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A state transition framework for patient-level modeling of engagement and retention in HIV care using longitudinal cohort data.

A state transition framework for patient-level modeling of engagement and retention in HIV care using longitudinal cohort data.
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Lee H, Hogan JW, Genberg BL, Wu XK, Musick BS, Mwangi A, Braitstein P,


Lee H, Hogan JW, Genberg BL, Wu XK, Musick BS, Mwangi A, Braitstein P, (click to view)

Lee H, Hogan JW, Genberg BL, Wu XK, Musick BS, Mwangi A, Braitstein P,

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Statistics in medicine 2017 11 22() doi 10.1002/sim.7502

Abstract

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.

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