Improving adherence and quality of care and prevention through mobile technology and patient

education.

IAS Workshop Rome 2011

Linda-Gail Bekker

The Desmond Tutu HIV Centre

UCT

Todays workshop…..

• The Importance of ART Adherence in HIV Treatment and Prevention

• Adherence Interventions - What the Science Tells Us

• Panel Discussion • Presentation of An Adherence Counseling

Program (Life Steps) • Key Components of Adherence Programming• Panel Discussion

Panel

• Conall O’Cleirigh, PhD• Kenneth Mayer, MD• Francois Venter, MD• Ian Sanne, MD• Daniella Mark, PhD• Linda-Gail Bekker, MD,PhD.

Optimal outcome

High quality care RECEIVED

High quality care delivered

Delivering high quality care is a necessary, but not sufficient, factor in achieving optimal outcomes

Adherence

• To Prevention• To Testing• To Care• To Treatment• To Programs

Why would poor adherence be a problem?

• Poor outcomes on the individual level– Treatment failure

• Resistance and fewer treatment options• Viral rebound• Illness• Death

• Poor outcomes in prevention effectiveness• Risk inhibition• Condom migration• Increased susceptibility

• Poor outcomes on the population level– Resistant virus emergence and fewer treatment options– Increased transmission– Higher morbidity and mortality burdens

The Challenge of Adherence

MEMS Adherence and Incomplete Viral Suppression

Paterson DL et al. Ann Intern Med. 2000:133:21

Adherence to therapy is a strong predictor of viral load suppression, immune recovery, lack of disease progression, and reduction in mortality.

Poor adherence can cost lives…

Mellors JW, Munoz A, Giorgi JV, et al. Ann Intern Med. 1997;126:946-954.

Near perfect adherence is required to maintain low viral load…..

• Clinical trials 80-90% remain undetectable at one year• Only 50 % undetectable in clinical practice (Deeks et al

Toronto 1997).

Adherence, Viral Load, and Resistance

Pill count percent adherence

Bangsberg D, et al. AIDS. 2000:14:357

Log

10 H

IV R

NA

cop

y nu

mbe

rs7

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100

Resistant*

Sensitive

*Primary Drug Resistant Mutation IAS-USA

10% Adherence difference = 21% reduction in risk of AIDSAdherence and AIDS-Free Survival

Bangsberg D, et al. AIDS. 2001:15:1181

Prop

orti

on A

IDS-

Free

Months from entry

P = .0012

0 5 10 15 20 25 30

0.00

0.25

0.50

0.75

1.00

AdherenceO 90–100%O 50–89%O 0–49%

Summary of Mean Adherence Using Objective Measures

Bangsberg AIDS 2000 67% MEMS

73% Unannounced pill count

Paterson Annals Int Med 2000 74% MEMS

Liu Annals Int Med 2001 63% MEMS

83% Clinic pill count

McNabb CID 2001 53% MEMS (drug exposure)

Arnsten CID 200180%53%

Clinic pill countMEMS

“[some] claim that a lack of compliance is the only reason for a treatment-naïve patient to fail therapy within the first 6 months”

[Don Smith 2000]

Will “widespread, unregulated access to antiretroviral drugs in sub-Saharan Africa, [in the absence of directly observed therapy] lead to the rapid emergence of drug resistant viral strains, spelling doom for the individual, curtailing future treatment options, and

[leading] to transmission of resistant virus?”

Harries AD, Nyangulu DS, Hargreaves NJ, Kaluwa O, Salaniponi FM. Preventing antiretroviral anarchy in sub-Saharan Africa. Lancet 2001; 358:410-4.

“Ask Africans to take their drugs at a certain time of day, and they do not know what you are talking about” [Natsios, USAIDS,2001].

“One of the barriers in the expansion of ARV programmes is the widely held prejudicial view that, due to poverty and lack of education, individuals in Africa may be less likely to maintain adherence to antiretroviral therapy than their HIV-positive counterparts in the developed world.” Orrell et al, Barcelona 2002

There is an expectation that patients in Africa will be poorly adherent to antiretroviral therapy:

The Back Story: 1990s - early 2000“Adherence seen as potential barrier to ART in RLS”

Directly Observed vs Self Administered Therapy During Incarceration: Proportion with < 50 Copies/ml

Fischl et al 8th CROI, 2001 abstract 528

HIV DOT in Haiti

• 60 patients with late stage clinical disease– Enteropathy with severe weight loss– CNS dysfunction or severe neuropathy– Repeated opportunistic infections unresponsive to

antimicrobials

• Excellent clinical response• Toxicity uncommon• Promoted as a model for resource poor settings

TuberculosisWitnessed Therapy vs Self Administered Therapy

• South Africa Zwarenstein Lancet 1998; 352:1340-3.

