Intergenerational transmission of tobacco smoking: The role of the child's behavioral difficulties. Data from the Danish National Birth cohort (DNBC).

AIMS: This study explores the role of offspring behavioral difficulties in the intergeneration transmission of tobacco smoking. METHODS: This longitudinal cohort study is based on children born in Denmark in 1996-2003 participating in the Danish National Birth Cohort (DNBC), followed-up until 18years of age. We included mother-child pairs with complete data regarding the exposure (4 trajectories of maternal daily smoking quantity during pregnancy: low, intermediate/stable, intermediate/decreasing and high), outcome (offspring daily smoking status at 18 years) and mediator (offspring symptoms of hyperactivity-inattention at 11 years), that is 24,588 mother-child pairs. RESULTS: In our study population, during pregnancy respectively 86.2%, 6.80%, 4.08% and 2.97% mothers belonged to the low, intermediate/stable, intermediate/decreasing and high smoking trajectory groups. After controlling for covariates using propensity scores, the direct effect of maternal smoking in pregnancy on offspring smoking in adolescence was statistically significant, especially when the mother belonged to the intermediate/stable smoking trajectory group (ORIPW = 2.09, 95% CI: 1.70 - 2.61) or to the high smoking trajectory group (ORIPW = 2.08, 95% CI: 1.52 - 3.11) compared to the low smoking trajectory group. None of the indirect effects of maternal smoking in pregnancy were statistically significant, and neither were the proportions mediated. CONCLUSION: Maternal pregnancy smoking seems to have an influence on offspring smoking in early adulthood, which does not appear to be mediated by offspring behavioral difficulties. Women should be strongly encouraged to quit smoking in pregnancy to reduce both short and long-term health risks among their offspring.

Epigenome-Wide Associations of Placental DNA Methylation and Behavioral and Emotional Difficulties in Children at 3 Years of Age.

The placenta is a key organ for fetal and brain development. Its epigenome can be regarded as a biochemical record of the prenatal environment and a potential mechanism of its association with the future health of the fetus. We investigated associations between placental DNA methylation levels and child behavioral and emotional difficulties, assessed at 3 years of age using the Strengths and Difficulties Questionnaire (SDQ) in 441 mother-child dyads from the EDEN cohort. Hypothesis-driven and exploratory analyses (on differentially methylated probes (EWAS) and regions (DMR)) were adjusted for confounders, technical factors, and cell composition estimates, corrected for multiple comparisons, and stratified by child sex. Hypothesis-driven analyses showed an association of cg26703534 (AHRR) with emotional symptoms, and exploratory analyses identified two probes, cg09126090 (intergenic region) and cg10305789 (PPP1R16B), as negatively associated with peer relationship problems, as well as 33 DMRs, mostly positively associated with at least one of the SDQ subscales. Among girls, most associations were seen with emotional difficulties, whereas in boys, DMRs were as much associated with emotional than behavioral difficulties. This study provides the first evidence of associations between placental DNA methylation and child behavioral and emotional difficulties. Our results suggest sex-specific associations and might provide new insights into the mechanisms of neurodevelopment.

Maternal Tobacco Smoking During Pregnancy and Children's Emotional and Behavioral Trajectories: The EDEN Mother-Child Birth Cohort Study.

