The characteristics and risk factors for HIV infection among Beijing drug users in different settings

Drug and Alcohol Dependence 113 (2011) 37–45

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Drug and Alcohol Dependence journal homepage: www.elsevier.com/locate/drugalcdep

The characteristics and risk factors for HIV infection among Beijing drug users in different settings Haibo Wang a,1 , Guiying Li b,1 , Katherine Brown c , Kumi Smith c,d , Xia Jin c , Ruiling Dong e , Ning Wang c,∗ a

Chinese Center for Disease Control and Prevention, 27 Nanwei Rd, Beijing 100050, PR China Department of AIDS/STD Control and Prevention, Beijing Center for Disease Control and Prevention, 16 Hepingli ZhongJie, Beijing 100013, PR China National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Road, Changping District, Beijing 102206, PR China d University of North Carolina, Chapel Hill, Gillings School of Global Public Health, Department of Epidemiology, Chapel Hill, NC 27599-7435, United States e Shenzhen International Travel Health Care Center, Shenzhen Port Hospital, Shenghuo District HuangGang Port, Shenzhen 518033, PR China b c

a r t i c l e

i n f o

Article history: Received 15 January 2010 Received in revised form 2 July 2010 Accepted 2 July 2010 Available online 21 August 2010 Keywords: Drug use Detoxification centers HIV China

a b s t r a c t Background: Treatment for drug addiction in China can take place in mandatory detoxification centers (MDC), voluntary detoxification centers (VDC), or at outreach programs located in the community. To date little is known about HIV prevalence or associated risk factors among the current and past drug users (DU) in each setting. Methods: Cross-sectional surveys were conducted at three different settings in Beijing, China; 795 subjects were enrolled at MDC, 824 at VDC, and 520 within the community. Subjects who provided informed consent took part in face-to-face interviews and provided blood samples for HIV and syphilis testing. Results: Significant differences were found across enrollment sites in terms of demographic, drug use and sexual behavior characteristics. Overall HIV sero-prevalence was 2.9%, and was particularly high in MDC (5.2% versus 1.0% in VDC and 2.3% among community drug users). Adjusted odds ratios (OR) for HIV infection were 50.5 (95% CI: 19.07–133.85) for being of Yi ethnicity, 29.4 (95% CI: 15.10–57.24) for Uyghur ethnicity, 3.4 (95% CI: 1.57–7.52) for injection drug users who did not share equipment, and 18.8 (95% CI: 8.31–42.75) among injection drug users who shared injection equipment. Conclusions: The vast differences among DU in various enrollment sites in terms of demographic characteristics, socioeconomic status, and HIV related risk profiles underscore the importance of familiarity with population characteristics and drug user environment to better inform targeted prevention programs. Prevention programs targeting DU in Chinese settings must also consider differences in ethnicities, culture, and residential status. © 2010 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Injection drug users (IDU) were the first group in China to be identified as HIV-sero-positive, beginning with the discovery of a group of 189 HIV-infected drug users in Ruili county of Yunnan province (Zheng et al., 1994; Zheng, 1989). Today, there are 1.16 million registered drug users in China, but it has been estimated that there are about 5 million drug users across the country (Human Rights Watch, 2010; Wu et al., 2007). IDU remain the most heavily affected by the epidemic (Jia et al., 2010) and over 38% of China’s cumulative AIDS cases have been attributed to unsafe injection practices, which is more than any other mode of transmission (China Ministry of Health and UNAIDS, 2007). Though there is evidence that China’s epidemic is transitioning to one predomi-

∗ Corresponding author. Tel.: +86 10 5890 0906; fax: +86 10 5890 0906. E-mail address: [email protected] (N. Wang). 1 These authors contributed equally to this manuscript. 0376-8716/$ – see front matter © 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.drugalcdep.2010.07.010

nantly driven by unprotected sex, IDU-related HIV transmission is still a primary concern in China given its potential to ‘seed’ more generalized epidemics in Asian settings (Saidel et al., 2003). The control of HIV among drug users in China is not without its unique challenges. First, rifts in strategies towards controlling drug addiction between public health authorities, who prioritize HIV prevention, and public security authorities, who prioritize drug control, have resulted in contradictory policy outcomes, and clashes between local level police and harm reduction programs are not uncommon. As a result, drug users may avoid using harm reduction services out of fear of facing arrest and incarceration, further exacerbating drug addiction and drug-related risky behaviors (Cohen and Amon, 2008; Human Rights Watch, 2010). Second, illicit drug use, particularly of heroin, has impacted ethnic minorities the hardest, with disproportionate numbers of HIV-infected drug users being of minority (non-Han) ethnicity. Part of the general drug problem in China can be linked to China’s geographic proximity to the major opium producing regions in Southeast and Central Asia, which has created a cheap availability of drugs. For ethnic

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H. Wang et al. / Drug and Alcohol Dependence 113 (2011) 37–45

minorities specifically, economic hardship, a history of tense relations with the Han-dominated government authorities, and poor access to healthcare services have also been considered drivers of drug use in these populations. Furthermore, HIV prevention services that target ethnic minorities and drug users specifically are limited. Such sociopolitical factors have played a significant role in China’s drug-use driven HIV epidemic, and understanding this context is essential to fighting HIV in China. Drug users in China are treated in voluntary or mandatory detoxification centers or in community-based treatment programs. Mandatory detoxification centers (MDC) are administered by the Ministry of Public Security and function as penitentiary, reeducation-through-labor camp, and detoxification center for repeat offenders of drug related charges. In the past, MDCs have come under harsh scrutiny for human rights abuses, including failure to treat drug-addiction withdrawal symptoms as well as failure to treat other general medical problems. Some researchers and human rights advocates have argued that such conditions in MDCs actually leads to relapse and risky behaviors (Cohen and Amon, 2008; Tang and Hao, 2007; Human Rights Watch, 2010). Although current sentinel surveillance in China documents HIV prevalence in detoxification centers, HIV risk factors in the three defined settings have not yet been quantified. To investigate HIV prevalence and the associated risk behaviors among drug users in Beijing, we conducted a cross-sectional study among drug users from the Beijing community and from Beijing MDC and voluntary detoxification centers (VDCs).

