Author ORCID Identifier

0000-0002-1767-4744

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Statistics, Computer Sciences, Epidemiology

Publication Details

Longitudinal Studies Conference, Wellcome Genome Campus, UK

Abstract

Models to predict stroke risk with the aim of stroke prevention often use age as a factor in the model (Choudhury et al., 2015; Conroy et al., 2003; D’Agostino etal., 2008; Wolf et al., 1991). However, stroke risk scores often underestimate risk
for specific age groups, particularly younger age groups and the contribution of different risk factors to overall stroke risk changes over time (Boehme et al., 2017; Seshadri et al., 2006). Additionally, because age is a strong predictor of stroke, age can dominate the risk score (Leening et al., 2017). Longitudinal Studies such as the Irish Longitudinal study on Aging (TILDA) allow us to track these change
in risk factors (TILDA, 2019). We aim to determine risk factors using an age group specific analysis in order to reduce the underestimation of risk for certain
age groups.

Funder

Precise4Q, ADAPT Centre for Digital Content Technology


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