1. PREDICTORS OF LOW ADHERENCE TO HAART AMONG INDIGENT HIV-INFECTED MEN WHO HAVE SEX WITH MEN, IN MIAMI/DADE COUNTY’S HIV DRUG ASSISTANCE PROGRAM Jason Gagnon, M.A., M.P.H., 1 Seth Welles, Sc.D., Ph.D., 2 Anthony J. Japour, MD 3 1 Division of Graduate Medical Science, Boston University School of Medicine, Boston, MA, 02118, 2 Department of Epidemiology, Boston University School of Public Health, Boston, MA, 3 Department of Medicine, University of Miami Miller School of Medicine, Miami FL, 33101 Background: This study evaluated whether demographic and behavioral factors, current illicit drug use and history of addiction, HIV disease indicators and treatment history are associated with low HAART adherence in populations seeking free healthcare. Results: Ninety-seven HIV+ MSM enrolled in an AIDS Drug Assistance Program in Miami participated in focus groups to identify barriers to HAART adherence and condom use. Surveys administered during focus groups collected data on demographics, illicit drug use/history of addiction, and history of HAART treatment adherence. Findings: Patients recruited were mature (median age: 40 yrs.) with lower level of education (77% ≤ high school), and high unemployment (79%); more than half were men of color. Greater than 50% of subjects reported being noncompliant to HAART ever, and over one-third reported noncompliance in the prior weekend. Factors associated with low adherence included marijuana use [odds ratio (OR): 4.67, p = 0.01], and years since HIV diagnosis [OR: 1.09 (9% increase per year), p< 0.03], and perceived barriers to ARV adherence [OR: 14.05; 95% CI: 2.40, 82.16; p= 0.003, for quartile 2 vs quartile 1 of total score for inventory of barriers; and OR:10.02; 95% CI:1.45, 69.13), for quartile 3 vs quartile 1 of total score]. Conclusion: In this patient group of HIV+ men receiving free HIV medications through government supported AIDS Drug Assistance program, it was current drug use and addiction history (not demographic profiles or ART treatment parameters) that were associated with HAART non-adherence. Our findings suggest that treatment of current illicit drug use may have optimal impact on treatment adherence in groups of lower income HIV+ men with long-term infection. Adherence to the HAART regimen is inarguably the most important determinant for long term virologic suppression and health in HIV+ individuals. Previous studies have identified a broad array of factors associated with lower HAART adherence, including: demographics (younger age, black race, education level, income, employment status1, transmission route) mental health indicators and correlates (depression, drug use, partner intimacy, sexual functioning) factors associated with HIV/AIDS disease progression, treatment, and clinical care (medical care status, higher plasma HIV-1 RNA level, treatment type/regimne) rigor of treatment, level of satisfaction with HAART information, fear of side effects, occurrence of side effects (e.g. including lipodistrophy causing the person to be “outed” as HIV+) and pill burden are major predictors of adherence. Mental health indicators and recreational drug use both impact adherence to antiretroviral therapy [ART] and are potentially modifiable. Depression is an independent predictor of adherence associated with low self-efficacy, inability to manage side effects, medication fatigue, and inadequate understanding that non-adherence can advance disease status—again, all of which are additional predictors of adherence. Levels of adherence changes over time along with changes of attitudes, beliefs, physical ability, and cognitive changes. We estimate rates of ART adherence and identify independent predictors of adherence in a clinic-based sample of men in Miami; the sample was intentionally enrolled to be reflective of the community served by the Miami-Dade County Health Department’s AIDS Drug Assistance Program, which includes approximately 50% white/Hispanic and 50% black/African-American. Participants 97 HIV-infected men recruited through Miami’s ADAP Program 54% are White/Hispanic, 29% Non-Hispanic Black, and 6% are Black/Haitian. 