2. Methods

2.3. Data collection Beijing CDC staff members surveyed all study participants in face-to-face interviews. All interviews were conducted in Mandarin and translators were used in interviews with subjects who did not speak or were not comfortable speaking in Mandarin. All interviewers and outreach workers received training in good clinical practice (GCP), human protection, and safeguarding confidentiality. Interviews were conducted in on-site private rooms and study protocol protected confidentiality through the use of personal identification numbers, the codebooks of which were safeguarded in the project office. Blood samples were collected and tested for HIV-1 antibodies using enzymelinked immunosorbent assay (ELISA, Vironostika HIV Uni-Form plus O, bioMerieux, Holland) and for syphilis using rapid plasma reagin (RPR) test (Diagnosis kit, Xinjiang Xindi Company, China). Positive ELISAs were confirmed by western blot (Diagnostics HIV Blot 2.2, Genelabs, USA) and positive RPR tests were confirmed by the treponema pallidum particle assay (TPPA) test (Serodia-PPA-Fujirebio, Fuji, Japan). Pre- and post-test counseling was given at time of testing and 4 weeks after time of testing, respectively. Four weeks after the survey, trained counselors returned to MDC to provide test results and post-test counseling to all tested participants. Counselors also arranged times for result disclosure and post-test counseling for VDC and community DUs by contacting participants according to the subject-provided contact information. Informed consent, pre-test counseling, interviews, and blood sample collection took about 40 min for each participant. 2.4. Statistical analysis Statistical tests were performed using SASTM 9.1 software (SAS Institute Inc., Cary, NC, USA). Fisher Exact tests and chi-square tests were used to compare subjects originating from different enrollment sites and subjects with different ethnicities. Univariate associations between risk factors and HIV sero-positivity were evaluated using simple logistic regression analysis. Adjusted ORs were calculated both for all drug users and separately by enrollment sites. Logistic models were built in a stepwise manner by first adjusting for factors with p < 0.20 in univariate analysis and then limiting the final multivariate models to risk factors or confounders that were statistically significant (p < 0.05) in the overall and the site-specific analyses. All statistical tests conducted were based on two-tailed alternatives with p < 0.05 considered significant.

2.1. Setting This study was conducted in three drug user settings in Beijing. There are about 700 MDCs in all of China (Cohen and Amon, 2008) and this study was conducted in the only MDC in Beijing. At the time of enrollment, MDCs housed second-time offenders who served mandatory 6–12 months sentences and those with three or more offenses who were sentenced up to 3 years. MDCs do not provide drug replacement therapy to addicts. Beijing has 13 VDCs, and we selected the two biggest VDCs as our research sites (together representing over 90% of VDC drug users in Beijing). Subjects in VDCs are participants who self-register or are registered by family members to participate in voluntary detoxification for a fee ranging from about 2000 to 5000 RMB (294–735 USD) for a 10-day opioid substitute program. All drug users in the community are eligible to take part in local outreach activities including needle exchange programs and methadone maintenance therapy (available to locally registered opioid addicted drug users for 10 RMB (about 1.50 USD) fee per dose). Beijing is home to 18 such centers and we recruited study participants through 11 of these programs as well as through snowball sampling.

2.2. Participant eligibility and recruitment Individuals from the three defined drug user settings in Beijing were recruited for study participation between September and December 2007. Eligibility criteria included age over 16 years, a self-reported history of drug use, and provision of informed consent to take part in the survey and provide a blood sample for HIV and syphilis testing. Our team collaborated with the municipal public security to recruit participants from the MDC during routine HIV testing. During surveillance testing, those in MDC settings were randomly selected and invited to participate in the current study. Special precautions were taken to ensure that all eligible subjects in the MDC were given fair opportunities to participate in the study, and to ensure that their decision would not in any way affect their sentence term or treatment while in custody. To recruit subjects in VDC settings we collaborated with VDC programs operating out of two hospitals in Beijing. Drug users in VDC were also randomly selected and invited to participate. Lastly, outreach workers from a local drug treatment program assisted us in recruiting drug users (DU) in the community to participate and refer fellow DU through snowball sampling. Eligible participants from all three settings were notified of potential risks and benefits of study participation and understood that participation was voluntary. Written informed consent was obtained from all subjects who were eligible and willing to participate and parental consent was obtained for all participants under 18 years old. Subjects were compensated 50 RMB (about 7 USD) for their participation, and the study protocol secured approval from the National China Institutional Review Board.

3. Results 3.1. Demographic, drug use, and sexual characteristics Of the 2159 drug users approached for participation, 20 declined participation due to refusal to provide blood samples. A total of 2139 subjects were enrolled and completed the study survey and testing. Overall, 61 (2.9%) subjects were HIV-positive and 99 (4.6%) were syphilis-positive. The majority of participants were male (83.7%) and of Han ethnicity (87.5%), however subjects of Yi (1.9%) and Uyghur (5.7%) ethnicity were also in the surveyed population. Most study participants reported a history of heroin use (89.1%), but opium (1.8%), methamphetamine (9.3%), ecstasy (5.0%), and ketamine (3.9%) were among the other drugs reported. A total of 863 (40.3%) participants reported having ever injected drugs and of them, 140 (16.2%) reported having shared injection equipment. Drug users were asked to recall their sexual behaviors and 1082 (50.6%) reported having had sex with a regular sexual partner within the past year. Of those with regular sexual partners, the vast majority (98.8%) reported that they did not consistently use condoms. Some participants also reported cases of transactional (1010, 47.2%) and casual (182, 8.5%) sex and of those who did, only 12.6% (127/1010) and 7.1% (13/182), respectively reported consistent condom use with those partners. 3.2. Characteristics by enrollment site Demographic and behavioral characteristics of subjects across sites are presented in Table 1. Prevalence rates for HIV and syphilis were highest among subjects enrolled in MDC (5.2% versus 1.0% in VDC and 2.3% among community subjects for HIV; 5.0% versus 4.7% in VDC and 3.9% among community subjects for syphilis). Subjects enrolled in MDC were, on average, younger, less educated, not married, and had higher proportions of subjects of Yi or Uyghur

