prevalence. There was no association between using a used syringe at last injection and HIV. Having>10 years of education had lower odds of HIV than being illiterate (OR 0.58 [0.35-0.95]). Having a regular sexual partner (OR 0.74 [0.57-0.97]) or paying for sex with the opposite sex (OR = 0.62 [0.45-0.85]) had lower odds of HIV than not. Conversely, PWID paying a man/hijra for sex had higher odds of HIV (OR 1.20 [1.00-1.43]). Receipt of clean needles varied by city of residence (0-97% coverage), whilst PWID with knowledge of HIV service delivery programs had higher odds of receiving clean needles (OR 4.58 [3.50-5.99]). Injecting behaviors were associated with HIV prevalence among PWID, though risks related to paying for sex remain complicated. Geographical variation in access to clean needles suggests potential benefits of more widely spread public health services.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".