P001 Socio-economic factors and virological suppression among people diagnosed with HIV in the UK: results from the ASTRA study Burch, L*; Smith, C; Anderson, J; Sherr, L; Rodger, A; O’Connell, R; Gilson, R; Elford, J; Phillips, A; Speakman, A; Johnson, M; Lampe, F (London, UK)

P002 Factors associated with the continuum of care of HIV-infected patients in BelgiumVan Beckhoven, D; Lacor, P; Moutschen, M; Piérard, D; Sasse, A; Vaira, D; Van den Wijngaert, S; Vandercam, B; Van Ranst, M; Van Wijngaerden, E; Vandekerckhove, L; Verhofstede, C; Verbrugge, R; Demeester, R*; De Wit, S; Florence, E; Fransen, K; Delforge, M; Goffard, J; Goubau, P; BREACH, (Charleroi, Belgium)

P003 Loss to follow-up of HIV-infected women after delivery: The Swiss HIV Cohort Study and the Swiss Mother and Child HIV Cohort StudyAebi-Popp, K*; Kouyos, R; Bertisch, B; Staehelin, C; Hoesli, I; Rickenbach, M; Thorne, C; Grawe, C; Bernasconi, E; Cavassini, M; Martinez de Tejada, B; Stoeckle, M; Lecompte, T; Rudin, C; Fehr, J (Bern, Switzerland)

P004 Patients’ willingness to take separate component antiretroviral therapy regimens for HIV in the The The NetherlandsEngelhard, E*; Smit, C; Vervoort, S; Kroon, F; Brinkman, K; Nieuwkerk, P; Reiss, P; Geerlings, S (Amsterdam, The The Netherlands)

P005 Real-world medication persistence with single versus multiple tablet regimens for HIV-1 treatmentSweet, D*; Song, J; Zhong, Y; Signorovitch, J (Wichita, USA)

P006 No difference in persistence to treatment with atazanavir or darunavir in HIV patients in a real world settingFarr, A; Johnston, S; Ritchings, C; Brouillette, M; Rosenblatt, L* (Cambridge, USA)

ADHERENCE

*Indicates presenting author.

Socio-economic factors and virologicalsuppression among people diagnosed withHIV in the UK: results from the ASTRA studyBurch L1, Smith C1, Anderson J2, Sherr L1, Rodger A1, O’Connell R3, Geretti AM4 Gilson R1, Elford J5,Phillips A1, Speakman A1, Johnson M6, Lampe F1

1UCL, London, UK; 2Homerton University Hospital, London, UK; 3Barts Health NHS Trust, London, UK; 4Institute of Infection andGlobal Health, University of Liverpool, UK; 5City University London, UK; 6Royal Free London NHS Foundation Trust, London, UK .

Among people with HIV who are treated with ART in the UK, the proportionwith suppressed viral load is high. However, there remains a significantminority with detectable viraemia.Although there are known socio-economic variations in prognosis for anumber of chronic diseases, there are few studies of the impact of socio-economic status on treatment outcomes for people with HIV in the UK.This is important as the HIV-positive population in the UK comprises distinctdemographic groups with variation in social circumstances.Any impact of socio-economic status on virological suppression is likely to bemediated in part through differential patterns of adherence to ART1,2.Using data from the Antiretrovirals, Sexual Transmission Risk and Attitudes(ASTRA) study, we investigated the cross-sectional association between socio-economic factors and viral suppression in people on ART in the UK.

ASTRA is a cross-sectional questionnaire study of 3258 individuals from 8 HIVoutpatient clinics in the UK in 2011/20123.The analysis includes 2445 participants with a recorded current viral load (VL)and who had received ART for >6 months at the time of the questionnaire(69% MSM, 20% women, 68% white, median age 46 years, median CD4 550cells/mm3 and median time on ART 8 years).We considered the following socio-economic factors: financial hardship,employment, housing, education, time in UK, English reading ability,supportive network (using the modified Duke-UNC functional social supportquestionnaire), children and current partner.Virological non-suppression was defined as a single VL>50 copies/mL (latestat the time of the questionnaire), using clinic-recorded VL.Self-reported ART non-adherence was assessed by questionnaire and definedas missing ART for ≥2 consecutive days in the previous 3 months.

We used modified Poisson regression to calculate prevalence ratios (PR) ofassociations of virological non-suppression with each socio-economic factorin turn, adjusted for (i) demographics (gender/sexual orientation, ethnicity,age) only and (ii) demographics and non-adherence.

Among people with HIV on ART in the UK, although overall prevalence of viralsuppression was high, virological non-suppression was more common amongthose with poorer socio-economic status. Our results suggest that much ofthe associations found were mediated through difficulties in taking ART,although other factors may also play a role.Emphasis should be placed on supporting adherence of people in thesehigher risk groups. We need a better understanding of the drivers of lowadherence in these groups to inform effective support strategies.

Background

Methods

Results

Figure 2: Association of socio-economic factors with VL>50 c/mL

Acknowledgments:All ASTRA study participantsASTRA clinic teamsRoyal Free Hospital: Alison Rodger; Margaret Johnson; Jeff McDonnell; Adebiyi AderonkeMortimer Market Centre: Richard Gilson; Simon Edwards; Lewis Haddow; Simon Gilson; Christina Broussard; RobertPralat; Sonali WayalBrighton and Sussex University Hospital: Martin Fisher; Nicky Perry; Alex Pollard; Serge Fedele; Louise Kerr; Lisa Heald;Wendy Hadley; Kerry Hobbs; Julia Williams; Elaney Youssef; Celia Richardson; Sean GrothNorth Manchester General Hospital: Ed Wilkins; Yvonne Clowes; Jennifer Cullie; Cynthia Murphy; Christina Martin;Valerie George; Andrew ThompsonHomerton University Hospital: Jane Anderson; Sifiso Mguni; Damilola Awosika; Rosalind ScourseEast Sussex Sexual Health Clinic: Kazeem Aderogba; Caron Osborne; Sue Cross; Jacqueline Whinney; Martin JonesNewham University Hospital: Rebecca O’Connell; Cheryl TawanaWhipps Cross University Hospital: Monica Lascar; Zandile Maseko; Gemma Townsend; Vera Theodore; Jas SagooASTRA study team: Fiona Lampe; Alison Rodger; Andrew Speakman; Andrew Phillips; Marina DaskalopoulouASTRA advisory group: Lorraine Sherr; Simon Collins; Jonathan Elford ; Alec Miners; Anne Johnson; Graham Hart; Anna-Maria Geretti; Bill BurmanCAPRA grant Advisory Board: Nick Partridge; Kay Orton; Anthony Nardone; Ann Sullivan

Figure 1. Percentage with VL>50 c/mL according to demographic and socio-economic factors (N=2445 on ART for >6 months)

Socio-economic factors are shown in Table 1. 10% (234/2445) participants hadVL>50 copies/ml (of these, 35% had VL>500, 30% had VL>1000 and 15% hadVL>10,000). 18% reported ART non-adherence in the past 3 months.Non-MSM men, non-white individuals and those aged <30 years were morelikely to have VL>50 copies/mL (Figure 1).Those in financial hardship, non-employed, non-homeowners, not universityeducated, those with non-fluent reading ability and those with children weresignificantly more likely to have VL>50 copies/mL (Figure 1).After adjustment for gender/sexual orientation, ethnicity and age, factorssignificantly associated with VL>50 copies/mL were: financial hardship, non-employment, non-homeownership, non-university education (Figure 2).Effects were much attenuated after additionally adjusting for ART non-adherence (Figure 2, 2nd column), although the associations of financial hardshipnon-employment, non-homeownership and non-university education withVL>50 copies/mL remained statistically significant.Results were similar after further adjustment for time on ART, AIDS at ARTinitiation or time since diagnosis.

