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Correlates of HIV Infection among Injection Drug Users — Unguja, Zanzibar, 2007

Correlates of HIV Infection among Injection Drug Users — Unguja, Zanzibar, 2007. Dita Broz, PhD, MPH Epidemiology and Strategic Information Branch Global AIDS Program Co-authors:

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Correlates of HIV Infection among Injection Drug Users — Unguja, Zanzibar, 2007

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  1. Correlates of HIV Infection among Injection Drug Users — Unguja, Zanzibar, 2007 Dita Broz, PhD, MPH Epidemiology and Strategic Information Branch Global AIDS Program Co-authors: Andrea Kim, Evelyn Kim, Abigail Holman, Ahmed Khatib, Asha Othman, MahmoudMussa, Lisa Johnston, Alfred Kangolle, Mohammed Dahoma

  2. Background • HIV in Sub-Saharan Africa* • 22.4 million • Heterosexual transmission • Data on other transmission routes is limited * UNAIDS HIV Epidemic Update, 2009

  3. Injection Drug Use and HIV • Global estimates of drug injection* • 16 million injection drug users (IDUs) • 19% of IDUs living with HIV • IDUs have increased risk for HIV • Sharing injection equipment • High risk sex practices • HIV transmission to the general population • IDUs have increased risk for other bloodborne infections, such as hepatitis C virus (HCV) *Mathers et al, Lancet 2008: 372:1733-45

  4. HIV Epidemic in Unguja • Unguja, Zanzibar* • Total population 621,000 • Most reside in rural areas • 97% Muslim • Adult HIV prevalence is 0.8%† • 0.9% females • 0.6% males • Concentrated HIV epidemic Unguja * Tanzania Population and Housing Census 2002 † Tanzania HIV/AIDS and Malaria Indicator Survey 2007-2008

  5. Injection Drug Use in Unguja • Increase in local drug markets and drug use • Exploratory study of IDUs in 2005* • 30% HIV prevalence • Unsafe injection practices • Reports of direct blood sharing *Dahoma et al, African J of Drug & Alc Studies 2006: 5(2):130-139

  6. Behavioral and Biological Surveillance Survey, 2007 Overall Goal: To provide information on the prevalence of HIV infection and associated risk factors from a representative sample of IDUs Analysis Objectives: • Describe socio-demographics and high-risk behaviors of IDUs • Estimate HIV and HCV seroprevalence • Assess independent correlates of HIV seroprevalence

  7. Survey Design • August – September, 2007 • Respondent-driven sampling (RDS) • Probability-based, peer-recruitment sampling • Designed to sample hard-to-reach populations • Eligibility • Injected drugs in the past 3 months • Age ≥15 years • Lived in Unguja in the past 3 months • Able to provide informed consent *Heckathorn, Soc Probl 1997: 44:174-99

  8. RDS Recruitment Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Seed (n=7) Note: Illustration is created for demonstration purposes only

  9. RDS Recruitment Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 • Limited number of referrals per subject • Statistical adjustment based on: • Social network size • Recruitment pattern Seed Note: Illustration is created for demonstration purposes only

  10. RDS Recruitment Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Note: Illustration is created for demonstration purposes only

  11. RDS Recruitment Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Note: Illustration is created for demonstration purposes only

  12. RDS Recruitment Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Note: Illustration is created for demonstration purposes only

  13. RDS Recruitment Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 . . . N=493 Note: Illustration is created for demonstration purposes only

  14. Data Collection • Behavioral questionnaire • HIV testing • Serial 2-test algorithm using rapid HIV tests • Discordant specimen retested using a 3rd rapid HIV test • HCV testing • Rapid test strips for detection of HCV antibody • Pre- and post-test counseling and referrals for follow-up care

  15. Statistical Analysis • Descriptive Analysis – RDS Analysis Tool (RDSAT) • Estimated population proportions • Adjusted for social network size and recruitment patterns • Logistic Regression - SAS • HIV seroprevalence weights generated by RDSAT • Univariate and stratified analyses • Multivariable analysis • Odds ratios (OR) and 95% confidence intervals (CI) for the final model

  16. Socio-demographics (N=493) *Proportion estimates and 95% confidence intervals (CI) are adjusted for RDS design

  17. Drug Using Characteristics (N=493) *Proportion estimates and 95% confidence intervals (CI) are adjusted for RDS design

  18. High Risk Injection Behaviors (N=493) *Proportion estimates and 95% confidence intervals (CI) are adjusted for RDS design

  19. Sexual Behaviors (N=493) STI =sexually transmitted infection symptoms included genital discharge , genital or anal sores *Proportion estimates and 95% confidence intervals (CI) are adjusted for RDS design

  20. HIV and HCV Seroprevalence 95% confidence interval 43 co-infections (54.4% of HIV+)

  21. Multivariable Model of Factors Associated with HIV Seroprevalence * OR’s are adjusted for age, sex and all variables listed in the table

  22. Summary of Key Findings • HIV seroprevalence among IDUs in Unguja, Zanzibar was high • Over half of HIV-positive IDUs were co-infected with HCV • HCV serostatus was the strongest correlate of HIV seroprevalence: biomarker of injection risk • Indicators of high-risk sex behaviors were associated with HIV seroprevalence

  23. Limitations • Cross-sectional data • Not able to assess temporality • Not able to assess directionality • Self-reported behavioral data • Potential social desirability bias • Misclassification • Unable to assess sexual contact with non-injecting partners • Small number of females

