This LIDA Seminar will be presented via Zoom
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Presentation 1: tbc
By: Tom Albone
Presentation 2: tbc
By: Paul Truman
Presentation 3: Mental ill health epidemic: assessing the impact of poor mental health on individuals, the community and services
By: Maike Gatzlaff
Mental health issues are prevalent within the Bradford District, with a mental ill health epidemic affecting children and young people (CYP). Mental health support has been identified as a top priority by community stakeholders in the Holme Wood area of the District. These stakeholders report a range of issues including problems with accessibility to support, fragmented and poorly coordinated services, and insufficient resources. Stakeholders suggest the focus is on crisis management rather than early intervention. There is general recognition across the system that tackling these issues will result in better outcomes at the individual and community level, and could potentially offer savings in health, social care and policing costs. Thus, long-term solutions are needed in order to improve mental health support.
This project aims to better understand gaps in mental health provisions available to the Bradford District and Holme Wood by: (i) mapping mental health inequalities and service accessibility and (ii) interrogating datasets (e.g. the Avon Longitudinal Study of Parents and Children and Born in Bradford) to better understand factors (e.g. alcohol and drug use) that influence mental health in CYP. Interactive maps will be used to aid in communication with policymakers, service providers, and local residents. The goal is to enable evidence-based policymaking and use data science to inform possible interventions that can improve mental health on the Holme Wood estate.
Presentation 5: Children’s Urgent Care Provided in the Right Place Every Time
By: Sam Relins
Abstract: Bradford is home to one of the country’s leading hospital-at-home services – the Ambulatory Care Experience or ACE. Children suffering with health problems, that may traditionally have been admitted to a hospital ward, can instead receive treatment at home. The familiar household setting reduces anxiety for child and parent and frees up much needed space and resources in hospitals. This project aims to use data analysis and machine learning to increase the accuracy of referrals and better inform decision making within ACE. Using anonymised data collected at the time a child is referred to the service, modern statistical methods can be used predict the most suitable treatment setting for each patient. In particular, the project aims to produce models that can not only predict the likelihood of successful treatment in the ACE service, but also the level of confidence (or lack thereof) in this prediction. This information can then be provided as a tool to clinicians, to aid them in making the best referral decision for every patient every time.
If you wish to ask the presenters any questions or offer feedback, please email LIDA