Estimating the impacts of fine particulate matter concentration from different sources on the mortality of cardiovascular diseases: a population-based cohort study
Bibliographic record
Abstract
Background: Evidence is limited about the health impacts of fine particulate matter (PM2.5) mass originated from different sources. Methods: We assessed the associations of cardiovascular mortality with PM2.5 mass from ten major sources including residential, transportation, industry, agriculture, wild fire, dust, sea salt, biogenic SOA, power generation, and others. We constructed a cohort that comprised all Ontario adults who, on 1 January 2001, were 35-85 years old (~5.26 million subjects) and were followed up until 31 December 2016. The Ontario Registrar General information on deaths was used to ascertain cardiovascular deaths. We assigned the estimates of PM2.5 mass from these sources to participants’ annual postal-code addresses during follow-up. Using standard Cox proportional hazards models, we calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for source-specific PM2.5 mass, adjusted for both individual- and neighborhood-level covariates. We considered models for PM2.5 mass from each source individually and in combination. Results: During follow-up, we identified 305,353 deaths from cardiovascular diseases. Transportation, industry, and residential sectors are three greatest contributors of PM2.5 mass in Ontario. The residential sector was strongly correlated with the transportation sector (r=0.89), moderately with the industry sector (r=0.46), and weakly correlated with other sectors. In the single-pollutant models, we found the elevated risk of cardiovascular deaths with each unit increase in exposure to PM2.5 mass from industry, transportation, agriculture, dust, and power generation sectors with HRs ranging from 1.001 to 1.612. In the multi-pollutant model with all ten sectors included, the strongest positive association was observed with power generation, followed by residential combustion, and wild fire. Conclusion: Our study suggests that PM2.5 mass from human-made sources might have a greater impact on cardiovascular diseases than that from natural sources. Future investigations are warranted to evaluate the joint health impacts of PM2.5 and related sources.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".