Spatial Hotspots and Sociodemographic Profiles Associated With Traumatic Brain Injury in Nova Scotia
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a leading cause of death and disability, primarily caused by falls and motor vehicle collisions (MVCs). Although many TBIs are preventable, there is a notable lack of studies exploring the association of geographically defined TBI hotspots with social deprivation. Geographic information systems (GIS) can be used to identify at-risk neighborhoods (hotspots) for targeted interventions. This study aims to determine the spatial distribution of TBI by major causes and to explore the sociodemographic and economic characteristics of TBI hotspots and cold spots in Nova Scotia. Patient data for TBIs from 2003 to 2019 were obtained from the Nova Scotia Trauma Registry. Residential postal codes were geocoded and assigned to dissemination areas (DA). Area-based risk factors and deprivation status (residential instability [RI], economic dependency [ED], ethnocultural composition [EC], and situational vulnerability [SV]) from the national census data were linked to DAs. Spatial autocorrelation was assessed using Moran's I, and hotspot analysis was performed using Getis-Ord Gi* statistic. Differences in risk factors between hot and cold spots were evaluated using the Mann–Whitney U test for numerical variables and the χ 2 test or Fisher's exact test for categorical variables. A total of 5394 TBI patients were eligible for inclusion in the study. The distribution of hotspots for falls exhibited no significant difference between urban and rural areas ( p = 0.71). Conversely, hotspots related to violence were predominantly urban ( p = 0.001), whereas hotspots for MVCs were mostly rural ( p < 0.001). Distinct dimensions of deprivation were associated with falls, MVCs, and violent hotspots. Fall hotspots were significantly associated with areas characterized by higher RI ( p < 0.001) and greater ethnocultural diversity ( p < 0.001). Conversely, the same domains exhibited an inverse relationship with MVC hotspots; areas with low RI and ethnic homogeneity displayed a higher proportion of MVC hotspots. ED and SV exhibited a strong gradient with MVC hotspots; the most deprived quintiles displayed the highest proportion of MVC hotspots compared with cold spots (ED; p = 0.002, SV; p < 0.001). Areas with the highest levels of ethnocultural diversity were found to have a significantly higher proportion of violence-related hotspots than cold spots ( p = 0.005). This study offers two significant contributions to spatial epidemiology. First, it demonstrates the distribution of TBI hotspots by major injury causes using the smallest available geographical unit. Second, we disentangle the various pathways through which deprivation impacts the risk of main mechanisms of TBI. These findings provide valuable insights for public health officials to design targeted injury prevention strategies in high-risk areas.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".