Cartography of Emergency Department Visits for Asthma – Targeting High‐Morbidity Populations
Bibliographic record
Abstract
BACKGROUND: Asthma education should be offered with priority to populations with the highest asthma-related morbidity. In the present study, the aim was to identify populations with high-morbidity for asthma from the Quebec Health Insurance Board Registry, a large administrative database, to help the Quebec Asthma and Chronic Obstructive Pulmonary Disease Network target its interventions. METHODS: All emergency department (ED) visits for asthma were analyzed over a one-year period, considering individual and medical variables. Age- and sex-adjusted rates, as well as standardized rate ratios related to the overall Quebec rate, among persons zero to four years of age and five to 44 years of age were determined for 15 regions and 163 areas served by Centres Locaux de Services Communautaires (CLSC). The areas with rates 50% to 300% higher (P<0.01) than the provincial rate were defined as high-morbidity areas. Maps of all CLSC areas were generated for the above parameters. RESULTS: There were 102,551 ED visits recorded for asthma, of which more than 40% were revisits. Twenty-one CLSCs and 32 CLSCs were high-morbidity areas for the zero to four years age group and five to 44 years age group, respectively. For the most part, the high-morbidity areas were located in the south-central region of Quebec. Only 47% of asthmatic patients seen in ED had also seen a physician in ambulatory care. CONCLUSION: The data suggest that a significant portion of the population seeking care at the ED is undiagnosed and undertreated. A map of high-morbidity areas that could help target interventions to improve asthma care and outcomes is proposed.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".