The Cumulative Effect of Expanding the Breadth and Scope of Coverage for Substance Use Disorder Treatment on Behavioral Health Acute Inpatient Admissions: Evidence from Virginia Medicaid
Bibliographic record
Abstract
We evaluated the impact of Medicaid policies in Virginia (VA), namely the Addiction and Recovery Treatment Services (ARTS) program and Medicaid expansion, on the number of behavioral health acute inpatient admissions from 2016 to 2019. We used Poisson fixed-effect event study regression and compared average proportional differences in admissions over three time periods: (1) prior to ARTS; (2) following ARTS but before Medicaid expansion; (3) post-Medicaid expansion. The number of behavioral health acute inpatient admissions decreased by 2.6% (95% CI [-5.1, -0.2]) in the first quarter of 2018 and this decrease gradually intensified by 4.9% (95% CI [-7.5, -2.4]) in the fourth quarter of 2018 compared to the second quarter of 2017 (beginning of ARTS) in VA relative to North Carolina (NC). Following the first quarter of 2019 (beginning of Medicaid expansion), decreases in VA admissions became larger relative to NC. The average proportional difference in admissions estimated a decrease of 2.7% (95% CI, [-4.1, -0.8]) after ARTS but before Medicaid expansion and a decrease of 2.9% (95% CI, [-6.1, 0.4]) post-Medicaid expansion compared to pre-ARTS in VA compared to NC. Behavioral health acute inpatient admissions in VA decreased following ARTS implementation, and the decrease became larger after Medicaid expansion.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".