On the compounding manifestations of racism shaping the US HIV/AIDS epidemic: why ending the HIV epidemic must address these factors for success
Bibliographic record
Abstract
OBJECTIVE: Growing racial/ethnic inequities in healthcare access and racially segregated sexual mixing contribute to persistent disparities in HIV incidence in the US. We aim to examine the extent to which eliminating racial/ethnic inequities in healthcare access could reduce disparities in HIV incidence and its interaction with assortative sexual mixing. DESIGN: A mathematical model. METHODS: We used two independently developed HIV transmission models to estimate HIV incidence among Black, Hispanic/Latino, and White/Other MSM and the corresponding incidence rate ratios (IRRs) comparing Black and Hispanic/Latino to White/Other as a measure of disparity in the four "Ending the HIV Epidemic (EHE)" counties in Georgia. We compared three scenarios: status quo; equal service access across racial/ethnic groups with reported assortative sexual mixing by race/ethnicity; and equal service access with random sexual mixing. We standardized both models to enhance comparability. RESULTS: Under the status quo, both models projected a reduction in overall HIV incidence but persistent racial/ethnic disparities, with an IRR as large as 8.3 between Black and White/Other MSM. Compared to the status quo, providing equal health service access resulted in a modest reduction in IRRs with reported assortative sexual mixing in 2030, but yielded a much greater reduction when sexual mixing was at random: IRR reduced by up to 38.8% and 58.3% between Black and White/Other MSM in the two models. CONCLUSION: This study highlights racially segregated sexual mixing as a barrier to efforts to mitigate racial/ethnic disparities in HIV incidence. Reaching EHE targets will require not only equitable healthcare access but also strategies addressing sexual racism and other structural barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".