Ecological analyses of the associations between injury risk and socioeconomic status, geography and Aboriginal ethnicity in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: The current study examines what factors contribute to higher injury risk among Aboriginal peoples, compared to the total British Columbia (BC) population. We explore socioeconomic, geographic, and cultural factors, and combinations of these factors, that contribute to increased injury risk for Aboriginal peoples. This follows from our previously reported findings of improvements in injury risk over time for both the total and Aboriginal populations. DATA AND METHODS: We use provincial population-based linked health care databases of hospital discharge records. We identify three population groups: total BC population, and Aboriginal populations living off-reserve, or on-reserve. For each group we calculate age and gender-standardized relative risks (SRR) of injury-related hospitalization, relative to the total population of BC, for two 5-year time periods (1999-2003, and 2004-2008). We use custom data from the 2001 and 2006 long-form Censuses that described income, education, employment, housing conditions, proportion of urban dwellers, proportion of rural dwellers, and prevalence of Aboriginal ethnicity. We use multivariable linear regression to examine the associations between the census characteristics and SRR of injury. RESULTS: The best-fitting model was an excellent fit (R(2) = 0.905, p < 0.001) among the three population groups within Health Service Delivery Areas of BC. We find indicators in all three categories (socioeconomic, geographic, and cultural) are associated with disparity in injury risk. While the socioeconomic indicators (income, education, housing, employment) were shown to be highly correlated, only living in housing that needs major repair and occupational hazardousness, along with rural residence and Aboriginal ethnicity, remained in the final model. Our data show that cultural density is not associated with injury risk for Aboriginal peoples, and that living off-reserve is associated with reduced injury by improving socioeconomic and geographic conditions (compared to living on-reserve). Finally, our analyses show that Aboriginal status itself is associated with injury risk. CONCLUSIONS: Our findings confirm previous research indicating that geographical differences differentiate injury risk, including for Aboriginal populations, and that socioeconomic determinants are associated with health risks. Our analyses showing that Aboriginal status itself contributes to injury risk is new, but we can only speculate about pathway, and whether the causes are direct or indirect.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 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) | 0.002 | 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".