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Record W3152099645 · doi:10.1016/s2542-5196(21)00026-7

Ambient carbon monoxide and daily mortality: a global time-series study in 337 cities

2021· article· en· W3152099645 on OpenAlexaff
Kai Chen, Susanne Breitner, Kathrin Wolf, Massimo Stafoggia, Francesco Sera, Ana María Vicedo-Cabrera, Yuming Guo, Shilu Tong, Éric Lavigne, Patricia Matus, Haidong Kan, Jouni J. K. Jaakkola, Niilo Ryti, Veronika Huber, Matteo Scortichini, Masahiro Hashizume, Yasushi Honda, Baltazar Nunes, Joana Madureira, Iulian‐Horia Holobâcă, Simona Fratianni, Ho Kim, Whanhee Lee, Aurelio Tobı́as, Carmen Íñiguez, Bertil Forsberg, Christofer Åström, Martina S. Ragettli, Yue Leon Guo, Bing-yu Chen, Shanshan Li, Ai Milojevic, Antonella Zanobetti, Joel Schwartz, Michelle L. Bell, Antonio Gasparrini, Alexandra Schneider

Bibliographic record

VenueThe Lancet Planetary Health · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsOttawa Public HealthUniversity of OttawaHealth Canada
FundersMedical Research CouncilMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaBundesministerium für Bildung und ForschungFundação para a Ciência e a TecnologiaNatural Environment Research CouncilScience and Technology Commission of Shanghai MunicipalityNational Health and Medical Research CouncilEuropean CommissionSight Research UK
KeywordsConfoundingPoisson regressionDemographyRandom effects modelAir pollutionPoisson distributionMultilevel modelGeneralized additive modelOzoneEffect modificationMedicineEnvironmental scienceGeographyStatisticsEnvironmental healthMeteorologyMathematicsPopulationConfidence intervalMeta-analysisChemistryInternal medicine

Abstract

fetched live from OpenAlex

Background Epidemiological evidence on short-term association between ambient carbon monoxide (CO) and mortality is inconclusive and limited to single cities, regions, or countries. Generalisation of results from previous studies is hindered by potential publication bias and different modelling approaches. We therefore assessed the association between short-term exposure to ambient CO and daily mortality in a multicity, multicountry setting. Methods We collected daily data on air pollution, meteorology, and total mortality from 337 cities in 18 countries or regions, covering various periods from 1979 to 2016. All included cities had at least 2 years of both CO and mortality data. We estimated city-specific associations using confounder-adjusted generalised additive models with a quasi-Poisson distribution, and then pooled the estimates, accounting for their statistical uncertainty, using a random-effects multilevel meta-analytical model. We also assessed the overall shape of the exposure–response curve and evaluated the possibility of a threshold below which health is not affected. Findings Overall, a 1 mg/m 3 increase in the average CO concentration of the previous day was associated with a 0·91% (95% CI 0·32–1·50) increase in daily total mortality. The pooled exposure–response curve showed a continuously elevated mortality risk with increasing CO concentrations, suggesting no threshold. The exposure–response curve was steeper at daily CO levels lower than 1 mg/m 3 , indicating greater risk of mortality per increment in CO exposure, and persisted at daily concentrations as low as 0·6 mg/m 3 or less. The association remained similar after adjustment for ozone but was attenuated after adjustment for particulate matter or sulphur dioxide, or even reduced to null after adjustment for nitrogen dioxide. Interpretation This international study is by far the largest epidemiological investigation on short-term CO-related mortality. We found significant associations between ambient CO and daily mortality, even at levels well below current air quality guidelines. Further studies are warranted to disentangle its independent effect from other traffic-related pollutants. Funding EU Horizon 2020, UK Medical Research Council, and Natural Environment Research Council.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0020.006
Science and technology studies0.0010.001
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 head0.055
GPT teacher head0.317
Teacher spread0.262 · 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

Citations83
Published2021
Admission routes1
Has abstractyes

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