The shifting age distribution of people with HIV using antiretroviral therapy in the United States
Bibliographic record
Abstract
OBJECTIVE: To project the future age distribution of people with HIV using antiretroviral therapy (ART) in the United States, under expected trends in HIV diagnosis and survival (baseline scenario) and achieving the ending the HIV epidemic (EHE) goals of a 75% reduction in HIV diagnoses from 2020 to 2025 and sustaining levels to 2030 (EHE75% scenario). DESIGN: An agent-based simulation model with mathematical functions estimated from North American AIDS Cohort Collaboration on Research and Design data and parameters from the US Centers for Disease Control and Prevention's annual HIV surveillance reports. METHODS: The PEARL (ProjEcting Age, MultimoRbidity, and PoLypharmacy in adults with HIV) model simulated individuals in 15 subgroups of sex-and-HIV acquisition risk and race/ethnicity. Simulation outcomes from the baseline scenario are compared with outcomes from the EHE75% scenario. RESULTS: Under the baseline scenario, PEARL projects a substantial increase in number of ART-users over time, reaching a population of 909 638 [95% uncertainty range (UR): 878 449-946 513] by 2030. The overall median age increased from 50 years in 2020 to 52 years in 2030, with 23% of ART-users age ≥65 years in 2030. Under the EHE75% scenario, the projected number of ART-users was 718 348 [703 044-737 817] (median age = 56 years) in 2030, with a 70% relative reduction in ART-users <30 years and a 4% relative reduction in ART-users age ≥65 years compared to baseline, and persistent heterogeneities in projected numbers by sex-and-HIV acquisition risk group and race/ethnicity. CONCLUSIONS: It is critical to prepare healthcare systems to meet the impending demand of the US population aging 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".