Health care utilization and costs following nonfatal firearm injuries for children and youth
Bibliographic record
Abstract
Abstract Little is known about the health care and economic burdens of nonfatal firearm injuries for children/youth beyond the initial admission. This study sought to estimate health care utilization and total direct health care costs of nonfatal firearm injuries one-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 a firearm injury with those who did not. Mean and median number of health care encounters and costs, and respective 95% confidence intervals (CIs) and interquartile ranges (IQR), were estimated for both groups as well as costs by weapon type and intent. This study found that children/youth who experienced a firearm injury had a higher number of health care encounters per year than those who did not, particularly for medical hospitalizations (0.25 [95% CI 0.23-0.27] versus 0.01 [95% CI 0.01-0.02]) and emergency department visits (1.74 [95% CI 1.69-1.80] versus 0.38 [95% CI 0.36-0.40]). Mean and median one-year costs for those with a firearm injury were $5,442 (95% CI $5,022-$5,863) and $1,464 (IQR = $600-$4,720), and $781 (95% CI $638-$925) and $137 (IQR = $24-$401) for those without. One-year costs were highest for handgun firearm injuries ($12,875 [95% CI $9,941-$15,808]), for intentional assault-related injuries ($11,035 [95% CI $9,722-$12,348]) and intentional self-injuries ($9,658 [95% CI $5,509-$13,808]). Conclusion: Firearm injuries have substantial health care and economic burdens beyond the initial injury-related admission; this should be accounted for when examining the overall impact of firearm injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".