1. NNRTI polymorphisms and response to NNRTI-based ART Lucy Garvey, Linda Harrison, Peter Tilston, Andrew Phillips, Caroline Sabin, Anna Maria Geretti, David Dunn, Nicola Mackie

2. Background Polymorphisms occur at codons within regions 90-108, 179-190 and 225-348 in ARV-naive individuals Although some confer low-level resistance to NNRTIs in vitro, their impact on virological response remains unclear Aims 1. To determine the prevalence of polymorphisms at the following codons* in ARV-naive subjects: 2. To assess their impact on early virological response to NNRTI-based therapy 9098100101103106108 138179181188190225227230234236238318348 * IAS Dec 2008 and Stanford database http://hivdb.stanford.edu accessed 07 Jan 2010http://hivdb.stanford.edu

3. Population ARV-naive patients starting NNRTI-based Rx with WT or polymorphisms only on baseline genotype (any major RT mutation excluded) Analysis Assess early virological response at week 4 (approx timing wk 2-6): WT versus any polymorphism WT versus individual codon (irrespective of amino acid mutation) Results to date 2235 eligible subjects 1221 (55%) at least one polymorphism Most frequently seen at codons 135 (39%), 179 (10%), 98 (8%) Average 2.4 log 10 drop by wk 4

4. Difference in reduction in viral load at week 4 n=71 n=176 n=39 n=35 n=51 n=877 n=79 n=217 n=35 90 98 101 103 106 135 138 179 238 Polymorphism -.5-.250.25.5 Average effect on reduction in viral load at week 4

5. Proposed Further Analysis Details on demographics including: calendar year, first-line ARV details (EFV or NVP), HIV subtype Assess time to VL<50 copies/mL: WT versus any polymorphism WT versus individual codon Look at number of polymorphisms per patient

6. Prevalence of PI mutations in HIV-infected UK adults treated with ritonavir-boosted lopinavir as their first PI Tristan Barber, David Dunn, Linda Harrison, Loveleen Bansi, Ian Williams, Deenan Pillay

7. Little is known about the clinical significance of PI mutations for successful sequencing of PIs The Quest laboratory database reported a novel LPV resistance pathway with L76V 1 Looked at data from the UK HIV Drug Resistance Database linked to the UK CHIC study for: –patients failing LPV/r containing ART with demonstrable resistance –the prevalence of L76V –other novel resistance mutations Background 1 Nijhuis, et al. Failure of treatment with first line lopinavir boosted with ritonavir can be explained by novel resistance pathways with protease mutation 76V. JID 2009; 200: 698-709.

8. Methods Population –PI naïve adults, starting LPV/r as their first PI Virological failure –viral load >400 c/ml after previously being <400 –OR viral load >400 c/ml for the first 6 months of LPV/r –Patients were censored if they stopped LPV/r or started another PI Resistance –For those failing, we looked for resistance tests –Resistance was defined as ≥1 major PI mutation on the IAS list (Dec 2008)

9. Results N = 3056 Previous ART: 1580 (52%) naïve 569 (19%) NNRTI HAART 907 (30%) other 811 (27%) rebounded: 370 (23%) naïve 139 (24%) NNRTI HAART 302 (33%) other

10. Resistance Of 811 rebounding: 291 (36%) had resistance tests Of 291 with tests: 32 (11%) had PI resistance 3 had L76V V82A5 Q58E3 M46L I84V2 M46LV82A2 L90M2 L33FM46LV82AI84VL90M1 V32IM46II47A/V V82A1 L33FM46LV82AL90M1 M46LV82TI84VL90M1 V32IM46II47A1 M46IL76VI84V1 M46IV82AV82S1 M46IL76V1 M46II84V1 M46LT74P1 L33F1 M46I1 M46I/L1 M46L1 I47V1 L76V1 V82T1 I84V1

11. Outcomes of 2 nd line ART: 1st line NNRTI to 2nd line PI/r Laura Waters, David Asboe, Anton Pozniak, Loveleen Bansi, Chloe Orkin, Erasmus Smit, Esther Fearnhill & Andrew Phillips. UK Resistance Database and UK CHIC Study

