Data-Driven Longitudinal Model with Application to HIV Differentiated Care - Pages 89-97

Stephen O. Weunda, Collins Odhiambo and Idah Orowe

https://doi.org/10.6000/1929-6029.2019.08.12

Published: 25 November 2019

Abstract: Background: Differentiated care is a new innovative approach for managing HIV/AIDS where ART treatment services are customized by staggering patients’ visits for stable status while reducing unnecessary burdens on the health system. Through provision of differentiated care, the health providers are able to reallocate resources and time to patients in who are failing treatment.

Objective: The main objective of this study is to develop a data-driven longitudinal model which is applicable to HIV differentiated care.

Method: We used routine data of HIV positive patients initiated to ART at the point of care from 4 medical facilities in Nairobi in the year 2018. Since both the GLMM and GEE are extensions of the GLM, we start with a brief overview of GEE then relooked at extensions of GLMM. We specify f (µ) and g (µ) to be dependent on the type of response Yi. For a binary Yi, we consider f (µ) as Bernoulli distribution and g (µ) as the logit function, g (µ) = log [µ/1−µ] resulting to GLM is the logistic regression.

Results and Conclusion: Results show the binary response which was differentiated care category fits well with GLMM. We also found TB-HIV co-infection to be the only significant predictor of differentiated care under both GEE and GLMM framework.

Keywords: Differentiated care, HIV/AIDS, Generalized Linear Models (GLM), Generalized Estimating Equations (GEE), and Generalized Linear Mixed Models (GLMM).

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Spatial Heterogeneity of Viral Suppression and Viral Rebound Patterns among ART Patients in Zimbabwe from 2004 to 2017: A Bayesian Mixed Effects Multistate Model  - Pages 113-98

Zvifadzo Matsena Zingoni, Tobias F. Chirwa, Jim Todd and Eustasius Musenge

https://doi.org/10.6000/1929-6029.2019.08.13

Published: 20 December 2019

Abstract: Augmenting the global efforts towards HIV control and prevention, spatial modelling helps identify areas with poor viral suppression to inform programme planning. This study aims to describe the spatial viral suppression and viral rebound trajectories among ART patients. This is the first application of the fully Bayesian geoadditive semiparametric multistate Markov models to account for unobserved geographical heterogeneity. Time-varying log-baseline effects of the transition intensities and non-linear effects of continuous covariates were estimated as smoothed functions of time using penalised splines. Non-parametric effects of fixed covariates and frailty effects to account for individual variability were also considered. Viral load was the preferred marker for better prediction of HIV/AIDS disease progression; therefore, a three staged model was proposed bases on two viral load transient states defined by undetectable viral cut-off limits and death as the third absorbing state. Model application was based on the routinely collected individual-level data of ART patients from the Zimbabwe national ART programme. Amongst 18,150 participants, both the log-baseline transition rates of attaining undetectable viral suppression and attaining a viral rebound increased with increase in ART duration. Viral rebound transition was significantly prevalent among patients living on the long-distance truck route region (Matabeleland North province) which borders with Botswana and Zambia. Interventions which address health literacy and misconceptions over ART benefits and the gravity of attaining and sustaining viral suppression are a priority in the fight of HIV to increase patients’ life expectancy and lower HIV transmission.

Keywords: Bayesian estimation, multistate Markov models, spatial heterogeneity, viral suppression, viral rebound.

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