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Supplemental Digital Content is Available in the Text. Background: Low-risk perception is an important barrier to the utilization of HIV services. In this context, offering an online platform for people to assess their risk of HIV and inform their decision to test can be impactful in increasing testing uptake. Using secondary data from the HIVSmart! quasirandomized trial, we aimed to identify predictors of HIV, develop a risk staging model for South African township populations, and validate it in combination with the HIVSmart! digital self-testing program. Setting: Townships in Cape Town, South Africa. Methods: Using Bayesian predictive projection, we identified predictors of HIV and constructed a risk assessment model that we validated in external data. Results: Our analyses included 3095 participants from the HIVSmart! trial. We identified a model of 5 predictors (being unmarried, HIV testing history, having had sex with a partner living with HIV, dwelling situation, and education) that performed best during external validation (area under the receiver operating characteristic curve, 89% credible intervals: 0.71, 0.68 to 0.72). The sensitivity of our HIV risk staging model was 91.0% (89.1% to 92.7%) and the specificity was 13.2% (8.5% to 19.8%) but increased when combined with a digital HIV self-testing program, the specificity was 91.6% (95.9% to 96.4%) and sensitivity remained similar at 90.9% (89.1% to 92.6%). Conclusions: This is the first validated digital HIV risk assessment tool developed for South African township populations and the first study to evaluate the added value of a risk assessment tool with an app-based HIV self-testing program. Study findings are relevant for application of digital programs to improve utilization of HIV testing services. [ABSTRACT FROM AUTHOR]

Background: The clinical implications of a polygenic risk score (PRS) for LDL-C (low-density lipoprotein cholesterol) are not well understood, both within the general population and individuals with familial hypercholesterolemia (FH)., Methods: We developed the LDL-C PRS using Least Absolute Shrinkage and Selection Operator regression in 377 286 White British participants from UK Biobank and tested its association with LDL-C according to FH variant carrier status in another 41 748 whole-exome sequenced individuals. Next, we tested for an enrichment of FH variant carriers among individuals with severe hypercholesterolemia and low LDL-C PRS. Last, we contrasted the effect of the LDL-C PRS, measured LDL-C and FH variant carrier status on risk of ischemic heart disease among 3010 cases and 38 738 controls., Results: Among the 41 748 whole-exome sequenced White British individuals, 1-SD increase in the LDL-C PRS was associated with elevated LDL-C among both FH variant carriers (0.34 [95% CI, 0.22-0.47] mmol/L) and noncarriers (0.42 [95% CI, 0.42-0.43] mmol/L). Among individuals with severe hypercholesterolemia, FH variant carriers were enriched in those with a low LDL-C PRS (odds ratio, 2.20 [95% CI, 1.66-2.71] per SD). Each SD increase in the LDL-C PRS was associated with risk of ischemic heart disease to the comparable magnitude as measured LDL-C (odds ratio, 1.24 [95% CI, 1.20-1.29] and odds ratio, 1.15 [95% CI, 1.09-1.23], respectively). The LDL-C PRS was not strongly associated with other traditional ischemic heart disease risk factors., Conclusions: An LDL-C PRS could be used to identify individuals with a higher probability of harboring FH variants. The association between ischemic heart disease and the LDL-C PRS was comparable to measured LDL-C, likely because the PRS reflects lifetime exposure to LDL-C levels.