PubMed Health⌕ Search

PubMed · 16343373

Managing HIV/AIDS in correctional settings.

Abstract

Approximately one quarter of people living with HIV/AIDS in the United States pass through the correctional system, resulting in a burden of infection on the correctional health care system that has challenged correctional and public health officials. The HIV epidemic behind bars results from the high prevalence of HIV risk behaviors among those incarcerated: illicit drug use, untreated mental illness, prostitution, homelessness, and poverty. Challenges to HIV care in correctional settings include management of comorbid conditions, remoteness from HIV care sites, organizational constraints, and access to effective therapies. Despite these challenges, prisoners with HIV have derived considerable benefit from HIV detection and treatment. In order to achieve parity in HIV outcomes among vulnerable populations, effective prison-release programs that incorporate effective case management with effective drug treatment and adherence strategies are required to extend the benefit of highly active antiretroviral therapy as prisoners transition back to community settings.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sandra A Springer, Frederick L Altice. 2005. Managing HIV/AIDS in correctional settings.. https://doi.org/10.1007/s11904-005-0011-9

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Random changepoint modelling of HIV immunologic responses.

We propose a changepoint model for the analysis of longitudinal CD4 T-cell counts for HIV infected subjects following highly active antiretroviral treatment. The profile of CD4 counts for each subject follows a simple, 'broken stick' changepoint model, with random subject-specific parameters, including the changepoint. The model accounts for baseline covariates. The longitudinal CD4 records are censored at the time of the subject going off-study-treatment. This is a potentially informative drop-out mechanism, which we address by modelling it jointly with the CD4 count outcome. The drop-out model incorporates terms from the CD4 model, including the changepoint. The estimation is done in a Bayesian framework, with implementation via Markov chain Monte Carlo methods in the WinBUGS software. Model selection using DIC indicates that the data support the complex random changepoint and informative censoring model.

Antiretroviral Therapy, Highly Active↗

Estimation and prediction with HIV-treatment interruption data.

We consider longitudinal clinical data for HIV patients undergoing treatment interruptions. We use a nonlinear dynamical mathematical model in attempts to fit individual patient data. A statistically-based censored data method is combined with inverse problem techniques to estimate dynamic parameters. The predictive capabilities of this approach are demonstrated by comparing simulations based on estimation of parameters using only half of the longitudinal observations to the full longitudinal data sets.

Antiretroviral Therapy, Highly Active↗