Introduction Implementation data for digital unsupervised HIV self-testing (HIVST) are sparse. We evaluated the impact of an app-based, personalised, oral HIVST program offered by healthcare workers in Western Cape, South Africa. Methods In a quasirandomised study (n=3095), we recruited consenting adults with undiagnosed HIV infection from township clinics. To the HIVST arm participants (n=1535), we offered a choice of an offsite (home, office or kiosk based), unsupervised digital HIVST program (n=962), or an onsite, clinic-based, supervised digital HIVST program (n=573) with 24/7 linkages services. With propensity score analyses, we compared outcomes (ie, linkages, new HIV infections and test referrals) with conventional HIV testing (ConvHT) arm participants (n=1560), recruited randomly from geographically separated clinics. Results In both arms, participants were young (HIVST vs ConvHT) (mean age: 28.2 years vs 29.2 years), female (65.0% vs 76.0%) and had monthly income <3000 rand (80.8% vs 75%). Participants chose unsupervised HIVST (62.7%) versus supervised HIVST and reported multiple sex partners (10.88% vs 8.7%), exposure to sex workers (1.4% vs 0.2%) and fewer comorbidities (0.9% vs 1.9%). Almost all HIVST participants were linked (unsupervised HIVST (99.7%), supervised HIVST (99.8%) vs ConvHT (98.5%)) (adj RR 1.012; 95% CI 1.005 to 1.018) with new HIV infections: overall HIVST (9%); supervised HIVST (10.9%) and unsupervised HIVST (7.6%) versus ConvHT (6.79%) (adj RR 1.305; 95% CI 1.023 to 1.665); test referrals: 16.7% HIVST versus 3.1% ConvHT (adj RR 5.435; 95% CI 4.024 to 7.340). Conclusions Our flexible, personalised, app-based HIVST program, offered by healthcare workers, successfully linked almost all HIV self-testers, detected new infections and increased referrals to self-test. Data are relevant for digital HIVST initiatives worldwide.

BackgroundHIV self-testing (HIVST) offers a potential for expanded test access; challenges remain in operationalizing rapid personalized linkages and referrals to care. We investigated if an app-optimized personalized HIVST strategy improved referrals, detected new infections and expedited linkages to care and treatment.MethodsIn an ongoing cohort study (n = 2,000) based in South Africa, from November 2016 to January 2018, to participants presenting to self-test at community township based clinics, we offered a choice of the following strategies: (a) unsupervised HIVST; (b) supervised HIVST. We also observed participants opting for conventional HIV testing (ConvHT) in geographically separated clinics. We observed outcomes (i.e., linkage initiation, referrals, disease detection) and compared it between the two (HIVST vs. ConvHT) for the same duration.ResultsOf 2,000 participants, 1,000 participants were on HIVST, 599 (59.9%) chose unsupervised HIVST, 401 (40.1%) on supervised HIVST; compared with 1,000 participants on ConvHT. Participants in HIVST vs. ConvHT were comparable young (mean age 27.7 [SD = 9.0] vs. 29.5 [SD = 8.4]); female (64.0% vs. 74.7%); poor monthly income <3,000 RAND ($253 USD) (79.9% vs. 76.4%). With HIV ST (vs. ConvHT), many more referrals (17.4% [15.1–19.9] vs. 2.6% [1.7–3.8]; RR 6.69 [95% CI: 4.47–10.01]), and many new infections (86 (8.6% (6.9–10.5)) vs. 57 (5.7% (4.3–7.3)); Odds Ratio 1.55 [95% CI 1.1–2.2]) were noted. Break up: 45 infections in supervised HIVST 45 (52.3%); 41 infections in unsupervised HIVST (47.6%)]. Preference for HIVST was at 91.6%. With an app-optimized HIVST strategy, linkages to care were operationalized within a day in all participants (99.7% (HIVST) vs. 99.2% (ConvHT); RR 1.005 [95% CI: 0.99–1.01]); 99.8% supervised HIVST, 99.7% unsupervised HIVST.ConclusionOur app-optimized HIVST strategy successfully increased test referrals, detected new infections, and operationalized linkages within a day. This innovative, patient preferred strategy holds promise for a global scale up in digitally literate populations worldwide.Disclosures All authors: No reported disclosures.

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Background Psychiatric illness was a major barrier for HCV treatment during the Interferon (IFN) treatment era due to neuropsychiatric side effects. While direct acting antivirals (DAA) are better tolerated, patient-level barriers persist. We aimed to assess the effect of depressive symptoms on time to HCV treatment initiation among HIV–HCV co-infected persons during the IFN (2003–2011) and second-generation DAA (2013–2020) eras. Methods We used data from the Canadian Co-infection Cohort, a multicentre prospective cohort, and its associated sub-study on Food Security (FS). We predicted Center for Epidemiologic Studies Depression Scale-10 (CES-D-10) classes for depressive symptoms indicative of a depression risk using a random forest classifier and corrected for misclassification using predictive value-based record-level correction. We used marginal structural Cox proportional hazards models with inverse weighting for competing risks (death) to assess the effect of depressive symptoms on treatment initiation among HCV RNA-positive participants. Results We included 590 and 1127 participants in the IFN and DAA eras. The treatment initiation rate increased from 9 (95% confidence interval (CI): 7–10) to 21 (95% CI: 19–22) per 100 person-years from the IFN to DAA era. Treatment initiation was lower among those with depressive symptoms compared to those without in the IFN era (hazard ratio: 0.81 (95% CI: 0.69–0.95)) and was higher in the DAA era (1.19 (95% CI: 1.10–1.27)). Conclusion Depressive symptoms no longer appear to be a barrier to HCV treatment initiation in the co-infected population in the DAA era. The higher rate of treatment initiation in individuals with depressive symptoms suggests those previously unable to tolerate IFN are now accessing treatment.

Background Depression is common in the human immunodeficiency virus (HIV)-hepatitis C virus (HCV) co-infected population. Demographic, behavioural, and clinical data collected in research settings may be of help in identifying those at risk for clinical depression. We aimed to predict the presence of depressive symptoms indicative of a risk of depression and identify important classification predictors using supervised machine learning. Methods We used data from the Canadian Co-infection Cohort, a multicentre prospective cohort, and its associated sub-study on Food Security (FS). The Center for Epidemiologic Studies Depression Scale-10 (CES-D-10) was administered in the FS sub-study; participants were classified as being at risk for clinical depression if scores ≥ 10. We developed two random forest algorithms using the training data (80%) and tenfold cross validation to predict the CES-D-10 classes—1. Full algorithm with all candidate predictors (137 predictors) and 2. Reduced algorithm using a subset of predictors based on expert opinion (46 predictors). We evaluated the algorithm performances in the testing data using area under the receiver operating characteristic curves (AUC) and generated predictor importance plots. Results We included 1,934 FS sub-study visits from 717 participants who were predominantly male (73%), white (76%), unemployed (73%), and high school educated (52%). At the first visit, median age was 49 years (IQR:43–54) and 53% reported presence of depressive symptoms with CES-D-10 scores ≥ 10. The full algorithm had an AUC of 0.82 (95% CI:0.78–0.86) and the reduced algorithm of 0.76 (95% CI:0.71–0.81). Employment, HIV clinical stage, revenue source, body mass index, and education were the five most important predictors. Conclusion We developed a prediction algorithm that could be instrumental in identifying individuals at risk for depression in the HIV-HCV co-infected population in research settings. Development of such machine learning algorithms using research data with rich predictor information can be useful for retrospective analyses of unanswered questions regarding impact of depressive symptoms on clinical and patient-centred outcomes among vulnerable populations.