Steep increases in fentanyl-related mortality west of the Mississippi River: synthesizing recent evidence from county and state surveillance
Bibliographic record
Abstract
ABSTRACT Background and Aims Overdose deaths from synthetic opioids (e.g., fentanyl), increased 10-fold in the United States from 2013-2018, despite 88% of deaths occurring east of the Mississippi River. Public health professionals have long feared that further spread of fentanyl could greatly exacerbate the opioid epidemic. We aimed to measure and characterize recent fentanyl deaths in jurisdictions west of the Mississippi River. Design Systematic search of states and counties in the Western U.S. for publicly available data on fentanyl-related deaths since the most recently published Centers for Disease Control and Prevention (CDC) statistics, which cover through December 2018. Longitudinal study using 2019 and 2020 mortality records to identify changes in fentanyl-involved mortality since most recent CDC statistics. Settings U.S. states west of the Mississippi River. Measurements Annual rate of fentanyl-involved deaths per 100,000 population. Proportion of fatal heroin-, stimulant, and prescription pill overdoses also involving fentanyl. Findings We identified nine jurisdictions with publicly available fentanyl death data through December 2019 or later - State of Arizona; Denver County, CO; Harris County, TX; Humboldt County, CA; King County, WA; Los Angeles County, CA; San Francisco County, CA; Siskiyou County, CA; Dallas-Fort Worth, TX metro area (Denton, Johnson, Parker, Tarrant counties. Fentanyl deaths increased in each jurisdiction. Their collective contribution to national synthetic narcotics mortality tripled from 2017 to 2019. First quarter 2020 data (available from all but San Francisco County) showed a 33% growth in fentanyl-mortality over 2019. Fentanyl-involvement in heroin, stimulant, and prescription pill deaths has grown substantially over time. Conclusions Fentanyl has spread westward, which could dramatically worsen the nation’s already severe opioid epidemic. Increasing standard-dose of naloxone, expanding Medicaid, improving coverage of addiction treatment, and public health educational campaigns should be prioritized.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".