IntroductionThe 2016 WHO consolidated guidelines on the use of antiretroviral drugs defines HIV virologic failure for low and middle income countries (LMIC) as plasma HIV-RNA ≥ 1000 copies/mL. We evaluated virologic failure and predictors in four African countries.Materials and methodsWe included HIV-infected participants on a WHO recommended antiretroviral therapy (ART) regimen and enrolled in the African Cohort Study between January 2013 and October 2017. Studied outcomes were virologic failure (plasma HIV-RNA ≥ 1000 copies/mL at the most recent visit), viraemia (plasma HIV-RNA ≥ 50 copies/mL at the most recent visit); and persistent viraemia (plasma HIV-RNA ≥ 50 copies/mL at two consecutive visits). Generalized linear models were used to estimate relative risks with their 95% confidence intervals.Results2054 participants were included in this analysis. Viraemia, persistent viraemia and virologic failure were observed in 396 (19.3%), 160 (7.8%) and 184 (9%) participants respectively. Of the participants with persistent viraemia, only 57.5% (92/160) had confirmed virologic failure. In the multivariate analysis, attending clinical care site other than the Uganda sitebeing on 2nd line ART (aRR 1.8, 95% CI 1·28–2·66); other ART combinations not first line and not second line (aRR 3.8, 95% CI 1.18–11.9), a history of fever in the past week (aRR 3.7, 95% CI 1.69–8.05), low CD4 count (aRR 6.9, 95% CI 4.7–10.2) and missing any day of ART (aRR 1·8, 95% CI 1·27–2.57) increased the risk of virologic failure. Being on 2nd line therapy, the site where one receives care and CD4 count < 500 predicted viraemia, persistent viraemia and virologic failure.ConclusionIn conclusion, these findings demonstrate that HIV-infected patients established on ART for more than six months in the African setting frequently experienced viraemia while continuing to be on ART. The findings also show that being on second line, low CD4 count, missing any day of ART and history of fever in the past week remain important predictors of virologic failure that should trigger intensified adherence counselling especially in the absence of reliable or readily available viral load monitoring. Finally, clinical care sites are different calling for further analyses to elucidate on the unique features of these sites.

Introduction With increased use of antiretroviral therapy (ART), HIV mortality rates are declining and people living with HIV (PLWH) are surviving longer. We characterized CD4 recovery and viral suppression among adults aged < 50 and ≥ 50 years living with HIV who initiated ART in the African Cohort Study (AFRICOS). Methods Beginning in January 2013, PLWH at twelve clinics in Kenya, Uganda, Tanzania and Nigeria underwent medical history review, CD4 and viral load testing as part of the ongoing African Cohort Study (AFRICOS). ART-naïve PLWH who initiated ART within 30 days of enrollment and had at least one year of follow-up were included in these analyses. To compare ART response in participants < 50 years and ≥ 50 years old, changes in CD4 count and viral load suppression after ART initiation were examined at different time points using linear and binomial regression with generalized estimating equations. Variables for time since ART initiation and the interaction between age group and time on ART were included in the model to evaluate longitudinal changes in CD4 recovery and viral suppression by age. Results Between January 2013 and September 2019, 2918 PLHV were enrolled in the cohort. Of these, 443 were ART naïve and initiated on ART within 30 days of enrollment, with 90% (n = 399) aged < 50 years old at ART initiation. At ART initiation, participants aged 50 and older had a higher median CD4 count compared to participants younger than 50 years of age although it did not reach statistical significance (306 cells/mm3, IQR:130–547 vs. 277cells/mm3, IQR: 132–437). In adjusted models examining CD4 recovery and viral suppression there were no significant differences by age group over time. By the end of follow-up viral suppression was high among both groups of adults (96% of adults ≥ 50 years old and 92% of adults < 50 years old). Conclusion This study found no difference in long-term CD4 recovery or viral suppression by age at ART initiation. We found that particularly among younger adults participants had lower median CD4 counts at ART initiation, suggesting the importance of identifying and putting this population on treatment earlier in the disease course.

Introduction A diverse class of products, “e-cigarettes” present surveillance and regulatory challenges because of nonstandard terminology used to describe subtypes, especially among young adults, where occasional e-cig use is most prevalent. Methods Young adults (n = 3364) in wave 9 (Spring 2016) of the Truth Initiative Young Adult Cohort were randomized to see two of five photos of common e-cig products (three varieties of first-generation e-cigs and one variety each of second- and third-generation e-cigs). Qualitative responses were coded into nine classifications: “e-cigarette, e-hookah, vape-related, mod, other or more than one kind of e-cig, marijuana-related, non-e-cig tobacco product, misidentified, and don’t know.” We characterized the sample and survey responses and conducted multivariable logistic regression to identify participant characteristics associated with correctly identifying the devices as e-cigs. Data were weighted to represent the young adult population in the United States in 2016. Results The majority of participants identified the pictured devices as some type of e-cig (57.7%–83.6%). The white first-generation e-cig, as well as the second- and third-generation e-cigs caused the greatest confusion, with a large proportion of individuals responding “don’t know” (12.2%–25.1%, depending on device) or misidentifying the e-cig as a non-nicotine product (3.4%–16.1%, depending on device) or non-e-cig tobacco product (1.4%–14.6%, depending on device). Conclusions Accurate surveillance and analyses of the effect of e-cigs on health behavior and outcomes depend on accurate data collection on users’ subtype of e-cig. Carefully chosen images in surveys may improve reporting of e-cig use in population studies. Implications Survey researchers using images to cue respondents, especially young adult respondents, should consider avoiding use of white or colorful first-generation e-cigs, which were commonly misidentified in this research, in preference for black or dark colored first-generation e-cigs, such as the blu brand e-cig. Given the sizable proportion of respondents who classified second- and third-generation e-cigs with terminology related to vaping, surveys specifically aimed at assessing use of these types of e-cigs should include the term “vape” when describing this subclass of devices.