Unemployment and opioid overdose death patterns in the United States from 2017 to 2019
Bibliographic record
Abstract
Abstract Background Unemployment has been linked to increased opioid-related harms such as opioid overdose deaths. Identifying hotspots and coldspots across the United States (US), specifically concerning opioid overdose deaths, can be crucial to understanding health resources and leveraging strategies, policies and programs to reduce the burden of opioid-related harms. Methods Using data from the US Bureau of Labour Statistics and the Center for Disease Control from 2017 to 2019, we describe how unemployment correlates with opioid overdose deaths in the US. Spatial clustering analyses were carried out to generate Moran’s global I values and create hotspot maps leveraging Moran’s local I to identify clusters and trends over time. Results There was an autocorrelation of opioid overdose death rates with surrounding states, particularly in the Midwest, Northeast and Southeast in 2017 and 2019. In contrast, only certain states in the Northeast showed greater clustering in 2018. A Poisson regression model showed a positive association between unemployment and opioid overdose deaths for the years 2017 and 2019. However, a protective effect of unemployment was seen in 2018. Overall, 2018 did not follow similar patterns seen in 2017 and 2019 in terms of the correlation between unemployment and opioid overdose rates. Conclusion Opioid-related deaths appear to be associated with unemployment rates in the US during 2017 and 2019, but less so in 2018. The Midwest, Northeast and Southeast were highly positively correlated with each other. Future studies which incorporate potential confounders such as age, sex, and race/ethnicity are needed to better understand the true association between unemployment and opioid overdose deaths.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".