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.
Delivering high quality care is a necessary, but not
sufficient, factor in achieving optimal outcomes
High quality care delivered
High quality care RECEIVED
Optimal outcome
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
% Patients with
viral load >400 copies/ml
MEMS Adherence and Incomplete Viral
Suppression
100
90
80
70
60
50
40
30
20
10
0
82.1
71.4
66.7
54.6
21.7
<70
70-80
80-90
% Adherent
Paterson DL et al. Ann Intern Med. 2000:133:21
90-95
95
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
Log10 HIV RNA copy numbers
7
6
5
Resistant*
4
Sensitive
3
2
1
0
0 10 20 30 40 50 60 70 80 90 100
Pill count percent adherence
Bangsberg D, et al. AIDS. 2000:14:357
*Primary Drug Resistant Mutation IAS-USA
Adherence and AIDS-Free Survival
10% Adherence difference = 21% reduction in risk of AIDS
Proportion AIDS-Free
1.00
0.75
0.50
0.25
P = .0012
0.00
0
5
10
15
Months from entry
Bangsberg D, et al. AIDS. 2001:15:1181
20
25
30
Adherence
O 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
CID 2001
53%
CID 2001
80%
53%
MEMS
(drug exposure)
Clinic pill count
MEMS
McNabb
Arnsten
“[some] claim that a lack of compliance is the only reason for a treatmentnaïve 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.
There is an expectation that patients in Africa will be poorly adherent to antiretroviral
therapy:
“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
“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].
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
100
90
80
70
60
50
40
30
20
10
0
DOT <50
SAT <50
w4
w8
w16 w24 w48 w64 w72 w80 w88
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
Tuberculosis
Witnessed 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
• Self report mean
Adherence = 90%
• UDVL = 71%
Compared to Avg US Adherence
~70-80%
AIDS 2003
Somerset Hospital data, Cape Town (Orrell et al):
• Adherence assessed by counting tablet returns.
– Increasing adherence significantly associated with reduction in VL.
Correlation: r = -.2855, p< 0.0001
3
2
1
0
-1
-2
95% confidence
V L c h a n g e (L o g 1 0 c o p ie s
-3
-4
-5
0.2
0.4
0.6
0.8
1.0
Adherence at week 48 (General cohort)
1.2
1.4
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
Data from Senegal:
Good adherence in 87.9% accessing ART through a government treatment
programme.
[AIDS 2002, 16: 1361]
The Response
2. Resistance patterns are different with
similar adherence to different regimens
• NNRTI
Resistance develops quickly and
nearly linearly
• Boosted PI
Resistance develops more slowly
and in a bell shaped curve
Bangsberg NY PRN 2009
%VL below detection
Adherence and virological outcome –
PIs
80
70
60
50
40
30
20
10
0
<70
70-80
80-90
90-95
95-100
%adherence
Ann Intern Med 2000;133:21
Rate per 100 person years
Relationship between
resistance & adherence -NNRTIs
45
40
35
30
25
20
15
10
5
0
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-analysis
Review 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?
First HIV RNA > 1000 copies/ml
0.15
0.20
First and second consecutive
HIV RNA > 1000 copies/ml
0.05
0.10
75%
0.00
Proportion of patients on program
0.25
Kaplan-Meier failure estimate for time to first, then second
consecutive HIV RNA level > 1000 copies/ml.
0
4
8
12
16
20
24
28
32
86
51
36
Duration on Treatment (months)
929
641
421
328
229
162
127
Patients at Risk of starting Second Line therapy
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)
Censored
Complete
Cumulative proportion remaining in care
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0
1
2
3
4
5
Time (years)
6
7
8
9
no breakthrough
re-suppressed
failed
Resistance at fist-line failure
Susceptible
Possible
low level
resistance
Low level
resistance
Intermediat
e
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%)
-
Of 110 people, most had dual class resistance. Only 7% wild-type.
Orrell, Antiviral Therapy , 2009
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-line
ART 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)
In the absence of viral loads – use
adherence measures as a marker.
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
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
Paterson
Haubrich
Steiner
Bosely
Charney
Caron
Gilbert
Blowey
Mushlin
2001
2000
1999
1995
1995
1967
1978
1980
1997
1977
JAIDS
Annals Int Med
AIDS
Arch Int Med
Eur Resp J
Pediatrics
Clin Pharmacol
Can Med Assoc J
Ped Nephrology
Arch Int Med
HAART
HAART
HAART
AZT
Inhaled terbutaline
Penicillin
Anatacids
Digoxin
Cyclosporin
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.
Approaches to managing adherence
• 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
• 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.
Approaches to managing adherence
• 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!!!
