One Size Fits (n)One: The Influence of Sex, Age, and Sexual Human Immunodeficiency Virus (HIV) Acquisition Risk on Racial/Ethnic Disparities in the HIV Care Continuum in the United States
Bibliographic record
Abstract
BACKGROUND: The United States National HIV/AIDS Strategy established goals to reduce disparities in retention in human immunodeficiency virus (HIV) care, antiretroviral therapy (ART) use, and viral suppression. The impact of sex, age, and sexual HIV acquisition risk (ie, heterosexual vs same-sex contact) on the magnitude of HIV-related racial/ethnic disparities is not well understood. METHODS: We estimated age-stratified racial/ethnic differences in the 5-year restricted mean percentage of person-time spent in care, on ART, and virally suppressed among 19 521 women (21.4%), men who have sex with men (MSM; 59.0%), and men who have sex with women (MSW; 19.6%) entering HIV care in the North American AIDS Cohort Collaboration on Research and Design between 2004 and 2014. RESULTS: Among women aged 18-29 years, whites spent 12.0% (95% confidence interval [CI], 1.1%-20.2%), 9.2% (95% CI, .4%-20.4%), and 13.5% (95% CI, 2.7%-22.5%) less person-time in care, on ART, and virally suppressed, respectively, than Hispanics. Black MSM aged ≥50 years spent 6.3% (95% CI, 1.3%-11.7%), 11.0% (95% CI, 4.6%-18.1%), and 9.7% (95% CI, 3.6%-16.8%) less person-time in these stages, respectively, than white MSM ≥50 years of age. Among MSM aged 40-49 years, blacks spent 9.8% (95% CI, 2.4%-16.5%) and 11.9% (95% CI, 3.8%-19.3%) less person-time on ART and virally suppressed, respectively, than whites. CONCLUSIONS: Racial/ethnic differences in HIV care persist in specific populations defined by sex, age, and sexual HIV acquisition risk. Clinical and public health interventions that jointly target these demographic factors are needed.
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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 | 0.024 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".