12. A combination of 2 NRTI + 1 NNRTI is the most common first line regimen worldwide A combination of 2 NRTI + 1 NNRTI is the most common first line regimen worldwide Treatment failures continue to occur Treatment failures continue to occur Second line regimens are usually PI/r based and should include at least 2 active agents 1 Second line regimens are usually PI/r based and should include at least 2 active agents 1 Unclear how many active NRTI are necessary with 2nd line PI/r based therapy Unclear how many active NRTI are necessary with 2nd line PI/r based therapy Background 1) 2008 BHIVA Guidelines

13. To identify factors associated with failing 2 nd line, ritonavir boosted PI-based ART To identify factors associated with failing 2 nd line, ritonavir boosted PI-based ART To investigate the importance of the number of new or fully active NRTI started at 2 nd line To investigate the importance of the number of new or fully active NRTI started at 2 nd line Objectives

14. Eligibility Eligibility Patients failing first line NNRTI-ART (VL >200 copies/ml after 4 months) and starting PI/r for the first time Exclusions Exclusions VL<200 copies/ml at start of 2 nd line VL<200 copies/ml at start of 2 nd line <4 months follow up <4 months follow up failed new NRTI between first and second-line therapy failed new NRTI between first and second-line therapy Methods 1

15. Virological failure of 2 nd line ART was defined as VL>200 copies/ml despite 4 months continuous use Virological failure of 2 nd line ART was defined as VL>200 copies/ml despite 4 months continuous use NRTI GSS calculated for 2 nd line regimens using Stanford NRTI GSS calculated for 2 nd line regimens using Stanford Statistical Analyses: Statistical Analyses: Kaplan-Meier: time to failing 2 nd line HAART Kaplan-Meier: time to failing 2 nd line HAART Logistic regression: identify factors associated with having a resistance test Logistic regression: identify factors associated with having a resistance test Cox regression: identify factors associated with failing 2 nd line HAART Cox regression: identify factors associated with failing 2 nd line HAART Methods 2

16. Patient disposition Patients starting NNRTI HAART9285 VL>200 copies/ml despite 4 months continuous use of NNRTI 1692 (18.2%) Patients starting new drugs after failing first line1103 Started 2 nd line PI/r601* VL>200 copies/ml at start of 2 nd line PI/r501 Excluding those without 4 months follow up445 Excluding those who failed a new NRTI after failing 1 st line and before starting 2 nd line HAART 403 *Patients not meeting 2 nd line criteria502 Started single PI45 Started new NNRTI138 Started new NRTIs only318 Started other drugs1

17. Patients starting new drugs 2 nd line (n = 1103) Started PI/r Regimen 2 nd line (n = 601) Started non-PI/r regimen 2 nd line (n = 502) Unboosted PI45 New NNRTI138 New NRTI only318 Other1 Excluded HIV-RNA <200 at 2 nd line100 <4 months follow-up56 Failed new NRTI between 1 st and 2 nd line42 Eligible for Study (n = 403)

18. Baseline characteristics Patients starting 2 nd line PI/r HAART403 Time to failing 1 st line HAART (months)Median (IQR)9.4 (5.3, 20.0) New PI/r started n (%)LPV222 (55.1) AZV92 (22.8) Other89 (22.1) New nucleosides started at 2 nd line069 (17.1) n (%)1113 (28.0) >2>2221 (54.8) Year of starting 2 nd line n (%)1999-2001195 (48.4) 2002-2004164 (40.7) 2005-200744 (10.9) White ethnicity n (%)161 (40.0) MSM n (%)154 (38.2) VL<200 after failing 1 st line and before starting 2 nd line n (%) 63 (15.6) CD4 at start of 1 st line (cells/mm 3 )Median (IQR)150 (54, 22.0) VL at start of 1 st line (log copies/ml)Median (IQR)5.1 (4.6, 5.5) CD4 at start of 2 nd line (cells/mm 3 )Median (IQR)213 (124, 310) VL at start of 2 nd line (log copies/ml)Median (IQR)4.4 (3.6, 5.0.)

