Correlates of HIV Risk Among Injecting Drug Users in Sixteen Ukrainian Cities
AIDS and Behavior ISSN 1090-7165 Volume 15 Number 1 AIDS Behav (2010) 15:65-74 DOI 10.1007/ s10461-010-9817-6
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Author's personal copy AIDS Behav (2011) 15:65–74 DOI 10.1007/s10461-010-9817-6
ORIGINAL PAPER
Correlates of HIV Risk Among Injecting Drug Users in Sixteen Ukrainian Cities Yuriy S. Taran • Lisa G. Johnston • Nataliia B. Pohorila • Tetiana O. Saliuk
Published online: 28 September 2010 Ó Springer Science+Business Media, LLC 2010
Abstract We present findings from a HIV survey using respondent driven sampling among 3,711 injecting drug users (IDUs) in 16 cities in Ukraine in 2008. Eligible participants were males and females who injected drugs in the past 1 month, C16 years and lived/worked in their respective interview area. The impact of injecting and sexual risk behaviors on HIV-infection were analyzed using four logistic models. Overall HIV prevalence was 32%. In the sexual risk model, paying for sex in the past 3 months and condom use during last sex increased the odds of HIV infection. Being female, having greater than 3 years of injection drug use, always sharing equipment and using alcohol with drugs in the past month remained significant in all four models. These findings indicate the urgent need to scale up peer education, needle exchange and methadone substitution programs for IDUs with specific programs targeting the needs of female injectors Keywords Ukraine HIV/AIDS Injecting drug users Sexual risk behaviors Respondent driven sampling
Y. S. Taran (&) Graduate School for Social Research, Nowy Swiat Street, 72, GSSR, PAN, Warsaw, Poland e-mail:
[email protected] L. G. Johnston Department of International Health & Development, Tulane University School of Public Health & Tropical Medicine, New Orleans, Louisiana, USA L. G. Johnston N. B. Pohorila T. O. Saliuk ICF International HIV/AIDS Alliance in Ukraine, Kyiv, Ukraine
Introduction Ukraine, a former soviet republic with a population of approximately 47 million people, is experiencing one of the fastest growing HIV epidemics in the world [1]. At the end of 2007 it was estimated that 395,296, or 1.6% of the population aged between 15 and 49 years, were living with HIV in Ukraine [2]. In 2008, 1,294 additional new cases of HIV-infection were officially registered compared to the previous year [3]. The areas most affected with HIV comprise the southern (Mykolaiv, Odesa, Kherson, and Simferopol) and eastern (Dnipropetrovsk, Donetsk, Luhansk, and Kharkiv) regions of Ukraine. Although only a third of the population lives in these regions, they represent two-thirds of all officially registered HIV cases. The western region (Lviv and Lutsk) remain the least affected [4]. The significant diversity of HIV prevalence between regions can be attributed to the differing socio-economic situations. The eastern region, best described as the industrial center of the country, was especially hard hit after the collapse of the Soviet Union when numerous factories, coal mines and enterprises were closed down [5]. The prevalence of injection drug use is also highest in this region [2]. The southern region, known as the ‘gateway’ for HIV infection into Ukraine, comprise Mykolaiv and Odessa cities which serve as important port towns and are considered conduits of HIV infection to and from other countries. These cities are also home to a large proportion of IDUs and female sex workers [6]. In 1997, data from routine monitoring recorded 83.6% of newly registered HIV cases among injecting drug users (IDUs), resulting in increased surveillance in this population [4]. Although the percentage of newly registered HIV cases among IDUs decreased in the following years to 37% by 2009 [3], HIV infection rates among IDUs are still
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alarmingly high, especially given that IDUs constitute a stigmatized and hidden population and routine sentinel surveillance is likely to miss many HIV-infected IDUs. In 2007, 22% of those newly infected with HIV through sexual transmission reported an injection drug user as a regular sexual partner in the previous 12 months [7]. According to recent estimations there are 230,000–360,000 IDUs in Ukraine [6]. Currently a plethora of information exists about the drug use practices and the impact these practices have on the increase of HIV among IDUs in Ukraine [8–10]. Qualitative research indicates that IDUs inject a variety of mostly homemade drugs using easily accessible over-the-counter ingredients such as cold medicines and household chemicals. Commonly used drugs include the opiate-based ‘‘hanka’’ or ‘‘hemia’’ (liquid poppy straw or latex involving a lengthy preparation process of mixing chemicals, cooking and straining) and amphetamine-based ‘‘shirka’’, ‘‘vint’’ or ‘‘jeff’’ (ephedrine, methamphetamine, or methcathernone involving a lengthy preparation process of mixing chemicals, cooking and straining) or ‘‘baltushka’’ (ephedrine based involving a cold-shaking process to separate tablets from liquid) [8, 10–12]. Economic, political and social instability, misinformation and incorrect beliefs about drug injection risks and HIV transmission, social stigmatization, police policies towards IDUs, gender differences in drug use practices are suggested contributors to the rise of HIV among IDUs [8, 13]. Most data collected about HIV among IDUs in Ukraine come from sentinel surveillance and a scattering of studies using convenience sampling methods, some of which report findings during ongoing data collection from an evolving sample in three Ukrainian cities [9, 11, 13, 14]. In 2008, ICF International HIV/AIDS Alliance undertook a large scale bio-behavioral surveillance study among IDUs to assess HIV prevalence and injecting and sexual risk behaviors using Respondent Driven Sampling (RDS) in 16 cities throughout Ukraine. This is the most extensive study of its kind conducted anywhere in the world using a probability based sampling method to acquire estimates of HIV prevalence among IDUs. This paper presents findings from this study and examines associations between IDUs who tested positive for HIV and their injecting and sexual risk behaviors.
