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1 Women’s Interagency HIV Study (WIHS) Prepared in part by WIHS Data Management and Analysis Center (WDMAC) Phone: 410-614-1340 (-7125 FAX)

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Presentation on theme: "1 Women’s Interagency HIV Study (WIHS) Prepared in part by WIHS Data Management and Analysis Center (WDMAC) Phone: 410-614-1340 (-7125 FAX)"— Presentation transcript:

1 1 Women’s Interagency HIV Study (WIHS) Prepared in part by WIHS Data Management and Analysis Center (WDMAC) Phone: 410-614-1340 (-7125 FAX) email: wdmac@jhsph.edu http://statepiaps.jhsph.edu/wihs Range of Data, specimens and analyses available to researchers investigating drug use and its ramifications Kathy Anastos MD

2 2 Women’s Interagency HIV Study (WIHS) SanFrancisco Bronx, NY Chicago Los Angeles WashingtonDC Brooklyn, NY Baltimore, MD (Data Center)

3 3 WIHS Sites and Principal Investigators  Consortia:  Bronx, NY (K. Anastos)  Brooklyn, NY (H. Minkoff)  Chicago, IL (M. Cohen)  Los Angeles, CA (A. Levine)  Northern California (R. Greenblatt)  Washington, DC (M. Young)  Data Coordinating Center (WDMAC):  Johns Hopkins University, Baltimore, MD (S. Gange)

4 4 WIHS Sponsoring Institutions (Project Officers)  National Institute of Allergy and Infectious Diseases (G. Sharp; C. Williams – Epidemiology Branch Chief)  National Cancer Institute (G. Dominguez)  National Institute of Child Health and Human Development (H. Watts)  National Institute on Drug Abuse (K. Davenny)

5 5 Areas of Focused Scientific Research in the WIHS  Neurocognition  Cancer  Cardiovascular Disease  Drug Use & Behavior  Genetics  Pharmacology  Gynecology/Menopause  HPV  Immunology/Pathogenesis  Metabolic  Hepatitis / Liver Disease  Epidemiology

6 6  Cardiovascular Disease: Assessment of aspects of cardiovascular disease among HIV+ and HIV- women, including cardiovascular risk factors, carotid artery intima- media thickness (IMT), and clinical cardiovascular events  Genetics and Disease Progression: Interaction between genetic factors, substance abuse, mood, cognitive impairment, and progression of HIV disease  Neurocognition: Interaction between aging and HIV/AIDS on neurocognitive functioning among HIV+ and HIV at-risk women  Physical Functioning: Cross-sectional assessment to identify and characterize physical impairment and frailty among HIV+ and HIV at-risk women WIHS Major Active Substudies

7 7 WIHS Major Active Substudies cont’d.  Metabolic : Cross-sectional and longitudinal assessment of metabolic parameters including fat distribution, lipoprotein levels, bone density and glucose tolerance determined via DXA scan, glucose tolerance testing (GTT), and MRI  Intensive Pharmacokinetics (PK): Bioavailability and clearance rates for five target antiretroviral medications  Sex Steroid: Effect of HIV infection on age of onset of diminished ovarian reserve

8 8 Semi-Annual Visit  Interview Administered Questionnaires :  Medical and OB/GYN History  Health Services  Physical and Gynecological Examination  Body Habitus Exam (anthropometrics, BIA)  Medication use (ART, OI prophylaxis, hepatitis, etc.)  Participant Samples :  Blood (virologic, immunologic, fasting lipodystrophy markers, liver/renal function, etc.)  Other (CVL, saliva, hair)  Local and National Repositories Demographics/Psychosocial Behavior

9 9 Continuous Outcome Ascertainment  Seroconversion  Clinical Outcomes :  AIDS Diagnoses  Malignancies  Mortality  Tuberculosis  Cardiovascular Diagnoses  Liver biopsies  Hysterectomies  Sources :  Medical Record Abstractions  Registries (Cancer, AIDS, TB)  National Death Index

10 10 WIHS Cohort (as of 3/06) * 3766 Seroprevalent: 2791 (74%) Seronegative: 975 (26%) AIDS (34%) AIDS (47%) 1378 (66%) AIDS baseline 715 (26%) AIDS-free baseline 2076 (74%) Seroconverter 17 (  1) * (2%) Seronegative 958 (98%) Dead (39%) Dead 375 (  16) (52%) Dead (9%) Dead 43 (  3) (4%) Dead 9 (53%) Dead 1 (  1) ** (25%) AIDS-free 273 (  20) 122 (  7) 698 (  18) *HIV and AIDS status as of the of visit 23 (3/31/06). Deaths reflect those ascertained through 12/31/04 using the National Death Index and through 4/7/06 using other reports. (11%) 2 (  1) **One seroconverter found at death. 8 (  1)

