Initiating Highly Active Antiretroviral Therapy and Continuity of HIV Care: The Impact of Incarceration and Prison Release on Adherence and HIV Treatment Outcomes
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of incarceration within 12 months of initiating highly active antiretroviral therapy (HAART) on non-adherence and HIV-1 RNA suppression. METHODS: We compared the adherence and virological outcomes among participants in a population-based HIV/AIDS Drug Treatment Program in British Columbia, Canada, by history of incarceration in a provincial prison. Participants who were HIV-infected, naive to HAART and who were prescribed treatment between 1 July 1997 and 1 March 2002 were eligible for this study. Logistic regression was used to determine the factors associated with non-adherence and Cox proportional hazards modelling was used to determine the factors associated with HIV-1 RNA suppression adjusting for age, gender, history of drug use, baseline HIV-1 RNA, baseline CD4 cell count, type of antiretroviral regimen [two nucleosides + protease inhibitor (PI) vs two nucleosides + non-nucleoside reverse transcriptase inhibitor (NNRTI)], physician's HIV-related experience for each subject and adherence as measured by pharmacy refill compliance. RESULTS: There were 1746 subjects (101 incarcerated/1645 non-incarcerated) who started antiretroviral therapy between 1 July 1997 and 1 March 2002. Of those incarcerated, 50 initiated HAART while in prison and 27 subjects were released but returned to prison in the follow-up period. Subjects received antiretroviral therapy while incarcerated for a median number of 4 months [interquartile range (IQR): 2-10]. Multiple logistic regression results showed that a history of incarceration within 12 months of initiating HAART independently increased the odds of non-adherence [adjusted odds ratio (AOR): 2.40; 95% confidence interval (95% CI): 1.54-3.75]. A history of injected drug use was also associated with non-adherence (AOR: 1.49; 95% CI: 1.17-1.90). The following factors were negatively associated with non-adherence: older age (AOR: 0.81; 95% CI: 0.72-0.91), male sex (AOR: 0.50; 95% CI: 0.38-0.65) and higher physician HIV-related experience (AOR: 0.97; 95% CI: 0.96-0.98). In addition, a history of incarceration within 12 months of initiating HAART reduced the odds of achieving HIV-1 RNA suppression [adjusted hazards ratio (AHR): 0.68; 95% CI: 0.51-0.89]. Other factors negatively associated with viral suppression included a history of drug injection (AHR: 0.79; 95% CI: 0.69-0.91), two nucleosides + PI vs two nucleosides + NNRTI (AHR: 0.77; 95% CI: 0.69-0.87), higher baseline HIV-1 RNA (AHR: 0.66; 95% CI: 0.62-0.70). Higher adherence was positively associated with viral suppression (AHR: 1.38; 95% CI: 1.34-1.42). Among the 101 subjects who were incarcerated in the first year of starting HAART, the time spent in jail was positively associated with HIV-1 RNA suppression (HR: 1.06; 95% CI: 1.02-1.10). CONCLUSION: HIV-infected subjects with a history of incarceration within 12 months of initiating HAART have higher odds of non-adherence and, consequently, lower probability of achieving HIV-1 RNA suppression. The longer their sentence, however, the higher the probability of virological suppression. The British Columbian provincial prison system provided a structured setting for HAART but subjects are unable to continue this level of adherence upon release. Strategies to ensure continuation of HIV/AIDS care for HIV-infected individuals leaving the criminal justice system must be a public health priority.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".