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recruitment is a stochastic process in which samples comprised of long recruitment chains can be analytically adjusted to represent the population sampled [15, 16]. Sampling began with a set number of initial eligible participants referred to as ‘seeds’ identified through local organizations working with IDUs in each city. Seeds were diverse with respect to age, place of work, injecting behaviors and educational level. Seeds, as well as each participant who completed the survey, received up to three recruitment coupons to use in recruiting other eligible IDUs. Coupons included a unique number which was used to track who recruited whom and to ensure anonymity in linking the questionnaire and biological specimens. Individuals who presented a valid coupon to the interview location were screened for eligibility, provided information about the survey, asked to provide consent and responded to questions about their injecting and sexual behaviors and use of HIV services and testing. Participants were interviewed face-to-face by trained interviewers either in Russian or in Ukrainian. Interviews were conducted using a standardized questionnaire developed from validated behavioral surveillance tools and indicators [17, 18], and the involvement of key stakeholders and local experts. To measure response biases at the time of the survey, interviewers were asked to assess the behavior of the participant once the interview was completed. The average interview (without blood screening) lasted about 1 h. Upon completing the interview, participants were screened on-site for HIV using blood from a finger prick and rapid testing (Formasco CITO TEST HIV-1/2). Respondents who wanted their test results were provided with pre- and post-test counseling and those with positive test results were provided referrals to a local AIDS center for further HIV testing by enzyme-linked immunosorbent assay (ELISA). No personal identifiers were collected and questionnaires, test results and referrals were linked using a number assigned to each participant’s recruitment coupon. Eligible participants received a primary incentive (*US$ 3.00) for completing the interview and a secondary incentive (*US$ 2.00) for each (no more than three) eligible recruit. Sample sizes varied by region, depending on IDU prevalence. The ethical review boards of the L. V. Gromashevsky Institute of Epidemiology and Infectious Diseases and the Sociological Association of Ukraine approved the protocol and materials.
Methods Eligibility, Sampling and Study Sites
Variables
RDS was used to recruit males and females, 16 years and older, who injected drugs in the previous 30 days and lived/worked in their respective interview area. RDS
Several injection and sexual risk behaviors were assessed in this paper. Table 1 provides dichotomous data on HIV prevalence, condom use at last sex and needle sharing at
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Table 1 Sample sizes, number of seeds, and RDSAT adjusted estimates and 95% confidence intervals (CI) for HIV status, condom use at last sexual intercourse and sharing injection equipment at last injection among injecting drug users in the survey cities, Ukraine, 2008 Sample cities and sizes
HIV status
Used a condom at last sexual intercourse
Shared injection equipment at last injection
Study cities
N
N
% (95% CI)
N
%(95% CI)
(Seeds) recruits N = 3,711
% (95% CI)
Eastern region Dnipropetrovsk
(6) 113
51
40.3 (29.4,52.8)
53
39.9 (24.3,55.6)
28
20.8 (12.8,29.1)
Donetsk
(6) 400
143
33.2 (26.9,39.7)
169
52.9 (44.4,62.2)
80
22.9 (16.0,28.4)
Luhansk
(6) 200
9
6.7 (2.3,12.2)
108
54.1 (44.9,63.3)
11
4.4 (1.3,8.5)
Kharkiv
(5) 175
18
10.6 (4.8,16.1)
53
41.5 (27.5,51.8)
29
11.3 (8.1,19.0)
Lutsk
(4) 175
45
26.7 (19.3,34.9)
67
41.4 (32.2,50.1)
14
6.2 (2.8,12.4)
Lviv
(7) 175
39
21.0 (15.2,29.9)
63
43.2 (33.3,52.8)
28
15.6 (9.4,22.7)
(4) 225 (6) 260
58 207
26.7 (19.9,34.4) 79.9 (70.2,88.0)
144 144
67.2 (58.2, 74.8) 65.9 (55.6,75.7)
10 56
5.1 (2.2.,10.3) 22.3 (15.4,27.4)
Odesa
(6) 400
150
36.8 (30.4,43.0)
171
53.7 (47.6,60.3)
56
15.6 (10.4,18.3)
Simferopol
(5) 265
185
65.5 (57.4, 73.1)
124
64.1 (57.2,76.5)
29
10.5 (6.0,17.4)
(5) 173
22
9.3 (4.6, 16.2)
62
43.6 (32.2,55.2)
49
29.2 (21.3,37.6)
Cherkasy
(3) 175
45
18.2 (11.6,27.0)
100
56.7 (47.3,69.1)
21
12.5 (5.6,19.5)
Khmelnytskyi
(7) 200
55
26.8 (18.2,36.5)
110
59.9 (51.1,70.8)
28
10.1 (5.8,16.5)
Kirovohrad
(4) 175
20
13.2 (8.1,18.8)
42
29.1 (19.7, 43.5)
15
9.5 (2.9,12.6)
Kyiv
(8) 400
160
30.8 (24.7,36.6)
190
59.0 (53.6, 67.4)
68
13.7 (9.0,16.7)
Poltava
(4) 200
59
23.7 (16.6,32.0)
122
67.2 (58.5, 77.7)
57
26.7 (17.2,33.1)
Western region
Southern region Kherson Mykolaiv
Northern region Sumy Central region
last injection for each of the survey cities. In Table 2 all survey data are aggregated to present categorical sociodemographic (gender, marital status, education, and occupation which included possible categories of being a student and being physically disabled); last time (sharing needles/ syringes) and past month (drug type used, alcohol and prefilled syringe use and drug use frequency, and needle/ syringe and drug preparation equipment sharing and using drugs from common drug preparation equipment) drug and injection use; past year and past month sexual contact and past 3 months sexual risk (with permanent, commercial, and occasional partners); condom use at last sexual contact (with permanent, commercial, occasional partners) and HIV testing (knows where to get an HIV test, had HIV test in lifetime and past year, was positive at last HIV test and HIV test results in the survey). Age group, years of injection drug use, frequency of past drug use, past month number of injection partners and frequency of sex partners in the past 3 months were continuous recoded into categorical variables. Years of drug injection were coded into ‘‘\1 year’’ (categorized to assess HIV-infection among those who recently initiated injection drug use), ‘‘1–3 years’’ (categorized to assess harm
