A Multi-Country Study on Ozone-Related Mortality
Bibliographic record
Abstract
Back&aim: Many studies have characterized the short-term association between ozone and mortality in different settings, but mostly across limited geographical areas and using different methodological approaches. In this study, we aimed at comprehensively assessing short-term ozone-related mortality associations in a multi-country analysis.Method: We collected daily mortality and air pollution data (ozone, O3, and particulate matter, PM10) from 245 locations in 16 countries (Australia, Brazil, Canada, Chile, Columbia, France, Japan, South Korea, Mexico, Portugal, Spain, Sweden, Switzerland, Taiwan, Thailand, and United States), included in the Multi-City Multi-Country Collaborative Research Network. We applied a two-stage time-series design. First, we modelled mortality-ozone associations across 21 days of lag using quasi-Poisson regression and distributed lag linear models (DLMs). Second, we performed a multilevel multivariate meta-regression to obtain pooled associations across locations nested within country. Best linear unbiased predictions (BLUPs) were derived at both location and country levels. We estimated season-specific ozone-related mortality through time-varying interaction DLMs.Results: On average, an increase in 10 μg/m3 in ozone was associated with a 0.6% increase in mortality risk [95%CI: 0.3 to 0.8%]. The positive association was lagged and persisted during the following 7 days. Country-specific BLUPs ranged between 0.2%[-0.05 to 0.5] in Brazil to 0.8%[0.5 to 1.2] in the US. The ozone-related mortality association was significantly larger in winter [0.8%, 0.5 to 1.0] vs summer [0.5%, 0.4 to 0.7]. Results were robust to sensitivity analyses, such as multi-pollutant models.Conclusion: This represents the largest epidemiological study on health effects of ozone. By using a common advanced statistical framework, we provide robust evidence on the association with all-cause mortality in different locations across the globe.On behalf of the MCC Network
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".