Ambient Particulate Matter and Paramedic Assessments of Acute Diabetic, Cardiovascular, and Respiratory Conditions
Bibliographic record
Abstract
BACKGROUND: Ambulance data provide a useful source of population-based and spatiotemporally resolved information for assessing health impacts of air pollution in nonhospital settings. We used the clinical records of paramedics to quantify associations between particulate matter (PM2.5) and diabetic, cardiovascular, and respiratory conditions commonly managed by those responding to calls for emergency ambulance services. METHODS: We evaluated 394,217 paramedic assessments from three states in Southeastern Australia (population 13.2 million) and daily PM2.5 concentrations modeled at 5 km resolution from 2009 to 2014. We used a time-stratified, case-crossover analysis adjusted for daily meteorology to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for each clinical outcome per 10 µg/m increase in daily PM2.5 at lags from 0 to 2 days. RESULTS: Increased PM2.5 was associated with increased odds of paramedic assessments of hypoglycemia (OR = 1.07; 95% CI = 1.02, 1.12, lag 0), arrhythmia (OR = 1.05; 95% CI = 1.02, 1.09, lag 0), heart failure (OR = 1.07; 95% CI = 1.02, 1.12, lag 1), faint (OR = 1.09; 95% CI = 1.04-1.13, lag 0), asthma (OR = 1.06; 95% CI = 1.01, 1.11, lag 1), chronic obstructive pulmonary disease (OR = 1.07; 95% CI = 1.01, 1.13, lag 1), and croup (OR = 1.09; 95% CI = 1.02, 1.17). We did not identify associations with cerebrovascular outcomes. CONCLUSIONS: Ambulance data enable the evaluation of important clinical syndromes that are often initially managed in nonhospital settings. Daily PM2.5 was associated with hypoglycemia, faint, and croup in addition to the respiratory and cardiovascular outcomes that are better established.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".