Asthma-related emergency admissions and associated healthcare resource use in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: There is a lack of real-world research assessing asthma management following asthma-related emergency department (ED) discharges. The objective of this study was to characterise follow-up care, healthcare resource use (HCRU) and medical costs following ED admissions in Alberta, Canada. METHODS: A retrospective cohort study was conducted on adults with asthma using longitudinal population-based administrative data from Alberta Health Services. Adult patients with asthma and ≥1 ED admission from 1 April 2015 to 31 March 2020 were included. ED admissions, outpatient visits, hospitalisations and asthma-specific medication use were measured in the 30 days before and up to 90 days after each asthma-related ED admission. Mean medical costs attributable to each type of HCRU were summarised. All outcomes were stratified by patient baseline disease severity. RESULTS: Among 128 063 patients incurring a total of 20 142 asthma-related ED visits, a substantial rate of ED readmission was observed, with 10% resulting in readmissions within 7 days and 35% within 90 days. Rates increased with baseline asthma severity. Despite recommendations for patients to be followed up with an outpatient visit within 2-7 days of ED discharge, only 6% were followed up within 7 days. The mean total medical cost per patient was $C8143 in the 30 days prior to and $C5407 in the 30 days after an ED admission. CONCLUSIONS: Despite recommendations regarding follow-up care for patients after asthma-related ED admissions, there are still low rates of outpatient follow-up visits and high ED readmission rates. New or improved multidimensional approaches must be integrated into follow-up care to optimise asthma control and prevent readmissions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".