Human Immunodeficiency Virus transmission by HIV Risk Group and Along the HIV Care Continuum: A Contrast of 6 US Cities
Bibliographic record
Abstract
BACKGROUND: Understanding the sources of HIV transmission provides a basis for prioritizing HIV prevention resources in specific geographic regions and populations. This study estimated the number, proportion, and rate of HIV transmissions attributable to individuals along the HIV care continuum within different HIV transmission risk groups in 6 US cities. METHODS: We used a dynamic, compartmental HIV transmission model that draws on racial behavior-specific or ethnic behavior-specific and risk behavior-specific linkage to HIV care and use of HIV prevention services from local, state, and national surveillance sources. We estimated the rate and number of HIV transmissions attributable to individuals in the stage of acute undiagnosed HIV, nonacute undiagnosed HIV, HIV diagnosed but antiretroviral therapy (ART) naïve, off ART, and on ART, stratified by HIV transmission group for the 2019 calendar year. RESULTS: Individuals with undiagnosed nonacute HIV infection accounted for the highest proportion of total transmissions in every city, ranging from 36.8% (26.7%-44.9%) in New York City to 64.9% (47.0%-71.6%) in Baltimore. Individuals who had discontinued ART contributed to the second highest percentage of total infections in 4 of 6 cities. Individuals with acute HIV had the highest transmission rate per 100 person-years, ranging from 76.4 (58.9-135.9) in Miami to 160.2 (85.7-302.8) in Baltimore. CONCLUSION: These findings underline the importance of both early diagnosis and improved ART retention for ending the HIV epidemic in the United States. Differences in the sources of transmission across cities indicate that localized priority setting to effectively address diverse microepidemics at different stages of epidemic control is necessary.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".