Tracking Opioid Prescribing Metrics in Washington State (2012‐2017): Differences by County‐Level Urban‐Rural and Economic Distress Classifications
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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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".