The human immunodeficiency virus (HIV) care cascade is a conceptual model used to outline the benchmarks that reflects effectiveness of HIV care in the whole HIV care continuum. The models can be used to identify barriers contributing to poor outcomes along each benchmark in the cascade such as disengagement from care or death. Recently, the HIV care cascade has been widely applied to monitor progress towards HIV prevention and care goals in an attempt to develop strategies to improve health outcomes along the care continuum. Yet, there are challenges in quantifying successes and gaps in HIV care using the cascade models that are partly due to the lack of analytic approaches. The availability of large cohort data presents an opportunity to develop a coherent statistical framework for analysis of the HIV care cascade. Motivated by data from the Academic Model Providing Access to Healthcare, which has provided HIV care to nearly 200,000 individuals in Western Kenya since 2001, we developed a state transition framework that can characterize patient-level movements through the multiple stages of the HIV care cascade. We describe how to transform large observational data into an analyzable format. We then illustrate the state transition framework via multistate modeling to quantify dynamics in retention aspects of care. The proposed modeling approach identifies the transition probabilities of moving through each stage in the care cascade. In addition, this approach allows regression-based estimation to characterize effects of (time-varying) predictors of within and between state transitions such as retention, disengagement, re-entry into care, transfer-out, and mortality. Copyright © 2017 John Wiley & Sons, Ltd.

Beyond the individual patient characteristics typically used to characterize retention in HIV care, we identified specific periods of time and points in the care continuum associated with elevated risk of transitioning out of care. Our findings can contribute to evidence-based recommendations to enhance long-term retention in CNICS. This approach can also be applied to other cohort data to identify retention strategies tailored to each population.

Approximately 71% of HIV-infected individuals live in sub-Saharan Africa. Alcohol use increases unprotected sex, which can lead to HIV transmission. Little research examines risky sex among HIV-infected individuals in East Africa who are not sex workers. The study purpose was to examine associations with unprotected sex in a high-risk sample of 507 HIV-infected sexually active drinkers in western Kenya. They were enrolled in a trial to reduce alcohol use. Past-month baseline alcohol use and sexual behavior were assessed using the Timeline Followback. A zero-inflated negative binomial model examined associations with occurrence and frequency of unprotected sex. Results showed heavy drinking days were significantly associated with unprotected sex occurrence across gender, and with unprotected sex frequency among women. Among women, transactional sex, alcohol-related sexual expectations, condom use self-efficacy, drinking-and-protected-sex days and age were associated with unprotected sex occurrence while alcohol-related sexual expectations, depressive symptoms and condom use self-efficacy were associated with unprotected sex frequency. Among men, alcohol-related sexual expectations, condom use self-efficacy, and age were associated with unprotected sex occurrence, while drinking-and-protected-sex days were associated with unprotected sex occurrence and frequency. Findings suggest robust relationships between heavy drinking and unprotected sex. Further research is needed elucidating the temporal relationships between drinking and unprotected sex in this population.