Evidence map›Paper›PMID 37967567›Full record

ArticlePaediatric and perinatal epidemiology2024

A prediction model for classifying maternal pregnancy smoking using California state birth certificate information.

Di He, Xiwen Huang, Onyebuchi A Arah, Douglas I Walker, Dean P Jones, Beate Ritz, Julia E Heck

Open access · hybridAbstract read
In one paragraph

Article in Paediatric and perinatal epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Childhood cancer risk in offspring of mothers occupationally exposed to hydrocarbon solvents.European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP) · 2026
    Article
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Di HeDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA.ORCID 0000-0002-3589-4484
Xiwen HuangDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA.
Onyebuchi A ArahDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA.
Douglas I WalkerGangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
Dean P JonesClinical Biomarkers Laboratory, Division of Pulmonary, Allergy, and Critical Care Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA.
Beate RitzDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA.
Julia E HeckDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA.ORCID 0000-0001-8713-8413
University of California, Los Angeles · USEmory University · USUniversity of North Texas · US

Funding

Scientific and Technical CoreP2CHD041022 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HIRAM BELTRAN-SANCHEZ · 2016 to 2026
$6.9M
Air Pollution and Childhood Cancers in CaliforniaR21ES018960 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HECK, JULIA · 2010 to 2011
$421k
Pesticide Exposure and Childhood Cancer StudyR21ES019986 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI RITZ, BEATE R. · 2011 to 2012
$409k
Metabolomic profiling of retinoblastoma (MPR)R03CA252788 · NCI · UNIVERSITY OF NORTH TEXAS · PI HECK, JULIA · 2020 to 2020
$149k
NCI NIH HHS R03 CA252788NICHD NIH HHS P2C HD041022NIEHS NIH HHS R21 ES018960NIEHS NIH HHS R21 ES019986NIH HHS
6 · The paper itself

Abstract

backgroundSystematically recorded smoking data are not always available in vital statistics records, and even when available it can underestimate true smoking rates.

objectiveTo develop a prediction model for maternal tobacco smoking in late pregnancy based on birth certificate information using a combination of self- or provider-reported smoking and biomarkers (smoking metabolites) in neonatal blood spots as the alloyed gold standard.

methodsWe designed a case-control study where childhood cancer cases were identified from the California Cancer Registry and controls were from the California birth rolls between 1983 and 2011 who were cancer-free by the age of six. In this analysis, we included 894 control participants and performed high-resolution metabolomics analyses in their neonatal dried blood spots, where we extracted cotinine [mass-to-charge ratio (m/z) = 177.1023] and hydroxycotinine (m/z = 193.0973). Potential predictors of smoking were selected from California birth certificates. Logistic regression with stepwise backward selection was used to build a prediction model. Model performance was evaluated in a training sample, a bootstrapped sample, and an external validation sample.

resultsOut of seven predictor variables entered into the logistic model, five were selected by the stepwise procedure: maternal race/ethnicity, maternal education, child's birth year, parity, and child's birth weight. We calculated an overall discrimination accuracy of 0.72 and an area under the receiver operating characteristic curve (AUC) of 0.81 (95% confidence interval [CI] 0.77, 0.84) in the training set. Similar accuracies were achieved in the internal (AUC 0.81, 95% CI 0.77, 0.84) and external (AUC 0.69, 95% CI 0.64, 0.74) validation sets.

conclusionsThis easy-to-apply model may benefit future birth registry-based studies when there is missing maternal smoking information; however, some smoking status misclassification remains a concern when only variables from the birth certificate are used to predict maternal smoking.

Indexed as

Birth CertificatesSmokingCaliforniaCase-Control StudiesChildFemaleHumansInfant, NewbornModels, StatisticalNeoplasmsPregnancyTobacco Smokingbirth certificatesmaternal pregnancy smokingneonatal blood spotsprediction modeltobacco biomarkers

Identifiers

PMID37967567
PMCPMC10922711
OpenAlexW4388686869

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.