Suboptimal adherence associated with virologic failure and resistance

mutations among patients on 1st line HAART in Bangalore, India

M. Ekstrand1,2,3, A. Shet2,4, S. Chandy4, G. Singh4, R. Shamsundar4, V. Madhavan5, S. Saravanan5, N. Kumarasamy5

1University of California, San Francisco, United States, 2St John's Research Institute, Bangalore, India, 

3University of California Berkeley, United States, 4St John's National Academy of Health Sciences, Bangalore, India

5YRGCARE, Chennai, India

Background

Optimal adherence to antiretroviral regimens is closely associated with achieving and maintaining HIV viral suppression and preventing the development of drug resistant virus.

Initial adherence rates in India encouraging, but recent data* from our team and from NACO suggest that adherence rates decline over time.

Little known about local adherence patterns or the relationship between adherence and treatment outcome in this setting.

No published data on the relationship between adherence and drug resistance in India

*Bachani et al. 2010, Ekstrand et al. 2010

Objectives

1. Document local adherence patterns in Bangalore, India

2. Examine the relationship between adherence, treatment outcome and resistance associated mutations3. Explore universal and local barriers to different patterns of non-adherence in this setting

Methods:Longitudinal 2-year cohort study, n=552 (baseline)

Participants referred by MD, clinic clerk, and study screener

Eligibility criteria: > 18 years old, HIV infected, on fixed dose antiretroviral medication, willing to participate in all follow-up visits.

Face-to-face interviews administered in separate study offices by trained staff every 3 months for two years (ongoing)

Blood drawn every 6 months, by trained staff phlebotomists. Analyzed by laboratories at Reliance Life Sciences, Mumbai and YRG CARE, Chennai.

Study cleared by SJRI and UCSF IRBs and by ICMR.

Measures

Demographics: gender, age, marital status, education, residence.

CD4 cell counts

HIV plasma Viral Load (sensitivity enables detection of an HIV RNA level to 100 copies/mL, Reliance Life Sciences, Mumbai)

Viral Genotyping (YRGCARE, Chennai, in-house method certified by TAQAS Program and interpreted using Stanford HIV-1 Sequence Database. All relevant RT mutations analyzed)

ART regimen current and past regimens, verified by prescription, pill container and chart review

Measures:Adherence Past month adherence (VAS) and >48 hour treatment interruptions combined:

• "Optimally adherent" : VAS score of ≥ 95 % and no tx interruptions • "Sub-optimally adherent" : <95% adherence, tx interruptions, or both.

Adherence barriers:Health-related: feeling too sick or too healthy, depressive sxRefill-related: ran out of meds, problems getting to pharmacy or clinicRegimen-related: meds side effects, perceives drugs as “toxic”, problems following MD’s instructionsLack of routine: no/little set daily routine, frequent travelAlcohol consumption (any)

Results: Demographics and medical historyMale 68% (n=375) Female 32% (n=177) Mean Age (range) 38 years (18-75) Marital Status Married 70% (n=388) Education < 10 yrs 44% (n=242) 10 yrs 28% (n=156) > 10 yrs 28% (n=154) Mean length on HAART 20 months HAART regimen: 3TC + AZT + NVP 50% (277) 3TC + d4T + NVP 33% (184) 3TC + d4T + EFV 6% (33) 3TC + AZT + EFV 8% (42) 2nd line (5 different comb.) 3% (16)

Adherence, viral load and resistance mutationsAdherence: N=552

<95% (last month, VAS) 6% (n=34)

> 1 treatment interruption 20% (n=110)

Sub-optimal adherence 23% (n=123)

CD 4 mean (range) 352 (17 – 1467)

Viral load

Detectable 24% (n=132)

>1,000 c/mL 18% (n=101)

Samples amplified n=92

RT associated mutations

TAMS

NRTI associated mutations

NNRTI associated mutations

>3 NNRTI associated mutations

86% (n=79/92)

48% (n= 44/92)

68% (n=63/92)

72% (n=66/92)

23% (n=21/92)

Association between adherence, virologic failure, and drug resistance

Detectable VL Any resistance associated mutation

(n=552) (n=92)

Treatment interruptions Yes 43%* ** 86%*

No 19% 68%

Adherence past mo. < 95% 62%*** 84%

≥95% 21% 74%

Suboptimal adherence Yes 42%*** 87%**

No 19% 65%

* ** p < .001. **p <.02, * p <.05,

Ekstrand et al. under review

Factors associated with non-adherence

Past month non-adherence

O.R. (95% C.I.)