Conclusions

N=2445 on ART for >6 months; N included in models ranges from 2353 to 2445, due to missing values*Each socio-economic factor is considered in a separate model; #aPR= adjusted Prevalence ratioAdjusted for demographic factors (gender/sexual orientation, ethnicity, age)Adjusted for demographic factors and non-adherence (missing ART for ≥2 consecutive days in the previous 3 months)

The ASTRA study presents independent research commissioned by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research funding scheme(RP-PG-0608-10142). The views expressed in this presentation are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Table 1. Socio-economic characteristics of participants (N=2445 on ART for >6 months)

Factor N %Gender/ sexualorientation

MSM 1678 69Non-MSM Men 285 12Women 482 20

Ethnicity White 1673 68Black 561 23Other/ missing 211 9

Age <30 years 72 330-49 years 1533 65≥50 years 754 32

Financial hardship(money for basicneeds?)

Always 1050 44Mostly 626 26Sometimes 423 18No 298 12

Employment Employed 1316 54Unemployed 435 18Sick/ disabled 348 14Retired 164 7Other/ missing 182 7

Housing Homeowner 861 35Renting from council 773 32Renting privately 528 22

Factor N %Housing (continued) Temporary /homeless 61 3

Staying with family 165 7Other/ missing 57 2

Education(highest level)

University degree orhigher

986 40

Secondary school 1024 44None 263 11Other/ missing 172 7

Time in UK Born in UK 1337 57> 5 years 917 39≤5 years 99 4

Reading ability Born in UK 1337 57Fluent 841 36Not fluent 185 8

Supportive network Most 1409 58Medium 752 31Least 253 10

Children Yes 685 28No 1742 72

Partner Yes 1385 57No 1041 43

* Chi-squared test~ Test for trend

References: 1) Robbins et al. JAIDS (1999). 2007;44(1):30. 2) Howard et al. AIDS. 2002;16(16):2175-82. 3) Speakman et al. PLoS One.2013;8(10):e77230

BACKGROUND We studied factors associated with the continuum of HIV-care in Belgium. METHODS Data of the national registration of new HIV diagnosis and of the national cohort of HIV-infected patients in care were combined to obtain estimates of and factors related with proportions of HIV-infected patients in each step of the continuum of care from diagnosis to suppressed viral load (VL). Associated factors were identified by multivariate logistic regression. Factors associated with ignorance of HIV seropositivity were analysed among patients co-infected with HIV & STI in the Belgian STI sentinel surveillance network. RESULTS

CONCLUSION Nationalities and risk groups (MSM/IDU) are the main factors associated with ignorance of HIV seropositivity, entry and retention in care, but once the HIV-patient retained in care, few effect of these factors on the proportions on ART and with suppressed VL are observed. The association of prenatal HIV diagnosis and proportions on ART and with suppressed VL could be biased by transitory CD4 disturbances during pregnancy and ART discontinuation after pregnancy. The higher probabilities of older patients to be on ART and have suppressed VL once retained in care could be influenced by factors not explored here like comorbidities, adherence or duration on ART.

Factors related to the continuum of care of HIV-infected patients in Belgium D. Van Beckhoven, P. Lacor, M. Moutschen, D. Piérard, A. Sasse, D. Vaira, S. Van den Wijngaert, B. Vandercam, M. Van Ranst, E. Van Wijngaerden, L. Vandekerckhove, C. Verhofstede, R. Verbrugge, M.-L. Delforge, R. Demeester, S. De Wit, E. Florence, K. Fransen, J.-C. Goffard, P. Goubau, Belgian Research on AIDS & HIV Consortium (BREACH)

ISP-WIV | rue Juliette Wytsmanstraat14 | 1050 Brussels | Belgium T +32 2 642 57 09 | dvanbeckhoven@wiv-isp.be | www.wiv-isp.be

Poster nr: P002

Among 4038 individuals diagnosed with HIV between 2007 and 2010, 90.3% were linked to care. Of 11684 patients in care in 2010, 90.8% were retained in care up to the following year, 88.3% of those were on ART, of whom 95.3% had suppressed viral load (<500 copies/ml).

In multivariate analyses, factors associated with ignoring HIV+ status were being younger (p<0.001), being heterosexual compared to MSM, and of a region of origin other than Belgium, Sub-Saharan Africa and Europe. Non Belgian regions of origin were associated with lower entry and retention in care (p<0.001 for both). Preoperative HIV testing was associated with lower entry in care (p=0.003). MSM had a higher retention in care (p<0.001), whilst IDU had lower retention (p=0.004). Low CD4 at first clinical contact and clinical reasons for HIV testing were independently associated with being on ART (p<0.001 for both); whilst prenatal HIV diagnosis was associated with lower proportion on ART (p=0.005) and lower proportion with suppressed VL among those on ART (p=0.005). Older age was associated with both being on ART and having suppressed VL among those on ART (p=0.002 & p<0.001 respectively), independently of duration since diagnosis of HIV infection. VL suppression was lower among patients on ART from Sub-Saharan Africa than among Belgians (p=0.04). Longer duration since HIV diagnosis was associated with higher retention in care, higher proportion on ART and more VL suppression (p<0.001, p<0.001, p=0.02 respectively).

Factors found to be associated with ignoring HIV status were studied in a subpopulation co-infected with HIV and STI and could therefore not be necessarily fully representative of all HIV-infected patients in Belgium.

Figure 1: Continuum of HIV care in Belgium

Figure 2: Adjusted OR for factors associated with each step of the continuum of HIV care

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Loss to follow-up of HIV-infected women after delivery: The Swiss HIV Cohort Study and the Swiss Mother and

Child HIV Cohort Study Karoline Aebi-Popp1, Roger Kouyos2, Barbara Bertisch3, Cornelia Stähelin1, Christoph Rudin4, Irene Hoesli5, Begoña Martinez de

Tejada6, Marcel Stoeckle5, Enos Bernasconi7, Matthias Cavassini8, Claudia Grawe2, Thanh Doco Lecompte4, Martin Rickenbach9, Claire

Thorne10, Jan Fehr2 and the Swiss Mother and Child HIV Cohort Study and the Swiss HIV Cohort Study

1University Hospital Bern, Switzerland, 2University Hospital Zuerich, Switzerland, 3Cantonal Hospital St.Gallen, Switzerland, 4University Children`s Hospital Basel, Switzerland, 5University Hospital Basel, Switzerland, 6University Hospital Geneva, Switzerland, 7Regional Hospital Lugano, Switzerland,

8University Hospital Lausanne, Switzerland, 9SHCS Data Center Lausanne, Switzerland, 10UCL Institute of Child Health, London, UK

Corresponding addresses: karoline.aebi-popp@insel.ch jan.fehr@usz.ch

BACKGROUND & AIMS

•  We used data on 719 pregnancies within the Swiss HIV Cohort Study from 1996 - 2012 and with information on follow-up visits available.

•  Definitions: delayed return to HIV care: >180 days after delivery; LTFU: no follow up > 365 days following delivery.

•  Logistic regression analysis was used to identify risk factors for a LTFU event after delivery.

RESULTS

Pregnant women with HIV require effective care intervention for PMTCT during pregnancy but are prone to disengage from sustainable HIV care beyond the point of giving birth. Ethnicity or migrant status did not influence retention in care in our setting. Failure to suppress HIV VL until delivery as well as history of IDU were associated with postnatal disengagement from care. The continuation of ART after pregnancy could have benefits in terms of maternal health and future pregnancy outcomes. There is an urgent need to improve interventions to counsel women regarding the importance of postnatal retention in HIV care.

CONCLUSION

MATERIALS AND METHODS

POSTER 003

Table 1: Baseline Characteristics

Figure 1:

Table 2: Factors associated with postnatal LTFU

•  HIV infected pregnant women are very likely to engage in HIV medical care to prevent transmission of HIV to their newborn.

•  After delivery however, childcare and competing commitments might lead to disengagement from HIV care.

•  The aim of this study was to quantify loss to follow up (LTFU) from HIV care after delivery and to identify risk factors for LTFU.

•  Median maternal age at delivery: 32 years (IQR 28-36) •  Ethnicity: 357 (50%) women black, 280 (39%) white, 56 (8%) Asian and 3%

other •  107(15%) women with history of IDU •  524 (73%) HIV diagnosis before pregnancy, of those 79% diagnosed with HIV >3

years and 65% already on ART at time of conception. •  181 diagnosed during pregnancy: 80 (44%) 1st trimester, 67 (37%) 2nd trimester

and 34 (19%) 3rd trimester •  Among women with HIV diagnosis before pregnancy and with last HIV clinical

visit < 3 months before conception 93% achieved an undetectable HIV-VL at delivery versus 72% with last HIV clinical visit > 6 months before conception (p=0.001)

•  In the year after delivery 247 (34%) women were delayed in returning to their HIV clinic (>180 days) and 86 (12%) were LTFU> 365 days with 43 (50%) of those not returning at all.

