The Relationship Between Income and Burn Incidence in Winnipeg, Manitoba, Canada: A Population Health Study
Bibliographic record
Abstract
Burns continue to be a common cause of morbidity around the world, and socioeconomic status has been linked to high-burn risk in developed and developing countries. The purpose of this study was to define in Winnipeg, Manitoba, Canada: 1) demographics of adult patients with severe burns; 2) the relationship between household income and burn incidence; and 3) specific geographic areas that may benefit from targeted burn prevention strategies. We conducted a retrospective study of adult (>18 years) patients in Winnipeg, with burns severe enough to require at least 1 day of hospitalization between 2006 and 2016. Area-level median household income data at two geographic levels were collected from 2011 Census based on patient postal codes. Of 207 patients that met study criteria, 156 (75.4%) were male. Mean age was 44.5 ± 16.9 years, and the most common cause of burns was fire/flame (52.2%). The analysis of income data revealed that lower area-level income groups had a higher incidence of burns, with the lowest income quintile group having 5.4 times higher incidence than highest income quintile group. Spatial analysis software was used to map the incidence rates, and clusters of high-risk burns were identified in and around the city center region. Overall, our study showed an inverse relationship between area-level income and burn injury incidence. The identification and mapping of high incidence hotspots will allow policy makers to target groups who will benefit most from burn prevention strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| 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".