Sex, Race, and HIV Risk Disparities in Discontinuity of HIV Care After Antiretroviral Therapy Initiation in the United States and Canada
Bibliographic record
Abstract
Disruption of continuous retention in care (discontinuity) is associated with HIV disease progression. We examined sex, race, and HIV risk disparities in discontinuity after antiretroviral therapy (ART) initiation among patients in North America. Adults (≥18 years of age) initiating ART from 2000 to 2010 were included. Discontinuity was defined as first disruption of continuous retention (≥2 visits separated by >90 days in the calendar year). Relative hazard ratio (HR) and times from ART initiation until discontinuity by race, sex, and HIV risk were assessed by modeling of the cumulative incidence function (CIF) in the presence of the competing risk of death. Models were adjusted for cohort site, baseline age, and CD4+ cell count within 1 year before ART initiation; nadir CD4+ cell count after ART, but before a study event, was assessed as a mediator. Among 17,171 adults initiating ART, median follow-up time was 3.97 years, and 49% were observed to have ≥1 discontinuity of care. In adjusted regression models, the hazard of discontinuity for patients was lower for females versus males [HR: 0.84; 95% confidence interval (CI): 0.79–0.89] and higher for blacks versus nonblacks (HR: 1.17; 95% CI: 1.12–1.23) and persons with injection drug use (IDU) versus non-IDU risk (HR: 1.33; 95% CI: 1.25–1.41). Sex, racial, and HIV risk differences in clinical retention exist, even accounting for access to care and known competing risks for discontinuity. These results point to vulnerable populations at greatest risk for discontinuity in need of improved outreach to prevent disruptions of HIV care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".