Male Predominance in West Virginia Unintentional Overdose Deaths Is Influenced by Alcohol and Co-Intoxicants
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine sex differences in overdose (OD) mortality based on substances involved. METHOD: We conducted a retrospective database analysis of West Virginia OD decedents (12,666 unintentional OD deaths, 2005-early 2023). Exposures were substances judged to contribute to death. The main outcome measure was determination of male to female death ratios with varying co-intoxicant involvement, particularly related to alcohol and fentanyl. Secondary outcomes included associations of fentanyl concentrations with alcohol concentrations and male sex, including fentanyl and inactive metabolite norfentanyl concentration variability between sexes. RESULTS: Alcohol co-intoxication in OD deaths was associated with higher male:female death ratios, from 2.0 (alcohol absent) to 3.3 (alcohol present). There was a greater increase over time in alcohol involvement in recent deaths involving females compared with males (relative increases of 52% vs. 6%, respectively). Male:female ratios with alcohol and fentanyl co-involvement ranged from 5.9:1 (only two drugs involved) to 2.4:1 (≥5 substances), with females significantly more likely to have multiple substances contributing to death. Overall, males had statistically significantly larger fentanyl to norfentanyl median concentration ratios compared with females (8.8 vs. 6.9, respectively). Multivariable analyses found that alcohol presence was associated with a statistically significant 22% reduction in predicted fentanyl concentrations. CONCLUSIONS: Male:female ratios in unintentional OD deaths were higher with greater alcohol involvement and lower with fewer co-intoxicants. Fentanyl and norfentanyl concentration differences by sex were observed. It is important to determine possible contributors to sex differences in OD death rates to better target prevention and treatment initiatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".