Tracking Opioid Prescribing Metrics in Washington State (2012‐2017): Differences by County‐Level Urban‐Rural and Economic Distress Classifications
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: High-risk opioid prescribing is a critical driver of prescription opioid-related morbidity and mortality. This study explored opioid prescribing patterns across urban-rural and economic distress classifications. Secondarily, this study explored the urban-rural distribution of relevant health services, economic factors, and population characteristics. METHODS: County-level opioid prescribing metrics were based on quarterly Washington State Prescription Monitoring Program data (2012-2017). Counties were classified using the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties, and Washington State unemployment-based distressed areas. County-level measures from Area Health Resources Files were used to describe the urban-rural continuum. FINDINGS: Persistent economic distress was associated with higher-risk opioid prescribing. The large central metropolitan category had lower-risk opioid prescribing metrics than the other 5 urban-rural categories, which were similar to each other and not ordered by degree of rurality. High-risk prescribing declined over time, without notable trend divergence by either urban-rural or economic distress classifications. CONCLUSIONS: The most striking urban-rural differences in opioid prescribing metrics were between large central metropolitan and all other categories; thus, we recommend caution when collapsing urban-rural categories for analysis. Further research is needed regarding geographic and economic patterning of opioid prescribing practices, as well as the dissemination of guidelines and best practices across the urban-rural continuum. Finally, the multiple intertwined burdens faced by rural communities-higher-risk prescribing practices, higher opioid morbidity and mortality rates, and fewer resources for primary care, mental health care, alternative pain treatment, and opioid use disorder treatment-must be addressed as an urgent public health priority.
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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 | 0.001 | 0.000 |
| 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 it