•  Being LTFU for 365 days was significantly associated with history of IDU (aOR 2.67, 95%-CI 1.24-5.72, p=0.012) and not achieving an undetectable VL at delivery (aOR 2.47, 95%-CI 1.19-5.101, p=0.015) after adjusting for maternal age, ethnicity, time of HIV diagnosis and being on ART at conception.

•  After LTFU of > 356 days, 19/38 (50%) of women returned with a CD4 count below 350 cells/ µl

We are grateful to our patients for their commitment and participation in the Swiss HIV Cohort Study (SHCS) and the Swiss Mother and Child HIV Cohort Study (MoCHiV) This study has been financed within the frameork of the wSwiss HIV Cohort Study, supported by the Swiss National Science Foundation .The data are gathered by the Five Swiss University Hospitals, two Cantonal Hospitals, 15 affiliated hospitals and 36 private physicians (listed in http://www.shcs.ch/31-health-care-providers). The members of SHCS and MoCHiV: Aubert V, Battegay M, Bernasconi E, Böni J, Brazzola P, Bucher HC, Burton-Jeangros C, Calmy A, Cavassini M, Cheseaux JJ, Drack G, Duppenthaler A, Egger M, Elzi L, Fehr J, Fellay J, Francini K, Furrer H, Fux CA, Gorgievski M, Grawe C, Günthard H (President of the SHCS), Haerry D (deputy of "Positive Council"), Hasse B, Hirsch HH, Hösli I, Kahlert C, Kaiser L, Keiser O, Klimkait T, Kovari H, Kouyos R, Ledergerber B, Martinetti G, Martinez de Tejada B, Metzner K, Müller N, Nadal D, Pantaleo G, Polli Ch, Posfay-Barbe K, Rauch A, Regenass S, Rickenbach M, Rudin C (Chairman of the Mother & Child Substudy), Schmid P, Schultze D, Schöni-Affolter F, Schüpbach J, Speck R, Staehelin C, Tarr P, Telenti A, Trkola A, Vernazza P, Weber R, Wyler CA, Yerly S

Figure 2: CD4 categories at return after LTFU >365 days (n=38)

Figure 3: Difference of CD4 count between last measurement at delivery and at return after LTFU> 365 days (n=24)

010

2030

40Pe

rcen

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<200 200-349 350-499 >500CD4 category (cells/ µl )

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† adjusted for time of delivery (calendar year), maternal age, ethnicity, history of IDU, Vl at delivery, ART at conception Abbreviations: LTFU= lost to follow up> 365 days , IDU= intravenous drug use, VL= viral load

!

No LTFU after delivery n= 633 (n,%)

LTFU after delivery

n=84 (n,%)

p-value

Maternal age (n=719) 0.078 17-24 68 (11) 5 (6) 25-32 270 (43) 47 (55) >33 295 (47) 34 (40) Ethnicity (n=718) 0.023 white 236 (37) 44 (52) black 322 (51) 35 (41) asian 54 (9) 2 (2) other 21 (3 ) 4 (5) Education (n=689) 0.70 no school 101 (17) 15 (19) mandatory school 193 (32) 21 (27) finished apprenticeship 201 (33) 25 (32) higher education 115 (19) 18 (23) History of intravenous drug use (IDU) (n=719) 80 (13) 27 (31) <0.001 Time since HIV diagnosis (n=719) 0.105 0-3 months 27 (4) 7 (8) 4-6 months 63 (10) 4 (5) 6-9 months 73 (12) 7 (8) 9 months- 2 years 33 (5) 8 (9) >2 years 424 (67) 59 (69) Last visit before pregnancy (n=719) <0.001 0-90 days 325 (72) 32 (50) 91-180days 86 (19) 13 (20) 181-270days 11 (2) 4 (6) >270days 32 (7) 15 (23) ART at time of conception (n=719) 0.023 no 322 (51) 55 (64) yes 311 (49) 31 (36) VL at delivery (n=649) 0.003 <400 528 (90) 51 (78) >=400 56 (10) 14 (22) CD4 at delivery (n=633) 0.521 <200 38 (7) 4 (6) 200-349 121 (21) 12 (19) 350-499 146 (26) 22 (34) >500 264 (46) 26 (41) Year of delivery <0.001 1996-1999 89 (14) 26 (30) 2000-2003 126 (20) 22 (26) 2004-2007 201 (32) 22 (26) 2008-2012 217 (34) 16 (19)

Odds ratio (95% CI) Adjusted odds ratio (95% CI)†

Maternal age 17-24 1 1 25-32 2.69 (0.799-9.08) p=0.110 2.98 (0.86-10.32) p=0.085 >32 2.09 (0.61-7.13) p=0.237 2.12 (0.58-7.71) p=0.255 Ethnicity white 1 1 black 0.62(0.36-1.06) p=0.079 0.98 (0.49-1.95) p=0.543 asian 0.28(0.06-1.19) p=0.085 0.38 (0.08-1.75) p=0.210 other 0.65 (0.14-2.91) p=0.572 0.77(0.16-3.81) p=0.157 History of IDU no 1 1 yes 2.77 (1.52-5.05) p=0.001 2.67 (1.24-5.72) p=0.012 VL at delivery undetectable 1 1 detectable 2.40 (1.23-4.695) p=0.010 2.47 (1.19-5.10) p=0.015 ART at conception no 1 1 yes 0.73(0.43-1.23) p=0.234 0.78 (0.43- 1.41) p=0.403

Patients’ willingness to take separate component antiretroviral therapy regimens for HIV in the NetherlandsEAN Engelhard1,2, C Smit2, SCJM Vervoort3, F Kroon4, K Brinkman5, PT Nieuwkerk6, P Reiss1,2,7, SE Geerlings1, on behalf of the ATHENA observational cohort and the Q-HIV study group.

1Academic Medical Center of the University of Amsterdam, Division of Infectious Diseases, Amsterdam, Netherlands; 2Stichting HIV Monitoring, Amsterdam, Netherlands; 3University Medical Center, Department of Internal Medicine and Infectious Diseases, Utrecht, Netherlands; 4Leiden University Medical Center, Department of Infectious Diseases, Leiden, Netherlands; 5Onze Lieve Vrouwe Gasthuis, Department of Internal Medicine, Amsterdam, Netherlands; 6Academic Medical Center of the University of Amsterdam, Department of Medical Psychology, Amsterdam, Netherlands; 7Academic Medical Center of the University of Amsterdam, AIGHD, Amsterdam, Netherlands

Methods• Stichting HIV Monitoring (SHM) collects data from all HIV

infected patients in care in the designated HIV treatment centres in The Netherlands.

• A total sample of 1000 adult patients on cART for ≥ 6 months was selected, whilst taking the treatment centresize into account and ensuring a minimum of 20 patients from each of the 26 HIV treatment centers.

• Using a standardized questionnaire, patients were asked whether they were willing to take separate pills (simultaneously) instead of a STR.

• Predictors for answering “yes” versus “maybe” or “no” were assessed with logistic regression.

• Variables with p <0.1 in the univariate analysis were entered in a multivariate model.

• Examined factors were age, gender, region of origin, mode of HIV transmission, socioeconomic status, duration of cART.

BackgroundThe costs of separate component regimens are generally much lower than of a single tablet regimen (STR) including the same active ingredients. The availability of generic versions of components of heavily prescribed regimens has raised the issue of shifting prescriptions from STRs to separate component regimens.

Aim• To evaluate whether patients in care in the Netherlands

would be willing to take separate component regimens instead of a STR.

• To examine whether willingness was associated with particular patient characteristics.

Conclusion• The likelihood of accepting to switch to a separate component regimen

seems less for patients of non-Dutch origin and for those who have commenced treatment more recently.

• Duration of cART use and region of origin may be factors to take into account when considering to prescribe a separate component regimen.

