+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The Effects of Different Smoking Patterns in Pregnancy on Perinatal Outcomes in the Southampton Women’s Survey

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          Maternal smoking during pregnancy has established associations with poor perinatal outcomes. Among continuing pregnant smokers, harm-reduction strategies have been suggested, including temporary cessation of smoking during pregnancy, also known as partial quitting. Support for this strategy, however, remains limited. Six hundred and ninety-seven women in the Southampton Women’s Survey who smoked at their last menstrual period were categorised into sustained quitters, partial quitters (quit in either the first or third trimester but not both) or sustained smokers (continued to smoke throughout pregnancy). In regression models, compared with infants born to sustained smokers, infants born to sustained quitters and partial quitters were heavier at birth by β = 0.64 standard deviations (SD) (WHO z-score) (95% CI: 0.47–0.80) and 0.48 SD (WHO z-score) (95% CI: 0.24–0.72) respectively, adjusted for confounders, with similar patterns seen for other anthropometric measures (head circumference and crown–heel length). Sustained quitters had longer gestations by β = 3.5 days (95% CI: 1.8–5.2) compared with sustained smokers, but no difference was seen for partial quitters. While sustained quitting remains the most desired outcome for pregnant smokers, partial quitting should be explored as a strategy to reduce some of the harmful effects of smoking on offspring in those who cannot achieve sustained quitting.

          Related collections

          Most cited references 59

          • Record: found
          • Abstract: found
          • Article: not found

          Birthweight and mortality in adulthood: a systematic review and meta-analysis.

          Small birth size may be associated with increased risk of cardiovascular diseases (CVD), whereas large birth size may predict increased risk of obesity and some cancers. The net effect of birth size on long-term mortality has only been assessed in individual studies, with conflicting results. The Meta-analyses of Observational Studies in Epidemiology (MOOSE) guidelines for conducting and reporting meta-analysis of observational studies were followed. We retrieved 22 studies that assessed the association between birthweight and adult mortality from all causes, CVD or cancer. The studies were systematically reviewed and those reporting hazard ratios (HRs) and 95% confidence intervals (95% CIs) per kilogram (kg) increase in birthweight were included in generic inverse variance meta-analyses. For all-cause mortality, 36,834 deaths were included and the results showed a 6% lower risk (adjusted HR = 0.94, 95% CI: 0.92-0.97) per kg higher birthweight for men and women combined. For cardiovascular mortality, the corresponding inverse association was stronger (HR = 0.88, 95% CI: 0.85-0.91). For cancer mortality, HR per kg higher birthweight was 1.13 (95% CI: 1.07-1.19) for men and 1.04 (95% CI: 0.98-1.10) for women (P(interaction) = 0.03). Residual confounding could not be eliminated, but is unlikely to account for the main findings. These results show an inverse but moderate association of birthweight with adult mortality from all-causes and a stronger inverse association with cardiovascular mortality. For men, higher birthweight was strongly associated with increased risk of cancer deaths. The findings suggest that birthweight can be a useful indicator of processes that influence long-term health.
            • Record: found
            • Abstract: found
            • Article: not found

            An overview of mortality and sequelae of preterm birth from infancy to adulthood.

            Survival rates have greatly improved in recent years for infants of borderline viability; however, these infants remain at risk of developing a wide array of complications, not only in the neonatal unit, but also in the long term. Morbidity is inversely related to gestational age; however, there is no gestational age, including term, that is wholly exempt. Neurodevelopmental disabilities and recurrent health problems take a toll in early childhood. Subsequently hidden disabilities such as school difficulties and behavioural problems become apparent and persist into adolescence. Reassuringly, however, most children born very preterm adjust remarkably well during their transition into adulthood. Because mortality rates have fallen, the focus for perinatal interventions is to develop strategies to reduce long-term morbidity, especially the prevention of brain injury and abnormal brain development. In addition, follow-up to middle age and beyond is warranted to identify the risks, especially for cardiovascular and metabolic disorders that are likely to be experienced by preterm survivors.
              • Record: found
              • Abstract: found
              • Article: not found

              Robust causal inference using directed acyclic graphs: the R package ‘dagitty’

              Directed acyclic graphs (DAGs), which offer systematic representations of causal relationships, have become an established framework for the analysis of causal inference in epidemiology, often being used to determine covariate adjustment sets for minimizing confounding bias. DAGitty is a popular web application for drawing and analysing DAGs. Here we introduce the R package 'dagitty', which provides access to all of the capabilities of the DAGitty web application within the R platform for statistical computing, and also offers several new functions. We describe how the R package 'dagitty' can be used to: evaluate whether a DAG is consistent with the dataset it is intended to represent; enumerate 'statistically equivalent' but causally different DAGs; and identify exposure-outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality enables epidemiologists to detect causal misspecifications in DAGs and make robust inferences that remain valid for a range of different DAGs. The R package 'dagitty' is available through the comprehensive R archive network (CRAN) at [https://cran.r-project.org/web/packages/dagitty/]. The source code is available on github at [https://github.com/jtextor/dagitty]. The web application 'DAGitty' is free software, licensed under the GNU general public licence (GPL) version 2 and is available at [http://dagitty.net/].

                Author and article information

                Int J Environ Res Public Health
                Int J Environ Res Public Health
                International Journal of Environmental Research and Public Health
                30 October 2020
                November 2020
                : 17
                : 21
                [1 ]Mater Misericordiae University Hospital, Eccles Street, D07 R2WY Dublin, Ireland; martinmodonnell@ 123456gmail.com
                [2 ]School of Medicine and Medical Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
                [3 ]MRC Lifecourse Epidemiology Unit, Southampton General Hospital, University of Southampton, Southampton SO16 6YD, UK; jb@ 123456mrc.soton.ac.uk (J.B.); cc@ 123456mrc.soton.ac.uk (C.C.); src@ 123456mrc.soton.ac.uk (S.R.C.); kmg@ 123456mrc.soton.ac.uk (K.M.G.); hmi@ 123456mrc.soton.ac.uk (H.M.I.)
                [4 ]NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
                [5 ]NIHR Applied Research Collaboration Wessex, Southampton Science Park, Southampton SO16 7NP, UK
                [6 ]Department of Obstetrics and Gynaecology, The Rotunda Hospital, D01 P5W9 Dublin, Ireland; mgeary@ 123456rotunda.ie
                [7 ]Discipline of Public Health and Primary Care, Trinity College Dublin, D24 DH74 Dublin, Ireland
                Author notes
                [* ]Correspondence: hayesc9@ 123456tcd.ie ; Tel.: +353-1-896-3716
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).



                Comment on this article