19. Time to failing 2 nd line HAART 222/403 (55.1%) experienced virological failure of 2 nd line

20. Independent factors associated with failure of 2 nd line HAART (N=403) HR (95% CI)P-value Age at start of 2 nd line (years)Per 10 years older1.08 (0.86, 1.34)0.52 Time from failing 1 st line to starting 2 nd line Per 1 month increase1.01 (1.00, 1.03)0.07 Number of new nucleosides00.83 (0.51, 1.33)0.44 started at 2 nd line 1 11.11 (0.79, 1.56)0.56 >2>21- EthnicityWhite1- Black0.95 (0.60, 1.51)0.95 Other0.52 (0.24, 1.12)0.52 Sex/ExposureMSM1- Hetero male1.93 (1.17, 3.20)0.01 Hetero female2.33 (1.26, 4.02)0.002 Other1.59 (0.62, 2.80)0. 11 Year of starting 2 nd linePer 1 year increase0.94 (0.86, 1.02)0.15 VL<200 after failing 1 st line and before starting 2 nd line0.76 (0.47, 1.24)0.28 CD4 at 2 nd line (cells/mm 3 )Per 50 cells higher0.89 (0.83, 0.94)<0.0001 VL at 2 nd line (copies/ml)Per 1 log increase1.23 (1.06, 1.42)0.01 1 HR=1.00 (0.82, 1.21), p=0.99 if fitted as a continuous variable

21. Characteristics of patients, stratified by whether or not they had a resistance test performed after failing 1 st line HAART Resistance test performedNoYesP- value N187216 Age at failing 1 st line (years)Median (IQR)35 (31, 39)36 (32, 40)0.31 Ethnicity n (%)White73 (45.3)88 (54.7)0.73 Black99 (48.1)107 (51.9) Other15 (41.7)21 (58.3) Sex/Exposure n (%)MSM69 (44.8)85 (55.2)0.06 Hetero male59 (56.7)45 (43.3) Hetero female47 (42.7)63 (57.3) Other12 (34.3)23 (65.7) Achieved VL<500 before starting 2 nd line n (%)137 (43.8)176 (56.2)0.05 Year of failing 1 st line n (%)1999-200154 (49.1)56 (50.9)0.16 2002-200490 (49.5)92 (50.6) 2005-200743 (38.7)68 (61.3) CD4 at failing 1 st line (cells/mm 3 )Median (IQR)220 (124,349)287 (168,390) 0.01 VL at failing 1 st line (log copies/ml)Median(IQR)4.0 (3.1, 4.8)3.7 (3.0, 4.5)0.08 None of the above factors were independently associated with having a resistance test

22. Failed 2 nd line HAART GSSAllNoYesP-value 12190 <1<133 (15.6)20 (60.6)13 (39.4)0.80 1 1.25-1.7571 (33.6)42 (59.2)29 (40.9) >2>2107 (50.7)59 (55.1)48 (44.9) 1 Chi-squared test Patients not receiving any NRTIs (N=5) excluded GSS amongst those who had a resistance test performed (N=211)

23. Independent factors associated with failure of 2 nd line HAART (N=211) HR (95% CI)P-value NRTI GSS 1 <1<10.73 (0.37, 1.41)0.34 1.25-1.750.70 (0.42, 1.15)0.16 >2>21 Time from failing 1 st line to starting 2 nd line Per 1 month increase1.01 (0.99, 1.02)0.44 Age at start of 2 nd line (years)Per 10 years older1.29 (0.94, 1.79)0.12 EthnicityWhite1- Black0.61 (0.30, 1.23)0.17 Other0.61 (0.23, 1.59)0.31 Sex/ExposureMSM1- Hetero male2.53 (1.14, 5.63)0.02 Hetero female2.79 (1.28, 6.08)0.01 Other1.32 (0.60, 2.90)0.50 Year of starting 2 nd linePer 1 year increase0.97 (0.86, 1.10)0.67 VL<200 after failing 1 st line and before starting 2 nd line0.63 (0.31, 1.27)0.19 CD4 at 2 nd line (cells/mm 3 )Per 50 cells higher0.85 (0.77, 0.95)0.004 VL at 2 nd line (copies/ml)Per 1 log increase1.26 (0.99, 1.59)0.06 1 HR=1.14 (0.76, 1.72), p=0.51 if fitted as a continuous variable

24. Of 403 patients who started 2 nd line PI/r, 216 (54%) patients had a resistance test performed after failing 1 st line HAART Of 403 patients who started 2 nd line PI/r, 216 (54%) patients had a resistance test performed after failing 1 st line HAART NRTI GSS was >2 for 50% of patients with resistance tests performed NRTI GSS was >2 for 50% of patients with resistance tests performed Neither NRTI GSS nor the number of new NRTI started at 2 nd line were associated with virological failure of 2 nd line HAART Neither NRTI GSS nor the number of new NRTI started at 2 nd line were associated with virological failure of 2 nd line HAART Summary