• Future studies should investigate whether an expressed willingness to switch will translate into maintained high levels of adherence and viral suppression.

Results• 300 patients completed the questionnaire.

• 49% answered “yes”, 24% “maybe” and 27% “no” to the question whether they were willing to switch to a separate component regimen.

• Reasons for answering “no”: difficulties swallowing pills, convenience of STR (travelling/ at work), and concerns about side-effects.

• Respondents who answered “maybe”: often indicated that they preferred STR's, emphasized the importance of taking the pills once-daily, and pointed out that efficacy/safety of a separate component regimen should not be less.

• Having to pay for medication was reported as a reason to consider switching to a separate component regimen.

• In the multivariate analysis, respondents who were born outside the Netherlands were less likely; and those with cART use ≥15 years were more likely to answer “yes” (table 1).

Figure 1. Willingness of respondents to use a separate component regimen

Willingness respondents

YESMAYBENO

CharacteristicsOdds of respondents answering: “yes” vs.

“maybe or no”

OR (95% CI) p - value

Region of origin

Netherlands ref

Other 0 .30 (0 .15 - 0.61) 0.001

Duration of cART use (yrs)

< 5 ref

5 -10 1.76 (0.89 - 3.44) 0.102

10 - 15 0.72 (0.34 - 1.52) 0.386

≥15 3.37 (1.56 - 7.28) 0.002

Table 1: Adjusted odds ratios (OR), 95% confidence intervals (95% CI) and p-values for respondents reporting to be willing to use a separate component regimen.

CLINICAL CENTRES: * denotes site coordinating physician. Academic Medical Centre of the University of Amsterdam: HIV treating physicians: J.M. Prins*, T.W. Kuijpers, H.J. Scherpbier, J.T.M. van der Meer, F.W.M.N. Wit, M.H. Godfried, P. Reiss, T. van der Poll, F.J.B. Nellen, J.M.A. Lange†, S.E. Geerlings, M. van Vugt, D. Pajkrt, J.C. Bos, W.J. Wiersinga, M. van der Valk, A. Goorhuis, J.W. Hovius. HIV nurse consultants: J. van Eden, A. Henderiks, A.M.H. van Hes, M. Mutschelknauss, H.E. Nobel, F.J.J. Pijnappel, A.M. Westerman. HIV clinical virologists/chemists: S. Jurriaans, N.K.T. Back, H.L. Zaaijer, B. Berkhout, M.T.E. Cornelissen, C.J. Schinkel, X.V. Thomas. Admiraal De Ruyter Ziekenhuis, Vlissingen: HIV treating physicians: M. van den Berge, A. Stegeman. HIV nurse consultants: S. Baas. HIV clinical virologists/chemists: D. Versteeg. Catharina Ziekenhuis, Eindhoven: HIV treating physicians: M.J.H. Pronk*, H.S.M. Ammerlaan. HIV nurse consultants: E.M.H.M. Korsten-Vorstermans, E.S. de Munnik. HIV clinical virologists/chemists: A.R. Jansz, J. Tjhie. Emma Kinderziekenhuis: HIV nurse consultants: A. van der Plas, A.M. Weijsenfeld. Erasmus Medisch Centrum, Rotterdam: HIV treating physicians: M.E. van der Ende*, T.E.M.S. de Vries-Sluijs, E.C.M. van Gorp, C.A.M. Schurink, J.L. Nouwen, A. Verbon, B.J.A. Rijnders, H.I. Bax, R.J. Hassing, M. van der Feltz. HIV nurse consultants: N. Bassant, J.E.A. van Beek, M. Vriesde, L.M. van Zonneveld. Data collection: A. de Oude-Lubbers, H.J. van den Berg-Cameron, F.B. Bruinsma-Broekman, J. de Groot, M. de Zeeuw- de Man, M.J. Broekhoven-Kruijne. HIV clinical virologists/chemists: M. Schutten, A.D.M.E. Osterhaus, C.A.B. Boucher. Erasmus Medisch Centrum–Sophia, Rotterdam: HIV treating physicians: G.J.A. Driessen, A.M.C. van Rossum. HIV nurse consultants: L.C. van der Knaap, E. Visser. Flevoziekenhuis, Almere: HIV treating physicians: J. Branger*. HIV nurse consultant and data collection: C.J.H.M. Duijf-van de Ven. HagaZiekenhuis, Den Haag: HIV treating physicians: E.F. Schippers*, C. van Nieuwkoop, R.W. Brimicombe. HIV nurse consultants: J.M. van IJperen. Data collection: G. van der Hut. HIV clinical virologist/chemist: P.F.H. Franck. HIV Focus Centrum (DC Klinieken): HIV treating physicians: A. van Eeden*. HIV nurse consultants: W. Brokking, M. Groot. HIV clinical virologists/chemists: M. Damen, I.S. Kwa. Isala Klinieken, Zwolle: HIV treating physicians: P.H.P. Groeneveld*, J.W. Bouwhuis. HIV nurse consultants: J.F. van den Berg, A.G.W. van Hulzen. Data collection: G.L. van der Bliek, P.C.J. Bor. HIV clinical virologists/chemists: P. Bloembergen, M.J.H.M. Wolfhagen, G.J.H.M. Ruijs. Kennemer Gasthuis, Haarlem: HIV treating physicians: S.F.L. van Lelyveld*, R. Soetekouw. HIV nurse consultants: N. Hulshoff, L.M.M. van der Prijt, M. Schoemaker. Data collection: N. Bermon. HIV clinical virologists/chemists: W.A. van der Reijden, R. Jansen. Leids Universitair Medisch Centrum, Leiden: HIV treating physicians: F.P. Kroon*, S.M. Arend, M.G.J. de Boer, M.P. Bauer, H. Jolink, A.M. Vollaard. HIV nurse consultants: W. Dorama, C. Moons. HIV clinical virologists/chemists: E.C.J. Claas, A.C.M. Kroes. Maasstad Ziekenhuis, Rotterdam: HIV treating physicians: J.G. den Hollander*, K. Pogany. HIV nurse consultants: M. Kastelijns, J.V. Smit, E. Smit. Data collection: M. Bezemer, T. van Niekerk. HIV clinical virologists/chemists: O. Pontesilli. Maastricht UMC+, Maastricht: HIV treating physicians: S.H. Lowe*, A. Oude Lashof, D. Posthouwer. HIV nurse consultants: R.P. Ackens, J. Schippers, R. Vergoossen. Data collection: B. Weijenberg Maes. HIV clinical virologists/chemists: P.H.M. Savelkoul, I.H. Loo. MC Zuiderzee, Lelystad: HIV treating physicians: S. Weijer*, R. El Moussaoui. HIV Nurse Consultant: M. Heitmuller. Data collection: M. Heitmuller. Medisch Centrum Alkmaar: HIV treating physicians: W. Kortmann*, G. van Twillert*, J.W.T. Cohen Stuart, B.M.W. Diederen. HIV nurse consultant and data collection: D. Pronk, F.A. van Truijen-Oud. HIV clinical virologists/chemists: W. A. van der Reijden, R. Jansen. Medisch Centrum Haaglanden, Den Haag: HIV treating physicians: E.M.S. Leyten*, L.B.S. Gelinck. HIV nurse consultants: A. van Hartingsveld, C. Meerkerk, G.S. Wildenbeest. HIV clinical virologists/chemists: J.A.E.M. Mutsaers, C.L. Jansen. Medisch Centrum Leeuwarden, Leeuwarden: HIV treating physicians: M.G.A.van Vonderen*, D.P.F. van Houte. HIV nurse consultants: K. Dijkstra, S. Faber. HIV clinical virologists/chemists: J Weel. Medisch Spectrum Twente, Enschede: HIV treating physicians: G.J. Kootstra*, C.E. Delsing. HIV nurse consultants: M. van der Burg-van de Plas, H. Heins. Data collection: E. Lucas. Onze LieveVrouwe Gasthuis, Amsterdam: HIV treating physicians: K. Brinkman*, P.H.J. Frissen, W.L. Blok, W.E.M. Schouten. HIV nurse consultants: A.S. Bosma, C.J. Brouwer, G.F. Geerders, K. Hoeksema, M.J. Kleene, I.B. van der Meché, A.J.M. Toonen, S. Wijnands. HIV clinical virologists/chemists: M.L. van Ogtrop. Radboud UMC, Nijmegen: HIV treating physicians: P.P. Koopmans, M. Keuter, A.J.A.M. van der Ven, H.J.M. ter Hofstede, A.S.M. Dofferhoff, R. van Crevel. HIV nurse consultants: M. Albers, M.E.W. Bosch, K.J.T. Grintjes-Huisman, B.J. Zomer. HIV clinical virologists/chemists: F.F. Stelma. HIV clinical pharmacology consultant: D. Burger. Rijnstate, Arnhem: HIV treating physicians: C. Richter*, J.P. van der Berg, E.H. Gisolf. HIV nurse consultants: G. ter Beest, P.H.M. van Bentum, N. Langebeek. HIV clinical virologists/chemists: R. Tiemessen, C.M.A. Swanink. Sint Elisabeth Hospitaal, Willemstad, Curaçao: HIV treating physicians: C. Winkel,A. Durand, F. Muskiet, R. Voigt. HIV nurse consultants: I. van der Meer. Sint Lucas Andreas Ziekenhuis, Amsterdam: HIV treating physicians: J. Veenstra*, K.D. Lettinga. HIV nurse consultants: M. Spelbrink, H. Sulman. Data collection: M. Spelbrink, E. Witte. HIV clinical virologists/chemists: M. Damen, P.G.H. Peerbooms. Slotervaartziekenhuis, Amsterdam: HIV treating physicians: J.W. Mulder, S.M.E. Vrouenraets, F.N. Lauw. HIV nurse consultants: M.C. van Broekhuizen, H. Paap, D.J. Vlasblom. Data collection: E. Oudmaijer Sanders. HIV clinical virologists/chemists: P.H.M. Smits, A.W. Rosingh. Stichting Medisch Centrum Jan van Goyen, Amsterdam: HIV treating physicians: D.W.M. Verhagen. HIV nurse consultants: J. Geilings. St Elisabeth Ziekenhuis, Tilburg: HIV treating physicians: M.E.E. van Kasteren*, A.E. Brouwer. HIV nurse consultants and data collection: B.A.F.M. de Kruijf-van de Wiel, M. Kuipers, R.M.W.J. Santegoets, B. van der Ven. HIV clinical virologists/chemists: J.H. Marcelis, A.G.M. Buiting, P.J. Kabel. Universitair Medisch Centrum Groningen, Groningen: HIV treating physicians: W.F.W. Bierman*, H.G. Sprenger, E.H. Scholvinck, S. van Assen, K.R. Wilting, Y. Stienstra. HIV nurse consultants: H. de Groot-de Jonge, P.A. van der Meulen, D.A. de Weerd. HIV clinical virologists/chemists: H.G.M. Niesters, A. Riezebos-Brilman. Universitair Medisch Centrum Utrecht, Utrecht: HIV treating physicians: A.I.M. Hoepelman*, M.M.E. Schneider, T. Mudrikova, P.M. Ellerbroek, J.J. Oosterheert, J.E. Arends, R.E. Barth, M.W.M. Wassenberg. HIV nurse consultants: D.H.M. van Elst-Laurijssen, L.M. Laan, E.E.B. van Oers-Hazelzet, J. Patist, S. Vervoort, Data collection: H.E. Nieuwenhuis, R. Frauenfelder. HIV clinical virologists/chemists: R. Schuurman, F. Verduyn-Lunel, A.M.J. Wensing. VU Medisch Centrum, Amsterdam: HIV treating physicians: E.J.G. Peters*, M.A. van Agtmael, R.M. Perenboom, M. Bomers, J. de Vocht. HIV nurse consultants: L.J.M. Elsenburg. HIV clinical virologists/chemists: A.M. Pettersson, C.M.J.E. Vandenbroucke-Grauls, C.W. Ang. Wilhelmina Kinderziekenhuis, UMCU, Utrecht: HIV treating physicians: S.P.M. Geelen, T.F.W. Wolfs, L.J. Bont. HIV nurse consultants: N. Nauta.

