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Findings from longitudinal research, globally, repeatedly emphasise the importance of taking an early life course approach to mental health promotion; one that invests in the formative years of development, from early childhood to young adulthood, just prior to the transition to parenthood for most. While population monitoring systems have been developed for this period, they are typically designed for use within discrete stages (i.e., childhood or adolescent or young adulthood). No system has yet captured development across all ages and stages (i.e., from infancy through to young adulthood). Here we describe the development, and pilot implementation, of a new Australian Comprehensive Monitoring System (CMS) designed to address this gap by measuring social and emotional development (strengths and difficulties) across eight census surveys, separated by three yearly intervals (infancy, 3-, 6-, 9- 12-, 15-, 18 and 21 years). The system also measures the family, school, peer, digital and community social climates in which children and young people live and grow. Data collection is community-led and built into existing, government funded, universal services (Maternal Child Health, Schools and Local Learning and Employment Networks) to maximise response rates and ensure sustainability. The first system test will be completed and evaluated in rural Victoria, Australia, in 2022. CMS will then be adapted for larger, more socio-economically diverse regional and metropolitan communities, including Australian First Nations communities. The aim of CMS is to guide community-led investments in mental health promotion from early childhood to young adulthood, setting secure foundations for the next generation.

Background: Bronchopulmonary Dysplasia (BPD) is a chronic condition that affects preterm infants and is associated with long-term complications. Haemodynamic effects of BPD can lead to right ventricular (RV) dysfunction. Objective: To synthesise and map the evidence of echo parameters used in identifying RV dysfunction in the first two weeks-after-birth (WAB) of preterm infants with early BPD. Information Sources: This scoping review included the databases: Medline, CINAHL, PubMed, EMBASE, Scopus, ProQuest, Web of Science, Cochrane Library, JBI Evidence-Based Practise and Gray Literature. Search Strategy: The search utilised Boolean operators and descriptors registered in Medical Subject Headings. Inclusion and exclusion criteria: Included were studies utilising echo parameters to examine RV function in preterm infants with early BPD in the first two WAB. Synthesis of results: The results are presented as a map of the extracted findings in a tabular format with a narrative summary. Results: Eight studies were included. Differences were observed in the number and timing of echo scans performed in the first two WAB and the variations in the echo parameters used to compare preterm infants with and without early BPD. Only echo scans performed at the end of the first WAB, demonstrated significant differences in the echo parameters measurements between preterm infants with and without BPD. Studies using RV Myocardial Performance Index (MPI) to identify RV-dysfunction associated with early BPD demonstrated similar findings. The Pulsed-Wave Doppler technique identified differences in RV-MPI between preterm infants with and without BPD, while Tissue-Doppler-Imaging did not demonstrate similar results. Speckle tracking can measure strain (S) and strain rate (SR) and diagnose RV-dysfunction. However, the findings of studies that utilised speckle tracking varied. Finally, two of the included studies added blood tests to their diagnostic model of early BPD, which was able to dem

Background: Therapeutic footwear and insoles are preventative strategies to reduce elevated plantar pressures associated with diabetic foot ulcer risk. An insole intervention appropriate for chairside delivery optimising plantar foot pressure reduction in people with diabetes has been developed. Aim: To explore the feasibility and acceptability of testing an optimised insole compared with an active control insole to reduce plantar pressures for people with diabetic peripheral neuropathy. Methods: A double-blinded multi-centre feasibility RCT with an embedded qualitative study. Participants were randomised to either an optimised insole group (intervention) or a standard cushioned insole group (active control). Participants were assessed at baseline, 3, 6, and 12 months with clinical outcomes of foot ulceration and mean peak plantar pressure (MPPP) reduction. An embedded qualitative study involved semi-structured interviews with 12 study participants and three podiatrists to explore their experiences of the intervention and trial procedures. Data were analysed using descriptive statistics (quantitative data) and thematic analysis (qualitative data). Results: Screened were142 patients from which 61 were recruited; 30 participants were randomised to the intervention group and 31 to the active control group. Forty-two participants completed the study. At 12 months, 69% of the patient-reported questionnaires were returned and 68% of the clinical outcomes were collected. There were 17 incidences of foot ulceration occurring in 7/31 of the active control group and 10/30 in the intervention group. Mean difference in MPPP between the intervention and active control groups for all regions-of-interest combined favoured the intervention. Thematic analysis revealed three themes; accepting the study, behaviour and support during study procedures, and impact from study participation. Conclusion: The results of the feasibility RCT suggest that the optimised insole holds promise as a

The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Red noise is essential to many operational parameterization schemes, helping model temporal correlations. We show how to build on the successes of red noise by combining the known benefits of stochasticity with machine learning. This is done using a physically-informed recurrent neural network within a probabilistic framework. Our model is competitive and often superior to both a bespoke baseline and an existing probabilistic machine learning approach (GAN) when applied to the Lorenz 96 atmospheric simulation. This is due to its superior ability to model temporal patterns compared to standard first-order autoregressive schemes. It also generalises to unseen scenarios. We evaluate across a number of metrics from the literature, and also discuss the benefits of using the probabilistic metric of hold-out likelihood.

