Spatial heterogeneity of risk factors associated with HIV prevalence among men who inject drugs in India: An analysis of the data from the integrated bio-behavioral surveillance, India
Bibliographic record
Abstract
People who inject drugs (PWID) are India's third-largest vulnerable population to human immunodeficiency virus (HIV) infection. PWID in India are confined to certain geographic locations and exhibit varying injecting and sexual risk behaviors, contributing considerably to increasing HIV trends in specific regions. Spatial heterogeneity in risk factors among vulnerable PWID influences HIV prevalence, transmission dynamics, and disease management. Stratified analysis of HIV prevalence based on risk behaviors and geographic locations of PWID will be instrumental in strategic interventions. To stratify the male PWID based on their risk behaviors in each state and determine the HIV prevalence for each stratum. The behavioral data and HIV prevalence of the national integrated biological and behavioural surveillance (IBBS), a nationwide cross-sectional community-based study conducted in 2014 to 2015, was analyzed. Data from 19,902 men who inject drugs across 53 domains in 29 states of India were included. Women who inject drugs were excluded at the time of IBBS, and hence PWID in this study refers to only men who inject drugs. PWID were categorized based on their risk profile, and the corresponding HIV prevalence for each state was determined. HIV prevalence was the highest (29.6%) in Uttar Pradesh, with a high prevalence of risk behaviors among PWID. High HIV prevalence ranging between 12.1% and 22.4% was observed in a few states in East and North-East India and most states in central and North India. Unsafe injecting and sexual practices were significantly (P < .05) associated with higher HIV prevalence and more significantly in National Capital Territory of Delhi (P < .001). Unsafe injecting practices among PWID were proportionally higher in Western and Central India, whereas unsafe sexual behaviors were widespread among most states. Unsafe sexual practices among male PWID were common. The high prevalence of unsafe injecting had significant HIV infection and transmission risks in Western and Central India. The results emphasize the need for stratified, region-specific interventions and combination approaches for harm reduction among PWID. Strengthening the measures that facilitate the reduction of high-risk behaviors, adoption of safe practices, and utilization of HIV services will positively impact HIV prevention measures among PWID.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".