– No difference

• Thailand Kamolratanakul Trans R Soc Trop Med Hyg 1999; 93:552-7.

– Rural areas: DOTS better than SAT– Urban areas: no difference

• Pakistan Walley Lancet 2001; 357:664-9.– Clinic DOTS, family DOTS, SAT: no difference

AIDS 2003

• Self report mean Adherence = 90%• UDVL = 71%

Compared to Avg US Adherence~70-80%

Somerset Hospital data, Cape Town (Orrell et al):

• Adherence assessed by counting tablet returns.– Increasing adherence significantly associated with reduction in VL.

C o rre la tio n : r = -.2 8 5 5 , p < 0 .0 0 0 1

0 .2 0 .4 0 .6 0 .8 1 .0 1 .2 1 .4

A d h e re n c e a t w e e k 4 8 (G e n e ra l c o h o rt)

-5

-4

-3

-2

-1

0

1

2

3

VL

cha

ng

e (L

og

10

cop

ies/m

L)

9 5 % c o n fid e n c e

Somerset Hospital data, Cape Town (Orrell et al):

Discontinuations

• 16.2% discontinued therapy over 48 wks -were younger, had higher viral loads, lower CD4 counts.

• Socioeconomic status, gender, home language, WHO stage not associated with discontinuation

• only 4% dropouts were due to adverse events

Somerset Hospital data, Cape Town (Orrell et al)

Factors predicting poor adherence: • Three times a day dosing• Younger age• Not speaking English (language of site staff)

Factors NOT predicting adherence:• Socio-economic status• Gender• Symptomatic HIV disease/baseline viral load

Somerset Hospital data, Cape Town (Orrell et al)

Factors predicting virological failure:

• Adherence <95%• Complex dosing (food, 3 times a day)• Dual nucleoside regimens• High baseline viral load / low baseline CD4

South Africa Clinical Trials: 63% VL<400 Sanne I, Ive P, Mcintyre J 1st IAS Conference on HIV Pathogenesis and

Treatment, Buenos Aires, 2001 #321

Good adherence in 87.9% accessing ART through a government treatment programme.

[AIDS 2002, 16: 1361]

Data from Senegal:

The Response

2. Resistance patterns are different with similar adherence to different regimens

• NNRTI Resistance develops quickly and

nearly linearly

• Boosted PIResistance develops more slowly

and in a bell shaped curve

Bangsberg NY PRN 2009

Adherence and virological outcome –PIs

0

10

20

30

40

50

60

70

80

%V

L b

elo

w d

etec

tio

n

<70 70-80 80-90 90-95 95-100

%adherence

Ann Intern Med 2000;133:21

Relationship between resistance & adherence -NNRTIs

05

1015202530354045

Rat

e pe

r 10

0 pe

rson

yea

rs

100 90-99 80-89 70-79 60-69 <60

% adherence

Clinical Infectious Diseases 2003; 37:1112–8

Adherence declines over time

Most recent meta-analysisReview of Adherence at 2 years

Rosen et al. PLoS 2007– 32 studies in SSA 1996-2007– ~75,000 patients in non-research ART

programs– Average follow-up time reported 9.9 mo, 77% retention– 6 mo = 80% pts retained– 12 mo = 60% pts retained

– At 2 Years*:• BEST CASE = 84% • WORST CASE = 46%• AVERAGE = 61%

61% at 24 months

Virological failure vs. single breakthrough?Kaplan-Meier failure estimate for time to first, then second consecutive HIV RNA level > 1000 copies/ml.

929 641 421 328 229 162 127 86 51

0.00

0.05

0.10

0.15

0.20

0.25

Patients at Risk of starting Second Line therapy

0.00

0.05

0.10

0.15

0.20

0.25

0 4 8 12 16 20 24 28 32 360 4 8 12 16 20 24 28 32 36

Duration on Treatment (months)

Prop

ortio

n of

pati

ents

on

prog

ram

First HIV RNA > 1000 copies/ml

First and second consecutive HIV RNA > 1000 copies/ml

75%

Antiviral Therapy 2007; 12: 83-88

Nonadherence Predicts Early Treatment Discontinuation

Initial 30 Day Adherence Discontinue w/in 6 Months

<50% (40/52) 77%

50-80% (4/43) 9%

81-90% (0/24) 0%

>90% (0/33) 0%

Total (44/152) 29%

REACH unpublished data

Retention in care• Adherence is more than just beginning therapy,

it is sticking to it. LTFU rates are high…

Proportion remaining in care (Kaplan-Meier)

