Updated Global Estimates of Respiratory Mortality in Adults ≥30Years of Age Attributable to Long-Term Ozone Exposure
Bibliographic record
Abstract
Background: Relative risk estimates for long-term ozone (O3) exposure and respiratory mortality from the American Cancer Society Cancer Prevention Study II (ACS CPS-II) cohort have been used to estimate global O3-attributable mortality in adults. Updated relative risk estimates are now available for the same cohort based on an expanded study population with longer follow-up. Objectives: We estimated the global burden and spatial distribution of respiratory mortality attributable to long-term O3 exposure in adults ≥30y of age using updated effect estimates from the ACS CPS-II cohort. Methods: We used GEOS-Chem simulations (2×2.5º grid resolution) to estimate annual O3 exposures, and estimated total respiratory deaths in 2010 that were attributable to long-term annual O3 exposure based on the updated relative risk estimates and minimum risk thresholds set at the minimum or fifth percentile of O3 exposure in the most recent CPS-II analysis. These estimates were compared with attributable mortality based on the earlier CPS-II analysis, using 6-mo average exposures and risk thresholds corresponding to the minimum or fifth percentile of O3 exposure in the earlier study population. Results: We estimated 1.04–1.23 million respiratory deaths in adults attributable to O3 exposures using the updated relative risk estimate and exposure parameters, compared with 0.40–0.55 million respiratory deaths attributable to O3 exposures based on the earlier CPS-II risk estimate and parameters. Increases in estimated attributable mortality were larger in northern India, southeast China, and Pakistan than in Europe, eastern United States, and northeast China. Conclusions: These findings suggest that the potential magnitude of health benefits of air quality policies targeting O3, health co-benefits of climate mitigation policies, and health implications of climate change-driven changes in O3 concentrations, are larger than previously thought. https://doi.org/10.1289/EHP1390
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".