11 11 Baseline Characteristics (1) (Barkan, Melnick,..., Feldman, Epidemiology 1998; 9:117-125)** 94/95 Cohort01/02 Cohort HIV+HIV-HIV+HIV- Median age 36343329 Race/ethnicity: African-American56%54%60%61% Latina 23%28%32%28% Exposure Category: Intravenous drug use34%28%10%13% Heterosexual risk42%26%41%24% Transfusion risk4%3%--*--* No identified risk20%43%48%63% * Transfusion risk not assessed in 01/02 cohort ** 01/02 cohort data added

12 12 94/95 Cohort01/02 Cohort HIV+HIV-HIV+HIV- Median per capita household income$4,500 $5,000$4,500 $4,500 No health insurance18%41%15%39% History of physical/sexual abuse66%67%9%16% Median CD4+ count (cells/mm 3 )3301028493984 Median viral load (copies/ml, NASBA assay)22,000---610--- Baseline Characteristics (2) (Barkan, Melnick,..., Feldman, Epidemiology 1998; 9:117-125)* * 01/02 cohort data added

13 13 Baseline Characteristics (3) (Bacon, Von Wyl,..., Young, CDLI 2005; 12:1013-1019) Parameter or condition 94/95 Cohort 01/02 Cohort HBV core antibodies4319 HBV surface antigen2.61.4 HCV antibodies3913 SIL on Pap test * 138 Diabetes4.44.6 Hypertension1712 Obesity5365 Smoking5842 *Low-grade SIL, high-grade SIL, or carcinomas were detected in situ on initial Pap smear. HBV, hepatitis B virus; HCV, hepatitis C virus; SIL, squamous intraepithelial lesion. Percent with comorbidity

14 14 WIHS Database (as of 3/06) Total number of PARTICIPANTS enrolled2807HIV+ 959HIV- Total number of PERSON-YEARS17,659HIV+ 5,627HIV- Median FOLLOW-UP TIME in years10.1094/95 3.5901/02 Total number of PERSON-VISITS34,233HIV+ 11,010HIV- Total number of CD4 MEASUREMENTS32,436HIV+ 6,978HIV- Total number of VIRAL LOAD MEASUREMENTS32,368 Total number of REPOSITORY ALIQUOTS* 1,842,688 * Available as of 11/14/2006

15 Year Incidence per 100 person years HAART Use Among HIV+ (%) HAART WIHS Self-Reported AIDS Incidence, Mortality and Use of HAART (Gange, Barrón,..., Muñoz, J Epi Comm Health 2002; 56:153-159) (as of 3/06) AIDS Death

16 Antiretroviral Therapy Use Among WIHS HIV+ (Cook, Cohen,..., Young, AJPH 2001, 92:82-87) (as of 3/06) 2001/2 Recruits Join Cohort July '95 Jan '96 July '96 Jan '97 July '97 Jan '98 July '98 Jan '99 July '99 Jan '00 July '00 Jan '01 July '01 Jan '02 July '02 Jan '03 July '03 Jan '04 July '04 Jan '05 July '05 Jan '06 Calendar Time % Receiving Therapy Monotherapy Combination Therapy HAART Therapy no PI HAART Therapy with PI 0 20 40 60 80 100

17 17 Antiretroviral Use Among WIHS HIV+ (Kirstein, Greenblatt, …, Gange, JAIDS 2002, 29:495) (as of 3/06) 0 20 40 60 80 100 Dec 95Dec 99Dec 03 % on NRTI Treatment Lamivudine Tenofovir Zidovudine Abacavir Emtriva Truvada* Combivir* Didanosine Trizivir* Stavudine Epzicom* Zalcitabine *Not exclusive 0 20 40 60 80 100 Dec 95Dec 99Dec 03 % on PI Treatment Ritonavir Atazanavir Lopinvir Nelfinavir Fosamprenavir Saquinavir Indinavir Tipranavir Amprenavir 0 20 40 60 80 100 Dec 95Dec 99Dec 03 % on NNRTI Treatment Efavirenz Nevirapine Delavirdine

18 18 Effect of Substance Use on HIV disease progression

19 19 Association of Race and Gender with HIV-1 RNA (Anastos, Gange,..., Greenblatt, JAIDS 2000; 24:218-226) 200  CD4 < 350 CD4 < 200 Relative Difference of HIV-1 RNA in WIHS Women relative to MACS Men 350  CD4 < 500500  CD4 Relative Difference of HIV-1 RNA in Whites relative to Non-whites  p < 0.05  p < 0.005      

20 20 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 Non-IDU IDU (267) (136) (345) (133) (516) (162) (986) (314) CD4 200-350 CD4 <200 CD4 350-500 CD4 >500 (n)(n) Median HIV RNA Anastos. JAIDS 2000;24:218. Rates  21% lower (P<.001) Association of HIV-1 RNA with history of Injection Drug Use with

