Search

Background: Adversity in childhood is increasing in the United Kingdom. Complex health and social problems affecting children cluster in families where adults also have high need, but services are rarely aligned to support the whole family. Household level segmentation can help identify households most needing integrated support. Thus, the aim was to develop a segmentation model to identify those households with children who have high levels of complex cross-sectoral needs, to use as a case-finding tool for health and social care services, and to describe characteristics of identified households, to inform service integration. Method: Working with stakeholders—in an English region of 2.7m population- we agreed a definition of families having complex needs which would benefit from service integration – including households with high intensity use, who had both physical and mental health problems amongst both adults and children and wider social risks. We then used individual and household linked data across multiple health and social care services to identify these households, providing an algorithm to be used in a case finding interface. Finally, to understand the needs of this segment, and to identify unmet need, to tailor support, we used descriptive statistics and Poisson regression to profile the segment and compare them with the rest of the population. Results: Twenty one thousand and five hundreds twenty seven households (8% of the population of the region) were identified with complex needs, including 89,631 people (41,382 children), accounting for 34% of health and social care costs for families with children, £362 million in total, of which 42% was on children in care of local authorities. The households had contact with 3–4 different services, had high prevalence of mental health problems, most frequently co-morbid with respiratory problems, with high mental health emergency service use particularly amongst teenage girls many of whom had no prior elective treatment for conditions. Conclusion: Our model provides a potentially useful tool for identifying households that could benefit from better integration of services and targeted family support that can help break intergenerational transfer of adversity. [ABSTRACT FROM AUTHOR]

We present the results of an experiment designed to investigate the way information is organized and stored in the human brain. In particular, we are using controlled stimuli to reverse engineer the networks of ideas and concepts in order to answer the following questions. (1) Are the networks of ideas and concepts in the human brain invoked by verbal and visual stimuli distinct from each other? The answer appears to be no for the network of ideas and inconclusive for the network of concepts. (2) What is the topology of these networks? Our experimental results show that both are small-world networks, with the network of ideas being random and the network of concepts scale-free. [ABSTRACT FROM AUTHOR]

To represent object movement, the conventional approach is to embed the motion information into parametric and/or statistical motion models. However, the inherent complexity of this description is ill suited for application requiring rapid and automatic, albeit significant, responses. Surveillance applications belong to this class and are emerging as the most active field of research in computer vision. For this purpose, we observe that the differential invariants obtained by dense optic flow are capable of accurately describing complex object motion without requiring setting up and initializing models. In this letter, we demonstrate the novel use of such motion descriptors for spatio-temporal object segmentation and delayering of sequences. Our results show the ability of this approach to describe simply and accurately differently moving objects and to be incorporated in segmentation processes that deliver a hierarchical description of object, producing evident improvements to the segmented objects while being computationally efficient. [ABSTRACT FROM AUTHOR]