Emergency department presentations related to asthma and allergic diseases in Central Queensland, Australia: a comparative analysis between First Nations Australians and Australians of other descents
Bibliographic record
Abstract
OBJECTIVE: To examine the overall incidence rate and trends in emergency department (ED) presentations related to asthma and allergic diseases in regional Australia with a particular focus on First Nations Australians. DESIGN: A retrospective analysis of data from the Emergency Department Information System. SETTING: This study used data from 12 public hospitals in Central Queensland, Australia, a region encompassing regional, rural and remote outback areas. PARTICIPANTS: A total of 813 112 ED presentations between 2018 and 2023. OUTCOME MEASURE: Asthma and allergic diseases were identified using the International Classification of Diseases-Tenth Revision-Australian Modification codes. RESULTS: There were 13 273 asthma and allergic disease-related ED presentations, with an overall prevalence of 1.6% (95% CI 1.6, 1.7). There was a significantly higher incidence rate of asthma and allergic disease-related ED presentations among First Nations Australians at 177.5 per 10 000 person-years (95% CI 169.3, 186.0) compared with 98.9 per 10 000 person-years (95% CI 97.2, 100.8) among Australians of other descents. The incidence rates, with corresponding 95% CIs, of the four most common cases among First Nations Australians and Australians of other descents, respectively, were as follows: asthma (87.8 (82.0, 93.8) and 40.2 (39.0, 41.3)), unspecified allergy (55.3 (50.8, 60.2) and 36.0 (34.9, 37.1)), atopic/allergic contact dermatitis (17.1 (14.6, 19.9) and 10.6 (10.0, 11.2)) and anaphylaxis (7.2 (5.6, 9.1) and 6.2 (5.7, 6.6)). CONCLUSION: Our findings highlight a significantly higher rate of asthma and allergic disease-related ED presentations among First Nations Australians compared with Australians of other descents. This underscores the urgent need for targeted healthcare interventions integrating culturally appropriate approaches, alongside additional research to understand causality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".