Evaluating Clinic-Based Interventions to Reduce Racial Differences in Mortality Among People With Human Immunodeficiency Virus in the United States
Bibliographic record
Abstract
BACKGROUND: Mortality remains elevated among Black versus White adults receiving human immunodeficiency virus (HIV) care in the United States. We evaluated the effects of hypothetical clinic-based interventions on this mortality gap. METHODS: We computed 3-year mortality under observed treatment patterns among >40 000 Black and >30 000 White adults entering HIV care in the United States from 1996 to 2019. We then used inverse probability weights to impose hypothetical interventions, including immediate treatment and guideline-based follow-up. We considered 2 scenarios: "universal" delivery of interventions to all patients and "focused" delivery of interventions to Black patients while White patients continued to follow observed treatment patterns. RESULTS: Under observed treatment patterns, 3-year mortality was 8% among White patients and 9% among Black patients, for a difference of 1 percentage point (95% confidence interval [CI], .5-1.4). The difference was reduced to 0.5% under universal immediate treatment (95% CI, -.4% to 1.3%) and to 0.2% under universal immediate treatment combined with guideline-based follow-up (95% CI, -1.0% to 1.4%). Under the focused delivery of both interventions to Black patients, the Black-White difference in 3-year mortality was -1.4% (95% CI, -2.3% to -.4%). CONCLUSIONS: Clinical interventions, particularly those focused on enhancing the care of Black patients, could have significantly reduced the mortality gap between Black and White patients entering HIV care from 1996 to 2019.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".