Expanded access to combination antiretroviral therapy (ART) in many countries in sub-Saharan Africa during the past decade  has remarkably improved the prognosis of HIV-1-infected individuals . Important deficiencies in health systems, such as lack of virological monitoring and intermittent drug supply, have raised concerns about the rapid emergence and spread of drug-resistant HIV-1 strains in Africa [3,4]. Increasing levels of transmitted drug-resistant HIV-1 variants (TDR) could compromise the effectiveness of standard first-line ART regimens [5,6], which has severe public health consequences in areas where treatment options are limited. With the wider use of ART in industrialized countries, TDR to nonnucleoside reverse transcriptase inhibitors (NNRTIs) in newly infected individuals steadily increased, in San Francisco from 0% in 1996–1997 to 13.2% in 2000–2001  and in Europe from 2.3% in 1996–1998 to 9.2% in 2001–2002 . Genotypic resistance to two or more classes of antiretroviral drugs increased from 2.5 to 13.2% .
Uganda was among the first African countries to distribute life-saving antiretroviral medication. By the end of September 2009, nationwide an estimated 200 413 patients were receiving ART, reaching 39% of those in need . In the capital city of Kampala, the massive scale-up of ART was initiated in the year 2000, following limited-scale distribution since the mid 1990s. A survey performed in Entebbe, situated in the greater Kampala area, in 2006–2007 did not detect any significant drug-resistance mutations among 47 newly HIV-1 diagnosed pregnant women with CD4 cell count more than 350 cells/μl attending an antenatal clinic .
We report the results of a subsequent survey in 2009–2010 that evaluated the prevalence of TDR among newly HIV-1 diagnosed young individuals attending voluntary counseling and testing (VCT) sites in Kampala, Uganda.
Study design and population
A cross-sectional survey was conducted among clients attending two large free-access, nongovernmental VCT sites in Kampala, Uganda: AIDS Information Centre (AIC), situated in Mengo area, and Naguru Teenage Health Information Centre (NTC), situated in Bugolobi area. The institutional review boards at the Academic Medical Center and the Uganda Virus Research Institute approved the study. Mandatory eligibility criteria, as defined by the WHO, were used to identify individuals who were likely to have been recently infected : newly diagnosed with HIV-1 and aged between 18 and 25 years, or laboratory evidence of recent HIV-1 infection (defined as a confirmed HIV-1 positive antibody test with a negative HIV-1 antibody test within the past 12 months, or an indeterminate/negative HIV-1 antibody test with detectable HIV-1 RNA or positive p24 antigen). Exclusion criteria were any previous antiretroviral use (also for the prevention of mother-to-child transmission of HIV-1), documented WHO clinical stage 4 event, and previous pregnancy (parity) . All participants provided written informed consent prior to study enrollment. During the enrollment period of maximum 12 months, the VCT clients were all screened and sequentially enrolled. A case report form was completed and a blood draw was performed in all participants.
Plasma was separated within 2 h from blood draw and stored immediately at −80oC. HIV-1 RNA was tested with the Amplicor MONITOR 1.5 (Roche Molecular Systems; Roche, Nutley, New Jersey, USA). HIV-1 RNA was extracted from 140 μl of blood plasma using the Qiamp viral RNA mini kit (Qiagen Inc., Chatsworth, California, USA). Polymerase gene-specific primers were used for reverse transcriptase, followed by nested PCR to amplify a 1030-base pair pol gene encompassing amino acids 1–99 of protease and 1–242 of reverse transcriptase. The PCR products were then purified with a QIAquick PCR purification kit (Qiagen, Valencia, California, USA) and sequenced in the sense and antisense direction with a set of nested primers . To ensure the quality of the dataset, each sequence was checked before inclusion using ViroScore Suite v8.1 (ABL, Paris, France).
Genotypic resistance and phylogenetic analysis
Samples were sequentially genotyped and TDR was analyzed. Surveillance drug resistance mutations (SDRMs) were identified according to the 2009 WHO list for surveillance of genotypic TDR updated in 2009, which excludes polymorphisms . For SDRM analysis, the Stanford calibrated population resistance analysis tool version 5.0 beta was used . Pol region subtype classification and recombinant patterns were determined using the REGA subtyping tool  and the Subtype Classification Using Evolutionary Algorithms application , further confirmed using phylogenetic analysis. We performed maximum likelihood phylogenetic reconstruction using PhyML based on the General Time-Reversible model with gamma distributed rate variation among nucleotide sites.