Complete Censored

no breakthrough re-suppressed failed

0 1 2 3 4 5 6 7 8 9

Time (years)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Cu

mu

lative

pro

po

rtio

n r

em

ain

ing

in

ca

re

Resistance at fist-line failureSusceptible Possible

low level resistance

Low level resistance

Intermediate resistance

High resistance

Lamivudine / emtricitabine

22 (20%) - - 4 (4.0%) 86 (78%)

Abacavir 20 (18%) 55 (50%) 15 (14%) 20 (18%) -

Zidovudine 98 (89%) 1 (1.0%) 6 (5.5%) 3 (2.7%) 2 (1.8%)

Stavudine 87 (79%) 6 (5.5%) 12 (11%) 5 (4.5%) -

Didanosine 76 (69%) 9 (8.1%) 9 (8.1%) 14 (13%) 2 (1.8%)

Tenofovir 97 (88%) 1 (1.0%) 4 (3.6%) 8 (7.3%) -

Efavirenz 10 (9.0%) 2 (1.8%) - 13 (12%) 85 (77%)

Nevirapine 10 (9.0%) 2 (1.8%) 1 (1.0%) 4 (4.0%) 93 (85%)

Etravirine 10 (9.0%) 15 (14%) 49 (45%) 32 (29%) 4 (4.0%)

Protease Inhibitors

93 (84%) 16 (15%) 1 (1.0%) -

Orrell, Antiviral Therapy , 2009

Of 110 people, most had dual class resistance. Only 7% wild-type.

Results from Gugulethu

Susceptible Possible low level resistance

Low level resistance

Intermediate resistance

High resistance

Lamivudine / emtricitabine

43 (98%) - - - 1 (2%)

Abacavir 41 (93%) 2 (5%) - - 1 (2%)

Zidovudine 42 (95%) - 2 (5%) - -

Stavudine 42 (95%) 1 (2%) 1 (2%) - -

Didanosine 41 (93%) 1 (2%) 1 (2%) - 1 (2%)

Tenofovir 44 (100%) - - - -

Efavirenz 29 (66%) - - 3 (7%) 12 (27%)

Nevirapine 29 (66%) - 1 (2%) - 14 (32%)

Protease Inhibitors

38 (86%) 6 (14%)* - - -

* T74S

Probability of virologic failure stratified by the interval of time between 1st-lineART failure and 2nd-line ART initiation.

Levison, AIDS 2011, in press

So we know adherence is key…..

• How do we then ensure it ?– At initiation– In a sustainable way

• How do we measure it– In the treatment setting– In the prevention setting

Objective vs. Subjective Adherence Measurement Tools

Subjective Measures• Patient interview

– Pill recognition– 3, 4, 7, 30 day patient report– Visual-analog scale– Rating scale– Computer assisted self

interview (CASI)

Objective Measures• Electronic monitoring• Announced pill count

-- Clinic/Private Practice• Unannounced pill count– Home or usual place of residence– Telephone a la Kalichman• Pharmacy refill• Drug/biomarker levels– Plasma– Hair– Breath

In the absence of viral loads – use adherence measures as a marker.

Monitoring adherence

• Physician assessment - poor (no better than random!)

• Questionnaires - specific, insensitive (only last 3 days)

• Pill counts - good (overestimate in general; pill dumpers)

• Pharmacy records – fair (monthly medicine collection)

• Drug levels - single time points only

• Electronic monitoring – better but expensive!

… use a combination

Physicians Predict Adherence Not Much Better Than Random

Bangsberg 2001 JAIDS HAARTPaterson 2000 Annals Int Med HAARTHaubrich 1999 AIDS HAARTSteiner 1995 Arch Int Med AZTBosely 1995 Eur Resp J Inhaled terbutalineCharney 1967 Pediatrics PenicillinCaron 1978 Clin Pharmacol AnatacidsGilbert 1980 Can Med Assoc J DigoxinBlowey 1997 Ped Nephrology CyclosporinMushlin 1977 Arch Int Med Hypertensive

Wisebag, Wisecase

REACH Adherence Measures

• 3-day patient report

• MEMS electronic cap

• Unannounced pill count – home or usual place of

residence

Other ways to monitor Drug levels

• Plasma• Other body fluids• PBMC• breath• Hair

Approaches to managing adherence

• Treatment readiness vs. adherence – data show that “readiness” is a distinct factor that influences adherence - Study in 828 people from Sweden (SÖdergard, Patient Educ Couns 2007)

focus on individuals readiness for change, examine factors than CAN change and be changed by the individuals.

• Psycho-social interventions: establishing provider-patient relationships. Adherence a process of negotiating a tailored plan – “flexible rigidity” (Reir, Soc Work Health Care 2006)

• Treating depression improves adherence (Yun, JAIDS 2005)

Approaches to managing adherence

Approaches to managing adherence

• Different population in developed world – more marginalised, homeless, drug users.

