Medicaid Policy and Hepatitis C Treatment Among Rural People Who Use Drugs
Bibliographic record
Abstract
BACKGROUND: Restrictive Medicaid policies regarding hepatitis C virus (HCV) treatment may exacerbate rural health care disparities for people who use drugs (PWUD). We assessed associations between Medicaid restrictions and HCV treatment among rural PWUD. METHODS: We compiled state-specific Medicaid treatment policies across 8 US rural sites in 10 states and merged these with participant survey data. We hypothesized that local restrictions regarding prescriber type, sobriety, and fibrosis estimates were associated with HCV treatment outcomes. We conducted a cross-sectional, ecological analysis of treatment restrictions and HCV treatment outcomes using bivariate analyses to characterize differences between PWUD who initiated HCV treatment and unadjusted logistic regressions to assess associations between restrictions and treatment. RESULTS: Among 944 participants, 111 (12%) reported receiving HCV treatment. Participants receiving treatment were older [median age (interquartile range): 42 (34-53) vs. 35 (29-42), P<0.001], more likely to receive disability support (32% vs. 20%, P=0.002), and less likely to be Medicaid-insured (57% vs. 71%, P < 0.001). More PWUD in states without any restrictions reported receiving treatment (17% vs. 11%, P=0.08) and achieving HCV cure/clearance (42% vs. 30%, P=0.01) than in states with restrictions. Restrictions were associated with lower odds of receiving HCV treatment (odds ratio=0.61, 95% CI: 0.35-1.06, P=0.08). Sensitivity analyses showed a similar association with HCV cure/clearance (odds ratio=0.60, 95% CI: 0.40-0.91, P=0.02). CONCLUSIONS: We identified significant unadjusted associations between Medicaid restrictions and receipt of HCV treatment and cure, which has substantial implications for health outcomes among rural PWUD. Lifting remaining Medicaid restrictions will be critical to achieving HCV elimination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".