Transcript Adherence Measures and Prediction of Clinical Outcomes in the China Adherence for Life (AFL) Cohort March 18, 2008 Lora Sabin Center for International Health and Development Boston.

Adherence Measures and Prediction
of Clinical Outcomes in the China
Adherence for Life (AFL) Cohort
March 18, 2008
Lora Sabin
Center for International Health
and Development
Boston University
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AFL: study collaborators
Boston University SPH
Dr. Lora Sabin, MA, PhD
Dr. Christopher J. Gill, MS, MD
Dr. Mary Bachman DeSilva, ScD
Dr. Davidson H. Hamer, MD
Dali Second People’s Hospital
Dr. Zhang Jianbo, MD
Ditan Hospital
Dr. Xu Keyi, MD
Horizon Research Group
Dr. Yuan Yue, MA, PhD
Fan Wen, MA
US CDC-GAP Office
World Health Organization – Beijing
Funding provided by: USAID, WHO/Beijing, US CDC
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Background
• China has one of the fastest growing HIV
epidemics in the world
• China is rapidly scaling up ART but
treatment programs are at an early stage
• Among Chinese patients on ART:
• Little is known about levels of adherence, particularly
among IDUs and former IDUs
• Little is known about factors that affect adherence
• Little is understood about how to improve adherence
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Study site, Dali, Yunnan Province
Dali
Yunnan province
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Overview of AFL study
• AFL was a 3-phase, pilot study conducted
over 2½ years, designed to assess feasibility
and provide policy-relevant information on:
• Adherence among Chinese patients on ART
• Factors that affect adherence
• Possible strategies for improving adherence
• Phase I focused on qualitative data
• Phase II was a 6-month longitudinal study
• Phase III involved assessing a pilot
intervention to improve adherence among
these patients
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Overview of AFL
(Control)
Continued passive
observation
(Intervention)
N=80
Patients enrolled
N=69
Patients randomized
Active EDM
feedback
Phase I
Phase II
Phase III
6 months
6 months
6 months
Qualitative investigations on
Adherence observed
Randomized controlled trial to
what patients/doctors in Dali
prospectively via EDM,
determine effectiveness of EDM
view as key barriers to
relationship between barriers and
data feedback strategy
adherence
actual adherence, clinical
outcomes measured
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Phase II objectives
1. To determine the best surrogate measure
of ART adherence in the study population
2. To determine ART adherence rates in the
population
3. To analyze the relationship between
adherence factors and measured
adherence rates (not presented)
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Methods: measures of adherence
(assessed monthly*, all measures averaged over 6 month period)
1. Self report
1.
2.
Visual analog scale (% of doses taken)
Simplified medication adherence questionnaire (SMAQ)
(% of months adherent)
1. 6-item version (validated by Spanish GEEMA group)
2. 10-item version (our modification, w/dose timing,
sharing)
2. Pill count (% of correct number of doses)
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Methods: measures of adherence
3. Electronic data monitoring (EDM)
(*continuous)
1.
2.
3.
Proportion of doses taken (# taken / # prescribed doses)
Proportion of doses taken on time (# taken +/- 1 hr of
schedule / # taken)
Composite EDM measure, incorporating proportion taken
and proportion taken on time (# taken +/- 1 hr of schedule /
# prescribed doses)
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Methods: clinical measures
(assessed at baseline and 6 months)
•
Viral load (binary; <400 copies=
“undetectable”)
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Methods: assessment of
adherence measures
1. Odds ratios via logistic regression
2. Area under receiver-operating
characteristic (ROC) curves
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Findings: characteristics of the sample:
demographic indicators
Characteristic
Number (%)
Gender
Male
Female
51 (73.9)
18 (26.1)
Age (Mean, SD)
Mean (SD)
35.67 (8.06)
Education
Elementary
Junior high
Senior high/technical school
20 (29.0)
38 (55.1)
11 (15.9)
Heroin use
Ever used heroin
Never used heroin
46 (67.7)
22 (32.4)
CD4 Count, Baseline (Mean, SD)
318.58 (184.55)
All patients on: D4T/AZT, 3TC, NVP/EFZ
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Findings: characteristics of the sample:
clinical indicators
Undetectable Viral Load (UDVL)
N (%)
UDVL, Baseline (yes)
UDVL, Month 6 (yes)
45 (66.2)
57 (87.7)
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Findings: mean adherence according to
different measures
Measure
N
Mean adherence (SD)
Self-report
Visual analog scale
SMAQ tool - 6-item version (proportion of visits adherent)
SMAQ tool - 10-item version (proportion of visits adherent)
69
69
69
0.98 (0.04)
0.37 (0.34)
0.29 (0.30)
Pill Count
68
0.96 (0.11)
Electronic data monitor (EDM)
Proportion of doses taken
Proportion of doses taken, taken on time
Composite EDM measure
69
69
69
0.97 (0.05)
0.89 (0.18)
0.86 (0.19)
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1. What is the best measure of
adherence?
Relationship between adherence and UDVL
(Odds of achieving viral suppression for each
10% increase in adherence to therapy)
Adherence measure
Odds Ratio [95% CI] P-value
Self-report
Visual analog scale
SMAQ tool - 6-item version (proportion of visits adherent)
SMAQ tool - 10-item version (proportion of visits adherent)
0.43 [0.01-14.48]
1.17 [0.91-1.58]
1.26 [0.90-1.76]
0.6395
0.2315
0.1716
Pill Count
1.36 [0.78-2.38]
0.2772
Electronic data monitor (EDM)
Proportion of doses taken
Proportion of doses taken, taken on time
Composite EDM measure
1.43 [0.40-5.14]
1.46 [1.06-2.00]
1.55 [1.10-2.20]
0.5840
0.0205
0.0128
note: N=65 due to 4 patients missing a Month 6 viral load measure
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What is the best measure of adherence?
Area under receiver-operating characteristic
(ROC) curves
(Comparing sensitivity and specificity for predicting UDVL,
aiming to maximize the area under the curve)
Adherence measure
Area under curve Std
Self-report
Visual analog scale
0.5581
0.
SMAQ - 6-item (propor. visits adherent)
0.6261
0.
SMAQ - 10-item (propor. visits adherent)
0.6371
0.
Pill count
0.7807
0.
Electronic drug monitor (EDM)
Proportion of doses taken
0.6952
0.
Proportion of doses taken, on-time
0.7851
0.
Composite EDM measure
0.7654
0.
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2. What is adherence in this
population?
Percent
Adherence groups (EDM composite measure)
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Mean adherence using this measure: 86%)
N=10
N=5
< 70%
70-80%
N=10
N=12
N=32
80-80%
90-95%
>= 95%
Adherence level
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Patient-level EDM view:
A near perfect patient profile
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Patient-level EDM view:
A patient with adherence problems
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Conclusions
1. EDM measures are best predictors of viral
suppression in this population.
2. Self report measures are poor predictors of
viral suppression in this population.
3. Patients have relatively high adherence
overall, though about one-half are below ideal
level.
4. Appropriate dose timing appears to play a
role in viral suppression.
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Thank you.
Questions?
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ROC curves for prediction of UDVL,
Month 6
Test of H0: equal
ar eas under cur ves - - p=0. 0122
Sensi t i vi t y
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1 - Speci f i ci t y
I ndex
EDM com
posi t e
Pi l l count pr
Fr om DeLong et al
EDM pr opor t i o
Vi sual anal og
( 1988, Bi om
et r i cs 44)
G
EEM
A 10 pr op
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