HIV incidence and factors associated with HIV risk among people who inject drugs engaged with harm-reduction programmes in four provinces in South Africa: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: HIV incidence among people who inject drugs in South Africa has never been estimated. We aimed to estimate HIV incidence and associations with risk and protective factors among people who inject drugs engaged with harm-reduction services. METHODS: For this retrospective cohort study we used programmatic data collected from April 1, 2019, to March 30, 2022, by the Networking HIV and AIDS Community of South Africa, which offers harm-reduction services and HIV testing to people who inject drugs. During this 3-year period, services were delivered through drop-in centres and outreach in four South African provinces: Gauteng, KwaZulu-Natal, Western Cape, and Eastern Cape. Our cohort comprised people who inject drugs who did not self-report being HIV positive, were HIV negative at first testing, and had at least one follow-up test. Data were collected by outreach teams. We estimated HIV incidence, assuming seroconversions occurred at the midpoint between the last negative test and first positive test. We assessed associations between HIV seroconversion risk and several factors with Cox regression models, including sociodemographic characteristics, primary drugs used, uptake of interventions (ie, number of harm-reduction packs and opioid agonist treatment [OAT]), and HIV testing interval. FINDINGS: Of 31 182 people who inject drugs accessing harm-reduction services, 20 955 (including 3409 self-reporting being HIV positive) were not tested for HIV. Of 10 227 people who tested at least once, 8152 were HIV negative at first test and of these, 2402 had at least two tests and formed the study cohort. Overall, 283 (11·8%) people who inject drugs acquired HIV over 2306·1 person-years. HIV incidence was higher in Gauteng (16·7 per 100 person-years; 95% CI 14·5-19·1) and KwaZulu-Natal (14·9 per 100 person-years; 11·3-19·3), than in the Eastern Cape (5·0 per 100 person-years; 2·3-9·6) and Western Cape (3·2 per 100 person-years; 1·9-4·9). In multivariable Cox models, HIV acquisition risk varied by race, primary drugs used, and interval between HIV tests. Additionally, people who injected drugs and received OAT in the past year had lower HIV risk (adjusted hazard ratio 0·48; 95% CI 0·22-1·03) than people who did not receive OAT, although the 95% CI was wide and crossed the null. INTERPRETATION: Our study highlights a pressing need for scale-up of HIV prevention strategies, particularly opioid agonist treatment, for people who inject drugs in South Africa. Dedicated investments are needed to develop monitoring systems for HIV incidence, risk behaviours, and uptake of interventions to ensure effective and equitable programmes. FUNDING: Wellcome Trust, Canadian Institutes of Health Research, and Global Fund to Fight AIDS, Tuberculosis and Malaria.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".