Neighborhood socioeconomic factors and characteristics correlated with avoidable emergency department visits: A spatial analysis of a Canadian academic hospital
Bibliographic record
Abstract
INTRODUCTION: The influence of neighborhood characteristics and socioeconomic status (SES) factors on avoidable emergency department (ED) utilization is not well understood in a universal healthcare system. We examined correlations between these factors and avoidable ED visits at a Canadian academic hospital. MATERIALS AND METHODS: We conducted a retrospective cohort study using administrative ED data from a hospital in Hamilton, Canada from April 1, 2018 to August 31, 2023, and neighborhood data from the Statistics Canada Census of Population 2021. Avoidable visits were classified using the Emergency Department Avoidability Classification (EDAC), and mapped to neighborhoods using Canadian postal codes. SES was defined primarily based on education attained, household income, employment and housing security. The top 20 postal codes with the highest avoidable ED visits were categorized into quartiles and analyzed for trends using chi-squared tests of spatial association and Spearman rank correlations. RESULTS: A consistent ordinal trend across quartiles was observed throughout the study period, with quartile 1 representing the lowest avoidable ED visits and quartile 4 the highest. The quartiles were unevenly distributed spatially, though there was a significant association between close proximity to the ED and avoidable visits (X2 = 7.07, p <0.05). The quartile with the highest avoidable ED visits (quartile 4) had the greatest proportion of one-person households (35.5%) and one-parent families (37.8%), and showed statistically significant positive correlations with male sex, living alone and having an indigenous identity. Quartile 4 had the highest rates of individuals not completing high school (18.6%, p < 0.05), unemployment (13.7%), households spending greater than 30% of their income on shelter (26.5%), and households earning less than $30,000 annually (16.6%, compared to 8.7% in quartile 1 with the lowest avoidable ED visits). DISCUSSION: In a universal healthcare setting, lower SES neighborhoods were correlated with higher rates of avoidable ED visits. Targeted interventions that address social determinants of health disparities in neighborhoods with lower SES could reduce the burden of avoidable ED visits, and promote more equitable healthcare utilization.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".