The multi-person use (‘sharing’) of needles and syringes is an efficient method for transmitting HIV among injecting drug users (IDUs). Extremely rapid spread of HIV has been observed in many populations of IDUs, with incidence rates of 10/100 person-years up to 50/100 person-years [1–3]. Heterosexual transmission of HIV is normally much less efficient than needle-sharing transmission , and the hierarchical classification of HIV transmission risk used by the Centers for Disease Control and Prevention has always ranked injecting drug use-related transmission above heterosexual transmission .
The large-scale implementation of programs to reduce syringe sharing, such as syringe exchange, may change the relative importance of injection-related and sexual transmission of HIV among IDUs. Recent HIV incidence studies among IDUs in San Francisco  and Baltimore  have suggested that sexual transmission may be of increasing importance among IDUs.
Comparisons of HIV among IDUs and non-injecting drug users (NIDUs) may provide insight into the relative importance of sexual transmission in a geographic area. In this report, we note a convergence of HIV prevalence among injecting and non-injecting heroin and cocaine users in New York City (NYC). High rates of non-injecting drug use among the IDUs and frequent social linkages between the IDUs and NIDUs were also observed.
The data reported here are from two different NYC studies: the Risk Factors (RF) study, which recruited participants from persons entering drug abuse treatment programs, and the Respondent Driven Sampling (RDS) study, which used community recruiting of drug users. The two studies were coordinated, using identical questions for most topics, including drug use histories and HIV risk behaviors. This report compares current heroin and/or cocaine IDUs with NIDUs. Current IDUs are defined as persons who had injected heroin and/or cocaine in the 6 months prior to interview, and current NIDUs as persons who used heroin and/or cocaine in the prior 6 months but who had never injected an illicit drug. HIV testing was conducted at the NYC Department of Health Laboratory using repeated enzyme-linked immunosorbent assays (ELISA) testing with western blot confirmation.
The Risk Factors study
The RF study comprises an ongoing series of surveys of drug users entering the Beth Israel Medical Center drug detoxification and methadone maintenance programs in NYC (methods previously described in [1,8–12]). Data for this report comes from current IDUs recruited between 2001 and 2004, and from current NIDUs recruited from 2002 to 2004.
Briefly, research staff rotated through the general admission wards of the detoxification program in a pre-set order and examined the intake records to identify patients admitted within the past 3 days. All of the eligible newly admitted patients in the specific ward were then asked to participate in the study. Participants entering methadone maintenance treatment were recruited at the central intake facility for the program. Project staff visited the facility starting early in the morning and remained until intake closed. All persons applying for methadone treatment were asked to participate in the study. An honorarium of US$ 20 was provided to each study participant.
The Respondent Driven Sampling study
During the summer of 2004, we conducted a study of street-recruited drug users in NYC utilizing RDS. Since a detailed description of the methods has been presented elsewhere, a short overview will be given here. RDS [13–15] is a structured form of peer referral sampling, in which persons who have participated in a study are then asked to recruit new participants. RDS starts with a limited number of peers – known as seeds–who are selected by study staff. After participating in the study, these seeds are asked to recruit new participants, who in turn are asked to recruit additional new participants. Recruiting continues in successive ‘waves’ until the desired sample size is reached. Subjects receive honoraria for study participation (US$ 20 in the current study) and for recruiting new subjects (US$ 10 per recruit). A coupon system is utilized to track who recruits whom, and information is obtained about the size and characteristics of the social networks of the subjects.
The RDS procedures led to substantial cross-recruiting among IDUs and NIDUs: 46% of the current IDUs were recruited by NIDUs and 44% of the current NIDUs were recruited by IDUs. (A separate report has been prepared on ‘former injectors’ – participants who had previously injected but had not injected for at least 6 months ).
Data from the RF study were analyzed using the SAS statistical programs . The RDS study data were analyzed using custom software designed to analyze RDS data sets, RDS Analysis Tool (RDSAT), version 5.0, which uses recruitment linkages and network sizes to estimate adjusted population prevalence values. The 95% confidence intervals (CI) for the adjusted population values were then calculated by applying the RDS weights to each case and using a procedure for complex survey design in SAS (Proc Surveyselect) .
Table 1 presents sociodemographics of the current IDUs (subjects who reported injecting in the 6 months prior to interview) and NIDUs (subjects who used heroin and/or cocaine within the 6 months prior to interview, but who reported never having injected an illicit drug) in the two studies. Subjects in the RDS study were older and more likely to be African American. The percentages of males and females were very similar across both injecting status groups and across both studies. In both studies, there were higher percentages of Hispanics and whites among the current IDUs, and higher percentages of African Americans among the NIDUs.
Many of the current IDUs also reported non-injecting drug use. In the RF study, 35% reported intranasal heroin use, 15% reported intranasal cocaine use, 9% reported intranasal speedball (combined heroin and cocaine), and 29% reported smoking crack cocaine in the 6 months prior to the interview. In the RDS study, 53% of the IDUs reported intranasal heroin use, 36% reported intranasal cocaine use, 22% reported intranasal speedball use, and 46% reported smoking crack cocaine in the 6 months prior to the interview.