This study was financially supported by Aids Fonds (grant number: 2011015) E-mail: e.a.engelhard@amc.uva.nl

The ATHENA database is supported by a grant from the Dutch Ministry of Health, Welfare and Sport and was set up and is maintained by Stichting HIV Monitoring.

Real-World Persistence with Single versus Multiple Tablet Regimens for HIV-1 Treatment

D.E. Sweet1

1 The University of Kansas School of Medicine – Wichita, Wichita, KS, United States.

P005

Presented at HIV Glasgow, 2–6 November 2014, Glasgow, UK

This study was supported by Gilead Sciences, Inc.

MeTHodS

BackgRound Adherence to, and persistence on, recommended antiretroviral therapy (ART) for HIV-1 is crucial to achieving optimal clinical outcomes1,2

Greater adherence and persistence to recommended ART is associated with better virologic response, improved CD4 cell count, lower risk of progression to clinical AIDS or death, reduced risk of hospitalization and reduced risk of transmission3–6

Typical ART consists of a backbone of two nucleoside reverse transcriptase inhibitors (NRTIs) plus one drug from another class to maximally suppress HIV2

Simplification of ART with once-daily single-tablet regimens (STRs) can improve adherence and persistence compared with multi-tablet regimens (MTRs)6–9

Until recently, three STRs were available, all with the same tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) backbone; the co-formulated third drugs are efavirenz (EFV), rilpivirine (RPV) and elvitegravir/cobicistat (EVG/COBI)*

*EFV/TDF/FTC, RPV/TDF/FTC, and EVG/COBI/TDF/FTC were approved by the FDA in 07/2006, 08/2011, and 08/2012, respectively; data on the real world performance of dolutegravir/ABC/3TC were not available at the time of this study.

The majority of patients receiving MTRs combine a third drug with either a TDF/FTC backbone or with an ABC/3TC backbone, both of which are available as fixed-dose combinations

The extent to which persistence may vary among different TDF/FTC-based STRs has not been fully described

The extent to which backbone effects may impact real-world persistence is not well understood

The influence of persistence on ART on medical costs has not been fully investigated

Understanding of backbone effects on real-world persistence could shed light on the potential real-world persistence of a recently approved ABC/3TC-based STR compared to the TDF/FTC-based STRs

Understanding the effects of persistence on medical costs could further provide evidence that help elucidate the importance of drug persistence in selecting different ART therapies in real-world clinical practice

To evaluate real-world persistence with initial ART among HIV-1 infected patients, with comparison of

STRs vs. MTRs

Differences among TDF/FTC-based STRs: EVG/COBI/TDF/FTC vs. RPV/TDF/FTC vs. EVF/TDF/FTC vs. MTR

Backbone effects: use of a TDF/FTC vs. ABC/3TC backbone

To assess the association between persistence on ART and all-cause and HIV-related medical service costs

oBjecTIVe

ReSuLTS

RefeRenceS1. US Department of Health and Human Services. Panel on antiretroviral guidelines for adults and adolescents: Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. 2013.

2. World Health Organization. Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection: Recommendations for a public health approach. Geneva: World Health Organization. 2013.

3. Ing EC, Bae JW, Maru DS, Altice FL. Medication persistence of HIV-infected drug users on directly administered antiretroviral therapy. AIDS and behavior. Jan 2013; 17(1): 113–121.

4. Strategies for Management of Antiretroviral Therapy Study G, El-Sadr WM, Lundgren J, et al. CD4+ count-guided interruption of antiretroviral treatment. The New England journal of medicine. Nov 30 2006; 355(22): 2283–2296.

5. Kitahata MM, Reed SD, Dillingham PW, et al. Pharmacy-based assessment of adherence to HAART predicts virologic and immunologic treatment response and clinical progression to AIDS and death. International journal of STD & AIDS. Dec 2004; 15(12): 803–810.

6. Sax PE, Meyers JL, Mugavero M, Davis KL. Adherence to antiretroviral treatment and correlation with risk of hospitalization among commercially insured HIV patients in the United States. PloS one. 2012; 7(2): e31591.

