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We recently reported that the risk of sexually acquired HIV-1 infection is increased significantly by variants in the gene encoding CD101, a protein thought to modify inflammatory responses. Using blood samples from individuals with and without these variants, we demonstrate that CD101 variants modify the prevalence of circulating inflammatory cell types and show that CD101 variants are associated with increased proinflammatory cytokine production by circulating T cells. One category of CD101 variants is associated with a reduced capacity of regulatory T cells to suppress T cell cytokine production, resulting in a reduction in the baseline level of immune quiescence. These data are supported by transcriptomics data revealing alterations in the intrinsic regulation of antiviral pathways and HIV resistance genes in individuals with CD101 variants. Our data support the hypothesis that CD101 contributes to homeostatic regulation of bystander inflammation, with CD101 variants altering heterosexual HIV-1 acquisition by facilitating increased prevalence and altered function of T cell subsets.

Respondent-Driven Sampling (RDS) is a network-based method for sampling hard-to-reach populations that is widely used by public health agencies and researchers worldwide. Estimation of population characteristics from RDS data is challenging due to the unobserved population network, and multiple point and variance estimators have been proposed. Research evaluating these estimators has been limited and largely focused on point estimation; this analysis is the first evaluation of multiple variance estimators currently in use. We evaluated the performance of RDS variance estimators via simulations of RDS on synthetic networked populations constructed from 40 RDS surveys of injection drug users in the United States. In these simulations, average design effects (DEs) were lower and average 95% confidence interval (CI) coverage percentages were higher than suggested in previous work: typical DE range=1-3; average 95% CI coverage=93%. However, DE and CI coverage vary across the 40 sets of simulations, suggesting that the characteristics of a given study should be evaluated to assess estimator performance. We also found that simulation results are sensitive to whether sampling is conducted with replacement and the approach used to create CIs. We conclude that CI coverage rates and DEs are often acceptable but not perfect and that RDS estimates are usually reliable in scenarios where RDS assumptions are met. While RDS estimation performed reasonably well, we found strong evidence that the simple random sample variance estimator and corresponding CIs significantly underestimate variance and should not be used to analyze RDS data.

The study of hard-to-reach populations presents significant challenges. Typically, a sampling frame is not available, and population members are difficult to identify or recruit from broader sampling frames. This is especially true of populations at high risk for HIV/AIDS. Respondent-driven sampling (RDS) is often used in such settings with the primary goal of estimating the prevalence of infection. In such populations, the number of people at risk for infection and the number of people infected are of fundamental importance. This article presents a case-study of the estimation of the size of the hard-to-reach population based on data collected through RDS. We study two populations of female sex workers and men-who-have-sex-with-men in El Salvador. The approach is Bayesian and we consider different forms of prior information, including using the UNAIDS population size guidelines for this region. We show that the method is able to quantify the amount of information on population size available in RDS samples. As separate validation, we compare our results to those estimated by extrapolating from a capture-recapture study of El Salvadorian cities. The results of our case-study are largely comparable to those of the capture-recapture study when they differ from the UNAIDS guidelines. Our method is widely applicable to data from RDS studies and we provide a software package to facilitate this.

Respondent-Driven Sampling (RDS) is n approach to sampling design and inference in hard-to-reach human populations. It is often used in situations where the target population is rare and/or stigmatized in the larger population, so that it is prohibitively expensive to contact them through the available frames. Common examples include injecting drug users, men who have sex with men, and female sex workers. Most analysis of RDS data has focused on estimating aggregate characteristics, such as disease prevalence. However, RDS is often conducted in settings where the population size is unknown and of great independent interest. This paper presents an approach to estimating the size of a target population based on data collected through RDS. The proposed approach uses a successive sampling approximation to RDS to leverage information in the ordered sequence of observed personal network sizes. The inference uses the Bayesian framework, allowing for the incorporation of prior knowledge. A flexible class of priors for the population size is used that aids elicitation. An extensive simulation study provides insight into the performance of the method for estimating population size under a broad range of conditions. A further study shows the approach also improves estimation of aggregate characteristics. Finally, the method demonstrates sensible results when used to estimate the size of known networked populations from the National Longitudinal Study of Adolescent Health, and when used to estimate the size of a hard-to-reach population at high risk for HIV.

Moderated regression analysis is commonly used to test for multiplicative influences of independent variables in regression models. D. Lubinski and L. G. Humphreys (1990) have shown that significant moderator effects can exist even when stronger quadratic effects are present. They recommend comparing effect sizes associated with both effect types and selecting the model that yields the strongest effect. The authors show that this procedure of comparing effect sizes is biased in favor of the moderated model when multicollinearity is high because of the differential reliability of the quadratic and multiplicative terms in the regression models. Fortunately, levels of multicollinearity under which this bias is most problematic may be outside the range encountered in many empirical studies. The authors discuss causes and implications of this phenomenon as well as alternative procedures for evaluating structural relationships among variables.