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Self-Report Overestimates Adherence: Electronic Drug Monitoring vs. Self-Report among HIV-Positive Patients in Yunnan, China Results from the “Adherence for Life” study Sabin L 1 ., Gill C 1 ., Bachman DeSilva M 1 , Wilson, I 2 ,Bobo M 1 , Zhang J 3 , Tao L 1 , Wu W 1 , Xu K 4 , and Hamer D 1 1 Center for International Health and Development, Boston University School of Public Health, Boston, MA, U.S.A. 2 Tufts University, Boston, MA, U.S.A. 3 Ditan Hospital, Beijing, China. 4 Dali Second People’s Hospital, Dali, China P-318 Results Background Objectives Methods China has one of the fastest growing HIV epidemics in the world; HIV is spreading most rapidly in border provinces like Yunnan China is rapidly scaling up antiretroviral therapy (ART) but treatment programs are at an early stage, especially outside major urban centers and in border provinces like Yunnan ART requires high adherence to be effective, but little is known about: levels of adherence among Chinese patients how best to measure adherence in Chinese populations Determine ART adherence rates among Chinese patients using multiple methods Self-reports Pill counts Electronic data monitoring (EDM) Determine which measure is most accurate, using change in CD4 as the biological outcome measure Followed 80 HIV-positive patients in Dali for 6 months, Collected monthly adherence data using 3 methods Calculated associations among self- reported adherence, pill counts, and EDM measures over six months using Spearman correlations Calculated association between each adherence measure and change in CD4 count between baseline and six months Table 1: Patient Demographics Conclusions 1.Measured adherence varies substantially among the three adherence methods • Very strong association between adherence via EDM and changes in CD4. • Self reported adherence rates are unrealistically high • No association between self report and changes in CD4 • Self report does not accurately measure adherence in this population 2.EDM is most accurate measure for predicting change in CD4 3.Individual level data suggest that EDM could be Dali China Acknowledgements Thanks to: Mary Jordan, Billy Pick, David Stanton, Lois Bradshaw, Neal Brandes, Connie Osborne, Ray Yip, Ann Hendricks, Steven Safren, and Anna Knapp. Special thanks to the medical staff at the Dali Second People’s Hospital as well as the Dali-based HIV/AIDS Figure 1: Average adherence, multiple measures Characteristic N um ber (% ) M ean (SD ) G ender M ale 50 (73.5) Fem ale 18 (26.5) A ge (M ean, SD ) 35.6 (8.1) Ethnic background H an Chinese 33 (48.5) Bai 31 (45.6) O ther 4 (5.9) M aritalstatus Single 31 (45.6) M arried 37 (54.4) Education Elem entary 20 (29.4) Juniorhigh 37 (54.4) Seniorhigh/technicalschool 11 (16.2) Em ploym entstatus Currently em ployed 17 (25.4) Currently notem ployed 50 (74.6) H ousehold size 3.8 (1.5) H eroin use Everused heroin 45 (67.2) H asneverused heroin 22 (32.8) Experience w ith detox center H asbeen in detox 40 (59.7) H asneverbeen in detox 27 (40.3) Selfreport Pillcount EDM n=68 n=67 n=68 Self-report* 1.000 0.044 p= .722 0.138 p= 0.262 Pillcount - 1.000 0.168 p= 0.175 EDM - - 1.000 * V isualA nalog Scale * M easuresare m onthly adherence ratesaveraged oversix m onths Table 2. Correlation among different adherence measures Selfreport Pillcount EDM n=45 n=45 n=45 % change in C D 4 after6 m onths r= -0.20 p = 0.18 r= 0.01 p = 0.93 r= 0.39 p < 0.01 Table 3. Correlation between adherence measures and change in CD4 cell counts after 6 months. Patient_ID=57 Date 17000 17010 17020 17030 17040 17050 17060 17070 17080 17090 17100 Hours from scheduled time 0 1 2 3 4 5 6 7 8 Patient_ID=3 Date 16960 16970 16980 16990 17000 17010 17020 17030 17040 17050 17060 17070 17080 17090 Hours from scheduled time 0 1 2 3 4 5 6 7 Figure 2: Scatter plots of individual patient adherence patterns (dose timing) 0 10 20 30 40 50 60 70 80 90 100 110 Selfreport Pill count ED M Mean adherence over 6 months (%) Minimum level Maximum level