reduction programs which started in 2004), ‘‘4–10 years’’ and ‘‘C11 years’’ (the latter two categorized based median cut-offs). Frequency of sex partners during the past 3 months was categorized as ‘‘1 partner’’ (the least risky category), ‘‘2–3 partners’’ and ‘‘more than 3 partners’’.
Data Analysis HIV proportion estimates and 95% confidence intervals (CI) for each city were calculated using the RDS Analysis Tool 6.0.1 (RDSAT), a software package specifically developed to analyze data collected through RDS (www.respondentdrivensampling.org). RDSAT was developed to minimize biases associated with chain referral sampling by weighting participants’ probability of recruitment (social network size) into the RDS sample and controlling for homophily and recruitment patterns [15]. All variables analyzed were assessed for equilibrium with convergence set at 2% of the sample estimate. For descriptive and multivariate regression analyses cities were aggregated and adjusted with RDSAT generated sample weights on HIV status (dependent variable) to derive
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Table 2 Selected demographic characteristics, injecting and sexual behaviors, service exposure, and HIV status and testing history among injecting drug users in Ukraine, 2008. Sample size and unadjusted estimates Injecting drug users (N = 3,711) N
Estimate %
Sociodemographic characteristics Gender
3,711
Male
74.6
Female Age group
25.4 3,711
16–19
3.0
20–24
17.1
25–34
44.3
35? Marital status Single
35.6
Table 2 continued Injecting drug users (N = 3,711) N 1 partner
45.4
2–3 partners
36.1
[3 partners
18.5
Frequency of sharing a needle/syringe with someone who used it to inject before participant in last month
3,670
Never
81.3
Sometimes
16.9
Always
1.8
Frequency of sharing a needle/syringe with someone who used it after participant in last month
3,640
Never
3,711
Estimate %
85.9
51.6
Sometimes
13.0
Married
42.5
Always
1.1
Divorced/widowed
3.6
Married w/other partners Education
2.3 3,711
Basic secondary
19.2
Complete secondary
64.6
Higher education
16.1
Occupation
3,711 25.3
Full-time employed
25.0
Occasional employment
36.0
Housekeeper
6.4
Physically disabled
3.4
Type of drug injected in last month (multiple answers possible)
3.9 3,711
Opiates
77.4
Methamphetamines
26.4
Combination of drugs
15.7
Used alcohol with drugs in last month Years of drug injection
3,711
9.2
3,711
25.7
Always
30.6
Frequency using drugs from common 3,661 drug preparation equipmenta in last month 42.2
Sometimes
27.4
Always
30.5
Sexual behaviors Had sexual contact in past year
3,637 87.0
Sexual contact in past month
3,181 85.6
Sex with permanent partner in past 3 months
3,198 76.3
Paid for sex in past 3 months
3,198 5.3
Received payment for sex in past 3 months
3,198 3.4
Sex with occasional partner in past 3 months
3,198 37.5
Frequency of sex partners in past 3 months
3,134
1 partner 2–3 partners
59.7 20.4
More than 3 partners
19.9 3,152 55.2 2,375 47.8
Used condom with commercial partner at last sex
271
29.2
Used condom with occasional partner at last sex
1,260 71.8
58.8
Frequency of condom use with occasional partner in past year
1,105
1–3 years
26.2
3–10 years
39.9
[10 years 3,695
41.2
Used prefilled syringe in last month
3,658
55.4
Shared injection needle/syringe at last injection
3,661
15.2
Number of persons with whom needles/ syringes were shared in last month
738
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43.7
Sometimes
Used condom with permanent partner at last sex
4.8
Every day or more
Never
Used condom at last sex
More than 1 year
Drug frequency in last month More than once a day
3,660
Never
Unemployed
Student or other Drug use and injecting behaviors
Frequency of sharing drug preparation equipmenta in last month
Always Not always
79.3
47.9 52.1
Knows where to get an HIV test
3,691 87.7
Had an HIV test in lifetime
3,670 57.1
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Table 2 continued Injecting drug users (N = 3,711) N
a
Estimate %
Had an HIV test in past year
2,081
61.1
Reported HIV positive status based on last HIV test
1,654
36.1
HIV positive status in current study
3,705
32.0
Spoons, cookers or any vessel used to prepare drugs
estimates. Multivariate regression analysis controlled for city differences in order to derive adjusted odds ratios (AORs). Multivariate Logistic Regression Table 2 variables were tested for significant relationships with HIV status using Pearson’s chi-square test for categorical variables and t-test for continuous variables. Variables that remained significant at P B 0.05 and did not exhibit multicollinearity (correlation r C 0.5) were retained in the initial model. Four multivariate regression models were constructed with manually entered variables to find the best fitting statistical models, controlling for city and weighted by HIV status. Model I includes respondents who were tested for HIV during the study (N = 3487), regardless of their HIV status before enrolling into the study. Model II includes only those respondents who were found to be HIV-positive or negative as a result of HIV testing during the study, but the respondents who already knew their HIV-positive status prior to our study were excluded (N = 2929). The underlying logic for this is to mitigate any bias resulting from respondents having changed their sexual and drug use behaviors as a result of already knowing they were HIV-positive. Model III includes those in Model II and is limited to those respondents who reported having sex during the last year (N = 2477). Model IV included those in Model II and is limited to those who reported sexual contact with an occasional partner in the past 3 months (N = 866).
