Markov modelling of viral load adjusting for CD4 orthogonal variable and multivariate conditional autoregressive mapping of the HIV immunological outcomes among ART patients in Zimbabwe

Abstract

Background This study aimed to jointly model HIV disease progression patterns based on viral load (VL) among adult ART patients adjusting for the time-varying “incremental transients states” variable, and the CD4 cell counts orthogonal variable in a single 5-stage time-homogenous multistate Markov model. We further jointly mapped the relative risks of HIV disease progression outcomes (detectable VL (VL ≥ 50copies/uL) and immune deterioration (CD4 < 350cells/uL) at the last observed visit) conditional not to have died or become loss to follow-up (LTFU). Methods Secondary data analysis of individual-level patients on ART was performed. Adjusted transition intensities, hazard ratios (HR) and regression coefficients were estimated from the joint multistate model of VL and CD4 cell counts. The mortality and LTFU transition rates defined the extent of patients’ retention in care. Joint mapping of HIV disease progression outcomes after ART initiation was done using the Bayesian intrinsic Multivariate Conditional Autoregressive prior model. Results The viral rebound from the undetectable state was 1.78times more likely compared to viral suppression among patients with VL ranging from 50-1000copies/uL. Patients with CD4 cell counts lower than expected had a higher risk of viral increase above 1000copies/uL and death if their VL was above 1000copies/uL (state 2 to 3 (λ23): HR = 1.83 and (λ34): HR = 1.42 respectively). Regarding the time-varying effects of CD4 cell counts on the VL transition rates, as the VL increased, (λ12 and λ23) the transition rates increased with a decrease in the CD4 cell counts over time. Regardless of the individual’s VL, the transition rates to become LTFU decreased with a decrease in CD4 cell counts. We observed a strong shared geographical pattern of 66% spatial correlation between the relative risks of detectable VL and immune deterioration after ART initiation, mainly in Matabeleland North. Conclusion With high rates of viral rebound, interventions which encourage ART adherence and continual educational support on the barriers to ART uptake are crucial to achieve and sustain viral suppression to undetectable levels. Area-specific interventions which focus on early ART screening through self-testing, behavioural change campaigns and social support strategies should be strengthened in heavily burdened regions to sustain the undetectable VL. Sustaining undetectable VL lowers HIV transmission in the general population and this is a step towards achieving zero HIV incidences by 2030.

Loss to Follow-Up Risk among HIV Patients on ART in Zimbabwe, 2009–2016: Hierarchical Bayesian Spatio-Temporal Modeling

“…This resulted in a high influx of HIV patients in the HIV prevention programme. Studies have shown that patients who start ART with a higher CD4 cell count are generally well which can trigger non-adherence or low-risk perception to long-term therapy; hence, become LTFU [ 8 , 18 , 19 ]. The country also introduced HIV strategies like “test and treat all” which targeted the high-risk groups such as those with tuberculosis co-infection and serodiscordant partners, and pregnant women.…”

Investigation of multiple nosocomial infections using a semi-Markov multi- state model

“…The flexible parameter MSM is employed for semi-Markov model analysis, enabling the selection of parameter distribution types such as Weibull, exponential, Gompertz, gamma, log-logistic, log-normal, generalized gamma (gengamma), original generalized gamma (gengama.orig), generalized F (genf), and original generalized F (genf.orig). To fit and optimize the parameter distribution, the Akaike information criterion (AIC) (15) was utilized to screen the data distribution parameters of the regression model. Subsequently, the distribution with the lowest AIC value was chosen to establish the parameter regression model.…”

“…According to the assumption of the Markov MSM, future transitions are only influenced by the current state, not by previous history; in other words, there is no aftereffect. However, in some diseases' actual development, it is unrealistic to adhere to the Markov hypothesis (14,15). For example, the occurrence of NI is related to the LOS, with longer stays increasing the infection risk (19).…”

HIV viral suppression and risk of viral rebound in patients on antiretroviral therapy: a two- year retrospective cohort study in Northern Tanzania

“…Nonetheless, viral rebound is now increasingly reported among people who had prior history of viral suppression. A study in Zimbabwe reported that the rates of rebound are higher than viral suppression [ 17 ]. However, there is paucity of data on viral rebound and the risk for rebound in most LMIC such as Tanzania.…”