Opioid crisis in rural and urban counties from 2010 to 2018: in light of the index of relative rurality
Bibliographic record
Abstract
Abstract Previous studies have documented the rural-urban disparities of the opioid crisis, but rarely consider the heterogeneities within rural and urban areas, nor the changing dynamics of the rural-urban boundaries. Taking advantage of a continuous measure of rurality, this study separates counties into “becoming-more-rural” and “becoming-more-urban” groups according to the change of rurality between 2000 and 2010, and compares the determinants of the development of prescription opioid-, heroin-, and synthetic opioid-related opioid mortality in the two groups of counties. The analysis uses longitudinal data of 2010–2018 from a variety of national datasets such as the confidential Multiple-Cause of Death data, the U.S. Opioid Dispensing Rate Maps, and the American Community Survey. The results show that while increasing rurality does not predict increasing prescription opioid- and synthetic opioid-involved mortality, the growth of heroin-involved mortality could be a severe issue for becoming-more-rural counties that have already observed heroin overdose deaths. The demographic groups that have higher risk of opioid overdoses may be different in different areas, for example increasing males and people engaged in manual labor occupations are associated with increasing prescription opioid-involved mortality for becoming-more-rural counties but in becoming-more-urban areas, heroin-involved mortality correlates to a higher percentage of Hispanics. Concentrations of socioeconomically disadvantaged populations like veterans and people in poverty increase opioid overdose deaths particularly for becoming-more-urban counties, while healthcare services shown more beneficial to becoming-more-rural areas. The findings suggest that the programs and regulations to reduce opioid-involved mortality should consider the changing nature of counties in the degree of rurality.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".