Adverse Maternal and Infant Outcomes of Women Who Differ in Smoking Status: E-Cigarette and Tobacco Cigarette Users.
Eline K NanningaStella WeilandMarjolein Y BergerEsther I Feijen-de JongJan Jaap H M ErwichLilian L PetersPublished in: International journal of environmental research and public health (2023)
The electronic cigarette (e-cigarette) became commercially available around 2004, yet the characteristics of pregnant women who use these devices and their effects on maternal and infant health remain largely unknown. This study aimed to investigate maternal characteristics and pregnancy outcomes according to maternal smoking status. We conducted a cross-sectional study of Dutch women with reported pregnancies between February 2019 and May 2022, using an online questionnaire to collect data on smoking status and demographic, lifestyle, pregnancy, and infant characteristics. Smoking status is compared among non-smokers, tobacco cigarette users, e-cigarette users, and dual users (tobacco and e-cigarette). We report descriptive statistics and calculate differences in smoking status between women with the chi-square or Fisher (Freeman-Halton) test. Of the 1937 included women, 88.1% were non-smokers, 10.8% were tobacco cigarette users, 0.5% were e-cigarette users, and 0.6% were dual users. Compared with tobacco users, e-cigarette users more often reported higher education, having a partner, primiparity, and miscarriages. Notably, women who used e-cigarettes more often had small infants for gestational age. Despite including few women in the e-cigarette subgroup, these exploratory results indicate the need for more research to examine the impact of e-cigarettes on pregnancy outcomes.
Keyphrases
- smoking cessation
- pregnancy outcomes
- pregnant women
- replacement therapy
- gestational age
- birth weight
- healthcare
- polycystic ovary syndrome
- type diabetes
- preterm birth
- randomized controlled trial
- public health
- machine learning
- climate change
- cross sectional
- physical activity
- social media
- hiv infected
- risk factors
- clinical trial
- deep learning
- artificial intelligence
- weight gain