Association between state Medicaid expansion status and naloxone prescription dispensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To test whether Medicaid expansion is associated with (a) a greater number of naloxone prescriptions dispensed and (b) a higher proportion of naloxone prescriptions paid by Medicaid. DATA SOURCES/STUDY SETTING: We used the IQVIA National Prescription Audit to obtain data on per state per quarter naloxone prescription dispensing for the period 2011-16. STUDY DESIGN: In this quasi-experimental design study, the impact of Medicaid expansion on naloxone prescription dispensing was examined using difference-in-difference estimation models. State-level covariates including pharmacy-based naloxone laws (standing/protocol orders and direct authority to dispense naloxone), third-party prescribing laws, opioid analgesic prescribing rates, opioid-involved overdose death rates, and population size were controlled for in the analysis. PRINCIPAL FINDINGS: Medicaid expansion was associated with 38 additional naloxone prescriptions dispensed per state per quarter compared to nonexpansion controls, on average (P = .030). Also, Medicaid expansion resulted in an average increase of 9.86 percent in the share of naloxone prescriptions paid by Medicaid per state per quarter (P < .001). CONCLUSIONS: Our study found that Medicaid expansion increased naloxone availability. This finding suggests that it will be important to consider naloxone access when making federal- and state-level decisions affecting Medicaid coverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it