21 21 Time from HAART initiation (years) Proportion Alive 012345678 0.0 0.2 0.4 0.6 0.8 1.0 No. No. of RH ( 95% CI ) Adjusted ( 95% CI ) Deaths RH* African-American 573 113 1 -- 1 -- White 184 25 0.63 ( 0.41, 0.98 ) 1.11 ( 0.64, 1.93 ) Hispanic 204 31 0.68 ( 0.46, 1.01 ) 0.81 ( 0.47, 1.39 ) Factors Associated with Response to HAART (1) (Anastos, Schneider,..., Cohen, JAIDS 2005; 39,5:537-544) * Adjusted for ART use prior to HAART initiation, age at last pre-HAART visit, pre-HAART AIDS status, pre-HAART nadir CD4 + cell count, pre-HAART peak HIV-1 RNA, self-reported baseline HIV-1 exposure category, depression, current drug use, cigarette smoking, income, ART used following HAART initiation.

22 22 Factors Associated with Response to HAART (2) (Anastos, Schneider,..., Cohen, JAIDS 2005; 39,5:537-544) ExposureVirologic Response Virologic Rebound Immunologic Response Immunologic Failure Incident ADI DeathAIDS Death ART naïve prior to HAART1.770.820.960.721.290.941.41 Age at last pre-HAART visit (per 10 years) 1.190.911.121.000.981.361.18 Pre-HAART AIDS0.981.040.930.922.191.351.62 Pre-HAART nadir CD4 + count (per 100 cells) 1.081.021.091.270.860.610.30 Pre-HAART peak HIV-1 RNA (per log 10 ) 0.621.481.020.891.381.611.63 Therapy used after HAART No therapy1111111 Mono/combo8.550.304.070.590.720.530.71 HAART16.140.267.040.310.740.460.33 Depression (CES-D > 16)0.811.220.961.981.621.651.06 Current drug use0.891.230.851.111.491.042.35 Currently smoke cigarettes0.721.301.040.931.181.381.05 Income <$12,0000.911.250.981.451.191.641.39 Adherence2.190.371.470.660.650.670.53 Note: Adjusted for HIV-1 exposure and race. Significant values are in bold and yellow.

23 23 Time to AIDS Among Non-injecting Drug Users (NIDU) in the HAART Era (Kapadia, Cook,..., Vlahov, Addiction 2005; 100:990-1002) LRT=0.001 Never User Former NIDU Inconsistent NIDU Consistent NIDU Years to AIDS Survival Probability LRT=0.007 Non User Depressant User Polydrug User Stimulant User Kaplan-Meier curves of cumulative probabilities of survival for AIDS by pattern and type of NIDU among HAART users, WIHS (n=1046).

24 24 Time to Death Among Non-injecting Drug Users (NIDU) in the HAART Era (Kapadia, Cook,..., Vlahov, Addiction 2005; 100:990-1002) Inconsistent NIDU Never User Consistent NIDU Former NIDU LRT=0.035 Depressant User Non User Polydrug User Stimulant User LRT=0.113 Kaplan-Meier curves of cumulative probabilities of survival for death by pattern and type of NIDU among HAART users, WIHS (n=1046). Survival Probability Years to Death

25 25 Association of Smoking with CD4 response to HAART Feldman J, Schneider M, Gange S, Cohen M, Watts H, Gandhi M, Young M, Mocharnuk R, and Anastos K. Impact of Cigarette Smoking on HIV Prognosis Among Women in the HAART Era- A Report from the WIHS. Am J Public Health 2006

26 26 Association of Smoking with HIV-1 RNA response to HAART Feldman J, Schneider M, Gange S, Cohen M, Watts H, Gandhi M, Young M, Mocharnuk R, and Anastos K. Impact of Cigarette Smoking on HIV Prognosis Among Women in the HAART Era- A Report from the WIHS. Am J Public Health 2006

27 27 Progression to AIDS-defining condition by smoking Feldman J, Schneider M, Gange S, Cohen M, Watts H, Gandhi M, Young M, Mocharnuk R, and Anastos K. Impact of Cigarette Smoking on HIV Prognosis Among Women in the HAART Era- A Report from the WIHS. Am J Public Health 2006

28 28 All-cause mortality by smoking Feldman J, Schneider M, Gange S, Cohen M, Watts H, Gandhi M, Young M, Mocharnuk R, and Anastos K. Impact of Cigarette Smoking on HIV Prognosis Among Women in the HAART Era- A Report from the WIHS. Am J Public Health 2006

