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Record W3096767555 · doi:10.1164/rccm.201909-1744oc

Air Pollution as a Risk Factor for Incident Chronic Obstructive Pulmonary Disease and Asthma. A 15-Year Population-Based Cohort Study

2020· article· en· W3096767555 on OpenAlexafffund
Saeha Shin, Li Bai, Richard T. Burnett, Jeffrey C. Kwong, Perry Hystad, Aaron van Donkelaar, Éric Lavigne, Scott Weichenthal, Ray Copes, Randall V. Martin, Alexander Kopp, Hong Chen

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of OttawaHealth CanadaOttawa Public HealthUniversity of TorontoDalhousie UniversityMcGill UniversityInstitute for Clinical Evaluative SciencesPublic Health Ontario
FundersCanadian Institutes of Health Research
KeywordsMedicineInterquartile rangeHazard ratioAsthmaCOPDCohortProportional hazards modelConfidence intervalPopulationCohort studyRisk factorInternal medicineRespiratory diseaseEnvironmental healthLung

Abstract

fetched live from OpenAlex

Abstract Rationale Current evidence on the relationship between long-term exposure to air pollution and new onset of chronic lung disease is inconclusive. Objectives To examine associations of incident chronic obstructive pulmonary disease (COPD) and adult-onset asthma with past exposure to fine particulate matter ≤ 2.5 μm in diameter (PM2.5), nitrogen dioxide (NO2), ozone (O3), and the redox-weighted average of NO2 and O3 (Ox) and characterize the concentration–response relationship. Methods We conducted a population-based cohort study of all Ontarians, aged 35–85 years, from 2001 to 2015. A 3-year moving average of residential exposures to selected pollutants with a 1-year lag were estimated during follow-up. We used Cox proportional hazard models and Aalen additive-hazard models to quantify the pollution–disease associations and characterized the shape of these relationships using newly developed nonlinear risk models. Measurements and Main Results Among 5.1 million adults, we identified 340,733 and 218,005 incident cases of COPD and asthma, respectively. We found positive associations of COPD with PM2.5 per interquartile-range (IQR) increase of 3.4 μg/m3 (hazard ratio, 1.07; 95% confidence interval, 1.06–1.08), NO2 per IQR increase of 13.9 ppb (1.04; 1.02–1.05), O3 per IQR increase of 6.3 ppb (1.04; 1.03–1.04), and Ox per IQR increase of 4.4 ppb (1.03; 1.03–1.03). By contrast, we did not find strong evidence linking these pollutants to adult-onset asthma. In addition, we quantified that each IQR increase in pollution exposure yielded 3.0 (2.4–3.6), 3.2 (2.0–4.3), 1.9 (1.3–2.5), and 2.3 (1.7–2.9) excess cases of COPD per 100,000 adults for PM2.5, NO2, O3, and Ox, respectively. Furthermore, most pollutant–COPD relationships exhibited supralinear shapes. Conclusions Air pollution was associated with a higher incidence of COPD but was not associated with a higher incidence of adult-onset asthma.

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)0.002
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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.022
GPT teacher head0.331
Teacher spread0.309 · 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

Citations173
Published2020
Admission routes2
Has abstractyes

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