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Record W4320066055 · doi:10.1289/isee.2022.p-0237

Air pollution impacts at very low levels: Shape of the concentration-mortality relationship in a large population-based Canadian cohort

2022· article· en· W4320066055 on OpenAlexaffabout
Michael Bräuer, Jeffrey R. Brook, Dan L. Crouse, Anders C. Erickson, Perry Hystad, Chi Li, Randall V. Martin, Jun Meng, Michael Tjepkema, Aaron van Donkelaar, Tanya Christidis, Lauren Pinault, Weiran Yuchi, Crystal Weagle, Scott Weichenthal, Richard T. Burnett

Bibliographic record

VenueISEE Conference Abstracts · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsDalhousie UniversityMcGill UniversityUniversity of British ColumbiaStatistics CanadaUniversity of Toronto
Fundersnot available
KeywordsMedicineHazard ratioCohortProportional hazards modelPopulationCohort studyCOPDDemographyInternal medicineEnvironmental healthConfidence interval

Abstract

fetched live from OpenAlex

Background Mortality is associated with long-term exposure to fine particulate matter, although the form of these associations remain poorly understood at lower concentrations. We applied novel 1 km satellite-derived estimates of PM2.5 exposure to a population-based cohort of 7.1 million Canadians. Methods Cox proportional hazard ratios (HRs) were estimated for nonaccidental and cause-specific mortality and 10-year moving average exposure in models adjusted for a wide array of individual and contextual covariates. The shape of the concentration–response function was investigated using restricted cubic splines, threshold models and an extension of the Shape Constrained Health Impact Function (eSCHIF). Analyses examined sensitivity to co-pollutants, concentration thresholds and regional variation. Results Each 10-µg/m3 increase in PM2.5 corresponded with a nonaccidental mortality HR of 1.084 (1.073 -1.096). PM2.5 was associated mortality from ischemic heart disease, pneumonia, COPD, diabetes, and cerebrovascular disease, but not with heart failure, lung cancer, or kidney failure mortality. HR predictions steeply increased from the minimum concentration of 2.5 µg/m3 to 4.5 µg/m3, flattened from 4.5 µg/m3 to 8.0 µg/m3, then increased for concentrations above 8.0 µg/m3, a pattern also reflected in threshold and eSCHIF results. When restricting to those with exposures <10 µg/m3 shapes indicated positive associations for concentrations >9 µg/m3 with indications of adverse effects on mortality at concentrations as low as 2.5 µg/m3. In sensitivity analyses, PM2.5 - mortality associations were only observed in the highest tertile of oxidant gases with shapes varying across airsheds. These differences, were not related to variation in cohort composition or its access to healthcare, suggesting a role of spatially varying pollutant mixtures not sufficiently characterized by PM2.5 mass concentrations. Conclusions In a large Canadian cohort, associations were observed between exposure to PM2.5 with mortality at concentrations as low as 2.5 µg/m3, with no clear evidence of a threshold.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.058
GPT teacher head0.301
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes2
Has abstractyes

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