<b>Short-term exposure to wildfire-specific PM</b><sub><strong>2.5</strong></sub><b> and hospitalization for diabetes morbidity: a study in multiple countries/territories</b>
Bibliographic record
Abstract
Objective To evaluate associations of wildfire fine particulate matter (PM2.5) with diabetes across multi-countries/territories. Research Design and Methods We collected 3,612,135 diabetes hospitalization data across 1008 locations in Australia, Brazil, Canada, Chile, New Zealand, Thailand, and Taiwan during 2000‒2019. Daily wildfire-specific PM2.5 were estimated through chemical transport models and machine learning calibration. Quasi-Poisson regression with distributed lag non-linear models and random-effects meta-analysis were applied to estimate associations between wildfire-specific PM2.5 and diabetes hospitalization. Subgroup analyses were by age, sex, location income-level, and country/territory. Diabetes hospitalizations attributable to wildfire-specific PM2.5 and non-wildfire PM2.5 were compared. Results Each 10 µg/m3 increase in wildfire-specific PM2.5 over the current and previous three days was associated with relative risks of 1.017 (95% confidence interval [CI]: 1.011‒1.022), 1.023 (1.011‒1.035), 1.023 (1.015‒1.032), 0.962 (0.823‒1.032), 1.033 (1.001‒1.066), 1.013 (1.004‒1.022) for all-cause, type 1, type 2, malnutrition-related, other specified, and unspecified diabetes hospitalization, respectively. Stronger associations were observed for all-cause, type 1, and type 2 diabetes in Thailand, Australia, and Brazil; unspecified diabetes in New Zealand; and type 2 diabetes in high-income locations. 0.67% (0.16%‒1.18%) all-cause and 1.02% (0.20%‒1.81%) type 2 diabetes hospitalizations were attributable to wildfire-specific PM2.5. Wildfire-specific PM2.5 posed greater risks of all-cause, type 1, and type 2 diabetes than non-wildfire PM2.5, responsible for 38.7% of PM2.5-related diabetes hospitalizations. Conclusions We show the relatively underappreciated links between diabetes and wildfire air pollution, which can lead to a non-negligible proportion of PM2.5¬-related diabetes hospitalizations. Precision prevention and mitigation should be developed for those in advantaged communities, or Thailand/Australia/Brazil
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".