Examining trends in polydrug overdose deaths across rural Midwest counties in the United States, 2022 through 2024
Bibliographic record
Abstract
PURPOSE: To examine trends in overdose deaths and the presence of xylazine in postmortem toxicology in a highly rural region of northern Minnesota with a significant American Indian/Alaska Native (AI/AN) population, using real-time local surveillance data. METHODS: We analyzed drug overdose death data from the Midwest Medical Examiner's Office, covering 36 counties from January 1, 2022 to December 31, 2024. Data included demographic characteristics, place of death, and substances detected in toxicology results. Age-adjusted mortality rates and disparities across racial/ethnic groups were calculated, and polydrug combinations were explored using network analysis. FINDINGS: Among 967 overdose deaths, most decedents were white (71.7 %), AI/AN (13.4 %), or Black/African American (10.3 %) with an overall mortality rate of 76.1. Mortality rates declined during the study period for white and AI/AN populations and increased slightly for Black/African American populations. However, rates remained disproportionately high for AI/AN populations, who were 6.19 times more likely to die of overdose than non-AI/AN in 2024. Xylazine was detected only in combination with fentanyl, with no racial/ethnic differences in its presence. Distinct polydrug patterns were observed by race: fentanyl-cocaine combinations were more prevalent among Black/African Americans, while fentanyl-methamphetamine combinations predominated among AI/AN and White decedents. CONCLUSIONS: Local surveillance in rural areas can detect emerging threats like xylazine and illuminate racial disparities obscured in national datasets. Findings highlight the urgent need for real-time, locally driven data to inform targeted prevention and harm reduction, particularly for AI/AN populations in rural communities.
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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 | 0.001 |
| 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".