Results Overview by City Data were gathered from 3,711 IDUs from 16 cities throughout Ukraine over 6–10 weeks (Table 1). HIV seroprevalence among IDUs in the 16 sampled cities ranged from 6.7% (CI. 2.3, 12.2) in Luhansk (eastern region) to 79.9% (CI. 70.2, 88.0) in Mykolaiv (southern region). In the eastern region, HIV seroprevalence was highest in Dnipropetrovsk (40.3%; CI. 29.4, 52.8). In the western
region, HIV among IDUs was 26.7% (CI. 19.3, 34.9) in Lutsk and 21% (CI. 15.2, 29.9) in Lviv. The southern region of the country had the highest HIV seroprevalence including Simferopol (65.5%, CI. 57.4, 73.1), in addition to Mykolaiv. The only city sampled in the northern region (Sumy) had an HIV seroprevalence of 9.3% (CI. 4.6, 16.2). In the central region, IDUs in Ukraine’s capital city, Kyiv, had an HIV seroprevalence of 30.8% (CI. 24.7, 36.6). Condom use at last intercourse was under 68% for all cities with the lowest being in Kirovohrad (29.1%, CI. 19.7, 43.5). IDUs in three of the four cities in the southern region reported the highest condom use (Kherson, 67.2%, CI. 58.2, 74.8; Mykolaiv, 65.9%, CI. 55.6,75.7, and Simferopol, 64.1%, CI. 57.2,76.5) as did one city in the central region (Poltava, 67.2%, CI. 58.5, 77.7). The highest percentage of IDUs reporting they shared injection equipment at last injection was in the northern region (Sumy, 29.2%, CI. 21.3, 37.6), whereas the lowest was in Luhansk (4.4%, CI. 1.3, 8.5). Sociodemographic Characteristics The majority of IDUs in all cities were male (74.6%). IDUs had a median age of 31 years (min. 16, max. 65 years), median age at first injection of 20 years (min. 5, max. 61 years) and a median of 8 years injecting drugs (min.\1, max. 46 years). The majority of IDUs was either single (51.6%) or married (42.5%), completed a secondary education (64.6%) and had occasional employment (36%) versus being fully employed (25%) or unemployed (25.3%). Drug Use and Injecting Behaviors Most IDUs reported using opiate type drugs (77.4%), compared to methamphetamines (26.4%) and drug combinations (15.7%). Few IDUs reported using alcohol with drugs in the previous month (9.2%). The majority of injectors reported injecting for more than 1 year: 39.9% for between 3 and 10 years and 29.2% for more than 10 years. More than half of IDUs reported using a prefilled syringe in the last month but only 15.2% reported sharing injection equipment at last injection. Among IDUs who reported injecting with someone else in the past month (N = 738), 45.4% generally shared needles and syringes with only one other partner. More than 80% of IDUs reported never sharing a needle/syringe with someone who used it before or passing it to another person after they had in last month. Although 43.7% of IDUs reported never sharing drug preparation equipment and 42.2% reported never using drugs from common drug preparation equipment in the past month, approximately 30% reported always participating in these behaviors.
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Sexual Behaviors The majority of IDUs reported having sexual contact in the past month (85.6%). Most participants had sexual contact with a permanent partner (76.3%) or/and with an occasional partner (37.5%), and less than 6% reported commercial sex in the previous 3 months. Sixty percent of IDUs reported having one sexual partner in the past 3 months. Just over half of IDUs reported using a condom during last sexual contact. Reported condom use was most common during last sex with any commercial partner (79.3%), followed by occasional partners (71.8%) and permanent partners (47.8%). HIV Testing and Prevalence Most IDUs