98% are at least 20 years old. Eligibility Men were eligible if they were at least 18 years old, HIV-positive, sexually active, self-identified as men who have sex with men (MSM), able to speak, read, and converse in English, Spanish, or Creole, and able to provide written informed consent. Data Collection Data for analyses were collected from 97 focus group participants. Data that were collected included: regarding self-reported adherence to HIV medications, barriers to adherence, recreational drug use, and condom use. Data Analysis Study variables: Age in years, race/ethnicity, sexual orientation identity, education, employment status, sexual behaviors, condom use during intercourse, recreational substance abuse, HIV medical history (most recent CD4 cell count and plasma HIV-1 viral load), and parameters of current HIV treatment regimen (types and numbers of specific ARVs). The dependent variable: Self-reported lower levels (<50%) of ART adherence Contingency table analysis to identify differences: Demographics, level of HIV knowledge, perceived barriers to ART adherence Continuous data: Tests for difference between groups included Wilcoxon rank-sum tests Categorical data: Tests for differences included Pearson Chi-square and Fischer Exact tests, as appropriate. Simple and multiple logistic regressions were conducted to identify independent risk factors for associations with low levels of ART adherence. All odds ratios are presented with associated 95% confidence intervals. This project would not have been possible without the continued support and expert contributions of my principal advisor and mentor, Dr. Seth Welles. Additionally, I would like to thank Dr. Anthony Japour for his assistance in making available the raw data available used in this research project as well as for his insights and contributions into the manuscript. Finally, I would like to express gratitude for the research subjects who ultimately made this study possible. Abstract 156575Methods and Data CollectionResults (Continued) Introduction Acknowledgements Results Table 2. HIV/AIDS Disease Indicators and Treatment Parameters, by Level of ART Adherence Table4. Associations of Substance Use, HIV Treatment History and Disease Indicators, and Levels of Perceived Barriers to ART Adherence with Self-Reported Low Adherence HIV Disease/Treatment Parameter Patients with Low Adherence (n=47) Patients with Higher Adherence (n=45) TotalP-value HIV Disease Indicators Median Plasma HIV-1 RNA copies/ml (25%-75% interquartile range) 18,500 (1,165, 240,000) 1,160 (25, 25,000) 11,000 (505, 135,000) 0.05 Median CD4 cell count/mm 3 (25%-75% interquartile range) 300 (165, 432) 416 (303, 603) 350 (219, 554) 0.02 Median Months on ARV (25%-75% interquartile range) 60 (36, 120) 48 (24, 96) 60 (24,120) 0.21 Overall ARV Regimen (%) NRTI-Containing39/46 (84.7)41/43 (95.3)80/89 (90.0)0.16 NNRTI-Containing14/46 (30.4)11/43 (25.6)25/89 (28.1)0.65 PI-Containing30/46 (65.2)25/43 (58.1)55/89 (61.8)0.52 Fuzeon-Containing3/46 (6.5)2/43 (4.7)5/89 (5.6)1.00 No. ARVs in Rx Regimen (%) One5/46 (10.9)1/43 (2.3)6/89 (6.7)0.09 Two17/46 (37.0)15/43 (34.9)33/89 (37.1) Three19/46 (41.3)18/43 (41.9)39/89 (43.8) Four5/46 (10.9)9/43 (20.9)16/89 (18.0) Table 3. Perceived