Detection of Suicide Clusters using Small-Area Geographic Data from the Virginia Violent Death Reporting System, 2010 - 2015.
Bibliographic record
Abstract
Introduction: From 1999 to 2020, the suicide rate in Virginia increased from 13.1 to 15.9 per 100,000 persons aged 10 years and older. Few studies have examined spatial patterns of suicide geographies smaller than the county level. Methods: We analyzed data from suicide decedents aged ≥10 years from 2010 through 2015 in the Virginia Violent Death Reporting System. We identified spatial clusters of high suicide rates using spatially adaptive filtering with standardized mortality ratio (SMR) significantly higher than the state SMR (p < 0.001). We compared demographic characteristics, method of injury, and suicide circumstances of decedents within each cluster to decedents outside any cluster. Results: We identified 13 high-risk suicide clusters (SMR between 1.7 and 2.0). Suicide decedents in the clusters were more likely to be older (40+ years), non-Hispanic white, widowed/divorced/separated, and less likely to have certain precipitating suicide circumstances than decedents outside the clusters. Suicide by firearm was more common in four clusters, and suicide by poisoning was more common in two clusters compared to the rest of the state. Conclusions: There are important differences between geographic clusters of suicide in Virginia. These results suggest that place-specific risk factors for suicide may be relevant for targeted suicide prevention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".