reported knowing where to obtain an HIV test (87.7%) and 57.1% had an HIV test in their lifetime of which 61.1% did so in the past 1 year. Among those who received their HIV test results from their last HIV test, 36.1% reported their test results as positive. Overall HIV prevalence for the 16 cities sampled during the course of this study was 32%. HIV Risk Factors Table 3 presents findings using four models to assess HIV impact on demographic characteristics, drug use and sexual behaviors. In Model I (N = 3487) those who tested positive for HIV during the study were significantly more likely to be female (AOR = 1.6, P = 0.000) or physically disabled (AOR = 2.5, P = 0.000) and less likely to have completed secondary (AOR = 0.8, P = 0.032) or higher (AOR = 0.7, P = 0.010) education (compared to basic education), or have full or occasional employment (AOR = 0.8, P = 0.047 and AOR = 0.7, P = 0.004, respectively) or to be a student (AOR = 0.3, P = 0.002). These participants were also significantly more likely to have used drugs for a longer duration ([10 years, AOR = 6.9, P = 0.000), to have used alcohol with drugs (AOR = 1.5, P = 0.006) and to have always (compared to never) shared drug preparation equipment (AOR = 1.5, P = 0.001) in the past month. When the model is reduced to only those respondents who tested positive during the course of the study and did not know their HIV-positive status prior to the study (Model II, N = 2929), those who tested HIV positive were significantly more likely to be female (AOR = 1.5, P = 0.000) or physically disabled (AOR = 2.2, P = 0.002) and less likely to have higher (AOR = 0.7, P = 0.041) education (compared to basic education) or to be a student (AOR = 0.4, P = 0.037). These participants were also significantly more likely to have used drugs for a
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longer duration ([10 years, AOR = 3.9, P = 0.000), used alcohol with drugs (AOR = 1.5, P = 0.013) or always (compared to never) shared drug preparation equipment (AOR = 1.5, P = 0.003) in the past month. Model III (N = 2477) found HIV-positive status to be significantly associated with being female (AOR = 1.8, P = 0.000), physically disabled (AOR = 2.2, P = 0.022), having longer duration of drug injection (more than 10 years, AOR = 3.0, P = 0.002), using alcohol with drugs (AOR = 1.8, P = 0.001) or to have sometimes (AOR = 1.4, P = 0.028) or always (AOR = 1.7, P = 0.000) (compared to never) sharing drug preparation equipment in the past month. HIV-positive IDUs in this model were also significantly more likely to have had sex with commercial partners in the past year (AOR = 1.9, P = 0.003) and to have used a condom during last sexual contact with any sexual partner (AOR = 1.9, P = 0.000). In Model IV (N = 866) HIV-positive status was significantly associated with being female (AOR = 1.75, P = 0.046), longer duration of drug injection ([10 years, AOR = 4.5, P = 0.017), alcohol use with drugs (AOR = 2.4, P = 0.012) or to have always (AOR = 1.9, P = 0.017) (compared to never) shared drug preparation equipment in the past month. In this model HIV-positive IDUs were more likely to have used condoms during last sexual contact with an occasional partner (AOR = 2.4, P = 0.001). Response Bias The majority of interviewers (67.8%) assessed that interviewed participants responded honestly to questions; whereas some participants were assessed as unable to follow the questions at times (17.1%), inattentive (15.5%), ‘‘in a sleepy state caused by drug intoxication’’ (15.1%), ‘‘closed’’ or ‘‘reluctant’’ to respond to questions (6.6%), dishonest (6.3%) and aggressive (2.2%).