25. Among patients who have failed an NNRTI 1st line then started a PI/r 2nd line there was extensive variability in the number of new NRTI started, hence in the predicted activity of the NRTI backbone Among patients who have failed an NNRTI 1st line then started a PI/r 2nd line there was extensive variability in the number of new NRTI started, hence in the predicted activity of the NRTI backbone We found little evidence that: We found little evidence that: number of new NRTI started number of new NRTI started predicted NRTI activity within the regimen, predicted NRTI activity within the regimen, were associated with risk of virologic failure of the 2nd line regimen Conclusions

26. These findings may reflect: These findings may reflect: the strong potency of the PI/r component and/or the strong potency of the PI/r component and/or a negative impact of initiating more new agents in terms of tolerability and/or adherence a negative impact of initiating more new agents in terms of tolerability and/or adherence However, further analyses are required to more extensively explore this lack of association before drawing firm conclusions However, further analyses are required to more extensively explore this lack of association before drawing firm conclusions Conclusions

27. UK CHIC Steering Committee: Jonathan Ainsworth, Jane Anderson, Abdel Babiker, David Dunn, Philippa Easterbrook, Martin Fisher, Brian Gazzard (Chair), Richard Gilson, Mark Gompels, Teresa Hill, Margaret Johnson, Clifford Leen, Chloe Orkin, Andrew Phillips, Deenan Pillay, Kholoud Porter, Caroline Sabin, Tariq Sadiq, Achim Schwenk, Nicky Mackie, Alan Winston, Valerie Delpech. Central Co-ordination: Medical Research Council Clinical Trials Unit (MRC CTU), London (David Dunn, Kholoud Porter, Stephen Sheehan); Royal Free NHS Trust and RFUCMS, London (Loveleen Bansi, Teresa Hill, Andrew Phillips, Caroline Sabin). Participating Centres: Barts and The London NHS Trust, London (Chloe Orkin, Kevin Jones, Rachel Thomas); Brighton and Sussex University Hospitals NHS Trust (Martin Fisher, Nicky Perry, Anthony Pullin, Duncan Churchill,Wendy Harris); Chelsea and Westminster NHS Trust, London (Brian Gazzard, Steve Bulbeck, Sundhiya Mandalia, Jemima Clarke); Health Protection Agency – Centre for Infections London (HPA) (Valerie Delpech); Homerton University Hospital NHS Trust, London (Jane Anderson, Selina Gann); King’s College Hospital, London (Philippa Easterbrook, Yasar Khan, Fatimah Karim, Eghosa Bazuaye, Stephen Duffell); Medical Research Council Clinical Trials Unit (MRC CTU), London (Abdel Babiker, David Dunn, Kholoud Porter, Stephen Sheehan); Mortimer Market Centre, Royal Free and University College Medical School (RFUCMS), London (Richard Gilson, Julie Dodds, Shuk-Li Man, Ian Williams); North Middlesex University Hospital NHS Trust, London (Achim Schwenk); Royal Free NHS Trust and RFUCMS, London (Margaret Johnson, Mike Youle, Fiona Lampe, Colette Smith, Helen Grabowska, Clinton Chaloner, Dewi Ismajani Puradiredja, Loveleen Bansi, Teresa Hill, Andrew Phillips, Caroline Sabin); St. Mary’s Hospital, London (John Walsh, Jonathan Weber, Christian Kemble, Mark Carder); The Lothian University Hospitals NHS Trust, Edinburgh (Clifford Leen, Alan Wilson).