7. Gardner EM, Burman WJ, Maravi ME, Davidson AJ. Selective drug taking during combination antiretroviral therapy in an unselected clinic population. Journal of acquired immune deficiency syndromes (1999). Nov 1 2005; 40(3): 294–300.

8. Portsmouth SD, Osorio J, McCormick K, Gazzard BG, Moyle GJ. Better maintained adherence on switching from twice-daily to once-daily therapy for HIV: a 24-week randomized trial of treatment simplification using stavudine prolonged-release capsules. HIV medicine. May 2005; 6(3): 185–190.

9. Sax PE, Tierney C, Collier AC, et al. Abacavir/lamivudine versus tenofovir DF/emtricitabine as part of combination regimens for initial treatment of HIV: final results. The Journal of infectious diseases. Oct 15 2011; 204(8): 1191–1201.

10. Holland PW, Welsch RE. Robust regression using iteratively reweighted least-squares. Communications in Statistics-Theory and Methods 6, no. 9 (1977): 813–827.

11. Huber PJ. Robust regression: asymptotics, conjectures and Monte Carlo. The Annals of Statistics (1973): 799–821.

12. Nachega, J.B., et al., Association of antiretroviral therapy adherence and health care costs. Ann Intern Med. 2010. 152(1): 18–25.

Among the 8,785 patients who initiated antiretroviral prescriptions, 5,483 (62%) patients initiated STRs and 3,302 (38%) initiated MTRs (Figure 2)

Among the 3,302 patients who initiated MTRs, 2,286 received the TDF/FTC backbone and 289 received the ABC/3TC backbone along with a recommended third drugs (Table 2)

Boosted PIs were the most commonly used third drugs among patients who initiated MTRs (Table 2)

Figure 2. Sample selection

TDF/FTCs

N=2,286

ABC/3TC

N=289

Adult patients with at least one medical diagnosis for HIV (ICD-9 CM 042.xx) betweenOctober 1, 2008 and March 31, 2014

N=118,089

Patients with at least one prescription fill for an oral antiretroviral HIV drug after diagnosis of HIV-1 infection

N=34,322

Patients continually enrolled in a healthcare plan for ≥180 days with an HIV-1 diagnosis but no prescription fill for oral antiretroviral HIV drug within 180 days prior to the index date

N=9,741

Patients whose regimen consisted of three antiretroviral agents, based on prescriptions filled within seven days after the index date

N=8,785

Patients who received a multiple tablet regimen (MTR) as index regimen

N=3,302

Patients who received a single tablet regimen (STR) as index regimen

N=5,483

Patients who received an MTR with a fixed-dose combination and a recommended third drug for treatment naïve patients

N=2,575EFV/TDF/FTC

N=4,409

RPV/TDF/FTC

N=483

EVG/COBI/TDF/FTC

N=591

Table 1. Baseline characteristics

CharacteristicsSTR

N=5,483MTR

N=3,302P

TDF/FTC N=2,286

ABC/3TC N=289

P

Age (Years), Mean ± SD 39.1 ± 11.3 41.3 ± 10.8 <0.001 * 40.7 ± 10.8 42.9 ± 10.9 0.001 *

Female, n (%) 644 (11.7) 635 (19.2) <0.001 * 376 (16.4) 51 (17.6) 0.606

Charlson Comorbidity Index (CCI)

CCI, Mean ± SD 0.394 ± 0.918 0.563 ± 1.164 <0.001 * 0.486 ± 1.060 0.730 ± 1.398 0.010 *

CCI ≤ 2, n (%) 5,242 (95.6) 3,048 (92.3) <0.001 * 2,144 (93.8) 254 (87.9) <0.001 *

2 < CCI < 5, n (%) 147 (2.7) 152 (4.6) <0.001 * 88 (3.8) 19 (6.6) 0.029 *

CCI ≥ 5, n (%) 50 (0.9) 57 (1.7) 0.001 * 30 (1.3) 10 (3.5) 0.011 *

Other Comorbidity, n (%)

Central nervous system toxicity 1,036 (18.9) 747 (22.6) <0.001 * 547 (23.9) 50 (17.3) 0.012 *

Gastrointestinal disease 1,428 (26.0) 922 (27.9) 0.054 665 (29.1) 69 (23.9) 0.064

Mental disorder 1,197 (21.8) 817 (24.7) 0.002 * 605 (26.5) 57 (19.7) 0.013 *

AIDS-defining condition 628 (11.5) 470 (14.2) <0.001 * 343 (15.0) 25 (8.7) 0.004 *

Substance abuse 456 (8.3) 298 (9.0) 0.251 227 (9.9) 22 (7.6) 0.209

Jaundice 16 (0.3) 10 (0.3) 0.927 8 (0.3) 0 (0.0) 0.609

Dyslipidemia 753 (13.7) 530 (16.1) 0.003 * 344 (15.0) 71 (24.6) <0.001 *

Number of Unique Prescriptions

Number of unique prescriptions, Mean ± SD

4.9 ± 4.9 5.2 ± 5.4 0.177 5.4 ± 5.3 4.9 ± 6.0 0.012 *

Notes: P-values based on Wilcoxon rank sum tests for continuous variables and Chi squared tests for categorical variables. * indicates statistical significance based on P-value < 0.05.

Table 2. Distribution of MTRs with backbones as fixed-dose combinations and recommended third drugs

Backbone

Third Drug ClassTDF/FTC N=2,286

ABC/3TC N=289

Boosted protease inhibitor (PI), n (%) 1,503 (65.7) 149 (51.6)

Integrase inhibitor (INSTI), n (%) 580 (25.4) 53 (18.3)

Non-nucleoside reverse-transcriptase inhibitors (NNRTI), n (%)

203 (8.9) 87 (30.1)

Comparing persistence with STRs vs. MTRs

The median treatment persistence (95% confidence interval [CI]) was 45.0 (41.0 – 48.6) months on STRs and 15.2 (14.0 – 16.9) months on MTRs (difference= 29.8 months; log rank test P < 0.001; Figure 3). The multivariate analysis also showed a lower rate of treatment discontinuation with STRs than MTRs (adjusted HR = 0.54; 95% CI 0.50 to 0.58; Figure 6)

Comparing persistence among STRs

Persistence differed among STRs (Figure 4 and 6). In particular, persistence on EVG/COBI/TDF/FTC and RPV/TDF/FTC was significantly longer than persistence on EVF/TDF/FTC (adjusted HR = 0.45 and 0.47; 95% CI 0.33 to 0.60 and 0.32 to 0.69, respectively; Figure 6)

Comparing backbone effects on persistence

Regimen persistence was significantly longer for patients receiving the TDF/FTC backbone compared with patients receiving the ABC/3TC backbone when both given as a part of MTRs (adjusted HR=0.77, 95% CI 0.64 to 0.93; Figure 5 and 6)

Persistence remained significantly longer for patients receiving the TDF/FTC backbone compared with patients receiving the ABC/3TC backbone in the subgroup of patients receiving a boosted PI as the third drug (the most commonly used third drug for MTRs) (adjusted HR=0.69, 95% CI 0.54 to 0.88; Figure 6)

Figure 3. Persistence with STRs vs. MTRs

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 12 24 36 48 60

Pro

po

rtio

n p

ersi

sten

t o

n t

reat

men

t

Time from initiation (months)

STR

MTR

P < 0.001

Median months on treatment (95% CI)STR (N=5,483) 45.0 (41.0 –48.6)MTR (N=3,302) 15.2 (14.0 –16.9)

Figure 4. Persistence among different STRs and MTRs

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 3 6 9 12 15 18

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ersi

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t o

n t

reat

men

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Time from initiation (months)

EFV/TDF/FTC

MTR

RPV/TDF/FTC

EVG/COBI/TDF/FTC

P-valueLogrank tests

EVF/TDF/FTC vs. MTRs <0.001

<0.001

<0.001

<0.001

<0.001

0.771

RPV/TDF/FTC vs. MTRs

EVG/COBI/TDF/FTC vs. MTRs

RPV/TDF/FTC vs. EVF/TDF/FTC

EVG/COBI/TDF/FTC vs. EVF/TDF/FTC

EVG/COBI/TDF/FTC vs. RPV/TDF/FTC

Figure 5. Difference in persistence with MTRs having TDF/FTC vs. ABC/3TC backbones

