The Impact of Chronic Ozone and Particulate Air Pollution on Mortality in Patients With Sepsis Across the United States
Bibliographic record
Abstract
Objective: The impact of chronic exposure to air pollution on mortality in patients with sepsis is unknown. We attempted to quantify the relationship between air pollution, notably excess ozone, and particulate matter (PM), with in-hospital mortality in patients with sepsis nationwide. Methods: The 2011 Nationwide Inpatient Sample (NIS) was linked with ambient air pollution data from the Environmental Protection Agency for both 8-hour ozone exposure and annual mean 2.5-micron PM (PM 2.5 ) pollution levels. A validated severity of illness model for sepsis using administrative data was used to control for sepsis severity. Results: The records of 8 023 590 hospital admissions from the 2011 NIS sample were analyzed. Of these, there were 444 928 patients who met the Angus definition of sepsis, treated in hospitals for which air pollution data were available. The cohort had an overall mortality of 11.2%. After adjustment for severity of sepsis, increasing exposure to ozone pollution was associated with increased risk of mortality (odds ratio [OR]: 1.04 for each 0.01 ppm increase, 95% confidence interval [CI]: 1.03-1.05; P < .01). Particulate matter was not associated with mortality (OR: 0.99 for each 5 µg/m 3 increase, 95% CI: 0.97-1.01; P = .28). When stratified by sepsis source, ozone pollution had a higher impact on patients with pneumonia (OR: 1.06, 95% CI: 1.04-1.08; P < .01) compared to those patients without pneumonia (OR: 1.02, 95% CI: 1.01-1.03; P < .01). Conclusion: Exposure to increased levels of ozone but not particulate air pollution was associated with higher risk of mortality in patients with sepsis. This association was strongest in patients with pneumonia but persisted in all sources of sepsis. Further work is needed to understand the relationship between ambient ozone air pollution and sepsis outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".