Impact of Viral Load Monitoring on Retention and Viral Suppression: A Regression Discontinuity Analysis of South Africa's National Laboratory Cohort

Long-term HIV care outcomes under universal HIV treatment guidelines: A retrospective cohort study in 25 countries

BACKGROUND

While national adoption of universal HIV treatment guidelines has led to improved, timely uptake of antiretroviral therapy (ART), longer-term care outcomes are understudied. There is little data from real-world service delivery settings on patient attrition, viral load (VL) monitoring, and viral suppression (VS) at 24 and 36 months after HIV treatment initiation.

METHODS AND FINDINGS

For this retrospective cohort analysis, we used observational data from 25 countries in the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium's Asia-Pacific, Central Africa, East Africa, Central/South America, and North America regions for patients who were ART naïve and aged ≥15 years at care enrollment between 24 months before and 12 months after national adoption of universal treatment guidelines, occurring 2012 to 2018. We estimated crude cumulative incidence of loss-to-clinic (CI-LTC) at 12, 24, and 36 months after enrollment among patients enrolling in care before and after guideline adoption using competing risks regression. Guideline change-associated hazard ratios of LTC at each time point after enrollment were estimated via cause-specific Cox proportional hazards regression models. Modified Poisson regression was used to estimate relative risks of retention, VL monitoring, and VS at 12, 24, and 36 months after ART initiation. There were 66,963 patients enrolling in HIV care at 109 clinics with ≥12 months of follow-up time after enrollment (46,484 [69.4%] enrolling before guideline adoption and 20,479 [30.6%] enrolling afterwards). More than half (54.9%) were females, and median age was 34 years (interquartile range [IQR]: 27 to 43). Mean follow-up time was 51 months (standard deviation: 17 months; range: 12, 110 months). Among patients enrolling before guideline adoption, crude CI-LTC was 23.8% (95% confidence interval [95% CI] 23.4, 24.2) at 12 months, 31.0% (95% CI [30.6, 31.5]) at 24 months, and 37.2% (95% [CI 36.8, 37.7]) at 36 months after enrollment. Adjusting for sex, age group, enrollment CD4, clinic location and type, and country income level, enrolling in care and initiating ART after guideline adoption was associated with increased hazard of LTC at 12 months (adjusted hazard ratio [aHR] 1.25 [95% CI 1.08, 1.44]; p = 0.003); 24 months (aHR 1.38 [95% CI 1.19, 1.59]; p < .001); and 36 months (aHR 1.34 [95% CI 1.18, 1.53], p < .001) compared with enrollment before guideline adoption, with no before-after differences among patients with no record of ART initiation by end of follow-up. Among patients retained after ART initiation, VL monitoring was low, with marginal improvements associated with guideline adoption only at 12 months after ART initiation. Among those with VL monitoring, VS was high at each time point among patients enrolling before guideline adoption (86.0% to 88.8%) and afterwards (86.2% to 90.3%), with no substantive difference associated with guideline adoption. Study limitations include lags in and potential underascertainment of care outcomes in real-world service delivery data and potential lack of generalizability beyond IeDEA sites and regions included in this analysis.

CONCLUSIONS

In this study, adoption of universal HIV treatment guidelines was associated with lower retention after ART initiation out to 36 months of follow-up, with little change in VL monitoring or VS among retained patients. Monitoring long-term HIV care outcomes remains critical to identify and address causes of attrition and gaps in HIV care quality.

HIV viral load testing and monitoring in Côte d'Ivoire: A survival analysis of viral load testing and suppression, and evaluation of adherence to national recommendations

Routine viral load (VL) monitoring is the standard of care in Côte d'Ivoire and allows for effective treatment guidance for people living with human immunodeficiency virus (HIV) to reach viral load suppression (VLS). For VL monitoring to be effective in reducing the impact of HIV, it must be provided in accordance with national guidance. This study aimed to evaluate VL testing, VLS rates and adherence to national guidance for VL testing using data collected from three national laboratories. We collected data on VL testing between 2015-2018 from OpenELIS (OE), an open-source electronic laboratory information system. We merged data by unique patient ID for patients (0-80 years old) who received multiple VL tests to calculate time between tests. We defined VLS as HIV RNA ≤1,000 copies/mL based on Côte d'Ivoire national and WHO guidance at the time of data collection. We used the Kaplan-Meier survival estimator to estimate time between ART (antiretroviral therapy) initiation and the first VL test, time between subsequent VL tests, and to estimate the proportion of people living with HIV (PLHIV) who were virally suppressed within 12 months of ART initiation. At the first documented VL test, 79.6% of patients were virally suppressed (95% CI: 78.9-80.3). Children under 15 were the least likely to be virally suppressed (55.2%, 95% CI: 51.5-58.8). The median time from ART initiation to the first VL sample collection for testing was 7.8 months (IQR:6.2-13.4). 72.4% of patients were virally suppressed within one year of treatment initiation (95% CI:71.5-73.3). Approximately 30% of patients received a second VL test during the 4-year study period. The median time between the first and second VL tests was 24.9 months (IQR: 4.7->40). Most PLHIV received their first VL test within the recommended 12 months of ART initiation but did not receive subsequent VL monitoring tests within the recommended time frame, reducing the benefits of VL monitoring. While VLS was fairly high, children were least likely to be virally suppressed. Our findings highlight the importance of regular VL monitoring after the first VL test, especially for children.