• Predictors of discontinuing therapy = injection drug use and early poor adherence. (Moss, CID 2004)

WATCH adherence at week 4 and 8. Viral loads highest at the beginning, so adherence then is especially key.

• Non-nucleoside regimens are more forgiving: may suppress viral load with adherence >55%! NNRTI have much improved outcomes compared to PIs at 55-75% adherence range.

• PI: only likely to have suppressed VL with adherence >95%(Bangsberg , CID 2006)

Remember reduced disease progression and mortality improves with every increase in adherence level … do not drop standards!!!

Approaches to managing adherence

Technologies use in managing adherence

• Pillboxes: simple and effective intervention and should be widely used – improves adherence by ~4.5% (drop VL 0.35 log)Best for intermittent non-adherence (80-90%). Not enough of a reinforcement for those with very poor adherence. Pill box of more benefit than changing to once a day therapy. (Petersen, CID 2007)

Technologies use in managing adherence

Examples of MEMscaps output

Treatment Regimen

A single tablet regimen is associated with higher adherence and viral suppression than multiple tablet regimens in HIV+ homeless and marginally housed people

• Bangsberg, David Ra; Ragland, Kathleenb; Monk, Alexb; Deeks, Steven Gb

Treatment Readiness Program empowers patient to be adherent….

Pre-treatmentCounsellor assigned to each patient. Education-group & individual treatment readiness. Home visit. Disclosure support

On-treatmentIndividual supportGroup sessionsCrisis managementAdherence monitoringRed Alert

Sizophila Treatment Support

Surprise pill counts

• The counsellor visits his client at home and checks pill counts, entering data into his cell phone and transferring info directly to clinic database.

Prevention: where to adherence??

"We are really groping in the dark" Salim S. Abdool KarimQuoted in the Washington Post, November 1, 2007

64

• BAT 24 coitally-related gel use– Insert 1 gel up to 12 hours Before sex, – insert 1 gel as soon as possible within 12

hours After sex, – no more than Two doses in 24 hours

HIVNET 012 nevirapine regimen CAPRISA 004 tenofovir gel regimenasap

asap72 hrs12 hrs

Onset of labour

Delivery

CAPRISA 004 assessed the safety and effectiveness of 1% tenofovir gel

HIV infection rates in the tenofovir and placebo gel groups: Kaplan-Meier survival probability

Pro

ba

bil

ity

of

HIV

in

fec

tio

n

0.0 0.5 1.0 1.5 2.0 2.5

Years

Months of follow-up 6 12 18 24 30

Cumulative HIV endpoints 37 65 88 97 98

Cumulative women-years 432 833 1143 1305 1341

HIV incidence rates(Tenofovir vs Placebo)

6.0 vs 11.2 5.2 vs 10.5 5.3 vs 10.2 5.6 vs 9.4 5.6 vs 9.1

Effectiveness (p-value)

47%

(0.069)

50%

(0.007)

47%

(0.004)40%

(0.013)39%

(0.019)

p=0.019

Tenofovir

Placebo

0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

p=0.017

(0.017)

• High Risk MSM• Randomized 1:1 Daily Oral PREP• FTC/TDF vs Placebo• Followed on Drug for:

- HIV seroconversion- Adverse Events (especially renal &

liver)- Metabolic Effects (Bone, Fat, Lipids)- HBV Flares among HBsAg+- Risk Behavior & STIs- Adherence- If infected

‣Drug Resistance‣Viral load‣Immune responses & CD4 Count

The iPrEx Study

FTCTDFHIV-

HIV+

Placebo

34 Samples26 PBMC0 Plasma0 Both

35Samples

1 unavailable specimen

33Samples

2 unavailable specimens1 control used for 2 cases

26Samples

Stopped testingafter 26

34 Samples34 PBMC33 Plasma33 Both

1 case > 7 days afterseroconvertion

31 Samples30 PBMC24 Plasma23 Both

2 cases off drug

Sampling for Case Control Study

FTC/TDFCases/ControlsN=36

Drug Levels

17/35 Detectable

TFV-

DF

(fm

ol/1

06 cel

ls)

Caprisa 004 and Iprex

• Motivational client centered counselling• Next step counselling

ConclusionsCaution is warranted against placing too

much confidence in indicators suggesting high adherence

More confidence can be placed in estimated lower level of adherence.

Validation work with measures matched on time frame and over time for patterns of adherence are needed

Self-report is criticalIt provides information that cannot be assessed with

alternative direct measures- challenges, facilitators, intermittent patterns of use

It is essential in open communication between prescribers of PrEP and those using it

How can we improve self-report?Address social desirability bias and minimize

memory/recall demandsCreate normative expectations for frank discussions

over high compliance

Conclusions