Discussion This study provides important information about the HIV prevalence, injecting and sexual risk behaviors among IDUs in 16 cities throughout Ukraine. IDUs in Ukraine have a HIV seroprevalence of 32% and engage in unsafe risk behaviors including using prefilled syringes, and sharing injection and drug preparation equipment and having unprotected sex. HIV prevalence among IDUs in the majority of cities was higher than 20% and alarmingly high among IDUs in Mykolaiv and Simferopol, the southern region of Ukraine. In a study of IDUs recruited through street outreach conducted in Makeevka/Donetsk, Kyiv and Odessa in 2005 [9], selected IDUs were found to have an HIV prevalence around 33% in all three cities;
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Table 3 Adjusted odds ratios (confidence Intervals) of multivariate logistic regressions for associations with HIV infection and select sociodemographic characteristics and injecting and sexual risk variables among injecting drug users in Ukraine using four models,a controlling for cities differences and weighting with RDSAT generated weights on HIV status Model I (N = 3,487)
Model II (N = 2,929)
Model III (N = 2,477)
Model IV (N = 866)
1.55 (1.27, 1.89)**
1.54 (1.22, 1.93)**
1.75 (1.35, 2.26)**
1.75 (1.01, 3.04)*
Married
1.13 (0.93, 1.36)
1.11 (0.89, 1.37)
1.23 (0.97, 1.57)
0.96 (0.58, 1.58)
Divorced/widowed
1.13 (0.73, 1.74)
0.95 (0.54, 1.67)
1.00 (0.50, 1.99)
1.18 (0.39, 3.55)
Married w/other partners
0.67 (0.38, 1.17)
0.73 (0.39, 1.34)
0.66 (0.33, 1.31)
0.72 (0.20, 2.60)
Sociodemographic characteristics Female (ref. male) Marital status (ref. single)
Education (ref. basic secondary) Complete secondary
0.78 (0.63, 0.98)*
0.78 (0.60, 1.00)
0.78 (0.59, 1.04)
0.55 (0.32, 0.92)*
Higher education
0.68 (0.51, 0.91)*
0.70 (0.50, 0.99)**
0.74 (0.51, 1.07)
0.65 (0.33, 1.32)
0.79 (0.62, 0.99)*
0.82 (0.62, 1.09)
0.80 (0.58, 1.09)
0.74 (0.38, 1.44)
Occupation (ref. unemployed) Full-time employed Occasional employment
0.73 (0.59, 0.90)**
0.87 (0.68, 1.13)
0.86 (0.65, 1.14)
0.82 (0.49, 1.37)
Housekeeper
1.23 (0.85, 1.77)
1.36 (0.90, 2.06)
1.07 (0.68, 1.70)
0.82 (0.27, 2.48)
Physically disabled Student
2.54 (1.58, 4.08)** 0.28 (0.12, 0.61)**
2.16 (1.24, 3.78)** 0.37 (0.15, 0.89)*
2.20 (1.12, 4.29)* 0.32 v(0.12, 0.83)*
0.70 (0.16, 3.14) 0.66 (0.18, 2.40)
Drug use and injecting behaviors Type of drug injected in last month (ref. average) Opiates
1.16 (0.92, 1.46)
0.98 (0.76, 1.28)
0.96 (0.72, 1.30)
1.00 (0.56, 1.77)
Methamphetamines
0.81 (0.66, 1.00)
0.85 (0.66, 1.09)
0.85 (0.64, 1.12)
1.04 (0.62, 1.74)
Combination of drugs
1.10 (0.87, 1.40)
1.20 (0.91, 1.57)
0.86 (0.63, 1.17)
1.18 (0.66, 2.09)
Years of drug injection (ref. less than a year) 1–3 years
1.95 (1.02, 3.73)*
1.83 (0.95, 3.53)
1.40 (0.70, 2.77)
1.79 (0.51, 6.25)
4–10 years
4.30 (2.28, 8.12)**
3.08 (1.62, 5.88)**
2.38 (1.21, 4.66)**
3.84 (1.15,12.84)*
[10 years
6.89 (3.63,13.09)**
3.91 (2.03, 7.54)**
3.00 (1.50, 5.98)**
4.52 (1.31,15.55)*
0.94 (0.76, 1.16)
0.92 (0.73, 1.17)
1.27 (0.81, 2.00)
1.53 (1.09, 2.14)**
1.84 (1.28, 2.64)**
2.37 (1.21, 4.67)*
0.99 (0.75, 1.30)
0.95 (0.70, 1.29)
1.20 (0.67, 2.14)
Drug frequency in last month (ref. less than once a day) Everyday or more
0.87 (0.72, 1.05)
Used alcohol with drugs in last month Yes
1.51 (1.13, 2.03)**
Shared injection needles/syringes at last injection Yes 1.29 (1.03, 1.63)*
Frequency of sharing drug preparation equipmentb in last month (ref. never share) Sometimes
1.07 (0.86, 1.33)
1.26 (0.98, 1.61)
1.36 (1.04, 1.80)*
1.22 (0.70, 2.08)
Always
1.45 (1.17, 1.79)**
1.46 (1.14, 1.88)**
1.67 (1.26, 2.21)**
1.91 (1.13, 3.31)*
0.86 (0.67, 1.11)
1.06 (0.77, 1.45)
—
—
1.93 (1.22, 3.05)**
—
Sexual behaviors Sexual contact in past year Yes
—
—
Paid for sex in past 3 months Yes
Frequency of sex partners in past 3 months (ref. 1 partner) 2–3 partners
—
—
0.81 (0.60, 1.09)
—
[3 partners
—
—
0.74 (0.54, 1.01)
—
—
1.93 (1.53, 2.42)**
—
Used a condom with any sexual partner at last sex Yes
—
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AIDS Behav (2011) 15:65–74
Table 3 continued Model I (N = 3,487)
Model II (N = 2,929)
Model III (N = 2,477)
Model IV (N = 866)
Used a condom with occasional partner at last sex Yes
—
—
—
2.42 (1.45, 4.05)**
Intercept
