HIV, Infectious Diseases, and Drug Use in Networks and Communities

1. HIV, Infectious Diseases, and Drug Use in Networks and Communities I would like to acknowledge: NIDA projects: R01 DA13128 (Networks, norms & HIV risk among youth) R01 DA13336 (Community Vulnerability and Response to IDU-Related HIV), P30 DA11041 (Center for Drug Use and HIV Research) R01 DA10870 (HIV Risk among Women IDUs Who Have Sex with Women) NIMH project R01 MH62280 (Local context, social-control action, and HIV risk) Hundreds of participants in these studies Many collaborators and co-authors

2. Background

3. Cumulative AIDS cases among US adults and adolescents by Exposure Category, Dec 2002

5. Partial list of predictors of an IDU’s being or becoming infected with HIV: • Behavioral: Sharing syringes, backloading, indirect sharing, MSM • Sociodemographic: Black, Puerto Rican, WSW, years of injection • Network: High risk injection or sex partners; member of sociometric “microstructure” • Note well: Non-injecting heroin users and crack smokers are also at increased risk of HIV, hepatitis B and C, syphilis, and HSV-2. The most likely reason is unprotected sex with IDUs.

6. What characteristics of US metropolitan areas are associated with the rate of IDUs per capita and with HIV prevalence rate among IDUs? Data from the Community Vulnerability and Response to IDU-related HIV study

7. Methods Unit of analysis: Large Metropolitan Statistical Areas • The 96 MSAs (in USA) with populations of 500,000+ in 1993 • MSAs are defined by: • County boundaries • Central city population of 50,000 or more • Based on economic and social integration with surrounding areas and on commuting patterns to central city • N’s vary depending on missing data: 96 for IDUs per capita, 91 for HIV prevalence rate.

8. Dependent Variable 1: IDUs per capita, 1998 IDUs per capita was estimated by: • Using average of four multipliers to allocate total number of IDUs in the USA to the 96 MSAs; • Dividing by the population of the MSA. See Friedman et al, J Urban Health, 2004

9. Dependent variable 2: HIV prevalence rate in IDUs in 1998 HIV prevalence rates in 91 MSAs were estimated by: • In 26 MSAs where research data exists, these data were used as the estimates. • In 65 other MSAs, we used the average of two results: • Regressing research results on HIV counseling and testing data, and using this equation to predict the HIV prevalence rate in the other 65 MSAs; and • Using the Lieb techniques to estimate HIV prevalence rates as the number of IDUs living with HIV (itself estimated as a function of IDUs living with AIDS) divided by the number of IDUs (Lieb et al, J Urban Health, 2004).

11. Predictors of IDUs per capita

13. Final predictors of HIV prevalence rate

14. Limitations • Causal mechanisms are hard to study at a single level of analysis • Lack of time series data makes causal inference difficult • We plan to conduct time series analyses in the near future

15. Summary of this part • Socioeconomic factors (median income; unemployment) of MSAs are associated with more IDUs/capita and with higher HIV prevalence among IDUs. • Black/white racial residential segregation is associated with fewer IDUs per capita (which is puzzling). • Health expenditures are associated with lower HIV prevalence. • Higher rates of arrest for hard drug use are associated with more IDUs and more HIV among IDUs.

16. Insights from the New York City HIV epidemic among IDUs • This section presents some overviews plus it uses new data from the Networks, Norms and HIV Risk among Youth study in the Bushwick section of Brooklyn

17. I will now show evidence that IDUs, crack smokers, and other community residents are active participants in the fight against HIV • Thus, theories that view IDUs as helpless victims of addiction or as uncaring spreaders of HIV and other infections seem to be misleading. • Likewise, theories that see public health interventions and drug abuse treatment as the actors in HIV prevention are incomplete.

19. Intravention and its implications • Currently, based on decades of experience with HIV/AIDS and with street drug use, and perhaps with prevention programs, in some neighborhoods such as Bushwick residents engage in activities to help others to protect themselves

20. Survey findings about other-protective action in the prior 3 months by “hardest” drug used in last 3 months

21. Qualitative interviews confirm that these reports refer to recent concrete actions rather than to abstract intentions or actions well in the past. • Other-protective actions directed at drug users are more likely by those who are hard drug users and thus, perhaps, more likely to interact with users • Drug users frequently act to urge others to protect themselves • These data suggest that: • Urging safer behaviors has been institutionalized into the community as a somewhat self-sustaining intravention. • Many IDUs take actions to protect others from infection. • HIV prevention efforts and other programs need to take pre-existing intraventions into account.

