​Department of Statistics & Quantitative Methods

Abstract Background All over the world, especially in the developed countries, maternal age at birth is rising. This study aimed to assess the role of maternal age on the occurrence of preterm birth (PTB) in a large birth cohort of Lombardy Region, Northern Italy. Methods This population-based study used data from regional healthcare utilization databases of Lombardy to identify women who delivered between 2007 and 2017. PTBs were defined as births before 37 completed weeks of gestation and considered according to the gestational age (two categories: 40 age categories). PTB before 32 completed weeks occurred more frequently in the same age categories, except that among multiples no association with advanced maternal age emerged. Conclusion Our study suggested that, after adjustment for potential confounders, both advance and young maternal age were associated with an increased risk of PTB.

Additional file 2. Cubic spline models. LPA = Leisure time physical activity,TPA = Total physical activity.

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Antibiotic exposure may be associated with atopic dermatitis (AD). We assessed the risk of developing AD among children early exposed to antibiotics. From the Italian Pedianet database, children aged 0-14 years between 2004-2017 were enrolled from birth up to at least one year. Cox proportional-hazards models were fitted to estimate Hazard Ratios (HR) and 95% Confidence Intervals (CI) for the association between antibiotic exposure during the first year of life with incident AD. Exposure was also considered as a time-varying variable.  73,816 children were included in the final cohort, of which 34,202 had at least one antibiotic prescription. Incident AD was present in 8% of unexposed and exposed children. Early antibiotic exposure was not associated with any excess risk of AD compared to unexposed children (HR: 1.02, 95% CI: 0.97-1.07), and no dose-response effect was observed. In the time-varying analysis, antibiotic exposure was significantly associated with AD onset (1.12, 1.07-1.17). However, when taking into account the time-lag between exposure and outcome, risks progressively decreased, suggesting possible protopathic bias. These results are not suggestive of any significant association between exposure to antibiotics and subsequent AD onset and support the possible presence of protopathic bias.

Gaussian Directed Acyclic Graphs (DAGs) represent a powerful tool for learning the network of dependencies among variables, a task which is of primary interest in many fields and specifically in biology. Different DAGs may encode equivalent conditional independence structures, implying limited ability, with observational data, to identify causal relations. In many contexts however, measurements are collected under heterogeneous settings where variables are subject to exogenous interventions. Interventional data can improve the structure learning process whenever the <i>targets</i> of an intervention are known. However, these are often uncertain or completely unknown, as in the context of drug target discovery. We propose a Bayesian method for learning dependence structures and intervention targets from data subject to interventions on unknown variables of the system. Selected features of our approach include a DAG-Wishart prior on the DAG parameters, and the use of variable selection priors to express uncertainty on the targets. We provide theoretical results on the correct asymptotic identification of intervention targets and derive sufficient conditions for Bayes factor and posterior ratio consistency of the graph structure. Our method is applied in simulations and real-data world settings, to analyze perturbed protein data and assess antiepileptic drug therapies. Details of the MCMC algorithm and proofs of propositions are provided in the supplementary materials, together with more extensive results on simulations and applied studies. Supplementary materials for this article are available online.

Additional file 1:Supplementary Figure 1. A directed acyclic graph (DAG).

Additional file 3:Supplementary Table 1. Distribution of METh per physical activity category during weekday presented for women and men in the Swedish National March Cohort.

Additional file 1.