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Comparison of secondhand smoke exposure measures during pregnancy in the development of a clinical prediction model for small-for-gestational-age among non-smoking Chinese pregnant women
  1. Chuanbo Xie1,
  2. Xiaozhong Wen2,
  3. Zhongzheng Niu1,
  4. Peng Ding1,
  5. Tao Liu3,
  6. Yanhui He3,
  7. Jianmiao Lin4,
  8. Shixin Yuan4,
  9. Xiaoling Guo5,
  10. Deqin Jia5,
  11. Weiqing Chen1
  1. 1Department of Biostatistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, China
  2. 2Division of Behavioural Medicine, Department of Pediatrics, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
  3. 3Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
  4. 4Shenzhen Women and Children's Hospital, Shenzhen, China
  5. 5Foshan Women and Children's Hospital, Foshan, China
  1. Correspondence to Professor Weiqing Chen, Department of Biostatistics and Epidemiology, School of Public Health, Sun Yat-Sen University, 74, Zhongshan Road 2, Guangzhou 510080, China; chenwq{at}mail.sysu.edu.cn

Abstract

Objective To compare predictive values of small-for-gestational-age (SGA) by different measures for secondhand smoke (SHS) exposure during pregnancy and to develop and validate a prediction model for SGA using SHS exposure along with sociodemographic and pregnancy factors.

Methods We compared the predictability of different measures of SHS exposure during pregnancy for SGA among 545 Chinese pregnant women, and then used the optimal SHS measure along with other clinically available factors to develop and validate a prediction model for SGA. We fit logistic regression models to predict SGA by single measures of SHS exposure (self-report, serum cotinine and CYP2A6*4) and different combinations (self-report+cotinine, cotinine+CYP2A6*4, self-report+CYP2A6*4 and self-report+cotinine+CYP2A6*4).

Results We found that self-reported SHS exposure alone predicted SGA (area under the receiver operating characteristic curve or area under the receiver operating curve (AUROC), 0.578) better than the other two single measures (cotinine, 0.547; CYP2A6*4, 0.529) or as accurately as combined SHS measures (0.545–0.584). The final prediction model that contained self-reported SHS exposure, prepregnancy body mass index, gestational weight gain velocity during the second and third trimesters, gestational diabetes, gestational hypertension and the third-trimester biparietal diameter Z-score could predict SGA fairly accurately (AUROC, 0.698).

Conclusions Self-reported SHS exposure at peribirth performs better in predicting SGA than a single measure of serum cotinine at the same time, although repeated biochemical cotinine assessments throughout pregnancy may be optimal. Our simple prediction model is fairly accurate and can be potentially used in routine prenatal care.

  • Secondhand smoke
  • Cotinine
  • Smoking Caused Disease

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