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….
Sizophila Treatment Support
Pre-treatment
Counsellor assigned to each patient. Education-group &
individual treatment readiness. Home visit. Disclosure
support
On-treatment
Individual support
Group sessions
Crisis management
Adherence monitoring
Red Alert
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??
HIV Prevention
Efficacy
Type of
Intervention
Positive Effect
(significantly
reduced HIV
incidence
compared with
control)
Adverse Effect
(significantly
increased HIV
incidence
compared to
control)
No Effect (no
effect either
way)
Total
Behavioural
Microfinance
Diaphragm
Vaginal
Microbicides
Preexposure
Prophylaxis
Male Circumcision
STI Treatment
Vaccine
ART in discordancy
Total
1
1
7
1
1
11
7
1
1
13
1
-
2
3
3
1
1
1
8
1
1
8
3
34
4
9
4
1
43
"We are really groping in the dark"
Salim S. Abdool Karim
Quoted in the Washington Post,
November 1, 2007
Efficacy vs effectiveness
objectively measure adherence (receipt of vaccines)
For user-dependent interventions (eg PrEP), phase III
trials measure both biologic efficacy & adherence
Provide unbiased measure of efficacy across average users
40% efficacy with <100% adherence implies higher efficacy
Eff Measured =
50%
40%
30%
20%
10%
0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
70%
80%
90%
100%
Adherence level
B
Receptive Anal Intercourse
100%
90%
EffInt = 25%
80%
EffInt = 50%
Eff Measured = 1 - RR
70%
EffInt = 75%
60%
50%
40%
30%
20%
10%
0%
0%
10%
20%
30%
40%
50%
60%
Adherence level
CAPRISA 004 assessed the safety and
effectiveness of 1% tenofovir gel
• 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
asap
72 hrs
12 hrs
Onset of
labour
64
Delivery
CAPRISA 004 tenofovir gel regimen
asap
CAPRISA 004: Adherence is critical
for efficacy against HIV
High (>80% gel adherence)
54% efficacy
n=336 (38%)
Intermediate (50-80% adherence) n=181 (20%)
38% efficacy
Low (<50% gel adherence)
28% efficacy
n=367 (42%)
Abdool Karim et al, Science 2010
HIV infection rates in the tenofovir and placebo
gel groups: Kaplan-Meier survival probability
Probability of HIV infection
0.20
0.18
Placebo
0.16
0.14
p=0.019
p=0.017
0.12
Tenofovir
0.10
0.08
0.06
0.04
0.02
0.00
0.0
Months of follow-up
0.5
6
1.0
12
Years
1.5
18
2.0
24
2.5
30
88
97
98
Cumulative HIV endpoints
37
65
Cumulative women-years
432
833
1143
1305
1341
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
47%
(0.069)
50%
(0.007)
47%
(0.004)
40%
(0.013)
39%
(0.017)
(0.019)
HIV incidence rates
(Tenofovir vs Placebo)
Effectiveness
(p-value)
The iPrEx Study
•
•
•
•
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
Sampling for Case Control Study
FTC/TDF
Cases/Controls
N=36
HIV+
1 unavailable specimen
35
Samples
1 case > 7 days after
seroconvertion
34 Samples
34 PBMC
33 Plasma
33 Both
FTC
TDF
HIV2 unavailable specimens
1 control used for 2 cases
33
Samples
Placebo
Stopped testing
after 26
26
Samples
2 cases off drug
31 Samples
30 PBMC
24 Plasma
23 Both
34 Samples
26 PBMC
0 Plasma
0 Both
TFV-DF (fmol/106 cells)
Drug Levels
17/35 Detectable
Detection of Any Drug (%)
Drug Detection by HIV Status
100%
75%
51%
50%
25%
0%
9%
HIV+
HIV-
Recorded Adherence and Efficacy
% of Visits
Efficacy
95% CI
<50%
50-90%
>90%
18%
33%
49%
16%
34%
68%
-54 - 54
-20 - 64
36 - 84
Caprisa 004 and Iprex
• Motivational client centered counselling
• Next step counselling
Partners PrEP adherence intervention
Counselors: barriers to pill-taking include changes in
sexual behavior, partner discord, travel, & life
changes
Participants: high levels of motivation to adhere to
PrEP, often driven by altruism
Adherence intervention for those with <80%
adherence in prior 3 mos
Assessment of sexual & pill-taking behaviors, motivational
interviewing, & optional couples session
>80%
in 72% of those who went through intervention
Psaros, IAPAC 2011
Conclusions
 Caution 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
Conclusions
 Self-report is critical
 It 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 demands
 Create normative expectations for frank discussions
over high compliance