H. Wang et al. / Drug and Alcohol Dependence 113 (2011) 37–45

39

Table 1 Comparison of demographic and behavioral characteristics by site of study enrollment. Demographic and behavioral characteristics

HIV and syphilis prevalence Syphilis sero-positive

Mandatory detoxification centers (N = 795) N (%)

Voluntary detoxification centers (N = 824) N (%)

Community (N = 520) N (%)

All the subjects (N = 2139) N (%)

p value

Yes No

40 (5.0) 755 (95.0)

39 (4.7) 785 (95.3)

20 (3.9) 500 (96.1)

99 (4.6) 2040 (95.4)

0.60

Yes No

41 (5.2) 754 (94.8)

8 (1.0) 816 (99.0)

12 (2.3) 508 (97.7)

61 (2.9) 2078 (97.1)

<0.01

Male Female

685 (86.2) 110 (13.8)

710 (86.2) 114 (13.8)

395 (76.0) 125 (24.0)

1790 (83.7) 349 (16.3)

<0.01

Age (years)

<30 30∼ 40∼

323 (40.6) 317 (39.9) 155 (19.5)

148 (18.0) 410 (49.7) 266 (32.3)

152 (29.2) 239 (46.0) 129 (24.8)

623 (29.1) 966 (45.2) 550 (25.7)

<0.01

Ethnicity

Han Yi Uyghur Other

621 (78.1) 23 (2.9) 97 (12.2) 54 (6.8)

785 (95.3) 0 (0) 10 (1.2) 29 (3.5)

466 (89.6) 17 (3.3) 14 (2.7) 23 (4.4)

1872 (87.5) 40 (1.9) 121 (5.7) 106 (4.9)

<0.01

Registered permanent residence

Beijing Sichuan Xinjiang Other provinces

226 (28.4) 39 (4.9) 95 (12.0) 435 (54.7)

22 (2.7) 2 (0.2) 11 (1.3) 789 (95.8)

328 (63.1) 36 (6.9) 12 (2.3) 144 (27.7)

576 (26.9) 77 (3.6) 118 (5.5) 1368 (64.0)

<0.01

Education level

Illiterate <6 years ≥6 years

60 (7.6) 179 (22.5) 556 (69.9)

1 (0.1) 56 (6.8) 767 (93.1)

17 (3.3) 88 (16.9) 415 (79.8)

78 (3.6) 323 (15.1) 1738 (81.3)

<0.01

Employment status

Unemployed Employed

448 (56.4) 347 (43.6)

11 (1.3) 813 (98.7)

234 (45.0) 286 (55.0)

693 (32.4) 1446 (67.6)

<0.01

Marital status

Cohabiting or married Single, separated, divorced or windowed

410 (51.6) 385 (48.4)

784 (95.1) 40 (4.9)

306 (58.8) 214 (41.2)

1500 (70.1) 639 (29.9)

<0.01

≤20 years >20 years

137 (17.2) 658 (82.8)

115 (14.0) 709 (86.0)

120 (23.1) 400 (76.9)

372 (17.4) 1767 (82.6)

<0.01

Drug use method at initiation

Injection Non-injection

64 (8.1) 731 (91.9)

105 (12.7) 719 (87.3)

47 (9.0) 473 (91.0)

216 (10.1) 1923 (89.9)

<0.01

IDU (ever)

Yes No

311 (39.1) 484 (60.9)

194 (23.5) 630 (76.5)

358 (68.8) 162 (31.2)

863 (40.3) 1276 (59.7)

<0.01

Age at initiation of injection

≤20 years >20 years

45 (14.5) 266 (85.5)

18 (9.3) 176 (90.7)

51 (14.2) 307 (85.8)

114 (13.2) 749 (86.8)

0.18

Injection drug use when alone

Yes No

186 (59.8) 125 (40.2)

28 (14.4) 166 (85.6)

248 (69.3) 110 (30.7)

462 (53.5) 401 (46.5)

<0.01

Used needle use at initiation

Yes No

16 (5.1) 295 (94.9)

8 (4.1) 186 (95.9)

9 (2.5) 349 (97.5)

33 (3.8) 830 (96.2)

0.20

Shared injection equipment (water, cotton, cookers, etc.) (N = 863)

Yes

52 (16.7)

28 (14.4)

60 (16.8)

140 (16.2)

0.74

HIV sero-positive

Demographic characteristics Gender

Drug using behaviors Age at initiation of drug use

No

259 (83.3)

166 (85.6)

298 (83.2)

723 (83.8)

Injected drugs in last 6 months (N = 863)

Yes

297 (95.5)

186 (95.9)

227 (63.4)

710 (82.3)

No

14 (4.5)

8 (4.1)

131 (36.6)

153 (17.7)

Frequency of injection in last 6 monthsa (N = 710)

≤3 times per day

221 (74.4)

88 (47.3)

185 (81.5)

494 (69.6)

>3 times per day

76 (25.6)

98 (52.7)

42 (18.5)

Yes

68 (22.9)

40 (21.5)

30 (13.2)

138 (19.4)

0.02

No

229 (77.1) 3.6

146 (78.5) 1.6

197 (86.8) 4.2

572 (80.6) 3.2

0.06

Yes

158 (19.9)

761 (92.4)

163 (31.3)

1082 (50.6)

<0.01

No

637 (80.1)

63 (7.6)

357 (68.7)

1057 (49.4)

Yes

0 (0%)

11 (1.4%)

No

158 (100%)

a

Equipment sharing in last 6 months (N = 710) Mean number of people shared needle with

Sexual behaviors Had sex with regular partner in past year Consistent condom use with regular partner

750 (98.6%)

2 (1.2%) 161 (98.8)

13 (1.2) 1069 (98.8)

<0.01

<0.01 216 (30.4)

0.32

40

H. Wang et al. / Drug and Alcohol Dependence 113 (2011) 37–45

Table 1 (Continued ) Demographic and behavioral characteristics

Mandatory detoxification centers (N = 795) N (%)

Drug use among any sexual partners

Yes No

64 (8.1) 731 (91.9)