–2.33**
–2.77**
–2.28**
–4.72**
Pseudo-R2 : Cox and Snell
0.232
0.114
0.141
0.158
Pseudo-R2 : Nagelkerke
0.324
0.177
0.221
0.264
a
Model I includes respondents who were tested for HIV during the study, regardless of their HIV status before enrolling into the study; Model II includes only respondents whose HIV status was determined during the study; Model III includes those in Model II and is limited to respondents who reported sexual intercourse during the last year; Model IV includes those in Model III and is limited to respondents who reported sexual intercourse with an occasional partner in the past 3 months b
Spoons, cookers or any vessel used to prepare drugs
* P \ 0.05; ** P \ 0.01
findings which are comparable to those found in the same three cities in our study. The majority of IDUs in this survey reported injecting opiates, most likely in the form of liquid poppy straw, often purchased in preloaded syringes [11]. Although few IDUs reported sharing a needle/syringe before or after someone else had injected with it in the previous month, over half of IDUs reported sharing and using drugs from common drug preparation equipment, and using a prefilled syringe in the last month. This suggests that IDUs could be extracting their drug solution from a common container with their own or dealer’s syringe without passing the actual syringe on to others for direct injection. Furthermore, the high percentage of IDUs who use prefilled syringes, which may or may not be sterile, and which may have been front or back loaded into the user’s syringe, sometimes with as many as 12 unrelated IDUs, suggests a very high potential for rapid HIV spread [11, 14]. Although we found that IDUs who share drug preparation equipment are at increased risk of being HIV-infected, there was no association between using a prefilled syringe and HIV infection. Expansion of secondary needle exchange programs whereby IDUs redistribute sterile needles/syringes to peers within social and drug-using networks is essential for IDUs who may not use fixed site needle exchange programs because of their fear of being targeted by law enforcement, privacy concerns, or accessibility issues [19]. Although few IDUs reported using alcohol with drugs in the past month, this variable was significantly associated with HIV in all models and especially so in model IV (IDUs who learned their HIV status during the study and reported occasional sex partners in the past 3 months). This is concerning since alcohol use and abuse are common in Ukraine [20] and alcohol use in conjunction with some drug use, especially among females, has been found to impact judgments about safe sexual practices, as well as
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increase the morbidity and mortality [21–24]. Harm reduction programs and HIV counseling for IDUs should include screening and treatment of alcohol abuse. Although IDUs who reported sexual contact with commercial sex partners in the previous year were at increased risk of HIV infection, the majority of those IDUs reported using condoms with commercial partners at last sex. Furthermore, HIV-positive IDUs (among those who learned their HIV status during the study and who reported sexual contact in the previous year) had almost double the odds of using a condom at last sexual contact with any sexual partner and HIV-positive IDUs (among those who learned their HIV status during the study and who reported occasional sexual partners in the past 3 months) had more than double the odds of using a condom at last sexual contact with an occasional partner. Similar results were found for IDUs living in Moscow, Russia in 2009 [25]. Twenty-five percent of IDUs were female which is in line with estimated gender ratios in other European countries (male to female ratio of 2.5) [26]. Using a peer recruitment process was successful in obtaining a substantial proportion of female IDUs who are often more hidden and more difficult than male IDUs to recruit into research [27, 28]. Female IDUs were significantly more likely to be infected with HIV than males. Research conducted in Ukraine and elsewhere indicates that females who inject drugs face more vulnerabilities than males because females tend to have male sexual partners who also inject drugs and that these relationships may encourage continued drug use and/or sex work to maintain drug habits [24, 28–30]. In addition, females are more likely to use contaminated injecting equipment compared to males as females often will inject after their male partner has done so, may be afraid to ask for clean injection equipment from her partner if it implies that she does not trust him and tend to share injecting equipment with more people in their social network than do males [24, 28–30]. Ukraine is
