Incidence of AIDS-Defining Conditions Among Adults With Perinatally Acquired HIV After Transition to Adult HIV Care in the United States and Canada, 2000–2022
Bibliographic record
Abstract
BACKGROUND: Little is known about the incidence of AIDS-defining conditions (ADCs) among people with perinatally acquired human immunodeficiency virus (PHIV) who transitioned to adult human immunodeficiency virus (HIV) care in the United States and Canada. We described the incidence among PHIV and compared it with that among those with non-perinatally acquired HIV (non-PHIV) and across calendar era. METHODS: Using data from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) from 2000-2022, we estimated weighted mean cumulative counts (MCCs) of ADCs, comparing people with PHIV and non-PHIV acquisition risk groups aged 18-40 years engaged in adult HIV care. The weights accounted for differences in characteristics among HIV acquisition risk groups as well as informative censoring. We calculated 95% confidence intervals using bootstrapping. We stratified results before and after 2012, when the immediate start of antiretroviral therapy was first recommended. RESULTS: There were 5429 ADCs among 22 950 people with HIV. Among those with PHIV, the MCC of ADCs by 3 years in adult HIV care was 26 per 100 persons (95% confidence interval, 15-40) in 2000-2011 and 16 per 100 (5-21) in 2012-2022. Within each calendar era, weighted MCCs of ADCs among people with PHIV were similar or lower than in non-PHIV groups. Within each HIV acquisition risk group, weighted MCCs of ADCs were lower in 2000-2011 than in 2012-2022. CONCLUSIONS: People with PHIV who transitioned to adult HIV care did not experience a greater ADC incidence than those with non-PHIV. This emphasizes the importance of continued engagement in adult HIV care, as it provides critical opportunities for ADC prevention and management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".