144 (17.5) 680 (82.5)

90 (17.3) 430 (82.7)

298 (13.9) 1841 (86.1)

<0.01

Transactional sex

Yes No

595 (74.8) 200 (25.2)

74 (9.0) 750 (91.0)

341 (65.6) 179 (34.4)

1010 (47.2) 1129 (52.8)

<0.01

Consistent condom use in transactional sex

Yes

46 (7.7)

41 (55.4)

40 (11.7)

127 (12.6)

<0.01

No

549 (92.3)

33 (44.6)

301 (88.3)

883 (87.4)

Had casual sex partners

Yes No

106 (13.3) 689 (86.7)

19 (2.3) 805 (97.7)

57 (11.0) 463 (89.0)

182 (8.5) 1957 (91.5)

Consistent condom use with casual sex partners

Yes

7 (6.6)

No

99 (93.4)

a

Voluntary detoxification centers (N = 824) N (%)

Community (N = 520) N (%)

All the subjects (N = 2139) N (%)

1 (5.3)

5 (8.8)

13 (7.1)

18 (94.7)

52 (91.2)

169 (92.9)

p value

<0.01 0.83

For subjects incarcerated for longer than 6 months, recall period extended back to 6 months prior to incarceration.

ethnicity compared to the other settings. Those enrolled from MDC and VDC were more likely to be registered residents of provinces outside of Beijing, while subjects enrolled in the community were mostly Beijing residents. Larger proportions of those recruited in MDC reported risky drug use and sexual behaviors including sharing of injection equipment (16.7% among those who inject), higher frequency of injection (25.6% injected more than three times a day in the 6 months prior to incarceration), and a higher average number of people with whom they had ever shared injection equipment. Among the three groups, those enrolled at VDCs exhibited the most stable socioeconomic characteristics; the largest proportion of them were educated, employed, and married; in addition, over 95% of them were of Han ethnicity. Lower proportions of them reported risky behaviors such as injection drug use or transactional sex, but risk levels among those who had a history of injection drug use were not insignificant: 14.4% among those injecting drugs reported that they ever shared injection equipment and over half (52.7%) of them had injected drugs in the last 6 months. Similarly, while only 2.3% reported having a causal sexual partner only 5.3% of them regularly used a condom with this type of partner. Subjects recruited in the community were demographically more similar to subjects in MDC though they were slightly older, better educated, and more likely to be married and employed. A similar portion of Yi subjects made up the VDC group, but there were comparatively few Uyghur subjects as compared to those in the MDC group. Subjects recruited in the community had the highest proportion of subjects who had initiated drug use before age 20 as well as those who had ever injected drugs (23.1% began drug use before age 20 versus 17.2% and 14% at MDC and VDC, respectively; 68.8% had ever injected drugs versus 39.1% and 23.5% at MDC and VDC, respectively). 3.3. Drug use treatment history Subjects in each setting reported whether they had received drug treatment in other settings. Of the 520 community-based sub-

jects, 232 (44.6%) had previously been incarcerated in MDCs, 227 (43.7%) had sought treatment in VDCs, and 196 (37.7%) had never been treated in MDCs or VDCs. Of the 824 subjects recruited from VDCs, 27 (3.3%) subjects had been incarcerated in MDCs. Of the 795 subjects recruited from MDC, 93 (11.7%) subjects had sought treatment in VDCs. In addition, 45 (5.7%) MDC subjects reported having been incarcerated at least one other time before the time period in which the study was conducted. 3.4. Multivariate analysis of risk factors for HIV infection Table 2 shows adjusted ORs and 95% CIs of potential risk factors or confounders for HIV that remained statistically significant after we included all variables with p < 0.20 in univariate analysis (results of which are presented in Table 3) in the multivariate logistic model; ORs are shown for the entire sample as well as according to their enrollment site. To investigate the association between HIV infection and drug using behaviors we analyzed participants based on whether they were non-injection drug users (DU), injection drug users who have ever shared equipment (S-IDU) and injection drug users who have never shared equipment (NS-IDU). Using DU as the referent group, we found that aOR for S-IDU was 18.8 and 3.4 for NS-IDU. When analyzed by recruitment sites, the following factors were significantly associated with HIV sero-positivity: S-IDU, Uyghur ethnicity, Yi ethnicity and being unemployed. Among the 795 subjects enrolled at MDC, the greatest risk factors were being of Yi (OR 80.8; 95% CI: 21.24–307.48) or Uyghur (OR 26.0; 95% CI: 9.93–67.85) ethnicity, being an NS-IDU (OR 2.8; 95% CI: 1.05–7.48), sharing injection drug equipment (OR 28.2; 95% CI: 9.40–84.82) and having casual sexual partners (OR 2.8; 95% CI: 1.08–7.45). Being employed was significantly associated with lower risk of HIV infection in this group. Among study subjects recruited at VDC, those of Uyghur ethnicity (OR 70.7; 95% CI: 12.80–390.64) and those who reported initiation of injection drug use at a younger age (OR 6.0; 95% CI: 1.05–34.75) were more likely

Table 2 Multivariate analysis, risk factors for HIV infection among all drug users and according to site of contact. Potential risk factors

All drug users (aOR, 95% CI)

Mandatory detoxification centers (aOR, 95% CI)

Voluntary detoxification centers (aOR, 95% CI)

Community (a OR, 95% CI)

Yi ethnicity (vs. Han ethnicity) Uyghur ethnicity (vs. Han ethnicity) Employed (vs. unemployed) IDU, did not share injection equipment (vs. never injected) IDU, shared injection equipment (vs. never injected) Age at initiation of injection ≤20 years (vs. >20 years) Have had casual sexual partners (vs. never had) Had sex with drug-using partners

50.5 (19.07–133.85) 29.4 (15.10–57.24) 0.4 (0.23–0.80) 3.4 (1.57–7.52) 18.8 (8.31–42.75) – – –

80.8 (21.24–307.48) 26.0 (9.93–67.85) 0.3 (0.13–0.83) 2.8 (1.05–7.48) 28.2 (9.40–84.82) – 2.8 (1.08–7.45) –

