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Record W3020882052 · doi:10.1186/s12940-020-00601-1

Systematic review and meta-analysis of case-crossover and time-series studies of short term outdoor nitrogen dioxide exposure and ischemic heart disease morbidity

2020· review· en· W3020882052 on OpenAlexafffund
David M. Stieb, Carine Zheng, Dina H. Salama, Rania Berjawi, Monica Emode, Robyn Hocking, Ninon Lyrette, Carlyn J. Matz, Éric Lavigne, Hwashin Hyun Shin

Bibliographic record

VenueEnvironmental Health · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsQueen's UniversityUniversity of British ColumbiaOttawa Public HealthUniversity of OttawaHealth Canada
FundersHealth CanadaCalifornia State University, East BayUniversity of California, San FranciscoUniversity of TorontoMcGill University
KeywordsMeta-analysisFunnel plotMedicinePublication biasOdds ratioCrossover studyMeta-regressionDemographyEnvironmental healthInternal medicinePathology

Abstract

fetched live from OpenAlex

Abstract Background Nitrogen dioxide (NO2) is a pervasive urban pollutant originating primarily from vehicle emissions. Ischemic heart disease (IHD) is associated with a considerable public health burden worldwide, but whether NO2 exposure is causally related to IHD morbidity remains in question. Our objective was to determine whether short term exposure to outdoor NO2 is causally associated with IHD-related morbidity based on a synthesis of findings from case-crossover and time-series studies. Methods MEDLINE, Embase, CENTRAL, Global Health and Toxline databases were searched using terms developed by a librarian. Screening, data extraction and risk of bias assessment were completed independently by two reviewers. Conflicts between reviewers were resolved through consensus and/or involvement of a third reviewer. Pooling of results across studies was conducted using random effects models, heterogeneity among included studies was assessed using Cochran’s Q and I2 measures, and sources of heterogeneity were evaluated using meta-regression. Sensitivity of pooled estimates to individual studies was examined using Leave One Out analysis and publication bias was evaluated using Funnel plots, Begg’s and Egger’s tests, and trim and fill. Results Thirty-eight case-crossover studies and 48 time-series studies were included in our analysis. NO2 was significantly associated with IHD morbidity (pooled odds ratio from case-crossover studies: 1.074 95% CI 1.052–1.097; pooled relative risk from time-series studies: 1.022 95% CI 1.016–1.029 per 10 ppb). Pooled estimates for case-crossover studies from Europe and North America were significantly lower than for studies conducted elsewhere. The high degree of heterogeneity among studies was only partially accounted for in meta-regression. There was evidence of publication bias, particularly for case-crossover studies. For both case-crossover and time-series studies, pooled estimates based on multi-pollutant models were smaller than those from single pollutant models, and those based on older populations were larger than those based on younger populations, but these differences were not statistically significant. Conclusions We concluded that there is a likely causal relationship between short term NO2 exposure and IHD-related morbidity, but important uncertainties remain, particularly related to the contribution of co-pollutants or other concomitant exposures, and the lack of supporting evidence from toxicological and controlled human studies.

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)0.027
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.027
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.077
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0240.039
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.115
GPT teacher head0.383
Teacher spread0.268 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations34
Published2020
Admission routes2
Has abstractyes

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