Hot spots for pediatric asthma emergency department visits in Ottawa, Canada
Bibliographic record
Abstract
Pediatric asthma emergency department (ED) visits and repeat visits place a substantial burden on healthcare. National and provincial level studies demonstrate geographic variation in asthma ED visits and links to marginalization, but preclude translation into practical targeting of healthcare delivery. It is important to understand the relationship between pediatric asthma ED visits and marginalization at a more granular level. To map the city-level geographic variation in pediatric asthma ED visit and re-visit rates at the Children’s Hospital of Eastern Ontario (CHEO) in Ottawa, Canada and the relationship with marginalization. We performed a single center retrospective cohort study of children ages 1–17 with one or more ED visits for asthma at the CHEO in Ottawa. Using postal codes, we linked patients to census tracts. Per census tract, we mapped pediatric asthma ED visit and re-visit rates within one year and identified overlap with the Ontario Marginalization Index. Of 1,620 children with an index ED visit, 18.5% had a repeat ED visit. We identified 10 hot spot census tracts each for pediatric asthma ED visit and re-visit rates. We identified an overlap between urban hot spots and areas with high ethnic concentration or low dependency. At a granular, city-wide level, pediatric asthma ED visit and re-visit rates are heterogeneous. Urban hot spots, in contrast to rural, have more overlap with marginalization, especially ethnic concentration. These methods can be used in other jurisdictions to inform practical community strategies for geographically-targeted prevention of pediatric asthma-related ED visits in vulnerable areas. Emergency department; Children’s Hospital of Eastern Ontario; Pediatric Respiratory Assessment Measure; Ontario Marginalization Index; Socioeconomic status; United States Supplemental data for this article can be accessed at publisher’s website.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.090 | 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 teacher head, 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".