Author's personal copy AIDS Behav (2011) 15:65–74
currently enhancing its peer outreach programs to focus on the special needs of females, however qualitative research is needed to assess the specific vulnerabilities of females related to their sexual and drug use behaviors and how these behaviors are impacted by their sexual partnerships. Model II (IDUs who learned their HIV status during this study) did not differ from Model I (all IDUs), except for the variable ‘‘shared injection equipment during last injection’’, which became insignificant. Neither did we observe significant changes from Model II to Model III (excluding those who reported not having sex in the last year) and IV (excluding those who reported not having sexual contact with an occasional partner in the past 3 months). We interpret these results as evidence of the explanatory variables having equal variance and consequently the relative homogeneity of IDUs’ injection practices and sexual behaviour among the subgroups included in the different models. Despite sample reductions from Model I to Model IV, injection drug use duration remained the most important predictor for HIV infection. Condom use and sterile syringe use were not important predictors for HIV infection either because safe practices are not consistent over time or were falsely reported. Although we are able to get some indication of the risk behaviors associated with HIV prevalence, this was a cross-sectional study, so causality cannot be determined. Possibly, a separate analysis of those IDUs who most recently initiated injection drug use and HIV status would provide a more precise model. However, IDUs with a long injecting history could increase variability in risky behaviors that may not accurately be captured in a cross sectional sampling method. This study was subject to some methodological and analytical limitations. As in all surveys asking participants to respond to personal questions about past sexual and drug use behaviors, this survey was subject to some social desirability and recall bias. Due to the size and scope of this study some IDUs may have participated in the study more than once and some coupons may not have been distributed by IDUs randomly to other IDUs within their social network. When these problems were identified by survey staff, IDUs were asked to turn in their coupon and not allowed to enroll. An important goal of this survey was to generate nationally representative HIV prevalence estimates among IDUs. However, RDS is based on the assumption that each sample comprises a single network component which was only fulfilled on the city level and not on the country level. To date there is no consensus on whether city level data can be aggregated to provide national estimates. Therefore, aside from the separate city level analyses adjusted with RDSAT and provided in Table 1, findings are not representative according to the theoretical premises upon which RDS are based [15].
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Despite the limitations, these findings clearly indicate the need for significant revisions and improvements to current harm reduction programs including needle/syringe exchange programs and opioid substitution treatment, with special emphasis on programs that target IDUs who are female. In addition, given the success of the RDS network sampling methodology among IDUs in Ukraine, social network-based programs could expand the reach of important prevention messages throughout the IDU population [30, 31]. Acknowledgments This study was coordinated by the Ukrainian International HIV/AIDS Alliance and supported by the global fund to fight AIDS, tuberculosis and malaria.
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