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Record W1975636895 · doi:10.1097/ede.0000000000000012

Residential Air Pollution and Lung Cancer

2013· letter· en· W1975636895 on OpenAlexaffabout
Perry Hystad, Paul A. Demers, Kenneth C. Johnson, Richard M. Carpiano, Michael Bräuer

Bibliographic record

VenueEpidemiology · 2013
Typeletter
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsCancer Care OntarioOccupational Cancer Research CentreUniversity of OttawaUniversity of British Columbia
Fundersnot available
KeywordsRecall biasEnvironmental healthConfoundingMedicineExposure assessmentLung cancerPopulationOdds ratioAir pollutionInformation biasStatisticsDemographySelection biasPathologyMathematics

Abstract

fetched live from OpenAlex

The authors respond: Thank you for the opportunity1 to further discuss the potential in our study for residual confounding by smoking and misclassification of exposure.2 Although these concerns1 have been raised regarding many epidemiologic studies of air pollution, it is unlikely that they are responsible for the positive associations we reported. Numerous studies3 have found that self-reported measures produce valid estimates of smoking behavior. We found negative correlations between all smoking variables (ie, smoking pack-years, years since cessation, and residential and occupational second-hand smoke exposure) and air pollution exposures, which limits any positive bias in our results. While the potential for response and recall bias exists in all case-control studies, this population-based study has a relatively high response rate for cases (62%) and controls (67%). Furthermore, we found no difference between cases and controls in the completeness of the self-reported residential histories that we used to assign air pollution exposures. Our long-term exposure assessment approach represents clear improvements over past studies. Specifically, we included complete residential histories over a 20-year period and applied multiple spatiotemporal models of PM2.5, NO2, and O3. While some degree of exposure misclassification is present, this error is likely nondifferential and thus would produce bias toward (rather than away from) the null. As reported,2 the increased lung cancer odds ratio for NO2 exposures derived from fixed-site monitors likely represents contributions from PM2.5 due to the high correlation of these two pollutants. Furthermore, all other sensitivity analyses using various spatiotemporal models revealed consistent associations. Thus, our study offers a useful contribution to the epidemiologic evidence regarding air pollution exposure and lung cancer incidence. While we made no claims of a causal association in our article, we concur with recent commentaries4 and systematic reviews and meta-analyses5,6 that the current weight of evidence supports an association of PM2.5 and NO2 exposures with lung cancer incidence. Whether these associations are causal is the focus of the upcoming International Agency for Research on Cancer monograph evaluating the carcinogenicity of ambient air pollution.7 We do note, however, that the Environmental Protection Agency’s Integrated Science Assessment8 mentioned by Drs. Sax and Goodman concluded that the evidence is “suggestive of a causal relationship between long-term exposures to PM2.5 and cancer.” We also highlight that NO2 itself is not likely to be responsible for the increase in lung cancer risk but rather is a marker for other traffic-related carcinogens. Perry Hystad School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada, [email protected] Paul A. Demers Occupational Cancer Research Centre, Cancer Care Ontario, Ontario, Canada Kenneth C. Johnson Department of Epidemiology and Community Health, University of Ottawa, Ottawa, Ontario, Canada Richard M. Carpiano Department of Sociology, University of British Columbia, Vancouver, BC, Canada Michael Brauer School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.149
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.069
GPT teacher head0.374
Teacher spread0.305 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2013
Admission routes2
Has abstractyes

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