Estimating inequalities in unmet clinical need in patients living with Alzheimer's disease
Approved Research ID: 90906
Approval date: September 29th 2022
Alzheimer's (AD) disease in dementia is becoming increasingly prevalent due to ageing populations and is costly, yet treatments for AD and cognitive decline are limited. Because of this, healthcare providers face increasing demands from increasingly complex patients who are living in poor health with multiple health conditions for longer. Understanding disease impacts and identifying patient groups with the highest unmet need can inform data-driven decisions on providing treatments and allocating health resources in the population. This is particularly important for cognitive decline and its links to AD, which is not consistently measured or monitored in clinical practice. Numerous early life factors for AD apart from cognitive decline are also not well studied. This complexity is best captured through the analysis of large-scale health databases that contain a rich variety of information, such as the UK Biobank.
The UK Biobank and a UK-based electronic health records database will be used to develop robust epidemiological analyses and statistical models, which identify patient characteristics that best describe cognitive decline and predict later life AD and health care resource use. The effects of medical treatments on limiting poor health outcomes and reducing health care resource use in different groups of people will also be analysed. The results of this study will allow us to identify patient groups who will benefit the most clinically, so that targeting treatment to these groups result in the largest reduction in healthcare burden and more equitable health outcomes for the population.