Skip to main content

Advertisement

SpringerLink
Log in

Use of Latent Class Analysis Approach to Describe Drug and Sexual HIV Risk Patterns among Injection Drug Users in Houston, Texas

  • Original Paper
  • Published:
AIDS and Behavior Aims and scope Submit manuscript

Abstract

Following latent class analysis (LCA) approach we examined patterns of HIV risk using two related domains of behavior: drug use, and sexual activity among 523 injection drug users (IDUs) recruited into the 2009 National HIV behavioral surveillance system. Using posterior probability of endorsing six drug and sexual items, we identified three distinct classes representing underlying HIV risk. Forty percent of our participants were at highest risk, 25 % at medium risk, and 35 % at lowest risk for HIV infection. Compared to the Lowest-risk class members, the Highest-risk class members had riskier drug and sexual behaviors and had higher prevalence of HIV cases (6 vs. 4 %). This analysis underscores the merit of LCA to empirically identify risk patterns using multiple indicators and our results show HIV risk varies among IDUs as their drug and sexual behaviors. Tailored and targeted prevention and treatment interventions for the dual risk pattern are required rather than for drug or sexual risk in silos.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Centers for Disease Control and Prevention. HIV Surveillance-United States, 1981–2008. MMWR Morb Mortal Wkly Rep. 2011;60(21):689–93.

    Google Scholar 

  2. Santibanez S, Garfein R, Swartzendruber A, Purcell D, Paxton L, Greenberg A. Update and overview of practical epidemiologic aspects of HIV/AIDS among injection drug users in the United States. J Urban Health. 2006;83(1):86–100.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Celentano DD, Latimore AD, Mehta SH. Variations in sexual risks in drug users: emerging themes in a behavioral context. Curr HIV/AIDS Rep. 2008;5(4):212–8.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Des Jarlais DC, Semaan S. HIV prevention for injecting drug users: the first 25 years and counting. Psychosom Med. 2008;70(5):606–11.

    Article  PubMed  Google Scholar 

  5. Kruse GR, Barbour R, Heimer R, et al. Drug choice, spatial distribution, HIV risk, and HIV prevalence among injection drug users in St. Petersburg, Russia. Harm Reduct J. 2009;6:22–9.

    Article  PubMed Central  PubMed  Google Scholar 

  6. Harzke AJ, Williams ML, Bowen AM. Binge use of crack cocaine and sexual risk behaviors among African-American, HIV-positive users. AIDS Behav. 2009;13(6):1106–18.

    Article  PubMed Central  PubMed  Google Scholar 

  7. Arasteh K, Des Jarlais DC. At-risk drinking and injection and sexual risk behaviors of HIV-positive injection drug users entering drug treatment in New York city. AIDS Patient Care STDS. 2009;23(8):657–61.

    Article  PubMed Central  PubMed  Google Scholar 

  8. McCutcheon A. Latent class analysis. Newbury Park: Sage Publications; 1987.

    Google Scholar 

  9. Muthen B, Muthen LK. Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol Clin Exp Res. 2000;24(6):882–91.

    Article  PubMed  CAS  Google Scholar 

  10. Vermunt JK, Magidson J. Latent GOLD choice 3.0 user’s guide. 2003.

  11. Muthen LK, Muthen BO. Mplus users’s guide. 1998–2010, vol. 6. Los Angeles: Muthen & Muthen; 2010.

    Google Scholar 

  12. Green TC, Kershaw T, Lin H, et al. Patterns of drug use and abuse among aging adults with and without HIV: a latent class analysis of a US veteran cohort. Drug Alcohol Depend. 2010;110(3):208–20.

    Article  PubMed Central  PubMed  Google Scholar 

  13. Monga N, Rehm J, Fischer B, et al. Using latent class analysis (LCA) to analyze patterns of drug use in a population of illegal opioid users. Drug Alcohol Depend. 2007;88(1):1–8.

    Article  PubMed  CAS  Google Scholar 

  14. Agrawal A, Lynskey MT, Madden PA, Bucholz KK, Heath AC. A latent class analysis of illicit drug abuse/dependence: results from the National epidemiological survey on alcohol and related conditions. Addiction. 2007;102(1):94–104.

    Article  PubMed  Google Scholar 

  15. Lynskey MT, Agrawal A, Bucholz KK, et al. Subtypes of illicit drug users: a latent class analysis of data from an Australian twin sample. Twin Res Hum Genet. 2006;9(4):523–30.

    Article  PubMed  Google Scholar 

  16. Carlson RG, Wang J, Falck RS, Siegal HA. Drug use practices among MDMA/ecstasy users in Ohio: a latent class analysis. Drug Alcohol Depend. 2005;79(2):167–79.

    Article  PubMed  Google Scholar 

  17. James S, McField ES, Montgomery SB. Risk factor profiles among intravenous drug using young adults: a latent class analysis (LCA) approach. Addict Behav. 2013;38(3):1804–11.

    Article  PubMed Central  PubMed  Google Scholar 

  18. Kuramoto S, Bohnert A, Latkin C. Understanding subtypes of inner-city drug users with a latent class approach. Drug Alcohol Depend. 2011;118(2):237–43.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  19. Watson C, Weng CX, French T, et al. Substance abuse treatment utilization, HIV risk behaviors, and recruitment among suburban injection drug users in long island, New York. AIDS Behav. 2013:1–11.

  20. Mackesy-Amiti ME, Ouellet LJ, Finnegan L, et al. Transitions in latent classes of sexual risk behavior among young injection drug users following HIV prevention intervention. AIDS Behav. 2013:1–9.

