Pregnant women and children constitute vulnerable populations during a pandemic. This is because of the specificity of their immune systems and the possibility of mother to child transmission, their non-deferrable needs for health services and the effects of environmental factors on adverse pregnancy outcomes, notably the strong association of social disadvantage with risks of perinatal morbidity and mortality. Furthermore, adverse perinatal events can have lifelong consequences. Mortality and morbidity resulting from infection are directly related to the pandemic, while indirect effects can stem from reductions in the accessibility or quality of health services or from mitigation policies and economic hardship. Because pregnant women and newborns are generally in good health and because many of these health consequences may depend on health care organization and the socioeconomic context, studies of these effects require large population-based samples. The COV-PERINAT study’s aim is to investigate the indirect effects of the pandemic on the health of pregnant woman and newborns using routine hospital and administrative databases in the French National Health Data System (SNDS). It complements an on-going population-based study on the direct effects of the infection on the health of pregnant women and their newborns in France (COROPREG). Specifically, this project will (1) evaluate changes in population measures of health and healthcare use among pregnant women and newborns associated with the pandemic and strategies to contain the pandemic, (2) investigate whether these consequences differ by healthcare organization, socioeconomic context and other environmental factors, (3) assess the strengths and the weaknesses of using these routine sources to evaluate the impact of the pandemic and generate knowledge to reinforce monitoring capacity for future outbreaks, and (4) contribute to other on-going and future studies that rely on these data sources, including the COROPREG study and a European study (PHIRI) to facilitate exchange of population data between European countries. Requested funding for the 12 month study period will cover the costs of an experienced statistician to work with perinatal health researchers to (1) establish a validated cohort of pregnant women and their newborns (births from January 2016 to December 2020) linked to contextual data about the pandemic, pandemic policies and area-based organizational, social and environmental factors, (2) describe and analyze changes in population measures of maternal and newborn health, taking into consideration seasonality using time series models, and (3) assess the influence of context on these changes. The team has extensive experience analyzing population data on perinatal health. A scientific committee will involve experts from professional societies and public health agencies in the interpretation and dissemination of the results. Complementarity with on-going projects and possibilities for new follow-up projects ensure that the results produced within the 12 month period will reinforce the foundation for further research on COVID-19 among pregnant women and newborns.

Over recent decades, devices with screens (e.g., television, video games, smartphones, tablets) have become increasingly widespread and accessible worldwide. The technological and digital revolutions have been so rapid and widespread that the potential health and social consequences of screen use have not been evaluated comprehensively. Children’s development and health is of particular concern because the course from birth to adulthood is marked by successive stages of great importance for physical, behavioural and cognitive development. Conflicting scientific statements and national/international public health guidelines on screen use coexist which highlights a lack of high-quality evidence on the impact of screens on children’s health and development. Careful literature review underlines several knowledge and methodological gaps, including lack of studies that are recent (>2010), longitudinal, accounting for newer devices and media content, and conducted among European and Asian children. Previous studies also failed to adequately account for confounders (e.g. sociocultural background, parent-child interactions) and other recreational activities (e.g., physical activity, non-screen-based activities and play) that compete with screen use. It is indeed unclear whether screen use affects child development directly or via displacement of the time spent in other daily activities. The iSCAN project aims to examine the patterns, trajectories, risk factors and impact on child health and development of screen use from birth to adolescence. It will utilize data already collected in three birth cohorts from France and Singapore: the EDEN (n=1,907 children born in 2003-2006), ELFE (18,329 children born in 2011) and GUSTO (1,257 children born in 2009-2010) studies. All cohorts have collected detailed data on screen use (type of device, time, media content, context of use) and other activities not involving screens (e.g., outdoor time, reading stories, playing with toys/puppets) at multiple time points from age 2 to 10 years. Axis 1 will describe and compare patterns/trajectories of screen use and examine their demographic, socioeconomic, behavioural, and genetic determinants. Axis 2 will examine the associations of screen use with repeated measures of child growth, adiposity, blood pressure and cardiometabolic biomarkers. Axis 3 will focus on the associations with sleep (quantity and quality) and neurodevelopmental outcomes, including verbal and nonverbal cognition, motor skills, academic learning, and internalizing and externalizing behaviour. Finally, Axis 4 aims to support collection of subjective and objective data on adolescents’ screen use as part of an upcoming time point in the EDEN study. Risk of bias (i.e., confounding and reverse causation) will be tackled by implementing state-of-the-art epidemiological and statistical methods (i.e., appropriate multivariable adjustment, propensity score matching, inverse probability weighting, Mendelian randomization, longitudinal analyses with repeated-measured linear and logistic regression). This iSCAN project will importantly update and extend current knowledge related to children’s screen use and their development and health. By bringing to light recent, longitudinal and comprehensive data, this project will strengthen the evidence base by assessing the extent to which young children’s screen use affects health and developmental outcomes. It will help fine-tune public health policies and guidelines and decrease the sense of confusion for the targeted populations. The iSCAN project will also inform future interventions for more efficiency against the negative impact of screens and help identify screen usage that may have virtuous effects for child health and development.

