There has been limited study of the syndemic link between HIV and intimate partner violence (IPV) among rural populations in the United States. We utilized the Revised Conflict Tactics Scale-2 to examine the past year prevalence, type (psychological aggression, physical assault, and sexual assault), and the impact of IPV on HIV clinical outcomes among men living with HIV in rural Appalachia. Approximately 39% of participants experienced some type of IPV in the preceding year, with 67% of those individuals experiencing more than 1 type of IPV. Approximately 77% of participants endorsing IPV exposure experienced psychological aggression. Most participants exposed to psychological aggression (70%) and/or physical assault (57%) were both victims and perpetrators, and those experiencing sexual assault reported being exclusively victims (65%). There were no significant differences in clinical outcomes including viral load and CD4 count, which may be secondary to small sample size derived from a clinic population with a high rate of virologic suppression (94%). This study demonstrates the need to assess IPV exposure in men living with HIV and further highlights the intricacies of relationship violence in these individuals.

Missing data is a common problem encountered in statistical analysis. However, little is known about how bias inducing missing at random missing data mechanisms affect predictive model performance measures such as sensitivity, specificity, error rate, ROC curves, and AUC. I investigate the effect of missing at random missing data mechanisms on a single layer artificial neural network with a sigmoidal activation function, equivalent to a binary logistic regression. Binary logistic regression is frequently used in health research and so it is a logical starting point to understand the effects of missing data on statistical learning models that could be used in health research. I then examine whether multiple imputation is a useful analytic correction for improving the predictive model performance measures relative to performing a complete case analysis.

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