Elucidating Drivers for Variations in the Explosive Human Immunodeficiency Virus Epidemic Among People Who Inject Drugs in Pakistan
Bibliographic record
Abstract
BACKGROUND: Pakistan's explosive human immunodeficiency virus (HIV) epidemic among people who inject drugs (PWID) varies widely across cities. We evaluated possible drivers for these variations. METHODS: Multivariable regression analyses were undertaken using data from 5 national surveys among PWID (n = 18 467; 2005-2017) to determine risk factors associated with variations in city-level HIV prevalence. A dynamic HIV model was used to estimate the population-attributable fraction (PAF; proportion of HIV infections prevented over 10 years when that risk factor is removed) of these risk factors to HIV transmission and impact on HIV incidence of reducing their prevalence. RESULTS: Regression analyses suggested that city-level HIV prevalence is strongly associated with the prevalence of using professional injectors at last injection, heroin use in last month, and injecting ≥4 times per day. Through calibrating a model to these associations, we estimate that the 10-year PAFs of using professional injectors, heroin use, and frequent injecting are 45.3% (95% uncertainty interval [UI], 4.3%-79.7%), 45.9% (95% UI, 8.1%-78.4%), and 22.2% (95% UI, 2.0%-58.4%), respectively. Reducing to lowest city-level prevalences of using professional injectors (2.8%; median 89.9% reduction), heroin use (0.9%; median 91.2% reduction), and frequent injecting (0.1%; median 91.8% reduction) in 2020 reduces overall HIV incidence by 52.7% (95% UI, 6.1%-82.0%), 53.0% (95% UI, 11.3%-80.2%), and 28.1% (95% UI, 2.7%-66.6%), respectively, over 10 years. CONCLUSIONS: Interventions should focus on these risk factors to control Pakistan's explosive HIV epidemic among PWID, including a concomitant expansion of high-coverage needle/syringe provision, opioid substitution therapy, and antiretroviral therapy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".