Table 2 shows HIV prevalence with 95% confidence intervals by injecting status in both studies, and by gender and race/ethnicity within injecting status in both studies. The RDS prevalence estimates are based on the weighting procedures calculated by RDS Analysis Tool, with confidence intervals calculated with SAS . These weighted prevalences are generally two to five percentage points lower than the observed values in the unweighted sample. The weighting procedures reduced the prevalence estimates because HIV positive subjects in this RDS study tended to recruit more subjects and tended to have larger drug using networks.
There were few differences in HIV prevalence between the current IDUs and the NIDUs in both studies. There was overlap in the 95% CIs for all comparisons of current IDUs to NIDUs for all of the demographic subgroups in each of the studies. Elevated HIV prevalence among African American IDUs versus NIDUs in the RF study was the only IDU versus NIDU comparison to reach statistical significance (P < 001). There were, however, clear racial/ethnic differences, with whites generally having lower HIV prevalence and African Americans having higher HIV prevalence. This pattern was consistent for both current IDUs and NIDUs.
The two studies reported here used very different sampling methods but obtained very similar results. This suggests that the findings are likely to apply to large numbers of IDUs and NIDUs in NYC.
Since injecting drug use is generally more stigmatized than non-injecting drug use, it is possible that some participants who reported that they had never injected might actually have injected. Hepatitis C virus (HCV) is strongly associated with injecting drug use and only modestly associated with non-injecting drug use. Anti-HCV testing has been conducted for the RDS study participants. Removing the anti-HCV positive participants from the RDS NIDU group lowered the HIV prevalence among the RDS NIDUs by only 2%, and did not change the observed equivalence of HIV prevalence among IDUs and NIDUs in the RDS study (data not presented, a full report on HCV prevalence is in preparation).
The legalization and large-scale expansion of syringe exchange and other HIV prevention services for IDUs in NYC, which began in 1992, was associated with dramatic reductions in both HIV prevalence and HIV incidence among IDUs in the city. HIV prevalence declined from approximately 50% in 1990 down to the current level and HIV incidence declined from an estimated 4/100 person-years to an estimated 1/100 person-years . The decline in HIV prevalence among IDUs was observed in multiple studies . Longitudinal data on HIV prevalence among NIDUs in New York are not available. Two previous studies that recruited non-injecting heroin and cocaine users found 11% prevalence , and 10% prevalence . These results are similar to the 12 and 17% found in the two studies reported here, suggesting that HIV prevalence is either stable or possibly increasing among NIDUs in the city.
There are several reasons to consider HIV infection among IDUs and NIDUs in New York as linked rather than separate phenomena. First, the RDS recruitment data indicate social linkages between IDUs and NIDUs, many IDUs recruited NIDUs and many NIDUs recruited IDUs. Information about HIV/AIDS and social norms about HIV risk behaviors are undoubtedly shared among IDUs and NIDUs. Second, many drug users transition between non-injecting use and injecting use of heroin and cocaine [21,22]. The transitions occur in both directions – from non-injecting use to injecting use and from injecting use to non-injecting use. The social linkages and transitions between IDUs and NIDUs also suggest that there are overlaps in sexual networks between the groups.
We are currently examining possible reasons for the relatively high HIV seroprevalance among the NIDUs in these two studies, including sexual risk behaviors, sexual networks, and the presence of other sexually transmitted diseases that would facilitate sexual transmission of HIV.
In conjunction with the expansion of syringe exchange programs in the US , HIV prevalence has been declining among IDUs in many areas in the US . There is not, however, sufficient data for assessing any national trends in HIV among NIDUs.
HIV prevalence among NIDUs in the two studies reported here was equal to the prevalence among IDUs and as high or higher than the HIV prevalence among IDUs in 85 of the 96 largest SMAs in the USA. . New programs to reduce HIV transmission among NIDUs are clearly needed in the city. As many non-injectors are socially networked with injectors, the new programs should probably address sexual transmission among both IDUs and NIDUs. We would also suggest that HIV/AIDS surveillance programs – both in New York and nationally – create a subcategory of ‘non-injecting heroin, cocaine or methamphetamine user’ within the category of ‘heterosexual transmission’ for better monitoring of HIV infection in this high-risk group.
Sponsorship: These studies were supported by grant 5R01 DA003574 from the National Institute on Drug Abuse and by the Centers for Disease Control and Prevention through program announcement number 00005.
1. Des Jarlais DC, Friedman SR, Novick D, Sotheran JL, Thomas P, Yancovitz S, et al
. HIV-1 infection among intravenous drug users in Manhattan, New York City, from 1977 through 1987. JAMA 1989; 261:1008–1012.
2. Robertson JR, Skidmore CA, Roberts JJK. HIV infection in intravenous drug users: a follow-up study indicating changes in risk-taking behaviour. Br J Addict 1988; 83:387–391.
3. UNAIDS. UNAIDS 2004 Report on the global AIDS epidemic
. Geneva: Joint United Nations Programme on HIV/AIDS; 2004.
