Prescriptions Written for Opioid Pain Medication in the Veterans Health Administration Between 2000 and 2016
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to identify national opioid pain medication (OPM) prescribing trends within the Veterans Health Administration (VA), and assess the impact of educational campaigns introduced in 2010 and 2013. METHODS: We created a national cohort that documents more than 21 million patient records and 97 million outpatient OPM prescriptions covering a 17-year period. We examined OPM prescriptions in emergency departments, outpatient clinics, and inpatient settings. RESULTS: The cohort accounted for 2.5 billion outpatient clinic visits, 18.9 million emergency department visits, and 12.4 million hospital admissions. The number of OPM prescriptions peaked in 2011, when they were provided during 5% of all outpatient visits and 15% of all emergency department visits. The morphine milligram equivalents (MMEs) peaked in 2014 at almost 17 billion in outpatient clinics and at 137 million in emergency departments. In 2016, OPM prescriptions were down 37% in outpatient clinics and 23% in emergency departments, and MMEs were down 30% in both settings. Prescriptions for hydrocodone and tramadol increased markedly between 2011 and 2015. OPM doses in inpatient settings continued to rise until 2015. CONCLUSIONS: We used a large national cohort to study trends in OPM prescriptions within the VA. Educational efforts to reduce the number of OPM prescriptions coincided with these reductions, but were initially associated with an increase in OPM dosage, an increase in the use of tramadol and hydrocodone, and an increase in the use of OPMs in inpatient settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".