Healthcare utilization and costs following non-fatal powdered and non-powdered firearm injuries for children and youth
Bibliographic record
Abstract
Little is known about the healthcare and economic burdens of non-fatal firearm injuries for children/youth beyond the initial admission. This study sought to estimate healthcare utilization and total direct healthcare costs of non-fatal powdered and non-powdered (air gun) firearm injuries 1-year post-injury. Using administrative data from 2003 to 2018 on all children/youth 0-24 years old in Ontario, Canada, a matched 1:2 cohort study was conducted to compare children/youth who experienced powdered and non-powdered firearm injuries with those who did not. Mean and median number of healthcare encounters and costs, and respective 95% confidence intervals (CIs) and interquartile ranges (IQRs), were estimated for both weapon type groups and controls and by intent. Children/youth who experienced a powdered and non-powdered firearm injury had a higher number of healthcare encounters and costs per year than those who did not. Mean 1-year costs for those with powdered and non-powdered firearm injuries were $8825 ($8007-$9643) and $2349 ($2118-$2578), respectively, versus $812 ($567-$1058) and $753 ($594-$911), respectively, for those without. Mean 1-year costs were highest for handgun injuries ($12,875 [95% CI $9941-$15,808]), and for intentional assault-related ($13,498 [$11,843-$15,153]; $3287 [$2213-$4362]), and intentional self-injuries ($14,773 [$6893-$22,652]; $6005 [$2193-$9817]) for both powdered and non-powdered firearm injuries, respectively. Conclusion: Firearm injuries have substantial healthcare and economic burdens beyond the initial injury-related admission; this should be accounted for when examining the overall impact of firearm injuries. What is Known: • Child/youth firearm injuries have significant health and economic burdens. • However, existing work has mainly examined healthcare utilization and costs of initial admissions and/or have been limited to single-center studies and no studies have provide cost estimates by weapon type and intent. What is New: • Children/youth who suffered powdered firearm injuries had higher mean healthcare utilization and costs than those with non-powdered firearm injuries as well as comparable healthy children/youth. • Mean 1-year costs were highest for handgun injuries ($12,875), and for intentional assault-related ($13,498; $3287), and intentional self-injuries ($14,773; $6005) for powdered and non-powdered firearm injuries, respectively.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".