Barriers to ART Adherence, for all Participants Possible Barriers to Adherence Frequency of Likelihood (%) for Each Barrier 1 Highly Unlikely 23 No Likely or Unlikely 45 Highly Likely Alcohol52 (54.2)11 (11.5)8 (8.3)2 (2.1)24.0 (23) Marijuana57 (60.0)6 (6.3)7 (7.4)2 (2.1)24.2 (23) Other Recreational drug use59 (62.8)5 (5.3)8 (8.5)2 (2.1)21.3 (20) Organization57 (61.3)10 (10.8)9 (9.7)3 (3.2)15.1 (14) Forgetting36 (37.5)23 (24.0)9 (9.4) 19.8 (19) Think you took meds but did not40 (42.6)24 (25.5)7 (7.5) 10.0 (16) Don ’ t believe in taking medications 69 (71.9)8 (8.3)2 (2.1)3 (3.3)14.6 (14) Use of alternative non-prescription drugs65 (69.2)8 (8.5)6 (6.4)5 (5.3)10.6 (10) Number of pills49 (52.1)11 (11.7)22 (23.4)4 (4.3)8.5 (8) Toxic side effects48 (50.5)11 (11.6)13 (13.7)8 (8.4 )15.8 (15) Physical side effects41 (42.7)12 (12.5)17 (17.7)7 (7.3)19.8 (19) Work Schedule62 (65.3)10 (10.5)8 (8.4)6 (6.3)9.5 (9) Cost of medications65 (68.4)5 (5.3)7 (7.4) 11.6 (11) Selling medications to cover other expenses73 (77.7)5 (5.3)1 (1.1) 14.9 (14) Selling medications to buy recreational drugs74 (78.7)2 (2.1)3 (3.2)1 (1.1)14.9 (14) Homelessness58 (61.7)6 (6.4) 7 (7.5)18.1 (17) Fear of people learning your HIV status55 (57.3)8 (8.3)9 (9.4)5 (5.2)19.8 (19) Food Requirements56 (58.3)10 (10.4)13 (13.5)4 (4.2)13.5 (13) Conclusions FactorUnadjusted OR (95% CI) Adjusted OR (95% CI) P-value (adjusted model) Marijuana Use6.05 (2.12, 17.25)4.67 (1.43, 15.27)0.01 Cocaine Use5.00 (1.51, 16.60)- - - Ever been in Drug Treatment Program2.62 (0.99, 6.90)- - - Years since HIV Diagnosis (increased risk per year) 1.07 (>1.00, 1.14)1.09 (1.0, 1.19)0.03 CD4 cell count (increased risk per 100 cells) 0.90 (0.80, 1.10)- - - Viral Load (copy number)1.00 (1.00, 1.00)- - - Viral Load (categorical) < 500 copies1.0 (referent)- - - 500 -9999 copies0.67 (0.21, 2.16)- - - > = 10000 copies2.34 (0.92, 5.97)- - - Perceived Barriers to Adherence Summary Score Quartile (%) Quartile 4 (Highest)2.91 (0.70, 12.0)3.02 (0.53, 17.24)0.21 Quartile 38.0 (1.84, 34.79)10.02 (1.45, 69.13)0.02 Quartile 28.0 (1.93, 33.18)14.05 (2.40, 82.16)0.003 Quartile 11.00 (referent) Study participants who used marijuana were 4 times as likely to be low HAART adherers Length since HIV diagnosis was significantly associated with low HAART adherence Demographics and other disease indicators were not associated with low adherence in this group. Findings suggest that treatment of current illicit drug use may have optimal impact on treatment adherence in groups of lower income HIV+ men with long- term infection. DemographicPatients with Low Adherence (n=47) Patients with Higher Adherence (n=45) Total study population P-value Age (yrs)45 (43,50)45 (41, 48)45 (41,49)0.53 Race (%) Black26/44 (59.1)20/42 (47.6)46/86 (53.5)0.29 Non-Black18/44 (40.9)22/42 (52.4) Ethnicity (%) Haitian3/40 (7.5)3/44 (6.8)6/84 (7.1)0.69 Hispanic19/40 (47.5)25/44 (56.8)44 /84(52.4) Non-Hispanic18/40 (45.0)16/44 (36.4)34/84 (40.5) Orientation (%) Gay19/46 (41.3)18/44 (40.9)37/90 (41.1)0.31 Bisexual12/46 (26.1)17/44 (38.6)29/90 (32.2) Heterosexual15/46 (32.6)9/44 (20.5)24/90 (26.7) Education (%) High School32/39 (82.1)30/41 (73.2)62/80 (77.5)0.34 College/Grad School 7/39 (17.9)11/41 (26.8)18/80 (22.5) Employment (%) Part-time or unemployed 10/43 (23.3)9/44 (20.5)19/87 (21.8)0.75 Full-time33/43 (76.7)33/44 (79.5)68/87 (78.2) Frequency MSM activity (%) Always13/42 (31.0)13/40 (32.5)26/82 (31.7)0.28 Usually4/42 (9.5)9/40 (22.5)13/82 (15.9) Sometimes9/42 (21.4)8/40 (20.0)7/82 (20.7) Rarely4/42 (9.5)2/40 (5.0)6/82 (7.3) When under influence /high 12/42 (28.6)8/40 (20.0)20/82 (24.4) Table 1. Demographics and Measures of Sexual Orientation/Homosexual Behaviors