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Record W2793404076 · doi:10.1016/s2542-5196(18)30028-7

Effect of long-term exposure to fine particulate matter on lung function decline and risk of chronic obstructive pulmonary disease in Taiwan: a longitudinal, cohort study

2018· article· en· W2793404076 on OpenAlexaff
Cui Guo, Zilong Zhang, Alexis K.H. Lau, Changqing Lin, Yuan Chieh Chuang, Jimmy W.M. Chan, Wun Kai Jiang, Tony Tam, Eng‐Kiong Yeoh, Ta‐Chien Chan, Ly-yun Chang, Xiang Qian Lao

Bibliographic record

VenueThe Lancet Planetary Health · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsHealth Research Foundation
Fundersnot available
KeywordsMedicineVital capacityCOPDQuartilePulmonary function testingCohortHazard ratioProportional hazards modelSpirometryCohort studyInternal medicineNational Health and Nutrition Examination SurveyPhysical therapyEnvironmental healthLung functionLungPopulationDiffusing capacityAsthmaConfidence interval

Abstract

fetched live from OpenAlex

Background Information on the effects of long-term exposure to fine particulate matter with an aerodynamic diameter of 2·5 μm or less (PM 2·5 ) on lung health is scarce. We aimed to investigate the associations between long-term exposure to PM 2·5 , lung function, and chronic obstructive pulmonary disease (COPD) in a large-scale longitudinal cohort. Methods We included 285 046 participants aged 20 years or older from the Taiwan MJ Health Management Institution cohort, who were recruited between 2001 and 2014 and had spirometric tests during the medical examination visit. We used a satellite-based spatiotemporal model to estimate the 2-year average ground concentration of PM 2·5 (for the calendar year of each participant's medical examination and for the previous year) at each participant's address. We used the generalised linear mixed model to examine the associations between PM 2·5 concentrations and lung function and the Cox proportional hazard regression model with time-dependent covariates to investigate the PM 2·5 effects on COPD development. Findings Every 5 μg/m 3 increment in PM 2·5 was associated with a decrease of 1·18% for forced vital capacity (FVC), 1·46% for forced expiratory volume in 1 s (FEV 1 ), 1·65% for maximum mid-expiratory flow (MMEF), and 0·21% for FEV 1 :FVC ratio. The decrease accelerated over time. Additional annual declines were observed for FVC (0·14%), FEV 1 (0·24%), MMEF (0·44%), and FEV 1 :FVC ratio (0·09%). Compared with the participants exposed to the first quartile of PM 2·5 , participants exposed to the fourth, third, and second quartiles of PM 2·5 had a hazard ratio of 1·23 (95% CI 1·09–1·39), 1·30 (1·16–1·46), and 1·39 (1·24–1·56) for COPD development, respectively. Interpretation Long-term exposure to ambient PM 2·5 is associated with reduced, and faster declines in, lung function. Long-term exposure to ambient PM 2·5 is also associated with an increased risk of the incidence of COPD. This study reinforces the urgency of global strategies to mitigate air pollution for improvement of pulmonary health and prevention of COPD. Funding Environmental Health Research Fund of the Chinese University of Hong Kong and PhD Studentship of the Chinese University of Hong Kong.

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)0.002
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.021
GPT teacher head0.307
Teacher spread0.287 · 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

Citations336
Published2018
Admission routes1
Has abstractyes

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