Toward Ending the HIV Epidemic: Temporal Trends and Disparities in Early ART Initiation and Early Viral Suppression Among People Newly Entering HIV Care in the United States, 2012–2018
Bibliographic record
Abstract
Abstract Background In 2012, the US Department of Health and Human Services updated their HIV treatment guidelines to recommend antiretroviral therapy (ART) for all people with HIV (PWH) regardless of CD4 count. We investigated recent trends and disparities in early receipt of ART prescription and subsequent viral suppression (VS). Methods We examined data from ART-naïve PWH newly presenting to HIV care at 13 North American AIDS Cohort Collaboration on Research and Design clinical cohorts in the United States during 2012–2018. We calculated the cumulative incidence of early ART (within 30 days of entry into care) and early VS (within 6 months of ART initiation) using the Kaplan-Meier survival function. Discrete time-to-event models were fit to estimate unadjusted and adjusted associations of early ART and VS with sociodemographic and clinical factors. Results Among 11 853 eligible ART-naïve PWH, the cumulative incidence of early ART increased from 42% in 2012 to 82% in 2018. The cumulative incidence of early VS among the 8613 PWH who initiated ART increased from 83% in 2012 to 93% in 2018. In multivariable models, factors independently associated with delayed ART and VS included non-Hispanic/Latino Black race, residence in the South census region, being a male with injection drug use acquisition risk, and history of substance use disorder (SUD; all P ≤ .05). Conclusions Early ART initiation and VS have substantially improved in the United States since the release of universal treatment guidelines. Disparities by factors related to social determinants of health and SUD demand focused attention on and services for some subpopulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".