Years of Life Lost due to Opioid Overdose in Ohio: Temporal and Geographic Patterns of Excess Mortality
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to quantify the burden of premature mortality because of opioid overdose in Ohio, document the role of fentanyl poisoning in contribution to this evolving epidemic, examine geographic, demographic, and temporal patterns of mortality burden within Ohio, and measure the effect of opioid overdose on lifespan in the state. METHODS: A serial cross-sectional analysis was performed for all fatal opioid poisonings (N = 12,782) in the state of Ohio between January 1, 2010 and December 31, 2016. The burden of fatal opioid overdose was calculated in Years of Life Lost (YLL). YLL were mapped with respect to geographic and cultural region. The geographic spread of fentanyl poisoning was also mapped, and the shifting contribution of fentanyl poisoning to overall opioid mortality burden was assessed over time. Finally, the negative effect of opioid overdose on average lifespan was calculated. RESULTS: Opioid overdose resulted in 508,451 total YLL. In the year 2016 alone, there were 136,679 YLL attributable to opioid poisoning. Fentanyl-related YLL rose from 7.5% of all YLL because of opioid overdose in 2010 to 69.0% in 2016. In the same year, opioid overdose lowered the lifespan of an average Ohioan by 0.97 years. CONCLUSIONS: Fatal opioid overdose accounted for over half a million YLL in Ohio during the 7-year study period. Opioid overdose mortality rose annually. Fentanyl involved overdoses accounted for a growing proportion of excess mortality. Burden was not equally distributed within the state. Two distinct geographical clusters of excess mortality were identified in the northeast and south.
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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 | 0.002 |
| 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 | 0.000 |
| 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".