The survey sample size was estimated from the hypothesis that the prevalence of TDR in the target population was initially low (estimated at 2%) and increased with time (estimated at 10%). To detect such increase with 80% power using a two-sided significance level of 0.05, the required number of HIV-1 sequences per geographic area was 78. Assuming 10% amplification failure, the target sample was 85 individuals. The proportions of sequences containing at least one SDRM were calculated overall and by each of the three main drug classes, that is, protease inhibitors, nucleoside reverse transcriptase inhibitors (NRTIs), and NNRTIs. TDR prevalence was estimated with a 95% confidence interval (CI) based on the binomial distribution. As a secondary analysis, the WHO-recommended truncated sampling technique was used to categorize TDR prevalence as low (<5%), moderate (5–15%), or high (>15%) for each of the three drug classes, based on the testing of the first ≤47 sequences . Categorical data were compared using χ2 test. Continuous data were investigated using Kruskal–Wallis or Student's t-test. All analyses were performed using Stata version 10 (StataCorp LP, College Station, Texas, USA).
Study enrollment took place from February 2009 to February 2010 at AIC and from May 2009 to May 2010 at NTC. A total of 884 individuals were screened, of whom 81 (9.2%) met the eligibility criteria. Excluding four individuals due to protocol violations (i.e., three did not meet the age criterion and one had a previous pregnancy), 77 participants (43 from AIC and 34 from NTC) were included in the analysis (Table 1). Seventy-six participants qualified based on the age criterion and one participant had a new confirmed HIV-1 diagnosis after a recent negative test. The mean age was 21.6 years (standard deviation, SD 2.1). Women constituted 70.1% (n = 54). The mean age was lower for women (21.1 years, SD 2.0), compared to men (22.7 years, SD 1.9, P = 0.0017). The median CD4 cell count was 417 cells/μl [interquartile range (IQR) 318.5–551.5 cells/μl] and the median HIV-1 RNA load was 4.49 log10 copies/ml (IQR 3.96–5.28 log10 copies/ml). A total of 94.8% (n = 73) of participants were Ugandan nationals. Nearly all (73, 94.8%) participants reported sexual encounters with the opposite sex, whereas other exposures were uncommon. The median age at sexual debut was 18 years, with a range between 14 and 27 years. During the 3 years prior, 71 (92.2%) participants reported to have engaged in unprotected sex, with an average of 2.1 (SD 1.8) sexual partners, and 23 (29.9%) reported a first episode of a sexually transmitted infection. Among participants who had a steady sexual partner, 68.8% was unaware of their partner's HIV-1 status. Baseline characteristics, except for mean age, did not differ between sites (Table 1).
Seventy samples were successfully genotyped and seven samples failed to amplify or had no valid genotype. One or more SDRMs were identified in six of the 70 valid sequences, yielding an estimated TDR prevalence of 8.6% with a 95% CI 3.2–17.7%. The proportion of sequences with SDRMs associated with NRTIs, NNRTIs, and protease inhibitors was 2.9% (two of 70), 4.3% (three of 70), and 1.4% (one of 70), respectively. We observed six different SDRMs: D67G, K101E, G190S, G190A, and L210W in reverse transcriptase and N88D in protease. TDR was confined to a single drug class in all six sequences. Table 2 summarizes the demographic and virological characteristics of the six participants who harbored an SDRM.
Using the WHO-recommended truncated sequential sampling technique, four of the first 47 sequences harbored an SDRM (moderate prevalence category), of which two were NRTI-associated (low prevalence category), one protease inhibitor-associated (low prevalence category), and one NNRTI-associated. HIV-1 subtype frequencies were A (36/70, 51.4%), C (two of 70; 2.9%), D (23/70, 32.8%), and A1/D recombinants (nine of 70, 12.9%).
This survey among 70 newly HIV-1 diagnosed young VCT clients in Kampala demonstrated an estimated prevalence of TDR of 8.6%, which is likely to represent an increase compared to the previous survey in 2006–2007 that did not detect any SDRMs among 47 pregnant women from the greater Kampala area . Identified SDRMs were associated with NNRTIs (three), NRTIs (two), and protease inhibitors (one), but in each sequence, TDR was confined to a single drug class. This study is among the first to suggest an increase in TDR between repeated surveys within the same geographic area in Africa, although the subsequent surveys targeted different subpopulations.
