Long-term exposure to black carbon and mortality: A 28-year follow-up of the GAZEL cohort
Bibliographic record
Abstract
Background The current evidence on health effects of long-term exposure to outdoor airborne black carbon (BC) exposure remains scarce. Objectives To examine the association between long-term exposure to BC and mortality in a large population-based French cohort, with 28 years of follow-up. Methods Data from the GAZEL cohort were collected between 1989 and 2017. Land use regression model with temporal extrapolation wa used to estimate yearly BC and PM 2.5 exposure at the residential addresses from 1989 until censoring for 19,906 participants. Time-varying Cox models with attained age as time-scale was used to estimate the associations between BC and all-cause and cardiovascular mortality, after adjusting for individual and area-level covariates. To handle confounding by PM 2.5 , we used the residual of BC regressed on PM 2.5 as an alternate exposure variable. For all-cause mortality, we also examined effect modification by sex, smoking status, BMI and fruit/vegetable intake. Results The median of 20-year moving average of BC exposure was 2.02 10 −5 /m in study population. We found significant associations between BC exposure and all-cause mortality (n = 2357) using both 20-year moving average of BC and residual of BC, with corresponding hazard ratios (HR) of 1.14 (95 %CI: 1.07–1.22) and 1.17 (95 %CI: 1.10–1.24) for an inter-quartile range (IQR) increase (0.86 10 −5 /m for BC and 0.57 10 −5 /m for residual of BC). We found a similar association between BC and cardiovascular mortality (n = 277) with a HR of 1.15 (95 %CI: 0.95–1.38). The dose–response relationship between BC and all-cause mortality was monotonic but nonlinear with a steeper slope at high BC levels. In addition, the effect of BC was higher among never-smokers and among those having fruit/vegetables less than twice a week. Conclusions There was a positive association between long-term exposure to BC and increased mortality risk, reinforcing the emerging evidence that BC is a harmful component of PM 2.5 .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".