Suchergebnisse

AbstractIntroductionEngaging at‐risk men in HIV prevention programs and services is a current priority, yet there are few effective ways to identify which men are at highest risk or how to best reach them. In this study we generated multi‐factor profiles of HIV acquisition/transmission risk for men in Durban, South Africa, to help inform targeted programming and service delivery.MethodsData come from surveys with 947 men ages 20 to 40 conducted in two informal settlements from May to September 2017. Using latent class analysis (LCA), which detects a small set of underlying groups based on multiple dimensions, we identified classes based on nine HIV risk factors and socio‐demographic characteristics. We then compared HIV service use between the classes.ResultsWe identified four latent classes, with good model fit statistics. Theolder high‐risk class(20% of the sample; mean age 36) were more likely married/cohabiting and employed, with multiple sexual partners, substantial age‐disparity with partners (eight years younger on‐average), transactional relationships (including more resource‐intensive forms like paying for partner's rent), and hazardous drinking. Theyounger high‐risk class(24%; mean age 27) were likely unmarried and employed, with the highest probability of multiple partners in the last year (including 42% with 5+ partners), transactional relationships (less resource‐intensive, e.g., clothes/transportation), hazardous drinking, and inequitable gender views. Theyounger moderate‐risk class(36%; mean age 23) were most likely unmarried, unemployed technical college/university students/graduates. They had a relatively high probability of multiple partners and transactional relationships (less resource‐intensive), and moderate hazardous drinking. Finally, theolder low‐risk class(20%; mean age 29) were more likely married/cohabiting, employed, and highly gender‐equitable, with few partners and limited transactional relationships. Circumcision (status) was higher among the younger moderate‐risk class than either high‐risk class (p < 0.001). HIV testing and treatment literacy score were suboptimal and did not differ across classes.ConclusionsDistinct HIV risk profiles among men were identified. Interventions should focus on reaching the highest‐risk profiles who, despite their elevated risk, were less or no more likely than the lower‐risk to use HIV services. By enabling a more synergistic understanding of subgroups, LCA has potential to enable more strategic, data‐driven programming and evaluation.

AbstractIntroduction: We describe the overall accuracy and performance of a serial rapid HIV testing algorithm used in community‐based HIV testing in the context of a population‐based household survey conducted in two sub‐districts of uMgungundlovu district, KwaZulu‐Natal, South Africa, against reference fourth‐generation HIV‐1/2 antibody and p24 antigen combination immunoassays. We discuss implications of the findings on rapid HIV testing programmes.Methods: Cross‐sectional design: Following enrolment into the survey, questionnaires were administered to eligible and consenting participants in order to obtain demographic and HIV‐related data. Peripheral blood samples were collected for HIV‐related testing. Participants were offered community‐based HIV testing in the home by trained field workers using a serial algorithm with two rapid diagnostic tests (RDTs) in series. In the laboratory, reference HIV testing was conducted using two fourth‐generation immunoassays with all positives in the confirmatory test considered true positives. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value and false‐positive and false‐negative rates were determined.Results: Of 10,236 individuals enrolled in the survey, 3740 were tested in the home (median age 24 years (interquartile range 19–31 years), 42.1% males and HIV positivity on RDT algorithm 8.0%). From those tested, 3729 (99.7%) had a definitive RDT result as well as a laboratory immunoassay result. The overall accuracy of the RDT when compared to the fourth‐generation immunoassays was 98.8% (95% confidence interval (CI) 98.5–99.2). The sensitivity, specificity, positive predictive value and negative predictive value were 91.1% (95% CI 87.5–93.7), 99.9% (95% CI 99.8–100), 99.3% (95% CI 97.4–99.8) and 99.1% (95% CI 98.8–99.4) respectively. The false‐positive and false‐negative rates were 0.06% (95% CI 0.01–0.24) and 8.9% (95% CI 6.3–12.53). Compared to true positives, false negatives were more likely to be recently infected on limited antigen avidity assay and to report antiretroviral therapy (ART) use.Conclusions: The overall accuracy of the RDT algorithm was high. However, there were few false positives, and the sensitivity was lower than expected with high false negatives, despite implementation of quality assurance measures. False negatives were associated with recent (early) infection and ART exposure. The RDT algorithm was able to correctly identify the majority of HIV infections in community‐based HIV testing. Messaging on the potential for false positives and false negatives should be included in these programmes.