Treatment interruptions

O.R. (95% C.I.) ≤ 10 yrs of education 1.77 (0.57 – 6.17) 1.67 (0.83 – 3.41) Paid for medication 0.79 (0.21 – 2.83) 0.87 (0.40 – 1.87) More health-related barriers 3.88 (0.95 – 15.43) 5.32 (1.48 – 25.39) More refill-related barriers 2.65 (1.42 – 5.32) 4.00 (2.02 – 9.27) More regimen-related barriers 2.66 (0.98 – 9.28) 1.04 (0.34 – 4.65) More ‘lack of routine’ barriers 1.76 (1.07 – 2.95) 0.86 (0.54 - 1.35) Used alcohol in past 3 mo. 5.25 (1.23 – 21.62) *

*not in model

Ekstrand et al. under review

Understanding medication adherence barriers:

•Perceptions of stigma and fear of discrimination prevented patients from disclosing their HIV+ status.•Unable to ask others for help remembering doses.•Problems explaining frequent clinic visits to family and employers & getting help with transportation.•Lack of privacy interfered with taking pills in front of others, especially during holidays/family gatherings. •Patients did not want to fill their prescriptions at the local pharmacy, due to lack of confidentiality.

Focus group data n=17 males & 13 females reporting adherence challenges

The role of stigma in adherence

ForgetfulnessLack of privacy

Worksite issuesTravel/

holidaysNo time off work

Clinic transportationAccessing local

pharmacy

Treatment interruptions

Occasional missed pills

Lack of disclosureNo/little social

supportFear of stigma

Treatment failure

& resistanc

e

Conclusions•Ongoing adherence levels comparable to other international settings

•Treatment interruptions most common form of non-adherence, most often associated with delays in prescription refills

•Non-adherence –especially in the form of treatment interruptions - are associated with both viral load and resistance mutations

•To improve treatment outcome and minimize the development of resistance, barriers to uninterrupted treatment must be addressed!

•AIDS stigma is a crucial factor underlying both occasional missed doses and treatment interruptions. Needs to be targeted in adherence interventions at the level of the individual, family, institutions, and society

THANK YOU!

Prerana Study Team

St John’s Research Institute, Bangalore

Genotypic mutational pattern among patients failing first-line therapy (N=92) NRTI-associated mutations:

At least 1 NRTI mutation:

Non-thymidine analogue mutations: M184V/MV/I/IM E44D/DE/A/K L74V V75M V118I T69D/DN K65R Q151M Thymidine analogue mutations: TAMs1 pathway: T215Y M41L/LM L210W TAMs2 pathway: T215F D67N/DN K70R/KR/E K219E/Q Patients with TAMs*: Patients with TAMs +

Percent (n)

68 % (63)

65 % (60) 9 % (8) 4 % (4) 4% (4) 4% (4) 3 % (3) 3 % (3) 2 % (2) 21 % (19) 25 % (23) 3 % (3) 12 % (11) 27 % (25) 22 % (20) 13 % (12) 48 % (44) 1 % (1)

NNRTI-associated mutations: At least 1 NNRTI mutation:

At least 3 NNRTI mutations

Y181C/CY/I/V K103N/KN/R K101E/EK/Q/KQ G190A/AG V108I A98G V106M/MV V90I Y188L E138K/EK

72 % (66)

23 % (21)

37 % (34) 26% (24) 14 % (13) 18 % (17) 15 % (14) 12 % (11) 8 % (7) 4 % (4) 4 % (4) 3 % (3)

Patients with ≥ 2 NNRTI/NRTI mutations 67 % (62) Patients without NRTI and NNRTI mutations 24 % (22)