Most studies from Africa that were conducted during the early scale-up of ART have reported low levels of TDR [7,8]. In Botswana, the 2007 threshold survey indicated that 5 years following the countrywide ART roll-out, TDR was still less than 5% . The International AIDS Vaccine Initiative cohort, however, of newly HIV-1-infected individuals in east and southern Africa reported a 5% overall prevalence of TDR, with an increase from 3% (four of 157) in 2005–2006 to 7% (12/169) in 2007–2008 . The proportion of participants who harbored TDR was particularly high in Entebbe (four of 17, 23.5%) and Kigali (eight of 68, 11.8%) . Consistent with this report, our study supports the hypothesis that increasing antiretroviral drug exposure in African populations, following the roll-out of antiretroviral drugs for treatment and prevention of mother-to-child transmission, may cause a rise in TDR and thereby new public health challenges.
In this study, the categorization of TDR using prevalence (six of 70) corresponded with the WHO-recommended truncated sequential sampling technique (four of 47), that is, ‘moderate’ overall and ‘low’ for each drug class separately. It should, however, be noted that the small sample sizes resulted in a wide CI, warranting caution in interpreting and extrapolating the results.
This study has several limitations. Given the challenges, especially in resource-limited settings, in identifying individuals during acute or recent HIV-1 infection, WHO recommends the use of proxy criteria for the surveillance of TDR. A recent study in Botswana, however, found poor agreement between the WHO criteria and two laboratory-based methods to detect new infection . The WHO approach could, therefore, lead to the inclusion of individuals with established infection, during which drug-resistant mutants may have reverted to wild-type virus [17–20], thereby possibly underestimating the true current prevalence of resistance transmission. Although the study specifically selected newly diagnosed, antiretroviral-naive individuals, it cannot be completely ruled out that some participants had unknown or undisclosed prior exposure to antiretroviral therapy and/or prophylaxis.
In conclusion, 10 years following the ART scale-up in Kampala, Uganda, this repeated survey demonstrated that 8.6% of newly HIV-1 diagnosed youth harbored TDR, which is likely to represent an increase compared to the previous survey. The study findings should trigger public health action in performing additional surveys in the upcoming years to evaluate the evolution of TDR in the country and can provide guidance to drug-resistance prevention strategies. This is especially urgent as current options for first-line therapy in Uganda are limited and access to second-line therapy is not widely available.
The authors thank all survey participants and the site staff at AIDS Information Centre (AIC), Naguru Teenage Health Information Centre (NTC), and Medical Research Council – Uganda Virus Research Institute (MRC-UVRI). We thank the International Center for Reproductive Health Kenya for developing the clinical database. The PharmAccess African Studies to Evaluate Resistance (PASER) is part of the LAASER program (Linking African and Asian Societies for an Enhanced Response to HIV/AIDS), a partnership of Stichting Aids Fonds, The Foundation for AIDS Research (amfAR) – TREAT Asia, PharmAccess Foundation and International Civil Society Support. The authors thank Dr Philippe Lemey for carefully reviewing the phylogenetic analysis.
PASER is an initiative of PharmAccess Foundation, with financial support provided by the Ministry of Foreign Affairs of The Netherlands through a partnership with Stichting Aids Fonds (grant 12454). Additional funding was provided by MRC-UVRI Uganda Research Unit on AIDS. The funders had no role in the study design, data collection, data analysis, data interpretation, decision to publish, or writing of the report. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the institutions mentioned above.
N.N., R.L.H., C.K., P.K., and T.F.R.W. conceived and designed the study. K.C.E.S. and C.W. coordinated the field work. B.M. supervised data collection. N.N. supervised analyzed the resistance data, and performed phylogenetic analysis and B.N. and F.L. performed and interpreted the laboratory testing. R.L.H. analyzed the clinical data and the resistance data. N.N. and R.L.H. drafted the paper. K.C.E.S. and T.F.R.W. reviewed the paper for important intellectual content. All authors read and approved the final manuscript. T.F.R.W. is the guarantor.
1. Government of Uganda
. UNGASS Country Progress Report 2010. http://data.unaids.org/pub/Report/2010/uganda_2010_country_progress_report_en.pdf
. [Accessed 6 February 2011].
2. Jahn A, Floyd S, Crampin AC, Mwaungulu F, Mvula H, Munthali F, et al
. Population-level effect of HIV on adult mortality and early evidence of reversal after introduction of antiretroviral therapy
in Malawi. Lancet 2008; 371:1603–1611.
3. Bennett DE, Bertagnolio S, Sutherland D, Gilks CF. The World Health Organization's global strategy for prevention and assessment of HIV drug resistance. Antivir Ther 2008; 13(Suppl 2):1–13.
4. Hamers RL, Derdelinkx I, Van Vugt M, Stevens W, Rinke de Wit TF, Schuurman R. The status of HIV-1
resistance to antiretroviral drugs in sub-Saharan Africa. Antivir Ther 2008; 13:625–639.
