A prediction model for classifying maternal pregnancy smoking using California state birth certificate information.
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| Název: | A prediction model for classifying maternal pregnancy smoking using California state birth certificate information. |
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| Autoři: | He D; Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA., Huang X; Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA., Arah OA; Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA., Walker DI; Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA., Jones DP; Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, and Critical Care Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA.; Department of Medicine, Emory University, Atlanta, Georgia, USA., Ritz B; Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA., Heck JE; Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, California, USA.; College of Health and Public Service, University of North Texas, Denton, Texas, USA. |
| Zdroj: | Paediatric and perinatal epidemiology [Paediatr Perinat Epidemiol] 2024 Feb; Vol. 38 (2), pp. 102-110. Date of Electronic Publication: 2023 Nov 15. |
| Způsob vydávání: | Journal Article |
| Jazyk: | English |
| Informace o časopise: | Publisher: Blackwell Scientific Publications Country of Publication: England NLM ID: 8709766 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-3016 (Electronic) Linking ISSN: 02695022 NLM ISO Abbreviation: Paediatr Perinat Epidemiol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford ; Boston : Blackwell Scientific Publications, [c1987- |
| Výrazy ze slovníku MeSH: | Birth Certificates* , Smoking*/epidemiology, Child ; Female ; Humans ; Infant, Newborn ; Pregnancy ; California/epidemiology ; Case-Control Studies ; Neoplasms ; Tobacco Smoking ; Models, Statistical |
| Abstrakt: | Background: Systematically recorded smoking data are not always available in vital statistics records, and even when available it can underestimate true smoking rates. Objective: To 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. Methods: We 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. Results: Out 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. Conclusions: This 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. (© 2023 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd.) |
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| Grant Information: | R21 ES018960 United States ES NIEHS NIH HHS; R03 CA252788 United States CA NCI NIH HHS; R21 ES019986 United States ES NIEHS NIH HHS; United States NH NIH HHS; P2C HD041022 United States HD NICHD NIH HHS |
| Contributed Indexing: | Keywords: birth certificates; maternal pregnancy smoking; neonatal blood spots; prediction model; tobacco biomarkers |
| Entry Date(s): | Date Created: 20231115 Date Completed: 20240222 Latest Revision: 20250206 |
| Update Code: | 20250206 |
| PubMed Central ID: | PMC10922711 |
| DOI: | 10.1111/ppe.13021 |
| PMID: | 37967567 |
| Databáze: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Background: Systematically recorded smoking data are not always available in vital statistics records, and even when available it can underestimate true smoking rates.<br />Objective: To 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.<br />Methods: We 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.<br />Results: Out 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.<br />Conclusions: This 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.<br /> (© 2023 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd.) |
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| ISSN: | 1365-3016 |
| DOI: | 10.1111/ppe.13021 |