Area-based socioeconomic disparities in mortality due to unintentional injury and youth suicide in British Columbia, 2009–2013
Bibliographic record
Abstract
INTRODUCTION: The association between health outcomes and socioeconomic status (SES) has been widely documented, and mortality due to unintentional injuries continues to rank among the leading causes of death among British Columbians. This paper quantified the SES-related disparities in the mortality burden of three British Columbia's provincial injury prevention priority areas: falls among seniors, transport injury, and youth suicide. METHODS: Mortality data (2009 to 2013) from Vital Statistics and dissemination area or local health area level socioeconomic data from CensusPlus 2011 were linked to examine age-standardized mortality rates (ASMRs) and disparities in ASMRs of unintentional injuries and subtypes including falls among seniors (aged 65+) and transport-related injuries as well as the intentional injury type of youth suicide (aged 15 to 24). Disparities by sex and geography were examined, and relative and absolute disparities were calculated between the least and most privileged areas based on income, education, employment, material deprivation, and social deprivation quintiles. RESULTS: Our study highlighted significant sex differences in the mortality burden of falls among seniors, transport injury, and youth suicide with males experiencing significantly higher mortality rates. Notable geographic variations in overall unintentional injury ASMR were also observed across the province. In general, people living in areas with lower income and higher levels of material deprivation had increasingly higher mortality rates compared to their counterparts living in more privileged areas. CONCLUSION: The significant differences in unintentional and intentional injury-related mortality outcomes between the sexes and by SES present opportunities for targeted prevention strategies that address the disparities.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".