– 70.7 (12.80–390.64) – – – 6.0 (1.05–34.75) – –

– 43.1 (10.75–173.19) – – – – – 4.0 (1.09–15.02)

Table 3 Univariate analysis, risk factors for HIV infection among all drug users and according to site of enrollment. Community (N = 520)

All the subjects

OR (95% CI)

HIV positiveN (%)

OR (95% CI)

HIV positiveN (%)

OR (95% CI)

HIV positiveN (%)

OR (95% CI)

Voluntary detoxification center Community Mandatory detoxification center Male Female

–

–

–

–

–

–

8 (1.0)

1.0

– –

– –

– –

– –

– –

– –

12 (2.3) 41 (5.2)

2.4 (0.98–5.94) 5.5 (2.58–11.91)

38 (5.6) 3 (2.7)

1.0 0.5 (0.15–1.57)

5 (0.7) 3 (2.6)

1.0 3.8 (0.90–16.17)

7 (1.8) 5 (4.0)

1.0 2.3 (0.72–7.41)

50 (2.8) 11 (3.2)

1.0 1.1 (0.58–2.20)

Age (years)

<30 30∼ 40∼

21 (6.5) 18 (5.7) 2 (1.3)

1.0 0.9 (0.45–1.66) 0.2 (0.04–0.81)

2 (1.4) 3 (0.7) 3 (1.1)

1.0 0.5 (0.09–3.25) 0.8 (0.14–5.04)

8 (5.3) 3 (1.3) 1 (0.8)

1.0 0.2 (0.06–0.88) 0.1 (0.02–1.14)

31 (5.0) 24 (2.5) 6 (1.1)

1.0 0.5 (0.28–0.84) 0.2 (0.09–0.51)

Ethnicity

Han Yi Uyghur Other

5 (0.8) 10 (43.5) 24 (24.7) 2 (3.7)

1.0 94.8 (28.38–316.50) 40.5 (15.00–109.40) 4.7 (0.90–25.02)

4 (0.5) 0 (0) 4 (40.0) 0 (0)

1.0 – 130.2 (26.23–645.91) –

6 (1.3) 1 (5.9) 5 (35.7) 0 (0)

1.0 4.8 (0.54–42.17) 42.6 (10.96–165.59) –

15 (0.8) 11 (27.5) 33 (27.3) 2 (1.9)

1.0 47.0 (19.87–110.99) 46.4 (24.32–88.64) 2.4 (0.54–10.55)

Registered permanent residence

Beijing

2 (0.9)

1.0

0 (0)

–

3 (0.9)

1.0

5 (0.9)

Sichuan Xinjiang Other provinces

10 (25.6) 25 (26.3) 4 (0.9)

38.6 (8.06–184.93) 40.0 (9.24–173.03) 1.0 (0.19–5.72)

0 (0) 4 (36.4) 4 (0.5)

– 112.1 (23.26–540.66) 1.0

4 (11.1) 5 (41.7) 0 (0)

13.5 (2.90–63.19) 77.4 (15.39–389.20) –

14 (18.2) 34 (28.8) 8 (0.6)

25.4 (8.85–72.79) 46.2 (17.59–121.48) 0.7 (0.22–2.06)

Education level

Illiterate <6 years ≥6 years

13 (21.7) 16 (8.9) 12 (2.2)

1.0 0.4 (0.16–0.79) 0.1 (0.03–0.19)

0 (0) 3 (5.4) 5 (0.7)

1.0 – –

3 (17.7) 2 (2.3) 7 (1.7)

1.0 0.1 (0.02–0.71) 0.1 (0.02–0.34)

16 (20.5) 21 (6.5) 24 (1.4)

1.0 0.3 (0.13–0.55) 0.1 (0.03–0.11)

Employment status

Unemployed Employed

28 (6.3) 13 (3.8)

1.0 0.6 (0.30–1.15)

0 (0) 8 (1.0)

1.0 –

7 (3.0) 5 (1.8)

1.0 0.6 (0.18–1.84)

35 (5.1) 26 (1.8)

1.0 0.3 (0.21–0.58)

Marital status

Cohabiting or married Single, separated, divorced or windowed ≤20 years

18 (4.4)

1.0

7 (0.9)

1.0

5 (1.6)

1.0

30 (2.0)

1.0

23 (6.0)

1.4 (0.74–2.61)

1 (2.5)

2.8 (0.34–23.71)

7 (3.3)

2.0 (0.64–6.50)

31 (4.9)

2.5 (1.50–4.16)

15 (11.0)

1.0

0 (0)

1.0

8 (6.7)

1.0

23 (6.2)

1.0

Enrollment site

Gender

Age at initiation of drug use

26 (4.0)

0.3 (0.17–0.65)

8 (1.1)

–

4 (1.0)

0.1 (0.04–0.48)

38 (2.2)

0.3 (0.20–0.57)

> 20 years Injection

9 (14.1)

1.0

5 (4.8)

1.0

3 (6.4)

1.0

17 (7.9)

1.0

Non-injection

32 (4.4)

0.3 (0.13–0.62)

3 (0.4)

0.08 (0.02–0.36)

9 (1.9)

0.3 (0.07–1.09)

44 (2.3)

0.3 (0.15–0.49)

IDU

Yes No

32 (10.3) 9 (1.9)

1.0 0.2 (0.08–0.35)

6 (3.1) 2 (0.3)

1.0 0.1 (0.02–0.50)

10 (2.8) 2 (1.2)

1.0 0.4 (0.09–2.01)

48 (5.6) 13 (1.0)

1.0 0.2 (0.09–0.33)

Age at initiation of injection drug use

≤20 years

11 (24.4)

1.0

0

1.0

5 (9.8)

1.0

16 (14.0)

1.0

Drug use method at initiation

H. Wang et al. / Drug and Alcohol Dependence 113 (2011) 37–45

Mandatory detoxification centers (N = 795) Voluntary detoxification centers (N = 824)

HIV positive N (%)

Demographic and behavioral characteristics

41

42

Table 3 (Continued ) Community (N = 520)

All the subjects

HIV positive N (%)

OR (95% CI)

HIV positiveN (%)

OR (95% CI)

HIV positiveN (%)

OR (95% CI)

HIV positiveN (%)

OR (95% CI)

>20 years

21 (7.9)

0.3 (0.12–0.60)

6 (3.4)

–

5 (1.6)

0.2 (0.04–0.55)

32 (4.3)

0.3 (0.15–0.52)

Injection drug use when alone

Yes

9 (4.8)

1.0

1 (3.6)

1.0

4 (1.6)

1.0

14 (3.0)

1.0

Used needle use at initiation

No

23 (18.4)

4.4 (1.98–9.95)

5 (3.0)

0.8 (0.09–7.46)

6 (5.5)

3.5 (0.97–12.73)

34 (8.5)

3.0 (1.57–5.61)

No

23 (7.8)

1.0

6 (3.2)

1.0

10 (2.9)

1.0

39 (4.7)

1.0

Shared injection equipment (needles, water, etc.)