Cohort profile: the South African National Health Laboratory Service (NHLS) National HIV Cohort

PURPOSE

South Africa's National Health Laboratory Service (NHLS) National HIV Cohort was established in 2015 to facilitate monitoring, evaluation and research on South Africa's National HIV Treatment Programme. In South Africa, 84.8% of people living with HIV know their HIV status; 70.7% who know their status are on ART; and 87.4% on ART are virologically suppressed.

PARTICIPANTS

The NHLS National HIV Cohort includes the laboratory data of nearly all patients receiving HIV care in the public sector since April 2004. Patients are included in the cohort if they have received a CD4 count or HIV RNA viral load (VL) test. Using an anonymised unique patient identifier that we have developed and validated to linked test results, we observe patients prospectively through their laboratory results as they receive HIV care and treatment. Patients in HIV care are seen for laboratory monitoring every 6-12 months. Data collected include age, sex, facility location and test results for CD4 counts, VLs and laboratory tests used to screen for potential treatment complications.

FINDINGS TO DATE

From April 2004 to April 2018, 63 million CD4 count and VL tests were conducted at 5483 facilities. 12.6 million unique patients had at least one CD4 count or VL, indicating they had accessed HIV care, and 7.1 million patients had a VL test indicating they had started antiretroviral therapy. The creation of NHLS National HIV Cohort has enabled longitudinal research on all lab-monitored patients in South Africa's national HIV programme, including analyses of (1) patient health at presentation; (2) care outcomes such as 'CD4 recovery', 'retention in care' and 'viral resuppression'; (3) patterns of transfer and re-entry into care; (4) facility-level variation in care outcomes; and (5) impacts of policies and guideline changes.

FUTURE PLANS

Continuous updating of the cohort, integration with available clinical data, and expansion to include tuberculosis and other lab-monitored comorbidities.

Variation in HIV care and treatment outcomes by facility in South Africa, 2011-2015: A cohort study

BACKGROUND

Despite widespread availability of HIV treatment, patient outcomes differ across facilities. We propose and evaluate an approach to measure quality of HIV care at health facilities in South Africa's national HIV program using routine laboratory data.

METHODS AND FINDINGS

Data were extracted from South Africa's National Health Laboratory Service (NHLS) Corporate Data Warehouse. All CD4 counts, viral loads (VLs), and other laboratory tests used in HIV monitoring were linked, creating a validated patient identifier. We constructed longitudinal HIV care cascades for all patients in the national HIV program, excluding data from the Western Cape and very small facilities. We then estimated for each facility in each year (2011 to 2015) the following cascade measures identified a priori as reflecting quality of HIV care: median CD4 count among new patients; retention 12 months after presentation; 12-month retention among patients established in care; viral suppression; CD4 recovery; monitoring after an elevated VL. We used factor analysis to identify an underlying measure of quality of care, and we assessed the persistence of this quality measure over time. We then assessed spatiotemporal variation and facility and population predictors in a multivariable regression context. We analyzed data on 3,265 facilities with a median (IQR) annual size of 441 (189 to 988) lab-monitored HIV patients. Retention 12 months after presentation increased from 42% to 47% during the study period, and viral suppression increased from 66% to 79%, although there was substantial variability across facilities. We identified an underlying measure of quality of HIV care that correlated with all cascade measures except median CD4 count at presentation. Averaging across the 5 years of data, this quality score attained a reliability of 0.84. Quality was higher for clinics (versus hospitals), in rural (versus urban) areas, and for larger facilities. Quality was lower in high-poverty areas but was not independently associated with percent Black. Quality increased by 0.49 (95% CI 0.46 to 0.53) standard deviations from 2011 to 2015, and there was evidence of geospatial autocorrelation (p < 0.001). The study's limitations include an inability to fully adjust for underlying patient risk, reliance on laboratory data which do not capture all relevant domains of quality, potential for errors in record linkage, and the omission of Western Cape.

CONCLUSIONS

We observed persistent differences in HIV care and treatment outcomes across South African facilities. Targeting low-performing facilities for additional support could reduce overall burden of disease.