Quitting Smoking before and after Pregnancy: Study Methods and Baseline Data from a Prospective Cohort Study.
Erica CruvinelKimber P RichterKathryn I PollakEdward F EllerbeckNicole L NollenByron GajewskiZoe Sullivan-BlumChuanwu ZhangElena SherginaTaneisha S ScheuermannPublished in: International journal of environmental research and public health (2022)
Smoking during pregnancy and postpartum remains an important public health problem. No known prior study has prospectively examined mutual changes in risk factors and women's smoking trajectory across pregnancy and postpartum. The objective of this study was to report methods used to implement a prospective cohort (Msgs4Moms), present participant baseline characteristics, and compare our sample characteristics to pregnant women from national birth record data. The cohort study was designed to investigate smoking patterns, variables related to tobacco use and abstinence, and tobacco treatment quality across pregnancy through 1-year postpartum. Current smokers or recent quitters were recruited from obstetrics clinics. Analyses included Chi-square and independent sample t-tests using Cohen's d . A total of 62 participants (41 smokers and 21 quitters) were enrolled. Participants were Black (45.2%), White (35.5%), and multiracial (19.3%); 46.8% had post-secondary education; and most were Medicaid-insured (64.5%). Compared with quitters, fewer smokers were employed (65.9 vs 90.5%, Cohen's d = 0.88) and more reported financial strain (61.1% vs 28.6%; Cohen's d = 0.75). Women who continue to smoke during pregnancy cope with multiple social determinants of health. Longitudinal data from this cohort provide intensive data to identify treatment gaps, critical time points, and potential psychosocial variables warranting intervention.
Keyphrases
- smoking cessation
- public health
- pregnancy outcomes
- pregnant women
- risk factors
- electronic health record
- healthcare
- replacement therapy
- big data
- preterm birth
- randomized controlled trial
- primary care
- quality improvement
- mental health
- type diabetes
- machine learning
- skeletal muscle
- adipose tissue
- combination therapy
- health information
- human health
- drug induced
- affordable care act