29 29 Association of Substance Use with Other Outcomes

30 30 Relative Hazard for New HPV Infection in HIV+ Women (Minkoff, Feldman,..., Anastos, JID 2004; 189:1821-1828) RH (95% CI)P Monotherapy vs. none0.81 (0.49 – 1.32).40 Combination therapy vs. none1.06 (0.64 – 1.75).83 HAART vs. none1.08 (0.66 – 1.77).76 Oral contraceptive use (yes vs. no)1.09 (0.69 – 1.72).72 Log CD4+ cell count0.90 (0.83 – 0.97).008 Log viral load1.06 (1.01 – 1.11).01 Smoking status (yes vs. no)1.33 (1.10 – 1.60).003 Parity1.02 (0.98 – 1.06).34 No. sex partners, recent or lifetime0.99 (0.97 – 1.03).80 Age at baseline0.988 (0.97 – 1.00).04 Age at first intercourse1.00 (0.97 – 1.03).79 Parity and age at first intercourse are fixed variables; smoking, number of sex partners, oral contraceptive use, CD4+ cell count, and viral load are time-dependent variables. 1797 women; 501 events.

31 31 Accidental Death in WIHS and MACS  IDU and smoking each independently associated with higher likelihood of accidental death:  IDU: OR=2.6, p=0.0007  Smoking:OR=1.63, p=0.027 Hessol N et al, CID 2007

32 32 Predictors of Menopause and Amenorrhea  Opiate use predicted amenorrhea, but not menopause  Menopause also predicted by age, BMI  Menopause not predicted by HIV status, or any HIV related parameter (CD4, Viral load) Cejtin et al, unpublished data

33 33 Association of Substance Use with Behaviors Including Adherence

34 34 0.1 1 10 Correlates of ART Adherence in the Women's Interagency HIV Study (Wilson, Barrón,..., Young, Clin Infect Dis 2002; 34:529-534) Odds Ratio Crack, Cocaine or Heroin Use < 200200 - 500 CD4 Cell Count (  500 reference) Undetectable HIV RNA Age (10 year ) Health Perception Score (10 pts )

35 35 Drug Use and Sexual Behavior  Women using crack, cocaine or heroin less likely to use condoms: OR=0.63, p<0.001 1  Women who undergoing treatment for drug use were less likely to be sexually active: OR=0.83, p=0.0001 2 1 Wilson et al, AIDS 1999; 13: 591 2 Latka et al. J Subs Abuse Treat 2005; 29:329

36 36 Domestic Violence and Childhood Sexual Abuse in WIHS Women (Cohen, Deamant,..., Melnick, Am J Public Health 2000; 90:560-565) Multivariate Behavioral Correlates of Childhood Sexual Abuse Drug use, ever (OR* = 4.25, p <.001) Male partner w/HIV risk (OR* = 2.07, p <.001) Lifetime male sex partners (>10) (OR* = 2.29, p <.001) Sex for drugs, money or shelter (OR* = 2.62, p <.001) *OR adjusted for HIV serostatus, age, race/ethnicity and annual household income NS p <.01 NS Baseline Prevalence

37 37 Hepatitis C

38 38 Association between Renal Disease and Outcomes in HIV-Infected Women Receiving HAART (Szczech, Hoover,..., Anastos, Clin Infect Dis 2004; 39:1199–1206)

39 39 Predictors of HCV viremia in adjusted models  Age (>35 years) OR= 1.6, p=0.005  African Ancestry OR= 1.6, p=0.04  Crack/cocaine use OR= 1.7, p=0.02  HBV viremia OR= 0.3 p=0.006  Current smoking OR= 1.7, p=0.009 Operkalski et al unpublished data

40 40 Opiate Use, HCV and incident Diabetes Howard et al, unpublished data 2007

41 41 Opiate Use, HCV and incident Diabetes, cont’d. Howard et al, unpublished data 2007

42 42 Translational Studies

43 43 CD4 cell count per mm 3 HIV-1 RNA (copies/mL) 1102005002,0001001101002005002,000804004,00040,000400,000 % HLADR+/DR38+/CD4+ HIV-negativeHIV-positive 2 3 4 5 6 7 8 9 10 20 100 90 80 70 60 50 40 30 100 90 80 70 60 50 40 30 20 10 9 8 7 6 5 4 3 2 ABC Impact of Drug Use on CD4 Cell Activation (Landay, Benning,..., Kovacs, JID 2003; 188:209-218) Drug Users Non-drug Users

44 44 HCV quasispecies  Women with active IDU more likely to experience shift in quasispecies of HCV over time: OR=5.12, p=0.004 Laskus…..Kovacs, unpublished data

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47 Cohort Composition  935 participants  715 HIV infected women  221 HIV negative women  ~50% of each group are women who survived genocidal rape  At 1.5 years follow-up 410 HIV+ women have initiated ART  No tobacco or drug use  20% use alcohol

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