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Record W4388900957 · doi:10.1289/ehp11532

Evidence Synthesis of Observational Studies in Environmental Health: Lessons Learned from a Systematic Review on Traffic-Related Air Pollution

2023· review· en· W4388900957 on OpenAlexaff
Hanna Boogaard, Richard Atkinson, Jeffrey R. Brook, Howard H. Chang, Gerard Hoek, Barbara Hoffmann, Sharon K. Sagiv, Evangelia Samoli, Audrey Smargiassi, Adam A. Szpiro, Danielle Vienneau, Jennifer Weuve, Frederick Lurmann, Francesco Forastiere

Bibliographic record

VenueEnvironmental Health Perspectives · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversité de MontréalPublic Health OntarioUniversity of Toronto
FundersU.S. Environmental Protection Agency
KeywordsObservational studyGrading (engineering)Environmental epidemiologyConfidence intervalEnvironmental healthSystematic reviewPsychologyMedicineMEDLINEEngineeringPolitical sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: There is a long tradition in environmental health of using frameworks for evidence synthesis, such as those of the U.S. Environmental Protection Agency for its Integrated Science Assessments and the International Agency for Research on Cancer Monographs. The framework, Grading of Recommendations Assessment, Development, and Evaluation (GRADE), was developed for evidence synthesis in clinical medicine. The U.S. Office of Health Assessment and Translation (OHAT) elaborated an approach for evidence synthesis in environmental health building on GRADE. METHODS: We applied a modified OHAT approach and a broader "narrative" assessment to assess the level of confidence in a large systematic review on traffic-related air pollution and health outcomes. DISCUSSION: We discuss several challenges with the OHAT approach and its implementation and suggest improvements for synthesizing evidence from observational studies in environmental health. We consider the determination of confidence using a formal rating scheme of up- and downgrading of certain factors, the treatment of every factor as equally important, and the lower initial confidence rating of observational studies to be fundamental issues in the OHAT approach. We argue that some observational studies can offer high-confidence evidence in environmental health. We note that heterogeneity in magnitude of effect estimates should generally not weaken the confidence in the evidence, and consistency of associations across study designs, populations, and exposure assessment methods may strengthen confidence in the evidence. We mention that publication bias should be explored beyond statistical methods and is likely limited when large and collaborative studies comprise most of the evidence and when accrued over several decades. We propose to identify possible key biases, their most likely direction, and their potential impacts on the results. We think that the OHAT approach and other GRADE-type frameworks require substantial modification to align better with features of environmental health questions and the studies that address them. We emphasize that a broader, "narrative" evidence assessment based on the systematic review may complement a formal GRADE-type evaluation. https://doi.org/10.1289/EHP11532.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.145
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.444
GPT teacher head0.473
Teacher spread0.029 · 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 teacher head, not a consensus.

Study designSystematic review
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

Citations19
Published2023
Admission routes1
Has abstractyes

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