22. Drug users’ organizations: IDUs can work collectively and formally against HIV • Thai drug users’ network • Rotterdam junkiebund • Australian Intravenous League and state organizations • Some US prevention projects are users’ groups too • Etc.

23. Users in the community IDUs and other users have many social relationships with others

24. We have already discussed drug users’ participation in intraventions • Now we will look at sexual networks in the Bushwick (Brooklyn) community

25. Gender/Sexuality (MSM=up triangle, WSW=down triangle, other female=circle, other male=square) by Hardest Drug Use Ever (from dark red to light pink: IDU, Crack, NI Heroin or Cocaine; blue=other)

26. This preliminary diagram shows that the two behavioral groups at highest HIV risk—IDUs and MSM—have many risk network connections. • It also shows that women who have sex with women may be at risk through injection and sexual networks with IDUs and with MSM (including MSM who are IDUs)—which may help explain other studies’ findings that WSW IDUs are at very high HIV risk. • Finally, it presents many instances of crack smokers and other non-injecting cocaine and heroin users who have sexual ties to IDUs—which may help explain why these groups are at enhanced HIV risk.

27. HIV-positive by Gender/Sexuality (MSM=up triangle, WSW=down triangle, other female=circle, other male=square) by Hardest Drug Use Ever (from dark red to light pink: IDU, Crack, NI Heroin or Cocaine; blue=other)

28. HCV-positive by Gender/Sexuality (MSM=up triangle, WSW=down triangle, other female=circle, other male=square) by Hardest Drug Use Ever (from dark red to light pink: IDU, Crack, NI Heroin or Cocaine; blue=other)

29. HSV2-positive by Gender/Sexuality (MSM=up triangle, WSW=down triangle, other female=circle, other male=square) by Hardest Drug Use Ever (from dark red to light pink: IDU, Crack, NI Heroin or Cocaine; blue=other)

30. Health Activism Star by Gender/Sexuality (MSM=up triangle, WSW=down triangle, other female=circle, other male=square) by Hardest Drug Use Ever (from dark red to light pink: IDU, Crack, NI Heroin or Cocaine; blue=other)

31. HBV (exposed=+, immunized=V) by Gender/Sexuality (MSM=up triangle, WSW=down triangle, other female=circle, other male=square) by Hardest Drug Use Ever (from dark red to light pink: IDU, Crack, NI Heroin or Cocaine; blue=other)

32. Classification: Core, Sex Partners, and Distance • Core: Men who have sex with men (MSM) and injection drug users (IDUs) are a core group for HIV and HBV infection, and perhaps for other infections N = 201 • Sex partners (SPs) are defined as sex partners of one or more core members N = 67 • D2 (distance = 2) are sex partners of sex partners N = 32 • D3+ are sex partners of D2 members, or sex partners of other D3+ sex partners N = 19 • Unlinked are non-core subjects who are not sexually linked to a core group member by a path of any length N = 94

33. Blood-borne virus infection (and hep B induced immunity) by network distance from core HIV* HCV* HBVª* HBV immune* Core (MSM &/or IDU) 18% 60% 58% 20% SPs 10% 4% 24% 25% D2 0% 3% 22% 38% D3+ 0% 0% 8% 45% Unlinked to core 0% 1% 4% 40% ªHBV exposure among the unvaccinated. *p chi-sq test for trend) < .001.

34. Sexually-transmitted infections by network distance from core nHSV2*HSV1*Syph.GCCT Core (MSM or IDU) 201 60% 58% 5% 1% 4% SPs 67 56% 87% 3% 2% 2% D2 32 28% 77% 0% 3% 9% D3+ 19 37% 79% 0% 0% 26% Unlinked to core 94 30% 69% 0% 0% 9% *p (chi-sq test for trend) < .001.

35. Implications • Treatment centers and other projects: • STI prevention and treatment for users • Some ethnographic evidence from WSW Project that services for drug users assume heterosexuality and that this may hurt MSM and WSW. • HCV education and treatment • Vaccination for hep B inadequate. Research & planning needed for HIV, HCV & HSV-2 vaccines • Macro issues matter—both economics and policy. They affect SEP, treatment, IDU/capita, HIV prevalence among IDUs • IDUs are part of the community both epidemiologically and as part of intraventions • IDUs, other users, and community residents can be ACTORS for public health—and thus allies.