Opportunities and challenges of using secondary analysis for analysing social policy questions in Early Childhood Education and Care and children’s food and nutrition

Aims: This integrative summary aims to critically assess the opportunities and challenges of using secondary data analysis of large-scale UK data for addressing social policy questions within Early Childhood Education and Care (ECEC) and children’s nutrition (two areas of my research and which both focus on aspects of childhood wellbeing and developmental health). A key and original contribution of the integrative summary is the proposal of identifying a core outcome set (COS) of indicators, underpinned using Bronfenbrenner’s ‘proximal’ and ‘distal’ factors (Bronfenbrenner and Morris, 2006), within studies of early childhood education and care (ECEC) and children’s food and nutrition. The development of these COS are recommended in order to support ongoing secondary analysis research in these areas e.g., to assess service or outcome quality. Research Questions: RQ1: What does the literature identify as important concepts, factors and indicators and what does this suggest for a scope of a COS in my two research areas? RQ2: What indicators have been operationalised and analysed within my work? RQ3: What are the ‘data gaps’ within current large-scale UK data collections regarding the operationalisation of indicators in ECEC and children’s food and nutrition? RQ4: What indicators could be included within a COS for ECEC or children’s food and nutrition? Methods: First, a critical review of seven of my candidate publications from the past 5 years and the literature on ECEC and children’s food and nutrition. Second, to follow the suggested steps within the Core Outcome Measures in Effectiveness Trials framework (COMET) for developing a COS by identifying suitable indicators using literature in my two subject areas. Findings: RQ1: In ECEC, literature suggests ‘good relationships’ are key for achieving ‘good quality outcomes’ for children and it is important to distinguish and measure both structural and process concepts (distal or process) of service quality, underpinned by what children need for their development. In children’s food and nutrition, the key concepts discussed are indicators of dietary quality in relation to key government targets (measured through consumption of single foods or analysis of the ‘whole diet’) and indicators of dietary intake that meet needs for health and social participation as part of wider living standards. RQ2: Relationships between adults and children in ECEC settings are not well measured within large-scale UK datasets. These so-called process or proximal concepts are difficult to identify and operationalise. Secondary data analysts instead have to rely on measuring and analysing structural/distal concepts available to them in datasets. Proxy indicators, such as staff qualifications, can be used but it is not clear if these are good for assessing service quality. In children’s food and nutrition, indicators developed in relation to nutritional benchmarking are often subjective indicators (because they are based on self-reported behaviour) and are mostly process/proximal indicators, centred on the child. RQ3: Indicators are often ‘fragmented’ (spread across datasets), which makes secondary analysis of large-scale data difficult for researchers who may have to combine datasets or carry out separate analysis using a range of datasets. In children’s food and nutrition, large-scale UK data have detailed nutritional information but may lack important contextual data and/or have issues with reliability in the data collected. RQ4: The beginnings of a COS has been identified for ECEC and children’s food and nutrition based on evidence from the earlier RQs (drawing on the literature) to identify indicators that are important to measure. However, each COS needs further refinement through consultation with a relevant group of experts in each subject area. Conclusions: Secondary analysis of existing data has enormous potential for monitoring outcomes for children and families, through for example, the identification of a core outcome set (COS) within ECEC and children’s food and nutrition. To overcome problems of data fragmentation (when indicators are spread across a number of datasets), a COS in my research areas is recommended to enhance efforts for data harmonisation (unifying measures across research studies). The success of implementing a COS in ECEC and children’s food and nutrition is reliant on three key things. First, common understandings and definitions of indicators (and here my work needs building to include consultation with expert groups). Second, development of outcome indicators that are sensitive to the context in which they are being developed and applied. Third, a clear understanding of the purpose or aims of the indicators being included through the inclusion of an agreed supporting theoretical framework to unite the indicators and guide their organisation within the COS. This last point is important for providing a sound evidence base for informing social policy and practice

Conceptualising the multifaceted nature of urban road congestion

Urban road congestion is not a new phenomenon and remains an outstanding problem that continues to impact people around the world. Road congestion costs the European Union an estimated 1-2% of GDP each year and is responsible for 27% of deadly C02 emissions. In addition, it can cause life-threatening delays in the emergency services response time. Road congestion has a multifaceted nature and lacks a clear and explicit definition. This makes the problem of tackling it very subjective, time and context dependent. There have been several approaches to both modelling and predicting road congestion. From a physical perspective, road congestion has been modelled using speed, capacity, velocity, and journey time; relatively road congestion has been classified using terms such as non-recurrent and recurrent congestion which tend to be relative to each stakeholder; conceptual models such as the bathtub, traffic flow, and origin to the destination have been used to ascertain the impact of road congestion on a city scale. This research presented tackles the problem of defining what is meant by congestion within an urban road network through defining a conceptual model that captures the semantics of road traffic congestion and its causes. The model is validated through the construction of a real-world dataset and the development of a visual tool which can be used to identify and alleviate congestion. The final stage of the project uses both the model and the dataset to investigate and implement a series of fuzzy systems to classify three types of congestion (non-recurrent, recurrent, and semi-recurrent). The fuzzy system results are then validated against human methods of classifying congestion. The main contributions of this thesis to world knowledge can be summarised as follows: The design and development of a novel universal Urban Road Congestion Conceptual (URCC) model. The URCC model is broken down into two main components: Analogical conceptualisation which builds upon the famous ‘bathtub’ model and will integrate with other analogies to create ‘a raindrop hitting a leaf inside the bathtub with ever changing water temperatures’. The second component is an ontological approach to modelling congestion thus providing a better understanding for decision-makers through providing a formal and explicit explanation for concepts within the domain of urban road congestion. Another contribution is the development of a real-world spatiotemporal quasi-real-time big data dataset known as the Manchester Urban Congestion Data (MUCD) dataset which was used to validate the URCC. A visualisation graphical user interface called TIM (Transport Incident Manager) was developed with stakeholders TfGM (Transport for Greater Manchester). TIM has the ability to fill the void left by the clear lack of visualisation tools that are capable of visualising real-world big data datasets, such as the MUCD and models of urban road congestion. The final contribution to knowledge is the design and development of two fuzzy decision-making systems which are not only capable of predicting urban road congestion on a link but the type of congestion occurring on a network of links. Using a fuzzy decision-making system allows for explainable and interpretable decisions, and also provided useful and meaningful qualitative context back to the relevant TfGM stakeholders. The non-optimised multi-classification fuzzy system had slightly worst accuracy than the J48 decision tree algorithm, however, the fuzzy system is easier to interpret and provides meaningful context compared to the J48 algorithm due to only requiring 12 rules compared to the 1184 learned rules in the J48 decision tree. Furthermore, once the fuzzy system has been optimised (future work) it is likely to have similar if not better performance than the J48 decision tree