5. Grant RM, Hecht FM, Warmerdam M, Liu L, Liegler T, Petropoulos CJ, et al
. Time trends in primary HIV-1
drug resistance among recently infected persons. JAMA 2002; 288:181–188.
6. Wensing AM, van de Vijver DA, Angarano G, Asjo B, Balotta C, Boeri E, et al
. Prevalence of drug-resistant HIV-1
variants in untreated individuals in Europe: implications for clinical management. J Infect Dis 2005; 192:958–966.
7. Ndembi N, Lyagoba F, Nanteza B, Kushemererwa G, Serwanga J, Katongole-Mbidde E, et al
. Transmitted antiretroviral drug resistance surveillance
among newly HIV type 1-diagnosed women attending an antenatal clinic in Entebbe, Uganda
. AIDS Res Hum Retroviruses 2008; 24:889–895.
8. Bennett DE, Myatt M, Bertagnolio S, Sutherland D, Gilks CF. Recommendations for surveillance
of transmitted HIV drug resistance in countries scaling up antiretroviral treatment. Antivir Ther 2008; 13(Suppl 2):25–36.
9. Ndembi N, Goodall RL, Dunn DT, McCormick A, Burke A, Lyagoba F, et al.Viral rebound and emergence of drug resistance in the absence of viral load testing: a randomized comparison between zidovudine-lamivudine plus nevirapine and zidovudine-lamivudine plus abacavir.J Infect Dis
10. Bennett DE, Camacho RJ, Otelea D, Kuritzkes DR, Fleury H, Kiuchi M, et al
. Drug resistance mutations for surveillance
of transmitted HIV-1
drug-resistance: 2009 update. PLoS One 2009; 4:e4724.
11. Gifford RJ, Liu TF, Rhee SY, Kiuchi M, Hue S, Pillay D, Shafer RW. The calibrated population resistance tool: standardized genotypic estimation of transmitted HIV-1
drug resistance. Bioinformatics 2009; 25:1197–1198.
12. de Oliveira T, Deforche K, Cassol S, Salminen M, Paraskevis D, Seebregts C, et al
. An automated genotyping system for analysis of HIV-1
and other microbial sequences. Bioinformatics 2005; 21:3797–3800.
13. Kosakovsky Pond SL, Posada D, Stawiski E, Chappey C, Poon AF, Hughes G, et al
. An evolutionary model-based algorithm for accurate phylogenetic breakpoint mapping and subtype
prediction in HIV-1
. PLoS Comput Biol 2009; 5:e1000581.
14. Myatt M, Bennett DE. A novel sequential sampling technique for the surveillance
of transmitted HIV drug resistance by cross-sectional survey for use in low resource settings. Antivir Ther 2008; 13(Suppl 2):37–48.
15. Bussmann H, de la Hoz Gomez F, Roels TH, Wester CW, Bodika SM, Moyo S, et al.Prevalence of transmitted HIV drug resistance (HIVDR) in Botswana: lessons learned from the HIVDR-Threshold Survey conducted among women presenting for routine antenatal care as part of the 2007 National Sentinel Survey.AIDS Res Hum Retroviruses
2010. [Epub ahead of print]
16. Price MA, Wallis CL, Lakhi S, Karita E, Kamali A, Anzala O, et al
. Transmitted HIV type 1 drug resistance among individuals with recent HIV infection in east and southern Africa.AIDS Res Hum Retroviruses
17. Little SJ, Frost SD, Wong JK, Smith DM, Pond SL, Ignacio CC, et al
. Persistence of transmitted drug resistance
among subjects with primary human immunodeficiency virus infection. J Virol 2008; 82:5510–5518.
18. Novak RM, Chen L, MacArthur RD, Baxter JD, Huppler Hullsiek K, Peng G, et al
. Prevalence of antiretroviral drug resistance mutations in chronically HIV-infected, treatment-naive patients: implications for routine resistance screening before initiation of antiretroviral therapy
. Clin Infect Dis 2005; 40:468–474.
19. Johnson JA, Li JF, Wei X, Lipscomb J, Irlbeck D, Craig C, et al
. Minority HIV-1
drug resistance mutations are present in antiretroviral treatment-naive populations and associate with reduced treatment efficacy. PLoS Med 2008; 5:e158.
20. Weber J, Chakraborty B, Weberova J, Miller MD, Quinones-Mateu ME. Diminished replicative fitness of primary human immunodeficiency virus type 1 isolates harboring the K65R mutation. J Clin Microbiol 2005; 43:1395–1400.