Wood Smoke PM10 and Hospital Admissions in Seven Regional Australian Towns
Bibliographic record
Abstract
Background While the toxicology and epidemiology of particulate emissions from industry and transportation is well understood the picture for wood smoke is less clear. Exposure to ambient wood smoke is associated with mortality and respiratory outcomes, with some evidence of associations with cardiovascular outcomes. The use of wood heating in regional Australian towns is common and ambient particulate exposures during winter can be high. Aims We aimed to quantify the association between hospital admissions and wood smoke derived PM10 in regional Australian towns. Methods Daily winter PM10 concentrations were available from seven towns (population range= 18,961-57,015) between 1999 and 2006 for varying periods (range= 1-8 years). We used a time-stratified case-crossover design to assess the association between winter time PM10 and hospital admissions (cardiovascular, respiratory and asthma). Odds ratios (OR) and 95% confidence intervals (CI) were estimated and models were adjusted for daily meteorology, influenza epidemics and holidays. Summary estimates for the seven towns were calculated using meta analyses for each lag and for the largest magnitude lag (maximum |z|). Results Town daily mean PM10 concentrations ranged from 15.5 to 27.2µg/m3. Meta analysis estimates found that a 10µg/m3 increase in PM10 was associated with a small magnitude increase in cardiovascular admissions (lag 2 days, OR=1.02[95%CI: 0.99 to 1.05]; lag maximum |z|, OR=1.02[95%CI: 1.00 to 1.05]), but was not associated with admissions for all respiratory conditions or asthma. Conclusions Our results add to the emerging evidence that exposure to wood smoke derived particulate is associated with cardiovascular conditions, although the relatively small populations of our seven study towns and the limited availability of PM10 exposure data means our results should be viewed with caution. Our study supports the development of policies to reduce PM10 from solid fuel combustion while maintaining access to affordable heating.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".