Using electronic health records to assess effectiveness of national policy in dementia recognition in the UK

Date of ISAC Approval: 
Lay Summary: 
Tackling dementia has rapidly become a key priority for UK national policy, in order to address the burden of the disease, affecting over 850,000 people in the UK (1), incurring a cost of over £26 billion per annum (2). In the last two decades, there have been a range of national policies, new medications introduced, and ever-increasing awareness of the disease amongst the public and clinicians. However, it is estimated that only two thirds of those with dementia have received a formal diagnosis, and diagnoses are delayed by up to three years. This study aims to use health information from primary care to evaluate the impact of key national policies on dementia monitoring and diagnosis in order to understand which interventions have been most effective in improving the diagnosis rates of a disease so often underdiagnosed. With an ever-growing focus on early detection and diagnosis of dementia, the results of this study will look to inform which approach in national policy has been the most effective in incentivising dementia recognition, and as a result how best to design policies around identifying patients earlier in their disease trajectory and providing better care.
Technical Summary: 
Background: The UK government has prioritised a number of key areas in dementia, as outlined in the Prime Minister's Challenge on Dementia 2020. Amongst these are improved diagnosis rate. The government has aimed to identify two thirds of people with dementia. More effective disease detection would allow patients to access more appropriate care, information and support. Information captured in the electronic health records (EHR) provides an important source of information regarding routine clinical practice and therefore an insight into how prescribing and diagnosis rates have varied over time, and as a result of what policy interventions. Objective: The primary objective of this study is to use primary care electronic health records (EHR) to identify which policies or medication launches were most effective in increasing rates of dementia monitoring and diagnosis Methods & Data analysis: We will describe the overall trend in diagnosis and monitoring of dementia, and compare a supervised and unsupervised change-point detection approach to assess the effectiveness of eight national dementia policies. Part I: We will determine the incidence rates of four dementia domains: dementia diagnosis, dementia-specific prescription, dementia monitoring and symptoms of cognitive decline, and estimate their time trends by calendar year using regression analyses. Part II: We will conduct a supervised single change-point analysis for each of the eight policy interventions at the point at which they were released, using segmented regressions, applied individually to the three dementia domains Part III: We will conduct unsupervised change-point analysis using the Pruned Exact Linear Time (PELT) algorithm applied to the four dementia domains
Health Outcomes to be Measured: 

Spiros Denaxas - Chief Investigator - University College London (UCL)
Arturo Gonzalez-Izquierdo - Collaborator - University College London (UCL)
Dr Kate Walters - Collaborator - University College London (UCL)
Professor Martin Rossor - Collaborator - University College London (UCL)
Maxine Mackintosh - Collaborator - University College London (UCL)
Spiros Denaxas - Corresponding Applicant - University College London (UCL)

Primary Care Vision