1 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

2 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

3 Background China has one of the fastest growing HIV epidemics in the worldChina has one of the fastest growing HIV epidemics in the world China is rapidly scaling up ART but treatment programs are at an early stageChina is rapidly scaling up ART but treatment programs are at an early stage Among Chinese patients on ART:Among Chinese patients on ART: Little is known about levels of adherence, particularly among IDUs and former IDUsLittle is known about levels of adherence, particularly among IDUs and former IDUs Little is known about factors that affect adherenceLittle is known about factors that affect adherence Little is understood about how to improve adherenceLittle is understood about how to improve adherence

4 Study site, Dali, Yunnan Province Yunnan province Dali

5 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: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 ARTAdherence among Chinese patients on ART Factors that affect adherenceFactors that affect adherence Possible strategies for improving adherencePossible strategies for improving adherence Phase I focused on qualitative dataPhase I focused on qualitative data Phase II was a 6-month longitudinal studyPhase II was a 6-month longitudinal study Phase III involved assessing a pilot intervention to improve adherence among these patientsPhase III involved assessing a pilot intervention to improve adherence among these patients

6 Overview of AFL (Control) Continued passive observation (Intervention) Active EDM feedback Adherence observed prospectively via EDM, relationship between barriers and actual adherence, clinical outcomes measured Phase I 6 months Phase II 6 months Phase III 6 months Qualitative investigations on what patients/doctors in Dali view as key barriers to adherence Randomized controlled trial to determine effectiveness of EDM data feedback strategy N=80 Patients enrolled N=69 Patients randomized

7 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)

8 Methods: measures of adherence (assessed monthly*, all measures averaged over 6 month period) 1.Self report 1.Visual analog scale (% of doses taken) 2.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)

9 Methods: measures of adherence 3.Electronic data monitoring (EDM) (*continuous) 1.Proportion of doses taken (# taken / # prescribed doses) 2.Proportion of doses taken on time (# taken +/- 1 hr of schedule / # taken) 3.Composite EDM measure, incorporating proportion taken and proportion taken on time (# taken +/- 1 hr of schedule / # prescribed doses)

10 Methods: clinical measures (assessed at baseline and 6 months) Viral load (binary; <400 copies= “undetectable”)Viral load (binary; <400 copies= “undetectable”)

11 Methods: assessment of adherence measures 1.Odds ratios via logistic regression 2.Area under receiver-operating characteristic (ROC) curves

12 Findings: characteristics of the sample: demographic indicators All patients on: D4T/AZT, 3TC, NVP/EFZ

13 Findings: characteristics of the sample: clinical indicators

14 Findings: mean adherence according to different measures

15 Relationship between adherence and UDVL (Odds of achieving viral suppression for each 10% increase in adherence to therapy) 1. What is the best measure of adherence?

16 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)

17 2. What is adherence in this population? N=10 N=5 N=10 N=12 N=32 Mean adherence using this measure: 86%)

18 Patient-level EDM view: A near perfect patient profile

19 Patient-level EDM view: A patient with adherence problems

20 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.

21 Thank you. Questions?

22 ROC curves for prediction of UDVL, Month 6