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The University of Southampton
CORMSIS Centre for Operational Research, Management Sciences and Information Systems

CORMSIS Seminar Event

15:00 - 16:00
12 March 2015
Room 02/1083

For more information regarding this event, please email Sally Brailsford at .

Event details

IMPACT-Better aging model (IMPACT-BAM): a discrete-time markov chain to model vascular risk and functional decline


The relation between cardio vascular risk and functional decline (physical, mental and cognitive) is a key aspect of prevention in need of increased research attention. In this context there is considerable scope for prevention of adverse old age outcomes such as physical impairment and dementia by lowering the residual components of vascular risk. In development by University of Liverpool jointly with UCL, and funded by the British Heart Foundation, we proposed a discrete-time Markov chain to model the progression of a healthy population (aged 35-100 years old) from England and Wales until 2030 into different health states characterised mainly by the presence (absence) of cardiovascular disease, cognitive impairment no dementia, dementia and functional impairment. Those transitions between states are governed by age and sex dependent probabilities extracted from longitudinal studies such as Whitehall II study and ELSA. We also used data from the Office of National Statistics (ONS) to inform the demographic structure of the model. IMPACTBAM can explore different scenarios where levels of risk factors in the simulated population are targeted by specific policy interventions at different time points on the age and calendar time scales, thus making model outputs more relevant when exploring and evaluating the timing of specific public health interventions in ageing populations.

Speaker information

Maria Guzman Castillo,University of Liverpool, Maria Guzman Castillo has an honours degree in Industrial Engineering, a Masters and a PhD in Operational Research. She is a postdoctoral research associate at the Department of Public Health and Policy of the University of Liverpool. She has research interests in statistical modelling for prediction of health outcomes, disease modelling, diagnosis and prognosis prediction, simulation and medical decision analysis.

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