Effects of National Adoption of Treat-All Guidelines on Pre-Antiretroviral Therapy (ART) CD4 Testing and Viral Load Monitoring After ART initiation: A Regression Discontinuity Analysis

Abstract

Background The World Health Organization’s Treat-All guidance recommends CD4 testing prior to antiretroviral treatment (ART) initiation, and routine viral load (VL) monitoring (over CD4 monitoring) for patients on ART. Methods We used regression discontinuity analyses to estimate changes in CD4 testing and VL monitoring among 547,837 ART-naïve patients enrolling in HIV care during 2006-2018 at 225 clinics in 26 countries where Treat-All policies were adopted. We examined CD4 testing within 12 months before and VL monitoring 6 months after ART initiation among adults (≥20 years), adolescents (10-19 years) and children (0-9 years) in low/lower-middle income countries (L/LMICs) and high/upper-middle income countries (H/UMICs). Results Treat-All adoption led to an immediate decrease in pre-ART CD4 testing among adults in L/LMICs, from 57.0% to 48.1% (-8.9 percentage points [pp]; 95% CI: -11.0, -6.8), and a small increase in in H/UMICs, from 90.1 to 91.7% (+1.6pp; 95% CI: 0.2, 3.0), with no changes among adolescents or children; decreases in pre-ART CD4 testing accelerated after Treat-All adoption in L/LMICs. In L/LMICs, VL monitoring after ART initiation was low among all patients just before Treat-All; while there was no immediate change at Treat-All adoption, VL monitoring trends significantly increased afterwards. In H/UMICs, VL monitoring increased among adults immediately after Treat-All adoption, from 58.2% to 61.1% (+2.9pp; 95% CI: 0.5, 5.4), with no significant changes among adolescents/children. Conclusions While on-ART VL monitoring has improved in L/LMICs, Treat-All adoption has accelerated and disparately worsened suboptimal pre-ART CD4 monitoring, which may compromise care outcomes for individuals with advanced HIV.

Regression discontinuity analysis demonstrated varied effect of Treat-All on CD4 testing among Southern African countries

“…Our study and the recent global IeDEA study used IeDEA data and regression discontinuity design, however the latter study pooled data within broad income-group classifications that likely obscured heterogeneity among countries [12]. Further, the absence in the global study of Zambia, which comprised nearly 85% of low and lower-middle income data in our study and had the most divergent results, may further explain dissimilar findings.…”

“…The effect of Treat-All on pre-ART CD4 testing varied in Southern Africa. Although a recent global IeDEA study found Treat-All adoption in low or lower-middle income countries led to an immediate decline in pre-ART CD4 testing among adults who enrolled in HIV care after Treat-All adoption [12], we found considerable heterogeneity in the direction and magnitude of the immediate effect in such countries [33], which also varied by sex. We found testing slightly increased in Zambia, but decreased substantially in Malawi and moderately in Mozambique, with no effect in Lesotho or Zimbabwe.…”

“…To estimate risk differences associated with Treat-All and predict outcomes at the Treat-All threshold, we compared local linear regression models calculated just before and after Treat-All adoption using Imbens-Kalyanaraman (IK) data-driven bandwidths [29,30] for a first-order polynomial with a rectangular (uniform) kernel: 12,17])…”

“…Despite the importance of CD4 and viral load testing, recent studies show declining pre-ART CD4 testing with HIV treatment expansion in several Southern African countries and no increases in viral load testing [10,11]. Additionally, a global study found that Treat-All adoption led to decreased pre-ART CD4 testing in low and lower-middle income countries while remaining high in upper-middle and high income countries [12]. Given the high HIV burden in many low and lower-middle income countries in Southern Africa and the increase in rapid ART initiation after Treat-All adoption [13,14], examining the effect of Treat-All on pre-ART CD4 testing and viral load monitoring within countries is critical for understanding its impact on patient care.…”

“…Observational data may identify policy implementation ramifications, but only with limited strength of evidence [12,13,15,17]. Regression discontinuity design, a quasi-experimental approach, mimics randomized experimental designs, allowing for causal interpretation of observed effects within observational data [17][18][19][20][21].…”