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Longitudinal HIV Risk Behavior Among the Drug Abuse Treatment Outcome Studies (DATOS) Adult Sample

Author

Listed:
  • Debra A. Murphy

    (University of California, Los Angeles)

  • Mary-Lynn Brecht

    (University of California, Los Angeles)

  • Diane Herbeck

    (University of California, Los Angeles)

  • Elizabeth Evans

    (University of California, Los Angeles)

  • David Huang

    (University of California, Los Angeles)

  • Yih-Ing Hser

    (University of California, Los Angeles)

Abstract

Longitudinal trajectories for HIV risk were examined over 5 years following treatment among 1,393 patients who participated in the nationwide Drug Abuse Treatment Outcome Studies. Both injection drug use and sexual risk behavior declined over time, with most of the decline occurring between intake and the first-year follow-up. However, results of the application of growth mixture models for both sets of trajectories indicated that a subgroup of individuals reverted to a high-risk behavior over time, with a higher level of risk at the 5-year follow-up than their original risk level at intake. Of clients who were engaged in regular injection drug use at intake, 76% continued to inject drug at a moderate—stable or increased rate during the 5-year follow-up.

Suggested Citation

  • Debra A. Murphy & Mary-Lynn Brecht & Diane Herbeck & Elizabeth Evans & David Huang & Yih-Ing Hser, 2008. "Longitudinal HIV Risk Behavior Among the Drug Abuse Treatment Outcome Studies (DATOS) Adult Sample," Evaluation Review, , vol. 32(1), pages 83-112, February.
  • Handle: RePEc:sae:evarev:v:32:y:2008:i:1:p:83-112
    DOI: 10.1177/0193841X07307411
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    References listed on IDEAS

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    1. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    2. Neaigus, Alan & Friedman, Samuel R. & Curtis, Richard & Des Jarlais, Don C. & Terry Furst, R. & Jose, Benny & Mota, Patrice & Stepherson, Bruce & Sufian, Meryl & Ward, Thomas & Wright, Jerome W., 1994. "The relevance of drug injectors' social and risk networks for understanding and preventing HIV infection," Social Science & Medicine, Elsevier, vol. 38(1), pages 67-78, January.
    3. van Empelen, Pepijn & Kok, Gerjo & van Kesteren, Nicole M. C. & van den Borne, Bart & Bos, Arjan E. R. & Schaalma, Herman P., 2003. "Effective methods to change sex-risk among drug users: a review of psychosocial interventions," Social Science & Medicine, Elsevier, vol. 57(9), pages 1593-1608, November.
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