Yes

9 (56.3)

15.2 (5.19–44.57)

0 (0)

–

0 (0)

–

9 (27.3)

7.6 (3.31–17.46)

No

12 (4.6)

1.0

2 (1.2)

1.0

6 (2.0)

1.0

20 (2.8)

1.0

Yes

20 (38.5)

12.9 (5.75–28.77)

4 (14.3)

13.7 (2.37–78.69)

4 (6.7)

3.5 (0.95–12.72)

28 (20.0)

8.8 (4.79–16.13)

Had sex with regular partner in past year

Yes

12 (7.6)

1.0

8 (1.1)

1.0

5 (3.1)

1.0

25 (2.3)

1.0

Consistent condom use with regular partner

No

29 (4.6)

0.6 (0.29–1.17)

0 (0)

–

7 (2.0)

0.6 (0.20–2.02)

36 (3.4)

1.5 (0.89–2.50)

Yes

0 (0)

1.0

0 (0)

1.0

0 (0)

1.0

0 (0)

1.0

Had sex with drug-using partner

No

12 (7.6)

–

8 (1.1)

–

5 (3.1)

–

25 (2.3)

–

Yes

8 (12.5)

1.0

2 (1.4)

1.0

5 (5.6)

1.0

15 (5.0)

1.0

No

33 (4.5)

0.3 (0.15–0.75)

6 (0.9)

0.6 (0.13–3.16)

7 (1.6)

0.3 (0.09–0.91)

46 (2.5)

0.5 (0.27–0.88)

Traded sex

Yes No

22 (3.7) 19 (9.5)

1.0 2.7 (1.45–5.17)

0 (0) 8 (1.1)

1.0 –

7 (2.1) 5 (2.8)

1.0 1.4 (0.43–4.38)

29 (2.9) 32 (2.8)

1.0 1.0 (0.59–1.64)

Consistent condom use while trading sex

Yes

2 (4.4)

1.0

0 (0)

1.0

2 (5.0)

1.0

4 (3.2)

1.0

No

20 (3.6)

0.8 (0.19–3.67)

0 (0)

–

5 (1.7)

0.3 (0.06–1.71)

25 (2.8)

0.9 (0.31–2.62)

Yes

14 (13.2)

1.0

0 (0)

1.0

2 (3.5)

1.0

16 (8.8)

1.0

No

27 (3.9)

0.3 (0.14–0.53)

8 (1.0)

–

10 (2.2)

0.6 (0.13–2.84)

45 (2.3)

0.2 (0.14–0.44)

Yes

0 (0)

1.0

0 (0)

1.0

0 (0)

1.0

0 (0%)

1.0

No

14 (14.1)

–

0 (0)

–

2 (3.9)

–

16 (9.5%)

–

No Yes

38 (5.0) 3 (7.5)

1.0 1.5 (0.45–5.19)

6 (0.8) 2 (5.1)

1.0 7.0 (1.37–35.95)

11 (2.2) 1 (5.0)

1.0 2.3 (0.29–19.06)

55 (2.7) 6 (6.1)

1.0 2.3 (0.98–5.55)

Had casual sex partners Consistent condom use with casual sex partners Syphilis sero-positive

H. Wang et al. / Drug and Alcohol Dependence 113 (2011) 37–45

Mandatory detoxification centers (N = 795) Voluntary detoxification centers (N = 824)

Demographic and behavioral characteristics

H. Wang et al. / Drug and Alcohol Dependence 113 (2011) 37–45

43

Table 4 Comparison of demographic and behavioral characteristics among Han, Yi and Uyghur ethnicities. Ethnicity, N (%)

Demographic and behavioral characteristics

P value

Han (N = 1872)

Yi minorities (N = 40)

Uyghur (N = 121)

Education level

Illiterate <6 years ≥6 years

28 (1.5) 224 (12.0) 1620 (86.5)

14 (35.0) 23 (57.5) 3 (7.5)

31 (25.6) 56 (46.3) 34 (28.1)

<0.01

Marital status

Cohabiting or married Single, separated, divorced or windowed Unemployed Employed

1356 (72.4)

16 (40.0)

58 (47.9)

<0.01

516 (27.6)

24 (60.0)

63 (52.1)

565 (30.2) 1307 (69.8)

18 (45.0) 22 (55.0)

64 (52.9) 57 (47.1)

<0.01

Registered permanent residence

Beijing Sichuan Xinjiang Other provinces

539 (28.8) 36 (1.9) 7 (0.4) 1290 (68.9)

0 40 (100) 0 0

3 (2.5) 1 (0.8) 110 (90.9) 7 (5.8)

<0.01

IDU

Yes No

752 (40.2) 1120 (59.8)

12 (30.0) 28 (70.0)

67 (55.4) 54 (44.6)

<0.01

Age at initiation of drug use

≤20 years >20 years

222 (11.9) 1650 (88.1)

8 (20.0) 32 (80.0)

46 (38.0) 75 (62.0)

<0.01

Drug use method at initiation

Injection Non-injection

186 (9.9) 1686 (90.1)

3 (7.5) 37 (92.5)

21 (17.4) 100 (82.6)

0.03

Frequency of injection within past 6 months

≤3 >3

415 (69.3) 184 (30.7)