4. CDC. Updated US public health service guidelines for the management of occupational exposures to HBV, HCV, and HIV and recommendations for posexposure prophylaxis
5. CDC. HIV/AIDS surveillance report, 2003
. New York: Department of Health and Human Services, Centers for Disease Control and Prevention; 2004.
6. Kral AH, Bluthenthal RN, Lorvick J, Gee L, Bacchetti P, Edlin BR. Sexual transmission of HIV-1 among injection drug users in San Francisco, USA: Risk-factor analysis. Lancet 2001; 357:1397–1401.
7. Strathdee S, Galai N, Safaeian M, Celentano DD, Vlahov D, Johnson L, et al
. Sex differences in risk factors for HIV seroconversion among injection drug users: A 10-year perspective. Arch Intern Med 2001; 161:1281–1288.
8. Des Jarlais DC, Friedman SR, Sotheran JL, Wenston J, Marmor M, Yancovitz SR, et al
. Continuity and change within an HIV epidemic: injecting drug users in New York City, 1984 through 1992. JAMA 1994; 271:121–127.
9. Des Jarlais DC, Marmor M, Friedmann P, Aviles E, Deren S, Torian LV, et al
. HIV incidence among injecting drug users in New York City, 1992–1997: Evidence for a declining epidemic. Am J Public Health 2000; 90:352–359.
10. Des Jarlais DC, Perlis T, Friedman SR, Chapman T, Kwok J, Rockwell R, et al
. Behavioral risk reduction in a declining HIV epidemic: injection drug users in New York City, 1990–1997. Am J Public Health 2000; 90:1112–1116.
11. Maslow CB, Friedman SR, Perlis TE, Rockwell R, Des Jarlais DC. Changes in HIV seroprevalence and related behaviors among male injection drug users who do and do not have sex with men: New York City, 1990–1999. Am J Public Health 2002; 92:382–384.
12. Des Jarlais DC, Perlis T, Arasteh K, Torian LV, Beatrice S, Milliken J, et al
. HIV incidence among injection drug users in New York City, 1990 to 2002: Use of serologic test algorithm to assess expansion of HIV prevention services. Am J Public Health 2005; 95:1439–1444.
13. Heckathorn DD. Respondent-driven sampling: a new approach to the study of hidden populations. Soc Problems 1997; 44:174–199.
14. Heckathorn DD, Semaan S, Broadhead RS, Hughes J. Extensions of respondent-driven sampling: A new approach to the study of injection drug users aged 18–25. AIDS Behav 2002; 6:55–67.
15. Heckathorn D. Respondent-driven sampling II: Deriving valid population estimates from chain-referral samples of hidden populations. Soc Prob 2002; 49:11–34.
16. Des Jarlais D, Arasteh K, Perlis T, Hagan H, Heckathorn DD, McKnight C, et al
. The transition from injection to non-injection drug use: Long-term outcomes among heroin and cocaine users in New York City
17. SAS. SAS/STAT® User's Guide, Version 9.1
. Version 8 ed
. Cary, North Carolina: SAS Institute Inc.; 2004.
18. Des Jarlais DC, Perlis T, Friedman SR, Deren S, Chapman TF, Sotheran JL, et al
. Declining seroprevalence in a very large HIV epidemic: Injecting drug users in New York City, 1991 to 1996. Am J Public Health 1998; 88:1801–1806.
19. Neaigus A, Miller M, Friedman SR, Des Jarlais DC. Sexual transmission risk among noninjecting heroin users infected with human immunodeficiency virus or hepatitis C virus. J Infect Dis 2001; 184:359–363.
20. Howe C, Fuller C, Ompad D, Galea S, Koblin B, Thomas D, et al
. Association of sex, hygiene and drug equipment sharing with hepatitis C virus infection among non-injecting drug users in New York City. Drug Alcohol Depend 2005; 79:389–395.
21. Des Jarlais DC, Casriel C, Friedman SR, Rosenblum A. AIDS and the transition to illicit drug injection: Results of a randomized trial prevention program. Br J Addict 1992; 87:493–498.
22. Neaigus A, Gyarmathy VA, Miller M, Frajzyngier V, Friedman SR, Des Jarlais DC. Transitions to injecting among non-injecting heroin users: Social network influence and individual susceptibility
. Presented at the 12th World Congress on AIDS
. Geneva 1998.
23. McKnight C, Des Jarlais D, Perlis T, Eigo K, Krim M, Auerbach J, et al
. Update: Syringe exchange programs – United States, 2002. MMWR 2005; 54:673–676.
24. Santibanez S, Garfein R, Swartzendruber A, Kerndt PR, Morse E, Ompad D, et al
. Prevalence and correlates of crack-cocaine injection among young injection drug users in the United States, 1997–1999. Drug Alcohol Depend 2005; 77:227–233.
25. Friedman S, Lieb S, Tempalski B, Cooper H, Keem M, Friedman R, et al
. HIV among injection drug users in large US metropolitan areas. J Urban Health 2005; 82:434–445.