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

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.

Exploring Relative Preferences for HIV Service Features Using Discrete Choice Experiments: a Synthetic Review

“…PLoS medicine . 2018;15 [ 8 ]:e1002636 3 Eshun-Wilson I, Mukumbwa-Mwenechanya M, Kim HY, Zannolini A, Mwamba CP, Dowdy D, et al Differentiated care preferences of stable patients on antiretroviral therapy in Zambia: a discrete choice experiment. Journal of acquired immune deficiency syndromes (1999).…”

“…The comparative progress of engaging PLWH in long-term HIV treatment services has, however, lagged behind. High disengagement rates [5] and repeated transitions in and out of care even for those established on antiretroviral therapy (ART) [6][7][8][9] have resulted in an increased focus on identifying effective strategies to improve linkage and retention in HIV services [10].…”

Reasons for Missed Visits and Influence of Phone Call Reminders on Retention of HIV-Infected and Uninfected Children Followed in Cameroon: The ANRS-PEDIACAM Cohort

“…States transition matrix was calculated under the rst-order Markov dependence assumption. To evaluate the in uence of the call reminder enhancement performed on retention, we use a multinomial regression model (characterized by: retention to the care; duration of consecutive missing scheduled visit and mortality) to estimated the Relative Rate Ratio (RRR) (21) associated with each. A p-value of 0.05 or less was considered to be statistically signi cant.…”

“…In this context, the modeling requires a step of the imputation of the missing data beforehand. In a recent paper, Lee and al (21) used a most recent observed value for imputing missing data, however this strategy to deal with missing data is not always appropriate. In future work, comparing this method with other imputation strategies may determine the best way to consider dynamic data in this framework.…”

Nonparametric tests for transition probabilities in nonhomogeneous Markov processes

“…The state occupation probabilities describe the marginal behavior, i.e. unconditional on the prior history, of the processes and are of interest in many applications, such as in HIV studies focusing on the event history of patients in HIV care (Lee et al, 2018). It is important to note that these probabilities can be consistently estimated using the Aalen-Johansen estimator even in non-Markov processes (Datta and Satten, 2001).…”