The Effects of Coexposure to Extremes of Heat and Particulate Air Pollution on Mortality in California: Implications for Climate Change
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Bibliographic record
Abstract
Abstract Rationale Extremes of heat and particulate air pollution threaten human health and are becoming more frequent because of climate change. Understanding the health impacts of coexposure to extreme heat and air pollution is urgent. Objectives To estimate the association of acute coexposure to extreme heat and ambient fine particulate matter (PM2.5) with all-cause, cardiovascular, and respiratory mortality in California from 2014 to 2019. Methods We used a case-crossover study design with time-stratified matching using conditional logistic regression to estimate mortality associations with acute coexposures to extreme heat and PM2.5. For each case day (date of death) and its control days, daily average PM2.5 and maximum and minimum temperatures were assigned (0- to 3-day lag) on the basis of the decedent’s residence census tract. Measurements and Main Results All-cause mortality risk increased 6.1% (95% confidence interval [CI], 4.1–8.1) on extreme maximum temperature-only days and 5.0% (95% CI, 3.0–8.0) on extreme PM2.5-only days, compared with nonextreme days. Risk increased by 21.0% (95% CI, 6.6–37.3) on days with exposure to both extreme maximum temperature and PM2.5. Increased risk of cardiovascular and respiratory mortality on extreme coexposure days was 29.9% (95% CI, 3.3–63.3) and 38.0% (95% CI, −12.5 to 117.7), respectively, and were more than the sum of individual effects of extreme temperature and PM2.5 only. A similar pattern was observed for coexposure to extreme PM2.5 and minimum temperature. Effect estimates were larger over age 75 years. Conclusions Short-term exposure to extreme heat and air pollution alone were individually associated with increased risk of mortality, but their coexposure had larger effects beyond the sum of their individual effects.
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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 it