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ASSOCIATION OF OZONE WITH LOW BIRTH WEIGHT IN MICHIGAN, 1990-2001, USING MULTIPLE IMPUTATION METHOD FOR MISSING OZONE VALUES

2005· article· en· W1979602572 on OpenAlexaboutno aff
H.Q. Le, Stuart Batterman, Alireza Sadeghnejad, Julia J. Wirth, Mary Lee Hultin, Michael Depa, Richard L. Wahl

Bibliographic record

VenueEpidemiology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyBirth certificateLow birth weightMedicineMissing dataBirth weightImputation (statistics)Gestational agePoisson regressionEnvironmental healthStatisticsPregnancyPopulationMathematics

Abstract

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ISEE-217 Introduction: Exposure to the criteria air pollutants carbon monoxide, particulate matter less than 10 μm, and sulphur dioxide, has been associated with low birth weight (LBW), premature birth and small for gestational aged neonates. Aim: Currently, the association of LBW and ozone (O3) is not well established. A semi-individual study was designed to explore this association. Additionally, a multiple imputation method was used to impute missing values of O3. Methods: Zip codes within a four km radius of monitoring stations providing O3 data were used to obtain birth certificate data among residents in southeastern Michigan from 1990–2001. Among infants of 16–45 year old mothers, live singletons weighing 750–4000g with gestational ages of 37–42 weeks were included. The primary adverse birth outcome of interest is full-term LBW (<2500g). We controlled for gestational age, gender of new born, maternal race, age, education level, smoking status, prenatal care level, season of birth, and site of residency in the study. In Michigan, ozone monitoring is performed only during the summer months, which are the O3 active seasons. To assess O3 exposure, daily maxima of 8-h-moving-average (8-h) O3 were computed. Missing 8-h O3 values were imputed using a multivariate cross-sectional model incorporating full 8-h O3 data for the same period in Windsor (Canada) and meteorological factors. Several performance indicators were tabulated for describing the goodness of fit of the model. Depending on the site, the range of index of agreement (Willmott with k set to 2) and coefficient of determinant were 0.84 to 0.99 and 0.51 to 0.71, respectively. To link two databases, 8-h O3 was matched temporally to the date of birth and length of gestation. For each birth, the maternal exposures over the pregnancy to mean 8-h O3 were assessed by months of gestation and trimesters. Single pollutant logistic regression models were used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) for LBW in relation to exposure to O3, which was expressed in quartiles. Results: Of 229,390 singleton births, 20,097 (8.8%) were LBW. In a single adjusted pollutant model for the East Seven Mile monitoring site, an increased risk of LBW was observed for mothers exposed to an increase of 40.8 ppb O3 (RR=1.19; 95%CI:1.05–1.35), of 40.9 ppb O3 (RR=1.14; 95%CI:1.01–1.28), and of 45.9 ppb O3 (RR=1.18; 95%CI:1.04–1.34) during the 1st, 3rd month of gestation, and the 1st trimester, respectively. For the Lansing site, an increased risk of LBW was observed for mothers exposed to an increase of 29.2 ppb O3 (RR=1.21; 95%CI:1.02–1.45) during the 3rd month of gestation. Discussion: These findings provide evidence of increases in full-term LBW risks with mother exposure to O3 during first and third month of gestation, and during the first trimester. Multiple pollutant logistic regression models are being examined to confirm the independent association of LBW and O3. The performance of multiple imputation models for imputation of missing 8-hr O3 values indicates its usefulness in this study design.

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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)0.006
metaresearch head score (Gemma)0.003
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: Methods · Consensus signal: none
Teacher disagreement score0.627
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
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 head0.066
GPT teacher head0.382
Teacher spread0.316 · 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
GenreMethods

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

Citations0
Published2005
Admission routes1
Has abstractyes

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