  24. Public Health Implications • Need for comprehensive services • Reduction of injection and sexual risk behaviors • Strategies to link to treatment and care services for substance abuse, HIV and other STIs • Integration of HCV prevention, counseling and testing • Routine behavioral and biological surveillance • Size estimation

  25. Acknowledgements • Survey Participants • Zanzibar Study Team • Zanzibar AIDS ControlProgramme • Mohammed Dahoma • Ahmed Khatib • Asha Othman • MahmoudMussa • Tulane University • Lisa Johnston • Leigh Ann Miller • CDC GAP Atlanta • Andrea Kim • John Aberle-Grasse • Sanny Chen • Amy Drake • Avi Hakim • Roberta Horth • Evelyn Kim • Janet Lee • William Levine • Abraham Miranda • Christopher Murrill • Joyce Neal • Wanjiru Maruiru • Aisha Yansaneh • Irum Zaidi • CDC Tanzania • Gilly Arthur • John Grove • Irene Benech • Abigail Holman • Mary Kibona • Alfred Kangolle The findings and conclusions in this presentation are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

  26. Thank you!

  27. Programs for IDUs Prior to 2007 • Few IDU prevention programs • Education campaign by NGOs, Zanzibar Associated of Information Against Drug Abuse and Alcohol, and religious leaders • Peer education/community mobilization • No specific harm reduction strategies (e.g., access to sterile equipment, bleach cleaning) • No substance abuse rehabilitation facilities • Little focus on MARPs for HIV testing, care and treatment

  28. Current Public Health Practice • Strategic plans • Substance Use and HIV/AIDS Strategic Plan 2007-2011 • Minimum Package for HIV Prevention • Currently implemented or planned programs • Mobile outreach services for HIV and STI testing • Drop-in centers • Sensitization training for health care workers • Distribution of bleach kits • Plans for medication assisted therapy • Plan for hepatitis B and C virus screening and interventions

  29. Substance Use and HIV/AIDS Strategic Plan 2007-2011 • To reduce HIV/STI infections by 50% by 2011 • Provide treatment care and support to IDUs and their families Strategies: • Outreach for HIV prevention (risk reduction, HIV testing) • Mobile HIV testing • Community education and media campaign • Links to substance abuse treatment and counseling • Sensitization of health care workers (HCW) • Training of HCW on management of HIV, STI among users • Increase access to HIV care and treatment

  30. HIV and HCV Seroprevalence, by sex Males (n=478) Females (n=16) HIV HCV HCV HIV

  31. Background: Hepatitis C Virus • Chronic bloodborne infection • Primary transmission through percutanous exposure to infectious blood • Infrequent transmission through sex and MTCT • HCV as a biomarker of past injection risk • HCV and HIV have common transmission route • Injection-related transmission probability 10x greater for HCV than for HIV • HCV propagates through injection drug using populations earlier and quicker than HIV

  32. Medical Injections, Unguja • Medical injection in the past 12 months, 15-49 years old (Unguja) • Average number of medical injections per person • Females 1.0 • Males 0.7 • For last injection, syringe and needle take from a new, unopened package • Females 98.8% • Males 95.4%

  33. Blood Transfusion • As of 2003, all donated blood in Zanzibar are screened for HIV, HCV, HBV, and Syphilis • HCV Prevalence in Zanzibar general population • Blood donor (Zanzibar, 2002): 5.5% • Pregnant women (Unguja, 2008): 0.2% • Risk of transfusion-transmitted HCV infections in Tanzania (1998-2008) • 678 per 100,000 donations

  34. Biological Test Performance HIV test performance • SD Bioline, Determine® and Unigold™ all have 100% sensitivity and 99.8% specificity HIV testing quality assurance • All positive and 10% of negative specimen retested using enzyme-linked immunosorbent assay (ELISA) HCV test performance • ACON® test strips have sensitivity >99% and specificity 99.6%

  35. Other Biological Testing • Syphilis • ACON® Syphilis Ultra Rapid Test for detection of Treponemapallidumantibodies • 2 tested positive, 0.3% (95% CI 0.0-0.9) • Hepatitis B • ACON® HBsAg virus test strips (surface antigen) • 29 tested positive, 6.5% (95% CI 3.7-9.8)

  36. RDSAT: Homophily • Mixing patterns in networks • Probability HIV + person being connected to another HIV + person, from population HIV + and – people • Either positive or negative (-1 to 1) • Positive homophily: preferential recruit people similar to self • Negative homophily: preferential recruitment people NOT similar to self • When 0 for all groups: equilibrium and sample proportions identical to RDS population

  37. RDSAT: Equilibrium • Equilibrium reached for IDU sample • Assessed key variables: HIV serostatus, age, sex, duration of injecting, income, education • Definition: point where proportions for each variable change minimally regardless of more participants • Attaining equilibrium overcomes biases introduced by non-random seed selection

  38. Traditional Probability Sampling and RDS POPULATION POPULATION Estimation Social Network Collection Collection Estimation SAMPLE SAMPLE Estimation Heckathorn & Salganik, 2002

  39. RDS Assumptions • Participants have well-connected social networks; connections are reciprocal • Network is composed of a single component; connections are dense • Sampling occurs with replacement • Respondents can accurately report their social network size • Peer recruitment is random

  40. Other Methods to Sample Hard-to-Reach Populations and Biases Snowball • Not representative of the target population Time-Location (TLS), venue-based • Only captures those who are visible Institutional sampling • Not representative of target population

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