Analyzing the effects of place on injury: Does the choice of geographic scale and zone matter?
Bibliographic record
Abstract
BACKGROUND: Recent studies have shown that the morbidity and mortality associated with injury of pedestrians are inversely related to socio-economic status (SES). However, in drawing inferences from this association, investigators have paid little attention to the modifiable artifacts related to scale and how the data are partitioned. The purpose of this population-based study was to identify the relation between SES and incidence patterns of pedestrian injury at 4 different geographic scales. METHODS: We used a Poisson generalized linear model, stratified by age and sex, to analyze the relation between each of 4 area measures of SES and incidence patterns of pedestrian injuries occurring in metropolitan Vancouver between 1 January 2001 and 31 March 2006. The 4 area measures of SES were based on boundaries of dissemination areas, census tracts, custom-defined census tracts (generated by reassignment of dissemination area boundaries by means of a geographic information system) and census subdivisions of the Canadian census. We measured the SES of the location where the injury occurred with the Vancouver Area Neighbourhood Deprivation Index. RESULTS: A total of 262 injuries in adults (18 years of age or older) were analyzed. Among adult men, the odds ratio (OR) for injury of pedestrians at the scale of dissemination area was 4.93 (95% confidence interval [CI] 2.89-8.42) for areas having the lowest SES relative to those with the highest SES. For the same population, the OR for injury was lower with increasing aggregation of data: 2.33 (95% CI 1.45-3.74) when census tracts were used, 3.26 (95% CI 2.06-5.16) when modified census tracts were used and 1.27 (95% CI 0.47-3.45) when census subdivisions were used. Among adult women, the OR for pedestrian injury by SES was highest at the scale of census subdivision within medium-low SES areas (4.33, 95% CI 1.23-15.22). At the census subdivision scale, the relation between SES and incidence pattern of injury was not consistent with findings at smaller geographic scales, and the OR for injury decreased with each increase in SES. INTERPRETATION: In this analysis, there was significant variability when different administrative boundaries were applied as proxy measures of the effects of place on incidence patterns of injury. The hypothesized influence of SES on prevalence of pedestrian injury followed a statistically significant socio-economic gradient when analyzed using small-area boundaries of the census. However, researchers should be aware of the inherent variability that remains even among the more homogenous population units.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".