10 (83.3) 2 (16.7)

47 (71.2) 19 (28.8)

0.56

Needle sharing within past 6 months

Yes No

90 (15.0) 509 (85.0)

9 (75.0) 3 (25.0)

30 (45.4) 36 (54.6)

<0.01

The mean number of people shared needle with

Mean ± SD Median

2.4 ± 2.8 2.0

10.4 ± 14.6 3.0

3.4 ± 2.7 3.0

<0.01a <0.01b

Traded sex

Yes No

858 (45.8) 1014 (54.2)

28 (70.0) 12 (30.0)

63 (52.1) 58 (47.9)

<0.01

Consistent condom use while trading sex

Yes No

104 (12.1) 754 (87.9)

11 (39.3) 17 (60.7)

7 (11.1) 56 (88.9)

<0.01

Had casual sex partners

Yes No

130 (6.9) 1742 (93.1)

21 (52.5) 19 (47.5)

24 (19.8) 97 (80.2)

<0.01

Consistent condom use with casual sex partners

Yes No

12 (9.2) 118 (90.8)

1 (4.8) 20 (95.2)

0 24 (100)

0.25

Syphilis sero-positive

Yes No

72 (3.9) 1800 (96.1)

4 (10.0) 36 (90.0)

13 (10.7) 108 (89.3)

<0.01

Employment status

a b

Analysis of variance was used as the analytical method. Non-parameter analysis method was used as the analytical method.

to be HIV sero-positive. For subjects recruited from the community, Uyghur ethnicity was found to be the most significant risk factor for HIV sero-positivity (OR 43.1; 95% CI: 10.75–173.19). Those who had sex with drug-using partners and were enrolled from the community were four times more likely to be HIV sero-positive (OR 4.0; 95% CI: 1.09–15.02). Detoxification setting was significantly associated with HIV in univariate analysis, but was no longer significant in the multivariate model when controlling for other factors. 3.5. Ethnicity Table 4 shows the differences in demographic and behavioral characteristics between the three major ethnicities of the study population. When analyzed by ethnicity, Yi and Uyghur subjects were found to have far higher levels of unemployment (52.9% for Uyghur and 45% for Yi; p < 0.0001) than their Han counterparts (30.2%) and were also far more likely to have shared needles in the past 6 months (75% for Yi and 45.4% for Uyghur versus 15.0% for Han; p < 0.0001). Uyghur and Yi participants were also more likely have registered residence outside of Beijing (90.0% of Uyghur participants had Xinjiang residence and 100% of Yi participants had Sichuan residence). Sexual behaviors were less closely correlated

with ethnicity, although results did indicate that higher proportions of Yi and Uyghur subjects had engaged in sex with commercial sex workers (70.0% among Yi and 52.1% among Uyghur) in comparison to Han respondents (45.8%; p < 0.01). 4. Discussion The results of this study found that demographic characteristics, drug use and sexual behaviors, and HIV sero-prevalence vary significantly among current and former drug users by enrollment site. There was little overlap between drug users currently in MDC who had previously been in VDC and vice versa, but the majority of drug users in the community had previously been incarcerated or treated in MDC, VDC, or both. In the overall population, subjects in MDC had higher HIV infection rates (5.2%) than those in VDC (1.0%) or those in the community (2.3%). However, when controlling for ethnicity and behavioral factors the relationship between HIV prevalence and enrollment venue was no longer significant, suggesting the confounding role that these factors play in the relationship between HIV prevalence and the environments in which transmission takes place. Multivariate regression analysis found that among all subjects, ethnicity was by far the most salient predictor of HIV status,

44

H. Wang et al. / Drug and Alcohol Dependence 113 (2011) 37–45

followed by employment status, needle sharing behavior, and sexual behaviors in certain venues, prompting further analysis of these factors below. Ethnicity was the most salient predictor of HIV sero-status in this study; when compared to Han subjects, subjects of Yi and Uyghur ethnicity were respectively 30 and 50 times more likely to be HIV positive. Larger proportions of Yi and Uyghur participants also practiced risky drug use and sexual behaviors. Although registered residency was not significantly associated with HIV infection in the multivariate model, this is likely due to multicolinearity between ethnicity and registered residence. This is best be understood in the context of China’s rigid household registration system, which has long maintained a strong link between birth province and ethnicity. As a result one’s registered residence is still a moderately accurate proxy for one’s ethnicity. This is evident in the fact that 100% of Yi respondents in this study were permanent residents of Sichuan province and 90.9% of Uyghur respondents were those of Xinjiang province, both regions in which each ethnicity has established communities. Internal migrants lacking Beijing residency may also have limited access to healthcare, drug addiction treatment, and HIV prevention services and previous research has found higher rates of HIV among migrant drug users. Furthermore, internal migrants may also have limited access to free antiretroviral (ART) programs and those who are HIV-sero-positive may thus have further compromised health due to the registration system (Todrys and Amon, 2009). Comparatively low levels of education and employment were also identified among Yi and Uyghur respondents, factors which past research has linked to prevalent drug use and low health seeking behaviors (Choi et al., 2007; Gill and Gang, 2006; Qian et al., 2005; Zhang et al., 2008). Furthermore, past research of Yi and Uyghur IDU in their home provinces have attributed some of their drug-related risk behaviors to cultural norms that are said to have formed around drug use and needle sharing (Choi et al., 2007; Chu and Levy, 2005; Zhang et al., 2008). How these behaviors differ when individuals are outside of their home provinces is unknown, however previous research on migrants suggests that HIV-risk behaviors may increase when away from home due to a relaxed sense of social rules (Yang et al., 2007). Finally, because most minority respondents were not registered residents of Beijing, it is likely that they live far from their regular sexual partners and they may therefore be more likely to seek out commercial or casual sexual partners. Other factors that significantly predicted HIV infection were injection intake of drugs, needle-sharing behavior, earlier age of drug-use initiation, and sex with casual partners who had ever used drugs. Injection drug use and needle sharing tend to proxy for the other given the dominant probability of transmission through needle sharing and it is not surprising that both were significant predictors for HIV infection in the whole sample. Interestingly, the adjusted odds ratios for injection drug use expanded greatly within the MDC sample when analysis was restricted by enrollment venue with the odds of HIV infection increasing from 18.8 (95% CI: 8.31–42.75) to 28.2 (9.40–84.82) when comparing injection users who shared equipment to those who never injected. This increase in risk in the MDC population may reflect the excess risk associated with these behaviors if they take place within the MDC itself, where a lack of clean needles and higher HIV prevalence could both increase the odds of infection. Cohen et al. not only found that drug use and equipment sharing practices were taking place within a MDC in Guangxi province, but also reported that some detainees exchanged sex for drugs with guards (Cohen and Amon, 2008). If such behaviors took place within the Beijing MDC surveyed in this study, the risks associated with injection drug use and needle sharing may be compounded by unmeasured factors including sexual contact with guards. Increased risk associated with equipment sharing behavior in MDC could also be due

