Electronic nicotine delivery systems and pregnancy: Recent research on perceptions, cessation, and toxicant delivery.
Alison B BrelandAndrea McCubbinKristin AshfordPublished in: Birth defects research (2019)
Electronic nicotine delivery systems (ENDS), which includes e-cigarettes (ECIGs), are a rapidly-expanding class of products that heat a liquid (which may or may not contain nicotine) to produce an aerosol. The variation of ECIG components is extensive as are their effects on users. Epidemiological data show that while both adults and youth use ECIGs, use among youth has increased dramatically in recent years. Other epidemiological data show that women of reproductive age and some pregnant women are also using ECIGs. The goal of this article is to provide readers with background information about ECIGs, with a focus on recent findings about ECIG use in pregnancy and potential implications. Among pregnant women, correlates of ECIG use include current cigarette smoking, among other factors. Regarding pregnant women's perceptions of ECIG use in pregnancy, two themes emerge from the literature: many pregnant women perceive ECIGs to be safer than conventional cigarettes, and that ECIGs can aid with smoking cessation. In contrast to these perceptions, there is little concrete evidence that ECIGs help smokers quit. In addition, there are concerns about ECIG nicotine and other toxicant delivery. Nicotine is a toxicant of particular concern for pregnant women, as nicotine is known to harm a developing fetus. There are many limitations to existing research, and the literature is scant in this area. Further, new "pod mod"-style ECIGs such as JUUL present new challenges. Overall, with limited evidence of their effectiveness, and concerns about developmental toxicology, the authors do not recommend that pregnant women use ECIGs.
Keyphrases
- smoking cessation
- pregnant women
- pregnancy outcomes
- replacement therapy
- healthcare
- systematic review
- primary care
- preterm birth
- physical activity
- young adults
- mental health
- electronic health record
- big data
- magnetic resonance
- computed tomography
- magnetic resonance imaging
- machine learning
- social media
- metabolic syndrome
- artificial intelligence
- deep learning
- adipose tissue
- contrast enhanced