correlates of hiv infection among injection drug users — unguja, zanzibar, 2007 dita broz, phd,...

Correlates of HIV Infection among Injection Drug Users — Unguja,

Zanzibar, 2007

Dita Broz, PhD, MPHEpidemiology and Strategic Information Branch

Global AIDS Program

Co-authors: Andrea Kim, Evelyn Kim, Abigail Holman, Ahmed Khatib, Asha

Othman, Mahmoud Mussa, 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

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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

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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

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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

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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:1. Describe socio-demographics and high-risk

behaviors of IDUs2. Estimate HIV and HCV seroprevalence3. Assess independent correlates of HIV seroprevalence

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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

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Wave 1 Wave 2 Wave 3 Wave 4 Wave 5

Seed (n=7)

RDS Recruitment

Note: Illustration is created for demonstration purposes only

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Seed

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


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11



12



13

RDS Recruitment Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 . . .

N=493


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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

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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

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Socio-demographics (N=493)Selected variables n % (95%CI)*

Median age 31 years (range 15-66)Males 478 96.9 (94.2-98.7)

Completed ≤7 years of education 151 34.4 (28.8-40.6)

Household income <100,000 TZS (~74 USD), past month 174 42.7 (36.6-48.4)

Current living situation Family Alone Spouse/partner Friends No stable address

31492591314

64.1 (58.1-70.4)18.6 (14.3-23.2)11.4 (7.8-15.8)

4.2 (1.2-7.4)1.6 (0.6-2.9)

*Proportion estimates and 95% confidence intervals (CI) are adjusted for RDS design

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Drug Using Characteristics (N=493)Selected variables n % (95%CI)*

Median duration of injecting 10 years (range 1-36)

Injected heroin, past 3 months 491 98.4 (96.8-100.0)

Injected several times per day, past month 481 97.5 (95.8-99.0)

Used non-injecting drugs (other than alcohol), past 3 months

Marijuana Powder cocaine Heroin Pain killers/prescription drugs

385

328212135187

77.4 (72.6-81.8)

66.7 (61.2-71.5)47.4 (41.5-52.4)38.2 (32.1-46.6)43.3 (38.1-48.6)


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High Risk Injection Behaviors (N=493)

Selected variables, past month n % (95%CI)*

Shared needles 251 53.4 (47.8-59.0)

Shared needles always/most of the time 162 34.6 (23.9-39.9)

Shared needles with steady sex partner 161 35.3 (29.7-40.9)

Prepared drugs with others using the same equipment always/most of the time 222 45.5 (39.7-50.5)

Direct blood sharing 22 3.3 (1.5-5.9)


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Sexual Behaviors (N=493)

Selected variables n % (95%CI)*

Sexually active, past month 257 52.7 (46.5-58.8)

Sex with steady partner, past month 189 38.9 (32.9-44.5)

Sex in exchange for money/gifts, past month 95 26.2 (23.5-30.1)

≥2 sex partners, past month 174 32.4 (27.2-38.0)

Never used condoms, lifetime 175 34.2 (28.8-39.1)

STI symptoms, past 6 months 91 19.2 (14.7-24.0)

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

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HIV and HCV Seroprevalence

HIV HCV0%

5%

10%

15%

20%

25%

30%

16.1%

26.4%

43 co-infections(54.4% of HIV+)

95% confidence interval

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Multivariable Model of Factors Associated with HIV Seroprevalence

AOR* (95%CI)

HCV-positive serostatus 3.1 (1.6-6.2)

Never used condoms, lifetime 2.5 (1.3-4.8)

STI symptoms, past 6 months 2.1 (1.1-4.2)

Completed ≤7 years of education 2.2 (1.1-4.4)

* OR’s are adjusted for age, sex and all variables listed in the table

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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

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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

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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

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Acknowledgements

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.

• Survey Participants

• Zanzibar Study Team

• Zanzibar AIDS Control Programme– Mohammed Dahoma– Ahmed Khatib– Asha Othman– Mahmoud Mussa

• 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

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Thank you!

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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

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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

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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

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HIV and HCV Seroprevalence, by sex

Series10%

10%

20%

30%

40%

50%

60%

70%

80%

14.2%

74.0%

26.1%

39.4%

HIV HCV HIV HCV

Males (n=478) Females (n=16)

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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

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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%

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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

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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%

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Other Biological Testing

• Syphilis– ACON® Syphilis Ultra Rapid Test for detection of

Treponema pallidum antibodies– 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)

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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

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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

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Traditional Probability Sampling and RDS

POPULATION

SAMPLE

Estimation

POPULATION

SAMPLE

SocialNetwork

Collection

Estimation

Estimation

Heckathorn & Salganik, 2002

Collection

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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

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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

correlates of hiv infection among injection drug users — unguja, zanzibar, 2007 dita broz, phd,...

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