Suicide, Homicide, and Other Violent Deaths Among People Experiencing Homelessness in the United States: A Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVES: Limited information exists about violent deaths among people experiencing homelessness (PEH) across the United States. Using data from a national reporting system, we describe characteristics of suicides, homicides, and other deaths classified as violent among PEH in the United States. METHODS: We obtained data on demographic characteristics, mechanisms of injury, and circumstances surrounding violent deaths from January 1, 2016, through December 31, 2018, in 31 states from the National Violent Death Reporting System. RESULTS: Of 122 113 violent deaths in 31 states during 2016-2018, 1757 (1.4%) occurred among PEH and 3952 (3.2%) occurred among people for whom homelessness status was unknown or missing. Of all violent deaths among PEH, 878 were suicides (1.1% of all suicides), 458 were homicides (1.6% of all homicides), 352 were of undetermined intent (2.8% of all deaths of undetermined intent), and 59 were the result of legal interventions (3.8% of all deaths due to legal interventions). Hanging/suffocation/strangulation was the most common mechanism of suicide among PEH (44.4%), followed by deaths due to firearms (21.6%). Firearms were the most common mechanism of homicide deaths among PEH (48.0%). Black PEH were more likely to die by homicide than by suicide, and White PEH were more likely to die by suicide than by homicide. Among the 843 suicide victims for whom additional information was known, 345 (40.9%) had a history of suicidal thoughts or plans, 245 (29.1%) had disclosed intent to die by suicide, and 183 (21.7%) were receiving treatment for a mental health condition. CONCLUSIONS: Efforts to reduce mortality and improve health outcomes among PEH should consider the high rates of violent deaths in this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".