Each year, 2.2 million infants are born very preterm (VPT), that is, at a gestational age (GA) younger than 32 weeks.¹ Accompanying improved survival, more children are at risk for long-term impairments associated with prematurity, such as cognitive difficulties.^2-5 The mean IQ, across childhood, for VPT infants has been reported to be almost 1 SD lower than their full-term counterparts, equivalent to a mean difference of approximately 13 IQ points.⁶ This finding has important implications for academic achievement, socioeconomic status, and physical and mental health.^7-12 Since 1990, the preterm infant mortality rate in high-income countries has decreased by 4% per year, yet no significant improvements in cognitive outcomes have been achieved.^2,6,13

E-cigarette-only use and dual-use are emerging behaviours among adolescent nicotine product users which have not yet been sufficiently explored. This study examines the prevalence of, and the factors associated with, nicotine product use in adolescence. The study is a cross-sectional analysis of the 2018 Planet Youth survey completed by 15–16 year olds in the West of Ireland in 2018. The outcome of interest was current nicotine product use, defined as use at least once in the past 30 days. A main effects multinomial logistic regression model was used to examine the association between potential risk and protective factors and nicotine product use. Among 4422 adolescents 22.1% were current nicotine product users, consisting of 5.1% e-cigarette only users, 7.7% conventional cigarette only users, and 9.3% dual-users. For risk factors, the odds of association were weaker for e-cigarette only use compared to conventional cigarette and dual use. Participating in team sport four times/week or more significantly reduced the odds of conventional cigarette and dual use but had no association with e-cigarette only use (Cig: adjusted odds ratio (AOR) 0.63, 95% confidence interval (CI) 0.44–0.90; Dual-use: AOR 0.63, 95% CI 0.43–0.93). Similarly, having higher value for conventional social norms reduced the odds of conventional cigarette and dual use but not e-cigarette only use. This is the first study to show, among a generalisable sample, that dual-use is the most prevalent behaviour among adolescent nicotine product users in Ireland. Risk factor profiles differ across categories of use and prevention initiatives must be cognisant of this.

The application of machine learning (ML) to address population health challenges has received much less attention than its application in the clinical setting. One such challenge is addressing disparities in early childhood cognitive development—a complex public health issue rooted in the social determinants of health, exacerbated by inequity, characterised by intergenerational transmission, and which will continue unabated without novel approaches to address it. Early life, the period of optimal neuroplasticity, presents a window of opportunity for early intervention to improve cognitive development. Unfortunately for many, this window will be missed, and intervention may never occur or occur only when overt signs of cognitive delay manifest. In this review, we explore the potential value of ML and big data analysis in the early identification of children at risk for poor cognitive outcome, an area where there is an apparent dearth of research. We compare and contrast traditional statistical methods with ML approaches, provide examples of how ML has been used to date in the field of neurodevelopmental disorders, and present a discussion of the opportunities and risks associated with its use at a population level. The review concludes by highlighting potential directions for future research in this area. Impact: To date, the application of machine learning to address population health challenges in paediatrics lags behind other clinical applications. This review provides an overview of the public health challenge we face in addressing disparities in childhood cognitive development and focuses on the cornerstone of early intervention. Recent advances in our ability to collect large volumes of data, and in analytic capabilities, provide a potential opportunity to improve current practices in this field. This review explores the potential role of machine learning and big data analysis in the early identification of children at risk for poor cognitive outcomes.

Acute upper gastrointestinal bleeding (UGIB) is the leading indication for emergency endoscopy. Scoring schemes have been developed for immediate risk stratification. However, most of these scores include endoscopic findings and are based on data from patients with nonvariceal bleeding. The aim of our study was to design a pre-endoscopic score for acute UGIB--including variceal bleeding--in order to identify high-risk patients requiring urgent clinical management. The scoring system was developed using a data set consisting of 586 patients with acute UGIB. These patients were identified from the emergency department as well as all inpatient services at the University Hospital of Cologne within a 2-year period (01/2007-12/2008). Further data from a cohort of 322 patients who presented to our endoscopy unit with acute UGIB in 2009 served for external/temporal validation.Clinical, laboratory, and endoscopic parameters, as well as further data on medical history and medication were retrospectively collected from the electronic clinical documentation system. A multivariable logistic regression was fitted to the development set to obtain a risk score using recurrent bleeding, need for intervention (angiography, surgery), or death within 30 days as a composite endpoint. Finally, the obtained risk score was evaluated on the validation set. Only C-reactive protein, white blood cells, alanine-aminotransferase, thrombocytes, creatinine, and hemoglobin were identified as significant predictors for the composite endpoint. Based on the regression coefficients of these variables, an easy-to-use point scoring scheme (C-WATCH) was derived to estimate the risk of complications from 3% to 86% with an area under the curve (AUC) of 0.723 in the development set and 0.704 in the validation set. In the validation set, no patient in the identified low-risk group (0-1 points), but 38.7% of patients in the high-risk group (≥ 2 points) reached the composite endpoint. Our easy-to-use scoring scheme is able to distinguish high-risk patients requiring urgent endoscopy, from low-risk cases who are suitable candidates for outpatient management or in whom endoscopy may be postponed. Based on our findings, a prospective validation of the C-WATCH score in different patient populations outside the university hospital setting seems warranted.