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The Impact of Air Pollution on Bronchiolitis

2006· article· en· W1978364386 on OpenAlexaff
Paul A. Demers, Catherine J. Karr, Mieke Koehoorn, Cornel Lencar, Lillian Tamburic, Michael Bräuer

Bibliographic record

VenueEpidemiology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEnvironmental healthMedicinePopulationDemographyBronchiolitisLogistic regressionAir pollutionAir quality indexPediatricsGeographyMeteorology

Abstract

fetched live from OpenAlex

SAB5-O-05 Introduction: Bronchiolitis, a major cause of infant morbidity, has been associated with environmental tobacco smoke. Its association with air pollution has been evaluated in several studies, but the results are mixed. We evaluated the impacts of several air pollutants on bronchiolitis in a nested case-control study using improved exposure methods. Methods: The study population was singleton children born in a metropolitan area in 1999 and 2000. Birth records were linked to health records to obtain data on all healthcare system encounters, as well as residential history and census tract information as part of the Border Air Quality Study. After excluding 723 missing census data and 22 with <25 weeks of gestation, 40,288 were available for analysis. Infants were followed from the second to the 12th month and 5678 outpatient and hospitalization cases were identified. Five controls born on the same date were incidence density matched to each case. Mean exposures to NOx, CO, SO2, PM10, PM2.5, and O3 were estimated using inverse distance-weighted average of all monitors in the study region. NOx and Black Carbon (filter absorbance) were estimated using temporally adjusted land-use regression (LUR) models. Exposures were estimated for the home address of the children for lifetime, 1 month, 2 months prior to diagnosis/reference date. Conditional logistic regression was used to adjust for infant sex, ethnicity [First Nations Status], and prematurity, as well as neighborhood income and maternal education. Results: Based on lifetime exposure, the strongest association was observed with O3 (OR = 1.38; 95% CI, 1.19–1.60 per 10 μg/m3). Smaller, suggestive observations were observed for PM10 (OR = 1.02; CI, 1.00–1.05, 1 μg/m3), PM2.5 (OR = 1.09; CI, 0.98–1.22, 1 μg/m3), and black carbon (OR = 1.05; CI, 0.95–1.16, 0.5 × 10−5 m−1 increase in filter absorbance). No association was observed for NOx based on either ambient monitors or LUR. Protective associations were observed for SO2 (OR = 0.95; CI, 0.92–0.97, 1 μg/m3) and CO (OR = 0.93; CI, 0.90–0.97, 100 μg/m3). These results were relatively unchanged when exposure in specific time windows was considered, with the exception of O3 where the effects were somewhat reduced (OR = 1.26; CI, 1.10–1.44, 1 month prior and OR = 1.28; CI, 1.11–1.48, 2 months prior). Seasonal stratification indicated that O3 and PM10 associations were highest in fall (ORs = 1.71 and 1.07, respectively) and winter (ORs = 1.69 and 1.05, respectively). Conclusions: This is the first study to observe an association between physician diagnosis and hospital admissions for bronchiolitis and ozone exposure. These findings add some support for previously reported associations between bronchiolitis and exposure to particulate matter.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.374
Teacher spread0.318 · 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.

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

Citations1
Published2006
Admission routes1
Has abstractyes

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