What Strategies Are Hospitals Adopting to Address the Opioid Epidemic? Evidence From a National Sample of Nonprofit Hospitals
Bibliographic record
Abstract
OBJECTIVES: Hospitals are on the front lines of the opioid epidemic, seeing patients who overdose or have complicated infections, but the extent of services offered or whether services are evidence-based is not known. The objective of our study was to assess the extent to which nonprofit hospitals are addressing opioid abuse, a critical public health issue, through their community benefit work and to identify which evidence-based strategies they adopt. METHODS: We reviewed community benefit documents from January 1, 2015, through December 31, 2018, for a sample (N = 446) of all nonprofit hospitals in the United States. We classified hospital opioid-related strategies into 9 categories. Using logistic regression, we predicted the likelihood of hospitals adopting various strategies to address opioid abuse. RESULTS: Of the 446 nonprofit hospitals in our sample, 49.1% (n = 219) adopted ≥1 clinical strategy to address opioid use disorder in their community. Approximately one-quarter (26.5%; n = 118) of hospitals adopted a strategy related to treatment services for substance use disorder; 28.2% (n = 126) had ≥1 program focused on connecting patients to a primary care medical home, and 14.6% (n = 65) focused on caring for patients with opioid-related overdoses in the emergency department. We also identified factors that predicted involvement in programs that were less common than clinical strategies, but potentially effective, such as harm reduction and prescriber initiatives (both 6.3% of hospitals). CONCLUSIONS: Evidence-based prevention and treatment require strong collaboration between health care and community institutions at all levels. Effective policy interventions may exist to encourage various types and sizes of nonprofit hospitals to adopt evidence-based interventions to address opioid abuse in their communities.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".