To determine the frequency and patterns of acquired antiretroviral drug resistance in a rural primary health care programme in South Africa. Cross-sectional study nested within HIV treatment programme. Adult (≥18 years) HIV-infected individuals initially treated with a first-line stavudine- or zidovudine-based antiretroviral therapy (ART) regimen and with evidence of virological failure (one viral load >1000 copies/ml) were enrolled from 17 rural primary health care clinics. Genotypic resistance testing was performed using the in-house SATuRN/Life Technologies system. Sequences were analysed and genotypic susceptibility scores (GSS) for standard second-line regimens were calculated using the Stanford HIVDB 6.0.5 algorithms. A total of 222 adults were successfully genotyped for HIV drug resistance between December 2010 and March 2012. The most common regimens at time of genotype were stavudine, lamivudine and efavirenz (51%); and stavudine, lamivudine and nevirapine (24%). Median duration of ART was 42 months (interquartile range (IQR) 32–53) and median duration of antiretroviral failure was 27 months (IQR 17–40). One hundred and ninety one (86%) had at least one drug resistance mutation. For 34 individuals (15%), the GSS for the standard second-line regimen was <2, suggesting a significantly compromised regimen. In univariate analysis, individuals with a prior nucleoside reverse-transcriptase inhibitor (NRTI) substitution were more likely to have a GSS <2 than those on the same NRTIs throughout (odds ratio (OR) 5.70, 95% confidence interval (CI) 2.60–12.49). There are high levels of drug resistance in adults with failure of first-line antiretroviral therapy in this rural primary health care programme. Standard second-line regimens could potentially have had reduced efficacy in about one in seven adults involved.

HIV-1 transmitted drug resistance (TDR) could reverse the gains of antiretroviral rollout. To ensure that current first-line therapies remain effective, TDR levels in recently infected treatment-naive patients need to be monitored. A literature review and data mining exercise was carried out to determine the temporal trends in TDR in South Africa. In addition, 72 sequences from seroconvertors identified from Africa Centre's 2010 HIV surveillance round were also examined for TDR. Publicly available data on TDR were retrieved from GenBank, curated in RegaDB, and analyzed using the Calibrated Population Resistance Program. There was no evidence of TDR from the 2010 rural KwaZulu Natal samples. Ten datasets with a total of 1618 sequences collected between 2000 and 2010 were pooled to provide a temporal analysis of TDR. The year with the highest TDR rate was 2002 [6.67%, 95% confidence interval (CI): 3.09–13.79%; n =6/90]. After 2002, TDR levels returned to <5% (WHO low-level threshold) and showed no statistically significant increase in the interval between 2002 and 2010. The most common mutations were associated with NNRTI resistance, K103N, followed by Y181C and Y188C/L. Five sequences had multiple resistance mutations associated with NNRTI resistance. There is no evidence of TDR in rural KwaZulu-Natal. TDR levels in South Africa have remained low following a downward trend since 2003. Continuous vigilance in monitoring of TDR is needed as more patients are initiated and maintained onto antiretroviral therapy.

To investigate the origins and evolutionary history of subtype C HIV-1 in Zimbabwe in a context of regional conflict and migration. HIV-1C pol sequence datasets were generated from four sequential cohorts of antenatal women in Harare, Zimbabwe sampled over 15 years (1991–2006). One hundred and seventy-seven HIV-1C pol sequences were obtained from four successive cohorts in Zimbabwe. Maximum-likelihood methods were used to explore phylogenetic relationships between Zimbabwean HIV-1C sequences and subtype C strains from other regions. A Bayesian coalescent-based framework was used to estimate evolutionary parameters for HIV-1C in Zimbabwe, including origin and demographic growth patterns. Zimbabwe HIV-1C pol demonstrated increasing sequence divergence over the 15-year period. Nearly all Zimbabwe sequences clustered phylogenetically with subtype C strains from neighboring countries. Bayesian evolutionary analysis indicated a most recent common ancestor date of 1973 with three epidemic growth phases: an initial, slow phase (1970s) followed by exponential growth (1980s), and a linearly expanding epidemic to the present. Bayesian trees provided evidence for multiple HIV-1C introductions into Zimbabwe during 1979–1981, corresponding with Zimbabwean national independence following a period of socio-political instability. The Zimbabwean HIV-1C epidemic likely originated from multiple introductions in the late 1970s and grew exponentially during the 1980s, corresponding to changing political boundaries and rapid population influx from neighboring countries. The timing and phylogenetic clustering of the Zimbabwean sequences is consistent with an origin in southern Africa and subsequent expansion. HIV-1 sequence data contain important epidemiological information, which can help focus treatment and prevention strategies in light of more recent political volatility in Zimbabwe.

