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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».