Variability in trends of opioid-related hospital utilization among U.S. Adults, 2016–2021 check
Bibliographic record
Abstract
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".