  21. Gallagher KM, Sullivan PS, Lansky A, Onorato IM. Behavioral surveillance among people at risk for HIV infection in the U.S.: the National HIV Behavioral Surveillance System. Public Health Rep. 2007;122(Suppl 1):32–8.

    PubMed Central  PubMed  Google Scholar 

  22. Lansky A, Sullivan PS, Gallagher KM, Fleming PL. HIV behavioral surveillance in the U.S.: a conceptual framework. Public Health Rep. 2007;122(Suppl 1):16–23.

    PubMed Central  PubMed  Google Scholar 

  23. Heckathorn DD. Extensions of respondent-driven sampling: analyzing continuous variables and controlling for differential recruitment. Sociol Methodol. 2007;37(1):151–207.

    Article  Google Scholar 

  24. Salganik MJ, Heckathorn DD. Sampling and estimation in hidden populations using respondent-driven sampling. Sociol Methodol. 2004;34:193–239.

    Article  Google Scholar 

  25. Noor SW. HIV prevalence and contextual risk-factors among injection drug users in harris county, Texas, (doctoral dissertation). The University of Texas health science center, USA, 2012. Retrieved from Pro-Quest dissertations and theses. (Accession/Order No. 10827).

  26. NOVA Research Company. QDS™ (questionnaire development system). 2009;2.6.

  27. Strathdee SA, Sherman SG. The role of sexual transmission of HIV infection among injection and non-injection drug users. J Urban Health. 2003;80(4 Suppl 3):7–14.

    Google Scholar 

  28. Lo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88(3):767–78.

    Article  Google Scholar 

  29. Magidson J, Vermunt J. Latent class models. In: Kaplan D, editor. The sage handbook of quantitative methodology for the social sciences thousand oaks. New York: Sage Publications; 2004.

    Google Scholar 

  30. Nylund KL, Asparouhov T, Muthen BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model. 2007;14(4):535–69.

    Article  Google Scholar 

  31. Ramaswamy V, Desarbo WS, Reibstein DJ, Robinson WT. An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Mark Sci. 1993;12(1):103–24.

    Article  Google Scholar 

  32. Volz E, Wejnert C, Cameron C, et al. Respondent-driven sampling analysis tool (RDSAT). 2012;5.6.

  33. StataCorp. Stata statistical software: release 12. 2012.

  34. Coady MH, Latka MH, Thiede H, et al. Housing status and associated differences in HIV risk behaviors among young injection drug users (IDUs). AIDS Behav. 2007;11(6):854–63.

    Article  PubMed  Google Scholar 

  35. Salazar LF, Crosby RA, Holtgrave DR, et al. Homelessness and HIV-associated risk behavior among African American men who inject drugs and reside in the urban south of the United States. AIDS Behav. 2007;11(6 Suppl):70–7.

    Article  PubMed  Google Scholar 

  36. Wodak A, Stowe A, Ross MW, Gold J, Miller ME. HIV risk exposure of injecting drug users in Sydney. Drug Alcohol Rev. 1995;14(2):213–22.

    Article  PubMed  CAS  Google Scholar 

  37. Ross MW, Wodak A, Gold J. Sexual behaviour in injecting drug users. J Psychol Human Sex. 1992;5(1–2):89–104.

    PubMed  CAS  Google Scholar 

  38. Lau JT, Tsui HY, Gu J, et al. Sexual mixing and condom use with different types of sex partners among non-institutionalized sexually active female injecting drug users in Sichuan, China. AIDS Behav. 2011;15(1):86–94.

    Article  PubMed  Google Scholar 

  39. Lau JT, Cheng F, Tsui HY, et al. Clustering of syringe sharing and unprotected sex risk behaviors in male injecting drug users in China. Sex Transm Dis. 2007;34(8):574–82.

    PubMed  Google Scholar 

  40. Hall HI, Green TA, Wolitski RJ, et al. Estimated future HIV prevalence, incidence, and potential infections averted in the United States: a multiple scenario analysis. J Acquir Immune Defic Syndr. 2010;55(2):271–6.

    Article  PubMed  Google Scholar 

  41. Chung H, Flaherty BP, Schafer JL. Latent class logistic regression: application to marijuana use and attitudes among high school seniors. J Royal Statistical Soc Series A (Stat Soc). 2006;169(4):723–43.

    Article  Google Scholar 

  42. Asparouhov T, Muthén B. Bayesian analysis using Mplus: technical implementation. Manuscript submitted for publication. 2010. www.statmodel.com.

Download references

Acknowledgments

The authors would like to acknowledge Houston Department of Health and Human Services for their continuing support. This project was partially supported by the Centers for Disease Control and Prevention Cooperative Agreement.

Author information

Authors and Affiliations

  1. Division of Epidemiology and Community Health, University of Minnesota School of Public Health, WBOB-300, 1300 2nd St South, Minneapolis, MN, 55454, USA

    Syed W. B. Noor

  2. Division of Health Promotion and Behavioral Sciences, University of Texas School of Public Health, Houston, TX, USA

    Michael W. Ross

  3. Division of Biostatistics, University of Texas School of Public Health, Houston, TX, USA

    Dejian Lai

  4. Division of Epidemiology, Human Genetics & Environmental Sciences, University of Texas School of Public Health, Houston, TX, USA

    Jan M. Risser

Authors
  1. Syed W. B. Noor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael W. Ross
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dejian Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jan M. Risser
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syed W. B. Noor.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Noor, S.W.B., Ross, M.W., Lai, D. et al. Use of Latent Class Analysis Approach to Describe Drug and Sexual HIV Risk Patterns among Injection Drug Users in Houston, Texas. AIDS Behav 18 (Suppl 3), 276–283 (2014). https://doi.org/10.1007/s10461-014-0713-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10461-014-0713-3

Keywords

Search

Navigation