Over the past decade, the microbiota has received an exponentially growing attention from the scientific community and the public as its role in human physiology was gradually being uncovered. In particular, gut microbial signatures were observed in various disease states, including obesity, diabetes, gastrointestinal disorders, colorectal cancer or neurodegenerative diseases, suggesting unbalanced host-gut microbiota interactions. Although the gut microbiota is considered as key for human health, the mechanisms involved and the definition of what constitutes a healthy gut microbiota remain important challenges to tackle. The gut microbiota feed on the fermentation of compounds brought by the human diet that are not or poorly absorbed in the small intestine and that end up more or less intact in the colon. Many such compounds belong to the family of carbohydrates and can be categorized as fibres (e.g., non-starch polysaccharides, resistant oligosaccharides, resistant starch) or FODMAPs (Fermentable Oligosaccharides (FOS and GOS), Disaccharides (lactose), Monosaccharides (fructose) and Polyols (ex. sorbitol, xylitol)). Fermentable carbohydrates therefore encompass a vast diversity of compounds (e.g., type and number of units, type of linkage, length) differing in their biological, physical and chemical properties as well as their physiological effects but also their interaction with the gut bacteria (e.g., selection of specialized bacterial species). These compounds are provided by various sources, each supplying a specific mix of fermentable carbohydrates: they can be naturally supplied by cereal products, fruit, vegetables, legumes, nuts or dairy products (lactose), but can also come from industrially processed foods where isolated fermentable carbohydrates (extracted from foods or synthesized) can be added to foods (as ingredients or additives) for technological purposes and/or to increase the fibre content. Different types of fermentable carbohydrates are likely to have differential effects on the gut microbiota and health outcomes. Indeed, while dietary fibres are generally associated to better health outcomes (e.g., obesity, colorectal cancer), FODMAPs have been studied almost exclusively in the context of irritable bowel syndrome where they were defined based on adverse gastrointestinal effects. Besides, the gut microbiota profile is likely to influence how fermentable carbohydrates will be associated with health outcomes. So far, no large-scale study has considered the whole range of fermentable carbohydrates and how they are combined in “real” individual diets in relation to gut microbiota and health outcomes, including i) whether fermentable carbohydrates come from whole foods or are provided as ingredients/additives in industrially-processed foods, ii) whether different profiles of fermentable carbohydrate intakes associates with different profiles of gut microbiota and health outcomes, iii) whether gut microbiota profiles mediate or modulate the association between fermentable carbohydrates and health. Hence, using the NutriNet-Santé cohort (N=170,000), the FeCaMic project will: 1) provide an in-depth characterisation of the profiles of intakes of fermentable carbohydrates, considering their various types and sources, and including those added as additives or ingredients, or consumed as dietary supplements; 2) investigate the associations between the detailed profiles of fermentable carbohydrate intakes and gut microbiota profiles in a large population-based sample (N=6,000 with stool samples collected and gut microbiota profiles sequenced), also taking into account the overall diet and other individual characteristics; 3) explore the mediation and modulation by the gut microbiota of the associations between the profiles of intakes of fermentable carbohydrates and two health outcomes relevant to both fermentable carbohydrates and the gut microbiota: weight status and gastrointestinal disorders.

PREPARE Preterm aims to develop early personalised risk prediction and patient stratification after very preterm birth (VPT, 1-2% of births, >50.000 annually in Europe) using expert-guided AI methods and the world’s most comprehensive data platform of children and adults born VPT. Survivors of VPT birth face high risks of multiple health and developmental problems compared with children born at term, leading to significant health, social and educational costs. Early personalised prediction is needed for patient stratification to optimise known effective interventions to prevent and mitigate adverse outcomes. Work on prediction models for individuals born VPT is very limited despite growing knowledge about risk and resilience factors and innovation in analytic and computational methods. Our mission is to mobilise expertise and innovation to shift research from describing the consequences of VPT birth to acting on and improving them. The project develops 4 components needed for early personalised prediction, effective patient stratification and societal impact: (1) a theoretical framework integrating lived experience that identifies life-course resilience factors to predict health, development, participation in society and quality of life; (2) integrated and dynamic data from population cohorts, e-cohorts and Big Data registers; (3) predictive models based on explainable and responsible AI (domain expert involvement, participatory research; protocols for sharing model performance, federated learning) and (4) tools that promote use of data for optimal healthcare decisions and health literacy. The project leverages a H2020 project that created an unrivalled platform of 23 European VPT cohorts and the Nordic registers and fosters Open Science (RECAP Preterm, project 01/03/2017-30/09/2021. To achieve its aims, this project will expand this platform to include data from population-based and clinical cohorts, including routinely generated real-life data, to integrate new computational capabilities and to create innovative tools for research and clinical use. These tools will facilitate follow-up using the platform which can allow inputs from geographic areas which have been underrepresented in research on VPT birth and expand and update prediction models to reflect advances in care for VPT babies. Finally, we will explore the opportunity for joint activities by networking with other European research platforms using birth data as well as infrastructure initiatives to promote population health research and data science. The development and validation of personalised prediction models for patient stratification based on AI and Big Data technologies by a leading interdisciplinary team with a proven record of working together will lead to clear improvement of outcomes for individuals, care systems and the wider society.

The systematic identification and synthesis of all available evidence on a specific clinical question is an integral part of the medical decision-making process and the planning of future research. For most medical conditions, a plethora of healthcare interventions are available and, thus, network meta-analysis (NMA) – the only tool enabling simultaneous comparisons between many interventions – is currently among the highest levels of evidence for the development of clinical guidelines. However, current NMA methodology has important limitations for properly synthesizing different study designs (randomised, cohort, case-control, etc.), which could increase the external validity of the findings. These limitations impose restrictions in most NMAs by usually allowing the synthesis of a selective sample of randomised controlled trials only, rendering their results rarely applicable in real settings of healthcare practice. The aim of the OptiCER project is a) to develop and evaluate new synthesis methods for integrating all different sources of evidence available for addressing a common research question and b) to develop new presentation and communication tools for presenting the findings of these ‘all-evidence’ NMAs targeting the needs of different stakeholders (clinicians, regulators, guideline developers). All new statistical models and tools will be accompanied by open-access and user-friendly software. The new methods will be used for the synthesis of the COVID-19 studies but will also be freely available for application in other medical fields. In this way, clinical recommendations will be based on a global view and synthesis of any piece of evidence rather than on a partial approach.