A
Ashley Akbari
Researcher at Swansea University
Publications - 205
Citations - 2763
Ashley Akbari is an academic researcher from Swansea University. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 18, co-authored 112 publications receiving 1233 citations. Previous affiliations of Ashley Akbari include National Institute for Health Research.
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Journal ArticleDOI
Making the Most of Big Data in Plastic Surgery: Improving Outcomes, Protecting Patients, Informing Service Providers.
John A.G. Gibson,John A.G. Gibson,Thomas D. Dobbs,Thomas D. Dobbs,Loukas Kouzaris,Arron Lacey,Simon Thompson,Ashley Akbari,Hayley A Hutchings,William C. Lineaweaver,Ronan A Lyons,Iain S. Whitaker,Iain S. Whitaker +12 more
TL;DR: An overview of the nascent field of big data analytics in plastic is provided and how it has the potential to improve outcomes, increase safety, and aid service planning is highlighted.
Journal ArticleDOI
Impact of the national home safety equipment scheme 'Safe At Home' on hospital admissions for unintentional injury in children under 5: a controlled interrupted time series analysis.
Trevor Hill,Carol Coupland,Denise Kendrick,Matthew Jones,Ashley Akbari,Sarah Rodgers,Michael Craig Watson,Edward G Tyrrell,Sheila Merrill,Elizabeth Orton +9 more
TL;DR: In this article, a Controlled interrupted time series analysis of unintentional injury hospital admission rates in small areas (Lower Layer Super Output Areas (LSOAs)) in England where the scheme was implemented and matched with LSOAs in England and Wales where it was not implemented (control areas, n=9466), with subgroup analyses by density of equipment provision.
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Identifying children with Cystic Fibrosis in population-scale routinely collected data in Wales: A Retrospective Review
Rowena Griffiths,Daniela K Schlüter,Ashley Akbari,Rebecca Cosgriff,David Tucker,David Taylor-Robinson +5 more
TL;DR: The challenges of accurately identifying a cohort of children with Cystic Fibrosis using EHR and their validation against the UK CF Registry are described and the benefits of linking cases across multiple data sources to improve quality are demonstrated.
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Factors affecting the choice of first-line therapy in Parkinson’s disease patients in Wales: A population-based study
TL;DR: In this article, a population-based study evaluated data from the Secure Anonymised Information Linkage (SAIL) Databank of residents in Wales, aged 40 years or older, newly treated with PD medications between 2000 and 2016.
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Staff-pupil SARS-CoV-2 infection pathways in schools in Wales: A population-level linked data approach
Daniel A Thompson,Hoda Abbasizanjani,Richard Fry,Emily Marchant,Lucy J Griffiths,Ashley Akbari,Joe Hollinghurst,Laura North,Jane Lyons,Fatemeh Torabi,Gareth Davies,Mike B. Gravenor,Ronan A Lyons +12 more
TL;DR: In this article, the authors estimated the odds of testing positive for SARS-CoV-2 infection for staff and pupils over the period August- December 2020, dependent on measures of recent exposure to known cases linked to their educational settings.