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Archives of Women's Mental Health

, Volume 16, Issue 4, pp 293–302 | Cite as

Exposure to environmental tobacco smoke and risk of antenatal depression: application of latent variable modeling

  • Alfred K. Mbah
  • Hamisu M. Salihu
  • Getachew Dagne
  • Ronee E. Wilson
  • Karen Bruder
Original Article

Abstract

This study sought to determine the impact of passive smoking on the risk for depressive symptoms during pregnancy. In this prospective study, 236 pregnant women were recruited at less than 20 weeks of gestation from a university-affiliated obstetric clinic from November 2009 through July 2011. Tobacco use/exposure was measured using questionnaire and confirmed by salivary cotinine analysis. The Edinburgh Perinatal Depression Scale (EPDS) was employed to capture perinatal depressive symptomatology. Traditionally, a cutoff of 13 is utilized to indicate depressive symptoms in the perinatal population. However, this approach is vulnerable to measurement errors that are inherent in assessing depression using cutoff points. Therefore, in this analysis, we apply a flexible approach (latent variable modeling) that accounts for measurement errors thereby reducing bias in the estimates of association. Significant differences were observed in the mean EPDS scores across non-smokers (mean ± SD = 4.8 ± 4.8), passive smokers (5.3 ± 5.5) and active smokers (7.4 ± 6.1) [p value = 0.02]. For each itemized response of the EPDS, passive smokers demonstrated an increased risk for depressive symptoms with the greatest risk exhibited by items 8 and 9 of the questionnaire (feeling sad or miserable and feeling unhappy [and]crying, respectively). In addition, for each item of the EPDS, a dose–response pattern was revealed with non-smokers having the least risk of depressive symptoms during pregnancy and active smokers having the greatest risk. Women who are exposed to secondhand smoke are at elevated risk for depressive symptoms during pregnancy.

Keywords

Passive smoking Environmental tobacco smoke Perinatal depressive symptoms Latent variable modeling 

Notes

Acknowledgments

This work was supported by the James and Esther King Biomedical Research Program, Florida Department of Health [Award number09KN-07].

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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Alfred K. Mbah
    • 1
  • Hamisu M. Salihu
    • 1
    • 2
    • 3
  • Getachew Dagne
    • 1
  • Ronee E. Wilson
    • 1
  • Karen Bruder
    • 2
  1. 1.Department of Epidemiology and Biostatistics, College of Public HealthUniversity of South FloridaTampaUSA
  2. 2.College of MedicineUniversity of South FloridaTampaUSA
  3. 3.TampaUSA

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