HIV vaulnerability among people who inject drugs (PWID): findings from the Bangladesh integrated biological and behavioural surveillance (IBBS) study 2020
Bibliographic record
Abstract
INTRODUCTION: People who inject drugs (PWID) contribute significantly to the global HIV burden. Various individual and contextual factors exacerbate the risk of HIV among PWID, this problem is particularly acute in low- and middle-income countries, where resource constraints impede effective prevention and treatment efforts. Although Bangladesh is classified as a low HIV prevalence country, and despite national efforts, including surveillance through the Integrated Biobehavioural Survey (IBBS), gaps in evidence and actionable insights persist. This study aims to analyse the latest IBBS 2020 data to identify risk factors associated with HIV vulnerability among PWID in Bangladesh, informing culturally relevant and targeted harm reduction strategies to mitigate these risks. METHOD: This study analysed data from IBBS, which used a two-stage cluster sampling and Time Location Sampling methods to recruit participants. Data were collected across four domains: (1) Sociodemographic characteristics, (2) Drug and Injection-Related Behaviours, (3) Sexual Behaviours, and (4) Co-infections, with blood samples collected for HIV, hepatitis-C, and syphilis serological testing. Sample weights were applied to adjust for the complex survey design. Descriptive statistics summarized participant characteristics and risk behaviours. Two binary logistic regression models were used to identify HIV risk factors: The Fully Adjusted Model, which included all plausible confounders and used the Wald backward elimination method to determine significant predictors, and the Partially Adjusted Model, which controlled for age, gender, and education to explore intermediate factors through adjusting confounding or mediation. RESULT: Overall, the prevalence of HIV among PWID was 4.1%. Social exclusion (AOR: 1.71, 95% CI 1.1, 2.7). Hepatitis C infection (AOR: 2.57, 95% CI 1.6, 4.0), drug use of more than 10 years (AOR: 3.74, 95% CI 1.3, 10.8), injecting once or more daily (AOR: 5.23, 95% CI 2.6, 10.7), having multiple injecting partners (AOR: 3.11, 95% CI 1.8, 5.3) sharing injecting accessories (AOR: 2.55, 95% CI 1.5, 4.4) and engaging with a commercial sex partner (AOR: 1.80, 95% CI 1.1, 2.9) emerged as significant predictors of HIV risk among PWID patients. CONCLUSION: This study reveals the heightened HIV risk among PWID in Bangladesh, driven by intertwined social, behavioural, biological, and structural factors. It underscores the urgent need for tailored, holistic interventions combining harm reduction, structural reforms, and biomedical strategies to address vulnerabilities, reduce high-risk behaviours, and improve healthcare access for this marginalized group.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".