Five-Year Mortality for Adults Entering Human Immunodeficiency Virus Care Under Universal Early Treatment Compared With the General US Population
Bibliographic record
Abstract
BACKGROUND: Mortality among adults with human immunodeficiency virus (HIV) remains elevated over those in the US general population, even in the years after entry into HIV care. We explore whether the elevation in 5-year mortality would have persisted if all adults with HIV had initiated antiretroviral therapy within 3 months of entering care. METHODS: Among 82 766 adults entering HIV care at North American AIDS Cohort Collaboration clinical sites in the United States, we computed mortality over 5 years since entry into HIV care under observed treatment patterns. We then used inverse probability weights to estimate mortality under universal early treatment. To compare mortality with those for similar individuals in the general population, we used National Center for Health Statistics data to construct a cohort representing the subset of the US population matched to study participants on key characteristics. RESULTS: For the entire study period (1999-2017), the 5-year mortality among adults with HIV was 7.9% (95% confidence interval [CI]: 7.6%-8.2%) higher than expected based on the US general population. Under universal early treatment, the elevation in mortality for people with HIV would have been 7.2% (95% CI: 5.8%-8.6%). In the most recent calendar period examined (2011-2017), the elevation in mortality for people with HIV was 2.6% (95% CI: 2.0%-3.3%) under observed treatment patterns and 2.1% (.0%-4.2%) under universal early treatment. CONCLUSIONS: Expanding early treatment may modestly reduce, but not eliminate, the elevation in mortality for people with HIV.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".