8.1%

13.0%

10.6%10.0%

0%

2%

4%

6%

8%

10%

12%

14%

Dif

fere

nce

in p

rop

ort

ion

per

sist

ent

on

tre

atm

ent

(TD

F/FT

C -

AB

C/3

TC)

Time from initiation (months)

Log-rank P-value=0.011

12 24 36 48

Figure 6. Adjusted hazard ratios for treatment discontinuation

Comparing persistence with STRs vs. MTRs

STRs vs. MTRs

STRs vs. NNRTI- or INSTI-based MTRs

EFV/TDF/FTC vs. MTRs

RPV/TDF/FTC vs. MTRs

EVG/COBI/TDF/FTC vs. MTRs

Comparing persistence among STRs

RPV/TDF/FTC vs. EFV/TDF/FTC

EVG/COBI/TDF/FTC vs. EFV/TDF/FTC

EVG/COBI/TDF/FTC vs. RPV/TDF/FTC

Comparing backbone effects on persistence

TDF/FTC vs. ABC/3TC

TDF/FTC + PI/r vs. ABC/3TC + PI/r

0.54 (0.50, 0.58)

0.61 (0.54, 0.68)

0.70 (0.57, 0.86)

0.33 (0.22, 0.48)

0.31 (0.23, 0.42)

0.47 (0.32, 0.69)

0.45 (0.33, 0.60)

0.95 (0.61, 1.48)

0.77 (0.64, 0.93)

0.69 (0.54, 0.88)

0.2 0.4 0.6 0.8 1 1.2 1.4

Adjusted HR (95% CI)

Favors STR Favors MTR

Favors First STR Favors Second STR

Favors TDF/FTC Favors ABC/3TC

Comparing 2-year medical service costs for patients with regimen persistence <2 vs. ≥2 years

Both all-cause medical costs and HIV-related medical service costs for patients with <2 years of persistence were significantly higher compared with those with ≥2 years of persistence (Figure 7)

Figure 7. Adjusted difference in 2-year medical service costs for patients with regimen persistence < 2 vs. ≥ 2 years

0

2000

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ervi

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s

$16,942

$12,799

$6,328

$5,245

IP

IP

IPIP

OP

OP

OP OPED ED ED ED

All-cause HIV-related

32% increaseP<0.001

21% increaseP<0.001

Persistence <2 years

Persistence ≥2 years

IP: Inpatient; OP: Outpatient; ED: Emergency Department

LIMITaTIonS The presence of a claim for a filled prescription does not provide data on the quantity of medication actually consumed or the timing of doses

The multivariable analyses adjusted for observed baseline characteristics, but unobserved confounders may still remain

EVG/COBI/TDF/FTC and RPV/TDF/FTC were not available for the entire study period, therefore the sample sizes of these two groups were smaller and observation periods were shorter than for other regimens

The commercial insurance claims database does not include patients on Medicaid or without insurance

The impact of non-persistence may not be fully captured in the 2-year observation period. There is evidence suggesting that non-persistence with ART is associated with greater increase in health care costs, particularly hospitalization costs, in the long term12

concLuSIonSAmong patients receiving their first ART:

Persistence with STRs was significantly longer than persistence with MTRs

Persistence was significantly longer with the two newer STRs EVG/COBI/TDF/FTC and RPV/TDF/FTC than with EVF/TDF/FTC

The TDF/FTC backbone was associated with significantly longer regimen persistence than the ABC/3TC backbone in patients initiating MTRs

Suboptimal persistence on ART is associated with increased medical service costs

Patients

This study was conducted in the Truven MarketScan® claims database between 10/2008 and 03/2014

The data represent the medical claims of insured employees and their dependents from over 40 national employers, as well as for Medicare-eligible retirees with employer-provided Medicare Supplemental plans in the United States

HIV-1-infected patients (ICD-9 CM 042.xx) initiating antiretroviral prescriptions were identified. Patients were excluded if they had ever been diagnosed with HIV type 2 (ICD-9 CM 079.53)

The index date was defined as the date of a patient’s first oral antiretroviral prescription after diagnosis of HIV-1 infection during the observation period. The index regimen was defined as the antiretroviral regimen prescribed within seven days after the index date

A patient was included in the study if he/she satisfied all of the following:

Age ≥ 18 years as of the index date

Continuous health plan enrollment without antiretroviral prescription fills for 180 days prior to the index date

The index regimen consisted of exactly three antiretroviral agents (not counting the pharmaco-enhancers ritonavir or cobicistat)

Patients were classified as receiving STRs if their index regimens were dosed as one tablet once daily; all other patients were classified as receiving MTRs

Analyses that included separate effects for specific TDF/FTC-based STRs utilized data only after the approval date of EVG/COBI/TDF/FTC (08/2012) to ensure that STRs were compared over parallel time periods of use

Backbone effects were studied only among MTRs to avoid confounding due to inclusion of the TDF/FTC backbone in all STRs available during the study period.

MTRs were further classified according to the use of the TDF/FTC or ABC/3TC fixed-dose combination

To isolate the backbone effect, we further restricted the analysis to patients using recommended classes of third drugs for treatment naïve patients1

In the analysis of persistence and medical service costs, patients who fulfil the inclusion criteria above are further required to have at least two years of continuous eligibility after initiation of first-line ART

Primary outcome: Regimen Persistence

Persistence was measured as the time from starting the index regimen to the end of the first 90-day prescription gap for any drug in the index regimen (to ensure that the patient actually discontinued treatment), or to the date of the first prescription fill for any antiretroviral not in the index regimen (Figure 1)

Patients were censored by loss of eligibility or end of data availability

Figure 1. Definition of time to discontinuation for STRs and MTRs

Discontinuation: end ofthe first 90-day prescriptiongap for the index STR

Discontinuation: the firstprescription fill for an antiretroviral different from the index regimen (including STRs)

End of supply of theindex regimen

180 days continuous eligibility without a prescription for an antiretroviral

End of supply for any antiretroviral in the index regimen

Discontinuation: end of thefirst 90-day prescription gapfor any antiretroviral in the index MTR

Discontinuation: the firstprescription fill for an antiretroviral different from the index regimen

STR

MTR

Secondary outcome: Medical Service costs

Medical service costs included inpatient costs (IP), outpatient costs (OP), and emergency department (ED) costs that were incurred during the two-year continuous eligibility period after the initiation of first-line ART

A medical service cost was considered HIV-related if it was associated with a diagnosis of HIV-1 (ICD-9 code 042.XX)

Prescription costs, including ART costs, were not considered in this analysis

Statistical Methods

Baseline characteristics were compared between patients initiating STRs vs. MTRs and among patients initiating MTRs with different backbones (TDF/FTC vs. ABC/3TC) and recommended third drugs

Persistence was described using Kaplan-Meier curves and compared using logrank tests

Adjusted comparisons of persistence between treatment groups were based on multivariable Cox proportional-hazards models with adjustment for age, gender, insurance type, region, active full time employment status, Charlson Comorbidity Index (CCI), central nervous system toxicity, gastrointestinal disease, mental disorder, any AIDS-defining condition, substance abuse, jaundice, dyslipidemia, number of unique prescriptions, and resource use during the pre-index period

Medical service costs were log-transformed and multivariable robust regression models were used to compare the 2-year all-cause and HIV-related medical service costs between patients with <2 years of persistence and those with ≥2 years of persistence on ART. Covariates included in the multivariable-adjusted model were the same as listed above

acknoWLedgeMenTSThe author is grateful to James Signorovitch, Jinlin Song, and Yichen Zhong for their contributions to the design and analysis of the study.

Compared with DRV/r, patients initiating ATV/r tended to be younger (Commercial = 41.9 years versus42.1; Medicaid = 40.8 years versus 41.6) and a smaller proportion were male (Commercial = 77.0%versus 83.6%; Medicaid = 45.3% versus 51.9%).

Compared to the ATV/r cohorts, the DRV/r cohorts tended to have larger average numbers of uniquediagnoses, larger proportions of patients with comorbidities, and larger average healthcare costs during the baseline period.

