Air pollution from bushfires and their association with hospital admissions in Sydney, Newcastle and Wollongong, Australia 1994–2007
Bibliographic record
Abstract
OBJECTIVE: We examined the association between validated bushfire smoke pollution events and hospital admissions in three eastern Australian cities from 1994 to 2007. METHODS: Smoke events were defined as days on which bushfire smoke caused the 24-hour citywide average concentration of airborne particles to exceed the 99(th) percentile of the daily distribution for the study period. We used a time-stratified case-crossover design to assess the association between smoke events and hospital admissions. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for cardiovascular and respiratory conditions on event days compared with non-event days. Models were adjusted for daily meteorology, influenza epidemics and holidays. RESULTS: Smoke events occurred on 58 days in Sydney (population: 3,862,000), 33 days in Wollongong (population: 406,000) and 50 days in Newcastle (population: 278,000). In Sydney, events were associated with a 6% (OR=1.06, 95%CI=1.02-1.09) same day increase in respiratory hospital admissions. Same day chronic obstructive pulmonary disease admissions increased 13% (OR=1.13, 95%CI=1.05-1.22) and asthma admissions by 12% (OR=1.12, 95%CI=1.05-1.19). Events were also associated with increased admissions for respiratory conditions in Newcastle and Wollongong. CONCLUSIONS: Smoke events were associated with increased hospital admissions for respiratory but not cardiovascular conditions. Large populations are needed to assess the impacts of brief exposures. Implications : Public health impacts from bushfire pollution events are likely to increase in association with a warming climate and more frequent severe fire weather.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".