As more human immunodeficiency virus (HIV)–infected patients access combination antiretroviral therapy (cART), higher proportions of newly infected patients may be infected with drug-resistant viruses. Regular surveillance of transmitted drug resistance (TDR) is required in southern Africa where high rates of transmission persist despite rapid expansion of ART. Dried blood spot samples from cART-naive participants from two rounds of an annual population-based HIV surveillance program in rural KwaZulu-Natal were tested for HIV RNA, and samples with HIV RNA >10,000 copies/ml were genotyped for drug resistance. The 2009 surveillance of drug resistance mutation (SDRM) list was used for drug resistance interpretation. The data were added to previously published data from the same program, and the χ² test for trend was used to test for trend in estimated prevalence of any TDR. Seven hundred and one participants' data were analyzed: 67 (2010), 381 (2011), and 253 (2012). No TDR was detected in 2010. Years 2011 and 2012 had 18 participants with SDRMs 4.7% and 7.1%, respectively (p = .02, χ² test for trend). The nonnucleoside reverse transcriptase inhibitor mutation, K103N, was the most common mutation, occurring in 27 (3.8%) of the participants, while nucleoside reverse transcriptase inhibitor (NRTI) SDRMs were detected in 10 (1.4%) of the participants, of whom eight had only a single NRTI SDRM. The increase in levels of drug resistance observed in this population could be a signal of increasing transmission of drug-resistant HIV. Thus, continued surveillance is critical to inform public health policies around HIV treatment and prevention.

Better understanding of drug resistance patterns in HIV-infected children on antiretroviral therapy (ART) is required to inform public health policies in high prevalence settings. The aim of this study was to characterise the acquired drug resistance in HIV-infected children failing first-line ART in a decentralised rural HIV programme.

A single-platform volumetric flow cytometer, the Partec Cyflow SL_3, was evaluated against a BD FACSCalibur/Sysmex XT1800i dual platform for measuring CD4(+) lymphocytes, total lymphocytes, and the percentage of CD4 lymphocytes in whole-blood samples for monitoring the immune systems of human immunodeficiency virus (HIV)/AIDS patients. Statistical analyses for precision, correlation, and agreement were performed. Coefficients of variation (CV) of 5.8, 4.6, and 3.9% were obtained for low, medium, and high CD4(+) cell counts, respectively, using the SL_3, and CV of 3.7, 4.0, and 0.94 were obtained for the same categories, using the BD FACSCalibur. Significant correlations (P < 0.005) between the two assays for CD4 counts, total lymphocyte counts, and percentages of CD4 were obtained, with correlation coefficients of 0.99, 0.96, and 0.99, respectively (n = 229). Using the Bland-Altman plot, mean biases of -18 cell/microl (95% confidence interval (CI); -91 to 54 cells/microl), -0.8% (95% CI; -3.6 to 2%), and -36.8 cells/microl (95% CI; -477 to 404 cells/microl) were obtained for comparisons of CD4 counts, percentages of CD4 cells, and total lymphocyte counts, respectively. The effects of the age of the samples on the three parameters were also analyzed by comparing results from the same samples analyzed at 6, 24, and 48 h after collection. The correlation coefficients for comparisons among different time points for the same machine and among all the time points for the two different machines were greater than 0.90. These data showed that the Partec Cyflow SL_3 assay is comparable to the BD FACSCalibur/Sysmex XT1800i dual-platform method for measuring the amount of CD4(+) cells and total lymphocytes and the percentages of CD4 cells in blood samples for the purpose of monitoring HIV/AIDS patients.