Overall, the prevalence of comorbid conditions was higher in Medicaid patients than Commercial patients. After propensity score matching, the matched Commercial sample included 1,694 ART-naïve patients

and 1,294 ART-experienced patients and the matched Medicaid sample included 806 ART-naïve patients and 712 ART-experienced patients.— The distribution of index date range differed between the cohorts after matching and therefore this

variable was included in regression models. There were no significant differences in time to discontinuation/switch between patients initiating ATV/r

and patients initiating DRV/r. Descriptively, incidence rates of discontinuation/switch were substantially higher in the Medicaid

sample when compared to the Commercial sample. Results were similar for adherence and healthcare costs, though comparisons between odds of

PDC≥80% in ART-naïve Medicaid patients and total costs in ART-naïve Commercial patients and ART-experienced Medicaid patients were marginally significant and favored ATV/r.

No Difference in Persistence to Treatment withAtazanavir or Darunavir in HIV Patients

in a Real-World SettingAmanda M. Farr, MPH1; Stephen S. Johnston, MA1; Corey Ritchings, PharmD2; Matthew Brouillette, MPH3; Lisa Rosenblatt, MD, MPH2

1Truven Health Analytics, Bethesda, MD; 2Bristol-Myers Squibb, Plainsboro, NJ, 3Truven Health Analytics, Cambridge, MA

Two protease inhibitors (PIs), atazanavir (ATV) and darunavir (DRV) boosted with ritonavir (/r), arelisted as “recommended” options for antiretroviral-naïve patients in the U.S. Department of Health andHuman Services guidelines1 and are approved for antiretroviral-experienced patients.2,3

A recent open-label clinical trial reported that discontinuation rates were higher for ATV/r comparedwith DRV/r.4

There is a lack of data comparing ATV/r and DRV/r in a real-world setting.

BACKGROUND

Results were similar when the full Commercial and Medicaid samples were analyzed without stratifyingby ART-naivety status.

The incidence rate of discontinuation/switch was higher among Medicaid patients (who may be socio-economically disadvantaged) than Commercial patients.

Discontinuation/switch rates from real-world, claims-based analyses are typically higher than those reported in clinical trials. Frequent contact with study personnel and distribution of medications throughregular visits as part of the clinical trial may increase persistence and adherence to study medications.

DISCUSSION

Primary: To compare persistence (time to discontinuation/switch) in ATV/r-treated and DRV/r-treatedpatients with HIV

Secondary: To compare adherence and all-cause healthcare costs in ATV/r-treated and DRV/r-treatedpatients with HIV

OBJECTIVES

1. Department of Health and Human Services. Panel on Antiretroviral Guidelines for Adults and Adolescents.Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. Available at:http://aidsinfo.nih.gov/ContentFiles/AdultandAdolescentGL.pdf. Accessed Aug 12 2014.

2. Bristol-Myers Squibb. Reyataz Prescribing Information. Available at: http://packageinserts.bms.com/pi/pi_reyataz.pdf. Accessed September 26, 2014.

3. Janssen Pharmaceuticals. Prezista Prescribing Information. Available at: https://www.prezista.com/sites/default/files/pdf/us_package_insert.pdf. Accessed September 26, 2014.

4. Lennox JL, et al. Efficacy and tolerability of 3 nonnucleoside reverse transcriptase inhibitor-sparing antiretroviral regimens for treatment-naïve volunteers infected with HIV-1: A randomized, controlledequivalence trial. Ann Intern Med 2014; 161(7): 461–71.

REFERENCES

There were no significant differences in persistence (time to discontinuation/switch) or adherence with initiated PI among ART-naïve and ART-experienced HIV patients between those initiating anATV/r-based regimen and those initiating a DRV/r-based regimen.

Additionally, there were no significant differences in all-cause healthcare costs between the two cohorts, but there was a trend towards lower costs in patients on ATV/r.

Boosted ATV is an effective therapy option for HIV patients who are being considered for a PI regimen.

CONCLUSIONS

Data Source This retrospective observational cohort study used de-identified U.S. administrative claims and

encounters data from the 2006–2013 Truven Health Analytics MarketScan® Commercial and Multi-State Medicaid Databases.

Annually, the Commercial and Multi-State Medicaid Databases include over 50 million and over 8 million covered lives, respectively, and the patient populations are geographically diverse.

The databases contain inpatient medical, outpatient medical, and outpatient prescription drug claimsfor individuals with employer-sponsored primary insurance, including fee-for-service and managedcare health plans; or Medicaid insurance.

Sample Selection Criteria Inclusion criteria:

— Initiated an ATV/r or DRV/r antiretroviral therapy (ART) regimen between July 1, 2006, and March31, 2013 • The date of first ATV or DRV prescription drug claim was designated the index date.• Patients with a claim for ritonavir within seven days before the index date or 13 days after the

index date were considered to have initiated a ritonavir-boosted regimen.— Aged 18 years or older on the index date— Had continuous enrollment with pharmacy benefits for six months before, and for at least three

months after the index date— A non-diagnostic claim with an International Classification of Disease, Clinical Modification, Ninth

Revision (ICD-9-CM) diagnosis code for HIV (ICD-9-CM 042, 795.51, V08) in any position duringsix months before or three months after the index date

Exclusion criteria:— Evidence of initiation of an ART regimen that did not consist of one PI, ritonavir, and two nucleoside

reverse transcriptase inhibitors (NRTIs) based on claims within 14 days of the index date— Evidence of a claim for ATV or DRV in the six-month baseline period

Patients with no claims for ART any time before initiation (using all available data extending to January1, 2004) were considered to be ART-naïve.

Study Period The study period consisted of a six-month baseline period preceding the index date, the index date,

and a variable-length follow-up period.— Patients were not required to remain on index regimens for any specified length of time during

follow-up. The follow-up period was defined as the index date until the earliest of the following events:

— A ≥30-day continuous gap without ATV or DRV available— A prescription claim for a PI other than initiated drug, non-nucleoside reverse transcriptase

inhibitor, or fusion inhibitor that was not part of their initial regimen— A ≥30-day continuous gap without ritonavir available— Disenrollment from insurance benefits— Study end date of June 30, 2013

Outcomes Persistence (time to discontinuation/switch)

— Measured using the service date and days’ supply fields on outpatient prescription claims— Patients were considered to be non-persistent if their follow-up period ended because of (1) a

≥30-day continuous gap without ATV or DRV available, or (2) a prescription claim for any thirdagent that was not part their initial regimen.

— All other patients were censored at the end of follow-up. Adherence

— Measured during the follow-up period using the proportion of days covered (PDC), which was calculated as the proportion of days during the follow-up period that the patient had ATV or DRVavailable based on the service date and days’ supply field reported on the claim

— ART adherence was measured as a series of categorical variables (e.g., PDC≥80% versusPDC<80%, PDC≥95% versus PDC<95%).

All-cause healthcare costs (2013 U.S. dollars)— Comprising inpatient and outpatient medical and outpatient pharmacy costs— Measured during the follow-up period and summarized as per-patient-per-month units to account

for variability in the duration of follow-up Statistical Analysis Propensity score matching was used to adjust for confounding.

— Logistic regression models were fit including demographic and clinical characteristics as the covariatesand a binary indicator for ATV/r vs. DRV/r as the outcome to generate a propensity score.

— With each stratum of ART-naivety status, patients in the two cohorts were matched 1:1 based onpropensity score.

Time to discontinuation/switch was compared using Cox proportional hazards regression in thematched samples.

Odds of being adherent (PDC≥80% or PDC≥95%) were compared using logistic regression in thematched samples.— Patients who had a follow-up of ≤30 days were excluded due to artificially high PDC values.

Per-patient per-month all-cause healthcare costs were compared using log linear regression in thematched samples. Incremental cost differences were also generated.

METHODS

RESULTS

Presented at the International Congress on Drug Therapy in HIV Infection; November 2–6, 2014; Glasgow, United Kingdom.This analysis was funded by Bristol-Myers Squibb.

Poster # P006

Persistence

Adherence*

All-Cause Costs*

Table 1. Persistence, Adherence, and Per-Patient Per-Month All-Cause Healthcare Costs among 1:1 Propensity-Score-Matched Patient Sample