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
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».