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

Alfred K Mbah1, Hamisu M Salihu, Getachew Dagne

  • 1Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL, USA.

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.

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Gene-Environment Interactions01:20

Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...