State-Level Trends in the Relationship between Opioid Pain Relievers and Medication Assisted Treatment: A Quantitative Analysis of Quarterly Medicaid Prescription Data for the 50 U.S. States and Washington, D.C. from 2010 Quarter 1 to 2019 Quarter 3
Bibliographic record
Abstract
Background: Higher opioid pain reliever (OPR) prescribing rates increase the risk of opioid use disorder (OUD).1–3 Three forms of evidence-based medication-assisted treatment (MAT) are used to treat OUD: methadone, buprenorphine, and naltrexone.4 Recommended guidelines call for a balance of limiting unnecessary OPR prescribing and increasing MAT prescribing for those with OUD.1,5 This study aims to research the relationship of the two prescribing rates as a ratio of OPR prescriptions to MAT prescriptions to view how states address the opioid epidemic among their Medicaid populations. The study uses descriptive statistics, regression analysis, and data visualization to assess differences in each quarterly prescribing rate and quarterly ratio by 1. Time (total quarters), 2. Year, and 3. State. Methods: This study utilizes the Medicaid State Drug Utilization Data for all 50 U.S. states and Washington, D.C. from 2010 Quarter 1 to 2019 Quarter 3. Filtering data by Product Names and NDCs, aggregating to the state-year-quarter level, and dividing by Medicaid population count yielded prescribing rates. Dividing total OPR prescriptions by total MAT prescriptions yielded ratio. For each prescribing rate and ratio, an ordinary least squares regression with year- and state-level fixed effects and time in the quadratic form was used to determine whether a linear or quadratic regression would better fit the data. A U.S. Map of states’ ratios, line graphs of ratio, and line graphs of prescribing rates were generated to visualize the data. Results: West Virginia had the highest average quarterly OPR prescribing rates and Texas had the lowest. Vermont had the highest average quarterly MAT prescribing rates and Arkansas had the lowest. Vermont had the lowest average quarterly ratios and Arkansas had the highest. Time coefficients were only statistically significant for total OPR prescriptions, suggesting a quadratic regression to be the better fit. Greatest OPR prescribing rates occurred in Year 2011 and Year and Quarter 2012 Quarter 3. For all variables, Year coefficients were largely statistically insignificant and State coefficients were largely statistically significant. Maine, Massachusetts, New Hampshire, West Virginia, and Vermont all saw ratios of fewer than 1 OPR prescription per MAT prescription in the most recent quarters. Click for unadjusted and adjusted dashboards. Implications: Future studies should further investigate the observed trends using differences-in-differences and time-series trends to study the impact of policies and programs such as academic detailing, prescription drug monitoring programs, and efforts to increase access to MAT. States with the highest ratios should consider applying to grants and cooperative agreements to reduce OPR prescribing and increase MAT prescribing. States with the lowest ratios should consider applying to serve as mentor through state-to-state learning opportunities. Collectors and creators of the Medicaid State Drug Utilization Data website should consider eliminating the 10-character limit on Product Name entries and standardizing reporting of Product Name and NDC entries to avoid misspelled, mistyped, and missing data.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".