Point of Care CD4 Testing in National Household Surveys - Results and Quality Indicators from Eleven Population-Based HIV Impact Assessment (PHIA) Surveys.

Population-based HIV Impact Assessments (PHIAs) are national household (HH) surveys that provide HIV diagnosis and CD4 testing with an immediate return of results. Accurate CD4 results improve HIV-positive participants' clinical care and inform the effectiveness of HIV programs. Here, we present CD4 results from the PHIA surveys that were conducted in 11 countries in sub-Saharan Africa between 2015 and 2018. All of the HIV-positive participants and 2 to 5% of the HIV-negative participants were offered Pima CD4 (Abbott, IL, USA) point-of-care (POC) tests. The quality of the CD4 test was ensured by conducting instrument verification, comprehensive training, quality control, a review of testing errors and an analysis of unweighted CD4 data by HIV status, age, gender, and antiretroviral (ARV) treatment status. Overall, CD4 testing was completed for 23,085 (99.5%) of the 23,209 HIV-positive and 7,329 (2.7%) of the 270,741 negative participants in 11 surveys. The instrument error rate was 11.3% (range, 4.4% to 15.7%). The median CD4 values among HIV-positive and HIV-negative participants (aged 15+) were 468 cells/mm3 (interquartile range [IQR], 307 to 654) and 811 cells/mm3 (IQR, 647 to 1,013), respectively. Among the HIV-positive participants (aged 15+), those with detectable ARVs had higher CD4 values (508 cells/mm3) than those with undetectable ARVs (385.5 cells/mm3). Among the HIV-positive participants (aged 15+), 11.4% (2,528/22,253) had a CD4 value of less than 200 cells/mm3, and approximately half of them (1,225/2,528 = 48.5%) had detectable ARVs, whereas 51.5% (1,303/2,528) had no detectable ARVs (P < 0.0001). We successfully implemented high quality POC CD4 testing using Pima instruments. Our data come from nationally representative surveys in 11 countries and provide unique insights regarding the CD4 distribution among HIV-positive individuals as well as the baseline CD4 values among HIV-negative individuals. IMPORTANCE The manuscript describes CD4 levels among HIV-positive individuals and baseline CD4 levels among HIV-negative individuals from 11 sub-Saharan countries, thereby highlighting the importance of CD4 markers in the context of the HIV epidemic. Despite increased ARV access in each country, advanced HIV disease (CD4 < 200 cells/mm3) persists among approximately 11% of HIV-positive individuals. Therefore, it is important that our findings are shared with the scientific community to assist with similar implementations of point-of-care testing and to conduct a review of HIV programmatic gaps.

HIV-1 Recent Infection Testing Algorithm With Antiretroviral Drug Detection to Improve Accuracy of Incidence Estimates.

BACKGROUND: HIV-1 incidence calculation currently includes recency classification by HIV-1 incidence assay and unsuppressed viral load (VL ≥ 1000 copies/mL) in a recent infection testing algorithm (RITA). However, persons with recent classification not virally suppressed and taking antiretroviral (ARV) medication may be misclassified. SETTING: We used data from 13 African household surveys to describe the impact of an ARV-adjusted RITA on HIV-1 incidence estimates. METHODS: HIV-seropositive samples were tested for recency using the HIV-1 Limiting Antigen (LAg)-Avidity enzyme immunoassay, HIV-1 viral load, ARVs used in each country, and ARV drug resistance. LAg-recent result was defined as normalized optical density values ≤1.5. We compared HIV-1 incidence estimates using 2 RITA: RITA1: LAg-recent + VL ≥ 1000 copies/mL and RITA2: RITA1 + undetectable ARV. We explored RITA2 with self-reported ARV use and with clinical history. RESULTS: Overall, 357 adult HIV-positive participants were classified as having recent infection with RITA1. RITA2 reclassified 55 (15.4%) persons with detectable ARV as having long-term infection. Those with detectable ARV were significantly more likely to be aware of their HIV-positive status (84% vs. 10%) and had higher levels of drug resistance (74% vs. 26%) than those without detectable ARV. RITA2 incidence was lower than RITA1 incidence (range, 0%-30% decrease), resulting in decreased estimated new infections from 390,000 to 341,000 across the 13 countries. Incidence estimates were similar using detectable or self-reported ARV (R2 > 0.995). CONCLUSIONS: Including ARV in RITA2 improved the accuracy of HIV-1 incidence estimates by removing participants with likely long-term HIV infection.