28. UK Collaborative Group on HIV Drug Resistance Steering Committee Jane Anderson, Homerton University Hospital; David Asboe, Anton Pozniak, Chelsea & Westminster Hospital, London; Sheila Burns, Royal Infirmary of Edinburgh; Sheila Cameron, Gartnavel General Hospital, Glasgow; Patricia Cane, Health Protection Agency, Porton Down; Ian Chrystie, Guy’s and St. Thomas’ NHS Foundation Trust, London; Duncan Churchill, Brighton and Sussex University Hospitals NHS Trust; Duncan Clark, St Bartholemews and The London NHS Trust; Valerie Delpech, Deenan Pillay, Health Protection Agency-Centre for Infections London; David Dunn, Esther Fearnhill, Hannah Green, Kholoud Porter, MRC Clinical Trials Unit, London; Philippa Easterbrook, Mark Zuckerman, King’s College Hospital, London; Anna Maria Geretti, Royal Free NHS Trust, London; Paul Kellam, Deenan Pillay, Andrew Phillips, Caroline Sabin, Royal Free and University College Medical School, London; David Goldberg, Health Protection Scotland, Glasgow; Mark Gompels, Southmead Hospital, Bristol; Antony Hale, Leeds Teaching Hospitals NHS Trust; Steve Kaye, St. Mary’s Hospital, London; Svilen Konov, Community Advisory Board; Linda Lazarus, Department of Health; Andrew Leigh-Brown, University of Edinburgh; Nicola Mackie, St. Mary’s Hospital, London; Chloe Orkin, St. Bartholemews Hospital, London; Erasmus Smit, Health Protection Agency, Birmingham Heartlands Hospital; Peter Tilston, Manchester Royal Infirmary; Ian Williams, Mortimer Market Centre, London; Hongyi Zhang, Addenbrooke’s Hospital, Cambridge. Central Co-ordination: Medical Research Council Clinical Trials Unit (MRC CTU), London (David Dunn, Esther Fearnhill, Hannah Green, Kholoud Porter); Funding The UK HIV Drug Resistance Database is partly funded by the Department of Health; the views expressed in the publication are those of the authors and not necessarily those of the Department of Health. Additional financial support is provided by Boehringer Ingelheim; Bristol-Myers Squibb; Gilead; Tibotec, a division of Janssen-Cilag Ltd; and Roche.

29. Prevalence and patterns of Raltegravir resistance in treated patients in Europe- CORONET Study. CORONET -European collaborative study in area of integrase resistance - repository of integrase sequences from 9 European centres and 2 multi- centre repositories (UK Drug Resistance database/ EuResist database). AIM - To survey patients experiencing virological failure on Raltegravir (RAL) based regimen within CORONET, and to assess the influence of HIV-1 subtype on patterns of RAL genotypic resistance that emerge. Study Group Integrase sequences available for: 255 patients- viraemic on RAL- based therapy plus 591 patients- prior to starting RAL- based therapy. - Analysis included major INI resistance-associated mutations (T66I, E92Q, F121Y, G140A/S, Y143R/C,S147G, Q148H/R/K and N155H) other non- classic mutations at the same codons and mutations implicated in INI resistance in vivo or in vitro (codons 51, 54, 68, 74, 95, 97, 114, 125, 128, 138, 145, 146, 151, 153, 154, 157, 160, 163, 203, 230, 263).

30. Distribution of HIV-1 subtypes among INI experienced and naïve patients. Treatment experienced Treatment naive

31. Prevalence and patterns of major Raltegravir Associated Mutations (RAMs) in INI-experienced patients ( n=255 ). RAM Number of RAMs (%)SubtypeRAM Number of RAMs (%)Subtype T66I0(0.0)Y143C4(1.6)B E92Q9(3.5)B, C, GS147G1(0.4)B F121Y0(0.0)Q148H28(11.0)B G140A4(1.6)B, GQ148R16(6.3)B, C, G G140S34(13.3)BQ148K1(0.4)B Y143R9(3.5)B, C,FN155H57(22.4) A,B, C, D, F, G, CRF02 Total114(44.7) Table 1: Prevalence of major integrase inhibitor RAMs in INI experienced patients. RAM pattern Number of RAMs (%) RAM pattern Number of RAMs (%) E92Q1(0.4)Y143R +N155H1(0.4) E92Q + N155H8(3.1)S147G + Q148H1(0.4) G140S1(0.4)Q148H/R/K6(2.4) G140A/S + Q148 H/R/K36(14.1)Q148H + N155H1(0.4) G140S + Q148H + N155H1(0.4)N155H46(18.0) Y143R/C12(4.7)Total114(44.7) Table 2: Patterns of major integrase inhibitor RAMs in INI experienced patients.

