Does air pollution modify the effect of heat on mortality during the warm season? Results from a multi-country study.
Bibliographic record
Abstract
Background: The health effects of heat on mortality are well known worldwide, less is known on the synergistic effect of heat and air pollutants. The aim of the study is to estimate the role of air pollution as effect modifier of the association between temperature and mortality effects during the warm season (6 months) in 24 countries across the globe.Methods: Time-series analysis was run for each city. Heat effects were estimated as the percent change in mortality for increases in mean temperature between the 75th and 99th percentile. To evaluate the interaction, a bivariate tensor product between mean air temperature (lag 0-1) and either PM10 or ozone (both lag 0-1) was defined and temperature estimates were extrapolated at three pollutant percentile levels: low (10th), medium (50th), and high (90th).Results: Daily mortality, mean temperature (°C) and air pollution data (PM10 and ozone) for 482 cities were considered. Meta-analytical results showed statistically significant effects of heat on mortality for increasing levels of ozone and PM10. Considering the interaction with PM10, temperature estimates rose from 7.1% (CI 95%:3.1%-11.2%) to 14.5% (CI 95%:10.2%-9.0%) in the low and high levels respectively. A similar trend was observed when considering effect modification by ozone, with estimates ranging from 3.4% (CI 95%:1.0%-5.8%) to 12.8 % (CI 95%: 8.7%-17.1%) in the low and high levels respectively. Considering country specific estimates some heterogeneity was observed with a positive trend in the effect of heat by levels of PM10 in Australia, Brazil, Canada, S. Korea, and most European countries and similarly in Australia, Canada, Japan, Thailand, USA and European countries for ozone.Conclusion: This study shows the synergistic effect of heat and pollution on mortality during summer, which is important when considering the future health impacts of climate change.On behalf of the MCC collaborative group.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".