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies.

Ensembles of chemistry-climate models (CCMs) are fundamental for the exploration of the chemistry-climate system. A particular focus of chemistry-climate modelling is stratospheric ozone, whose concentrations have been decreased by anthropogenic releases of ozone depleting substances. In conjunction with observational data, CCM ensembles have been relied upon to simulate historic effects of ozone depletion and to project future ozone recovery. However, many widely used ensemble analysis methods are simplistic and are based upon incorrect assumptions about the design of the ensemble. Multi-model means used to construct future ozone projections do not account for variable model performance or similarity and therefore give biased and inaccurate projections. Similarly, simplistic linear regression methods used to infill historic ozone records underestimate interannual variability and are inaccurate in regions of sparse data coverage. Moreover, given advances in machine learning and data science and their increased use in environmental science, it is timely to apply more advanced tools to CCM ensembles. To address this methodological deficit, this thesis presents a set of novel tools to improve the predictions and projections from CCM ensembles of stratospheric ozone. A process-based weighted mean is developed which accounts for model performance and similarity in CCM ensembles. This improvement over pre-existing methods was used to generate accurate ozone hole recovery projections. This thesis also developed a Bayesian neural network (BNN) which fuses together CCMs with observational data to produce accurate and uncertainty-aware predictions. The BNN framework was used to produce historic continuous datasets of total ozone column and vertically resolved ozone, and represents a significant improvement in methods used to ensemble models. Though designed for CCM ensembles these flexible tools have the potential to be applied to other environmental modelling disciplines to

The COVID-19 lockdown had a series of intended and unintended consequences, including reduced infections and changes in activities and behaviours. Some of these changes may have been beneficial to perinatal outcomes; however, other factors such as reduced access to face-to-face healthcare may have contributed negatively to antenatal care. The aim of this audit was to evaluate neonatal admissions in the South-West of England during the COVID-19 pandemic in 2020 and the previous two years 2018–2019. Anonymised birth and neonatal admission rates from January to December 2020 was obtained and compared to data from 2018 to 2019. The results demonstrate a decreasing in neonatal unit admissions between 2018 and 2020, 9.48% of live births in 2018 (95% CI 9.17, 9.80) to 8.89% (95% CI 8.65, 9.13) in 2020 (p = 0.002). Conclusion: There were no significant differences across gestational groups. It is unclear without nationwide data whether our observed trends, decreased neonatal admissions over the past 3 years, are generalisable and related to the COVID-19 pandemic. Future research exploring the impact of lockdowns on behaviour change during pregnancy and support services is warranted to understand the implications of pandemics on pregnancy and preterm birth.What is Known:• The COVID-19 lockdown had a series of intended and unintended consequences; some of which may have been beneficial to perinatal outcomes.• Research suggests that preterm births have not significantly changed overall, but they have decreased in high-income countries.What is New:• In our audit, analysing retrospective data of regional birth and neonatal admission from the South-West of England, we observed a decrease in live birth rates between 2018 and 2020.• A reduction in neonatal unit admissions was observed from 2018 to 2020 with no significant differences across gestational groups. The reduction from 2019 to 2020 was smaller than that from 2018 to 2019 implying that the COVID-19 pandemic in 2020 was not

Background: Bronchopulmonary dysplasia (BPD) is a chronic disease that affects the immature lungs of preterm infants. Infants born before 32 weeks of gestation are at a greater risk of developing BPD due to the need for respiratory support with higher oxygen requirement. Pulmonary vascular remodelling in early BPD can impose an additional burden on the right ventricle (RV) and RV dysfunction. This protocol outlines the study design and aims to formulate a prediction model to identify early BPD through the data generated from echo scans analysis. Methods: The mixed-methods observational cohort feasibility study, which comprises three work-packages (WPs), will be conducted at the regional neonatal unit, University Hospital Plymouth, Plymouth, UK. WP-I will recruit 40 preterm infants; each participant will have two heart scans performed in the first ten days after birth (DABs). WP-II will collect the documentation of the participating preterm infants’ parents in the study neonatal unit diaries in the first 10 DABs. WP-III will involve semi-structured interviews of 10–15 parents of participating preterm infants and 10–15 health professionals who participated in WP-I. The study recruitment will be conducted over 18-months. The start date is 01 June 2022. WP-I and WP-II recruitment will occur during this period, while WP-III recruitment will occur during the second half. The results are expected to be submitted for publication by mid-2024. Discussion: This paper outlines the study design. If the study successfully identifies the most sensitive echo parameter in recognising the RV dysfunction associated with early BPD, it will be an important finding in constructing an early BPD prediction model. Trial registration: ClinicalTrials.gov Identifier is NCT05235399.