32. Non-classic mutations at major INI resistance codons detected in INI-experienced patients ( n=255 ). RAM Number of RAMs (%)SubtypeRAM Number of RAMs (%)Subtype T660(0.0)-Y143H/A/S5(2.0)B, D E92A/P2(0.8)BS147I1(0.4)B F1210(0.0)-Q1480(0.0)- G1400(0.0)-N155D/Q2(0.8)B

33. Prevalence of other mutations implicated in INI- resistance among INI experienced patients n=255. Mutation Number (%) Major INI RAMs MutationNumber(%)Major INI RAMs H51Y1(0.4)-Q146P1(0.4)1/1 V54I1(0.4)-V151I*25(9.8)19/25 L68I/V1(0.4)-M154I/L28(11.0)14/28 L74I/M22(8.6)13/22E157Q6(2.4)4/6 Q95K2(0.8)2/2K160Q/T8(3.1)5/8 T97A*20(7.9)16/20G163E/R17(6.7)11/17 T125A/V114(44.7)48/114I203M13(5.5)9/9 E138D/K*12(4.7)11/12S230N17(7.2)9/9 P145L1(0.4)1/1 *P value< 0.0001 vs. INI- naive patients. (Fisher’s exact test)

34. Novel mutations associated with INI experience. Mutation INI- experienced n (%) INI- naive n (%) P value K159Q/R4 (1.6)0 (0.0)0.008 I161L/M/N/T/V6 (2.4)1 (0.2)0.004 E170A/G4 (1.6)0 (0.0)0.008 *P values by Fisher’s exact test

35. 55.3% of viraemic patients on RAL lacked major INI resistance- associated mutations- overall, 114/255 (44.7%) RAL experienced patients had ≥1 major INI RAMs. Of 3 major recognised pathways of genotypic resistance to RAL : N155H and Y143R/C occurred in both B and non- B HIV1 subtypes. Q148H/R/K- significantly more prevalent in subtype B. Q148 was highly conserved among INI naive patients infected with either subtype B or non-B virus in contrast with INI experienced counterpart. T97A, E138D/K and V151I significantly more common in RAL experienced patients. Conclusions.

36. Identified 3 novel mutations that were more prevalent in RAL experienced patients in comparison with RAL drug naive: K159Q/R, I161L/M/N/T/V and E170AG. K159Q/R observed in subtype B only- in 1 patient with major INI RAMs and in 3 other patients with other INI associated mutations. I161L/M/N/T/V seen in subtype B and CRFO2- in 1 patient with major INI RAMs and in 5 patients with other mutations implicated in INI resistance. E170AG seen in subtype B viruses in 2 patients alongside 2 major INI RAMs and in all patients with other INI associated resistance mutations. Require further clarification of their impact across subtype on drug susceptibility to 1 st and 2 nd generation INIs. Conclusions.

37. Comparison of subtypes B and C accessory mutations observed in high level NRTI resistance Codon 43 Codon 118 Statistically significant differences (p<0.001) between subtype B and C in detection of mutants at codons 43 and 118 with accumulation of TAMS. Impact of 43, 44, and 118 on resistance and fitness in subtype B and C backgrounds now being analysed in phenotypic assays (Tamyo Mbisa).

38. PLATO II Project of COHERE: Analysis of predictors of triple class virologic failure (TCVF) and outcomes for patients with TCVF. Results so far relating to resistance (EACS Cologne, manuscript drafted) 722 patients who developed TCVF and for whom at least one resistance test was available at some point up to time of TCVF (1514 tests). 444 / 618 (72%) patients with a resistance test while on an NRTI after NRTI failure had an NRTI mutation. 372 / 427 (87%) patients with a resistance test while still on an NNRTI after NNRTI failure had an NNRTI mutation. 65 / 240 (27%) patients with a resistance test while on a PI after failing a PI/r had a PI mutation. Risk factors for PI resistance: longer time on a PI/r regimen since PI/r failure, being treated with an NNRTI-containing regimen, and for having previously used a greater number of PIs.

39. In the second round of the project (in progress) we will expand the scope of the resistance data pooled to include all resistance tests performed in people with TCF, including those tests performed beyond the time of TCF.

40. The specific aims in the second round will be in people with TCVF: 1. To assess the proportion of people with TCVF for whom resistance mutations to the three original classes, and to the newer drug classes, are documented, either up to the time of TCVF or beyond. 2. To document calendar time trends in the GSS for people on ART. The GSS at any calendar time point will be based on cumulative resistance tests up to that point (and so could be calculated for those with VL < 50 as well as for those with higher VL). 3. In people on ART, to assess the extent to which the documented increasing trend over calendar time in proportion of people with VL < 50 is explained statistically by the current GSS (i.e. the extent to which the rate ratio for the effect of calendar time on VL < 50 moves to 1 after adjustment for the current GSS).