to other unmeasured factors not captured in the model. The link between earlier drug use initiation and HIV infection has been explored widely, and can be attributed to both its ability to cause a longer lifetime duration of drug use as well as a propensity to engage in other HIV-related risk behaviors (Battjes et al., 1992; Cheng et al., 2006; Perngmark et al., 2003; Strathdee et al., 2001). The link between sex with casual partners and HIV infection may be attributed to risk of transmission through unprotected sex, but its link with enrollment venue is difficult to discern and underscores the need for targeted research into the sexual behaviors of drug users. Although this study found significant differences in subjects’ risk profiles across enrollment sites, this association is likely to be confounded by determinants of location where addicted individuals receive drug treatment. Whereas a large portion of participants enrolled in the community reported past treatment experience in MDCs (232/520 or 44.6%) or VDCs (227/520 or 43.7%), the MDC and VDC groups appeared to be far more mutually exclusive with a lower portion of overlap (11.7% of MDC had ever been in VDC and 3.3% of VDC had ever been in MDC). Potential confounders of the association between HIV-related risk behaviors and enrollment site might include ethnic profiling by law enforcement officials carrying out the arrest and sentencing of drug addicted individuals, as well as monetary resources and family involvement which contribute to VDC enrolled users to pay for non-penal commercial treatment. Finally, the overcrowded and tightly regulated living conditions in MDCs may also play a role. The lack of psycho-social treatment or treatment for drug withdrawal symptoms are thought to potentially exacerbate drug addiction problems by creating a cycle of abuse, incarceration, relapse, and recidivism (Cohen and Amon, 2008; Human Rights Watch, 2010). Although only 45 out of the 795 MDC enrolled subjects (5.7%) had been treated in an MDC at least once before, chronic recidivism has been reported as a significant shortcoming of China’s MDC system (Cohen and Amon, 2008; Malinowska-Sempruch and Bartlett, 2006; Tang and Hao, 2007; Human Rights Watch, 2010) and may also be a potential confounder in this study as well. There is little research regarding patterns in drug relapse or recidivism among drug users who have attended VDC or other types of treatment clinics. Further research is also needed to understand unique features of the ethnic minority experience with law enforcement and exposure to different types of drug treatment programs. Arrest, sentencing, and treatment towards inmates of ethnic minority status may very well be different than for Hans, but we are not able to estimate the extent of this potential discrimination within this current study. Finally, the ethnic makeup of drug using populations in Beijing is poorly understood, and further research is needed into the city’s Uyghur and Yi communities and the prevalence of drug use among them. Although we did have significant findings, our study did have several limitations. Information on personal and sensitive topics was collected through participant self-report, and our results may have been influenced by social desirability bias. However, self-reporting of drug users has generally been considered valid and this bias should have a negligible impact on the associations between risky behaviors and HIV sero-prevalence (Latkin et al., 1993). Significantly, drug use may have taken place within detoxification centers, but to protect the safety and comfort of study subjects, we did not ask about drug use while in MDC or VDC. Future research that looks into drug use inside such centers will help us better understand the impact these environments have on drug use. Furthermore, we recruited drug users in the community through snowball sampling and our sample may not be representative of the general population of drug users in the Beijing community. Moreover, this study was conducted in Beijing and our results may not apply to the risk behaviors of drug users in other Chinese cities.

H. Wang et al. / Drug and Alcohol Dependence 113 (2011) 37–45

Systematic study of drug users in detoxification centers and in the communities of other provinces is needed. Finally, because of the cross-sectional nature of our study, causality cannot be determined. Despite these limitations, these findings provide valuable information for future interventions. Significant differences between enrollment sites were found across all demographic variables, drug use and sexual behaviors, suggesting that drug users should not be treated as a homogeneous group. Specifically, we found a much higher HIV prevalence among Yi and Uyghur participants and these findings suggest the need for development of appropriate, culturally relevant prevention strategies that target marginalized populations in China. Furthermore, higher rates of IDU and higher rates of younger injection initiation were found among community drug users. Community outreach programs may need to develop specific programs targeting young drug users. Perhaps more significant in impacting drug addiction in China, law enforcement and public health officials need to unite on drug use policy as unification will help expand drug users’ access to drug treatment and HIV prevention programs. Significantly, in June 2008, a new drug policy, that took on a more treatment-based approach to drug addiction, was put in effect. Although the Chinese government has been praised for taking a more medically-based approach to solving the drug use problems in MDCs and the larger community, the policy has also been criticized for its ambiguous language and clashes between harm reduction and law enforcement have still been reported (Human Rights Watch, 2010). Further research on the impact of this policy on drug use among drug users in all settings is needed. Most current national sentinel surveillance in China is based on drug users in detoxification centers, but it is important that we broaden our understanding of the impact of the surrounding environment on relevant risk behaviors. This will both help inform drug control policy as well as provide more information for public health experts to more accurately synthesize this data for policy decisions that will affect drug users across all of these communities. Conflicts of interest No conflict declared. Role of funding source: Nothing declared. Contributors: Authors Ning Wang, Ruiling Dong, Guiying Li, Haibo Wang and Xia Jin designed the study and protocol. Guiying Li and Haibo Wang performed all statistical analysis. Haibo Wang, Kumi Smith, and Katherine Brown wrote the first draft of

45

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