INTRODUCTION: The nature of the relationship between maternal tobacco smoking during pregnancy and the occurrence of children's behavioral problems is still a matter of controversy. We tested this association using data collected among a sample of pregnant women and their offspring followed up from birth to early adolescence (age 12 years), accounting for multiple parent, child, and family characteristics. AIMS AND METHODS: Data come from 1424 mother-child pairs participating in the Étude des Déterminants pré et post-natals précoces du développement psychomoteur et de la santé de l'ENfant mother-child cohort in France. Using repeated measures (3, 5.5, 8, and 11.5 years) of the mother-reported Strengths and Difficulties Questionnaire, we estimated trajectories of children's emotional and behavioral difficulties. Two aspects of maternal smoking were studied: The timing (nonsmoker, smoking during the periconceptional period, or throughout pregnancy) and the level of use (cigarettes/day) during the first trimester of pregnancy. Robust Poisson regression models controlled for confounding factors including maternal mental health and socioeconomic characteristics using propensity scores with the overlap weighting technique. RESULTS: Contrary to bivariate analyses, in propensity score-controlled regression models, maternal smoking throughout pregnancy was no longer significantly associated with offspring emotional or behavioral difficulties. Maternal heavy smoking (≥10 cigarettes/day) remained significantly associated with intermediate levels of conduct problems (RR 1.25, 95% CI 1.19 to 1.31). CONCLUSIONS: The association between maternal smoking in pregnancy and offspring's emotional and behavioral difficulties appears to be largely explained by women's other characteristics. However, maternal heavy smoking appears to be related to offspring behavioral difficulties beyond the role of confounding characteristics. IMPLICATIONS: The relationship between maternal smoking during pregnancy (in two modalities: Timing and level of smoking) and behavioral difficulties in children is still a matter of debate. While the relationship between any maternal tobacco use and offspring behavioral difficulties appears to be largely explained by confounding factors, heavy maternal smoking in the first trimester of pregnancy seems to be associated with offspring behavioral difficulties beyond the socioeconomic and mental health characteristics transmitted across generations.

Use of tobacco cessation aids and likelihood of smoking cessation: A French population-based study.

Although smoking prevalence has been decreasing worldwide, sustained tobacco cessation remains a challenging goal for many smokers. Several types of tobacco cessation aids are available such as nicotine replacement therapy (NRT) and electronic cigarette, the effectiveness of the latter is still a matter of debate. This study aims to test differences in successful smoking cessation according to the type of aid used, considering selection and confounding factors. We used data from the 2017 French Health Barometer, a cross-sectional survey conducted by France's Public Health Agency. We studied the relationship between e-cigarette and NRT use and three distinct outcomes collected retrospectively: smoking status 6, 12 and 24 months after the cessation attempt (yes vs no). All results were weighted to be nationally-representative and controlled for propensity scores included via overlap weighting (OW). The use of an e-cigarette was significantly associated with tobacco cessation at 6 months (OWeighted OR = 1.38, 95 % CI: 1.03-1.99) as well as at 12 months (OWeighted OR = 1.61, 95 % CI: 1.13-2.27) and 24 months (OWeighted OR = 1.61, 95 % CI: 1.01-2.57). The use of NRT was negatively associated with tobacco cessation at 12 months (OWeighted OR = 0.62, 95 % CI: 0.43-0.89) and 24 months (OWeighted OR = 0.57, 95 % CI: 0.35-0.92). While the use of an e-cigarette alone or combined with NRT is associated with an increase in the likelihood of smoking cessation, the effects of the use of NRT alone on long-term smoking abstinence are probably limited.

Pandemic lockdown, isolation, and exit policies based on machine learning predictions.

The widespread lockdowns imposed in many countries at the beginning of the COVID-19 pandemic elevated the importance of research on pandemic management when medical solutions such as vaccines are unavailable. We present a framework that combines a standard epidemiological SEIR (susceptible-exposed-infected-removed) model with an equally standard machine learning classification model for clinical severity risk, defined as an individual's risk of needing intensive care unit (ICU) treatment if infected. Using COVID-19-related data and estimates for France as of spring 2020, we then simulate isolation and exit policies. Our simulations show that policies considering clinical risk predictions could relax isolation restrictions for millions of the lowest risk population months earlier while consistently abiding by ICU capacity restrictions. Exit policies without risk predictions, meanwhile, would considerably exceed ICU capacity or require the isolation of a substantial portion of population for over a year in order to not overwhelm the medical system. Sensitivity analyses further decompose the impact of various elements of our models on the observed effects. Our work indicates that predictive modeling based on machine learning and artificial intelligence could bring significant value to managing pandemics. Such a strategy, however, requires governments to develop policies and invest in infrastructure to operationalize personalized isolation and exit policies based on risk predictions at scale. This includes health data policies to train predictive models and apply them to all residents, as well as policies for targeted resource allocation to maintain strict isolation for high-risk individuals.