The impact of Medicaid expansion under the Affordable Care Act on HIV care continuum outcomes across the United States
Bibliographic record
Abstract
Abstract HIV care continuum outcome disparities by health insurance status have been noted among people with HIV (PWH). We therefore examined associations between state Medicaid expansion and HIV outcomes in the United States. Adults (≥18 years) with ≥1 visit in NA-ACCORD clinical cohorts from 2012-2017 contributed person-time annually between first and final visit or death; in each calendar year, clinical retention was ≥2 completed visits > 90 days apart, antiretroviral therapy (ART) receipt was receipt of ≥3 antiretroviral agents, and viral suppression was last measured HIV-1 RNA < 200 copies/mL. CD4 at enrollment was obtained within 6 months of enrollment in cohort. Difference-in-difference (DID) models quantified associations between Medicaid expansion changes (by state of residence) and HIV outcomes. Across 50 states, 87 290 PWH contributed 325 113 person-years of follow-up. Medicaid expansion had a substantial positive effect on CD4 at enrollment (DID = 93.5, 95% CI: 52.9, 134 cells/mm3), a small negative effect on proportions clinically retained (DID = −0.19, 95% CI: −0.037, −0.01), and no effects on ART receipt (DID = 0.001, 95% CI: −0.003, 0.005) or viral suppression (DID = −0.14, 95% CI: −0.34, 0.07). Medicaid expansion had a positive effect on CD4 at entry, suggesting more timely HIV testing and care linkage, but generally null effects on downstream HIV care continuum measures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".