Geospatial, racial, and educational variation in firearm mortality in the USA, Mexico, Brazil, and Colombia, 1990–2015: a comparative analysis of vital statistics data
Bibliographic record
Abstract
BACKGROUND: Firearm mortality is a leading, and largely avoidable, cause of death in the USA, Mexico, Brazil, and Colombia. We aimed to assess the changes over time and demographic determinants of firearm deaths in these four countries between 1990 and 2015. METHODS: In this comparative analysis of firearm mortality, we examined national vital statistics data from 1990-2015 from four publicly available data repositories in the USA, Mexico, Brazil, and Colombia. We extracted medically-certified deaths and underlying population denominators to calculate the age-specific and sex-specific firearm deaths and the risk of firearm mortality at the national and subnational level, by education for all four countries, and by race or ethnicity for the USA and Brazil. Analyses were stratified by intent (homicide, suicide, unintentional, or undetermined). We quantified avoidable mortality for each country using the lowest number of subnational age-specific and period-specific death rates. FINDINGS: Between 1990 and 2015, 106·3 million medically-certified deaths were recorded, including 2 472 000 firearm deaths, of which 851 000 occurred in the USA, 272 000 in Mexico, 855 000 in Brazil, and 494 000 in Colombia. Homicides accounted for most of the firearm deaths in Mexico (225 000 [82·7%]), Colombia (463 000 [93·8%]), and Brazil (766 000 [89·5%]). Suicide accounted for more than half of all firearm deaths in the USA (479 000 [56·3%]). In each country, firearm mortality was highest among men aged 15-34 years, accounting for up to half of the total risk of death in that age group. During the study period, firearm mortality risks increased in Mexico and Brazil but decreased in the USA and Colombia, with marked national and subnational geographical variation. Young men with low educational attainment were at increased risk of firearm homicide in all four countries, and in the USA and Brazil, black and brown men, respectively, were at the highest risk. The risk of firearm homicide was 14 times higher in black men in the USA aged 25-34 years with low educational attainment than comparably-educated white men (1·52% [99% CI 1·50-1·54] vs 0·11% [0·10-0·12]), and up to four times higher than in comparably-educated men in Brazil, Colombia, and Mexico. In the USA, the risk of firearm homicide was more than 30 times higher in black men with post-secondary education than comparably educated white men. If countries could achieve the same firearm mortality rates nationally as in their lowest-burden states, 1 777 800 firearm deaths at all ages and in both sexes could be avoided, including 1 028 000 deaths in men aged 15-34 years. INTERPRETATION: Firearm mortality in the USA, Mexico, Brazil, and Colombia is highest among young adult men, and is strongly associated with race and ethnicity, and low education levels. Reductions in firearm deaths would improve life expectancy, particularly for black men in the USA, and would reduce racial and educational disparities in mortality. FUNDING: Canadian Institutes of Health Research and the University of Toronto Connaught Global Challenge.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".