Variability in trends of opioid-related hospital utilization among U.S. Adults, 2016–2021 check
Notice bibliographique
Résumé
Background: Understanding trends in opioid-related hospital utilization is crucial for informing public health policies; however, existing research is often limited in scope and methodology. This study provides national estimates from 2016 to 2021, emphasizing the variability in trends across different opioid categories and subpopulations. Methods: This study employed a repeated cross-sectional analysis using data from the National Inpatient Sample (NIS) and Nationwide Emergency Department Sample (NEDS). Analyses were performed in two periods: 2016-2019 and 2019-2021 (during the COVID-19 pandemic). Outcomes included rates of opioid-related diagnoses and three types of opioid use disorder-related clinical events: nonfatal opioid overdose, injection drug use-related acute infection, and substance abuse treatment. Further analyses were conducted by opioid category (e.g., heroin and synthetic opioids as a proxy for fentanyl), as well as subgroup analyses based on predefined demographic characteristics, including age, sex, race/ethnicity, socioeconomic status, and geographic location. Findings: Between 2016 and 2019, in the NIS, there was a significant decrease in the rate of opioid-related diagnoses (relative change: -5.4%, 95% Cl: -9.4 to -1.3), nonfatal opioid overdose (-18.4%, -21.7 to -15.0), and substance abuse treatment (-25.1%, -45.9 to -4.3). Conversely, the rate of injection drug use-related acute infection increased significantly (14.4%, 7.3-21.4). In the NEDS, the rates of these outcomes did not change significantly. Notable variations were observed; for instance, in the NIS, the rate of nonfatal synthetic opioids as a proxy for fentanyl overdose increased by 21.1% (11.6-30.5), and heroin-related adverse event or poisoning increased by 51.8% (16.8-86.8) among adults aged 65-84. Between 2019 and 2021, in both the NIS and NEDS, the rate of nonfatal opioid overdose increased significantly (NIS: 8.1%, 3.5-12.7; NEDS: 24.8%, 11.5-38.0), in the NIS, a significant increase was found in the rate of injection drug use-related acute infection (relative increase: 8.2%, 1.2-15.1), while the rates of the other outcomes did not change significantly. Significant variations were also identified; for example, in the NIS, the rate of nonfatal opioid overdose did not show significant change among females, non-Hispanic whites, and adults with higher socioeconomic status. Interpretation: The significant variability in opioid-related hospital utilization trends among U.S. adults underscores the need for careful consideration in the design of future policies, especially during crises. Management strategies should be tailored to specific subpopulations, opioid categories, and OUD-related clinical events to maximize success rates. Funding: Taishan Scholars Program of Shandong Province-Pandeng Taishan Scholars.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».