Gender Differences in HIV-1 RNA Rebound Attributed to Incomplete Antiretroviral Adherence Among HIV-Infected Patients in a Population-Based Cohort
Bibliographic record
Abstract
BACKGROUND: There have been growing concerns about possible gender-related differences in rates of responses to highly active antiretroviral therapy (HAART). We therefore examined the association between gender and time to HIV-1 RNA rebound in antiretroviral-naive HIV-infected patients initiating HAART in a population-based setting. METHODS: We evaluated all antiretroviral-naive HIV-infected men and women who achieved HIV-1 RNA suppression at least once (HIV RNA <500 copies/mL) after initiating HAART between August 1, 1996 and July 31, 2000 and who were followed until March 31, 2002 in a province-wide HIV treatment program. We evaluated time to HIV-1 RNA rebound (> or =500 copies/mL) using Kaplan-Meier methods and Cox proportional hazards regression. RESULTS: In total, 844 (87.0%) men and 126 (13.0%) women initiated HAART during the study period and achieved HIV-1 RNA suppression at least once. Overall rates of rebound were 47.4% and 34.0% for women and men, respectively (log-rank, P < 0.021). Women were less likely to be > or =95% adherent (P = 0.001) and more likely to have a history of injection drug use (P = 0.001). In multivariate analysis, incomplete adherence was found to be highly predictive of HIV-1 RNA rebound (adjusted relative hazard [ARH] = 4.00, 95% confidence interval [CI]: 3.33-5.00). Although female patients had higher rates of HIV-1 RNA rebound in univariate analysis (relative hazard [RH] = 1.39, 95% CI: 1.05-1.82), this was no longer statistically significant once other known confounders such as adherence and injection drug use were adjusted for (RH = 0.95, 95% CI: 0.71-1.28). When the analyses were stratified based on history of injection drug use, we found that rates of rebound were higher among injection drug-using women than among injection drug-using men (P = 0.048), whereas there was no gender difference among non-injection drug users with respect to rebound (P = 0.345). CONCLUSIONS: We found that higher rates of HIV-1 RNA rebound among women were primarily explained by incomplete adherence, which was more prevalent among women in this cohort. Our findings suggest that psychosocial factors such as drug use and incomplete adherence predict HIV-1 RNA rebound and that gender differences in time to rebound can be largely attributed to a disproportionate prevalence of these factors among women in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".