TY - JOUR

T1 - Modelling the spatial risk pattern of dementia in Denmark using residential location data

T2 - A registry-based national cohort

AU - Amegbor, Prince M.

AU - Sabel, Clive E.

AU - Mortensen, Laust H.

AU - Mehta, Amar J.

N1 - Publisher Copyright: © 2024 Elsevier Ltd

PY - 2024

Y1 - 2024

N2 - Dementia is a major global public health concern that is increasingly leading to morbidity and mortality among older adults. While studies have focused on the risk factors and care provision, there is currently limited knowledge about the spatial risk pattern of the disease. In this study, we employ Bayesian spatial modelling with a stochastic partial differential equation (SPDE) approach to model the spatial risk using complete residential history data from the Danish population and health registers. The study cohort consisted of 1.6 million people aged 65 years and above from 2005 to 2018. The results of the spatial risk map indicate high-risk areas in Copenhagen, southern Jutland and Funen. Individual socioeconomic factors and population density reduce the intensity of high-risk patterns across Denmark. The findings of this study call for the critical examination of the contribution of place of residence in the susceptibility of the global ageing population to dementia.

AB - Dementia is a major global public health concern that is increasingly leading to morbidity and mortality among older adults. While studies have focused on the risk factors and care provision, there is currently limited knowledge about the spatial risk pattern of the disease. In this study, we employ Bayesian spatial modelling with a stochastic partial differential equation (SPDE) approach to model the spatial risk using complete residential history data from the Danish population and health registers. The study cohort consisted of 1.6 million people aged 65 years and above from 2005 to 2018. The results of the spatial risk map indicate high-risk areas in Copenhagen, southern Jutland and Funen. Individual socioeconomic factors and population density reduce the intensity of high-risk patterns across Denmark. The findings of this study call for the critical examination of the contribution of place of residence in the susceptibility of the global ageing population to dementia.

KW - Bayesian spatial modelling

KW - Contextual factors

KW - Dementia

KW - Socioeconomic factors

KW - Stochastic partial differential equation (SPDE)

U2 - 10.1016/j.sste.2024.100643

DO - 10.1016/j.sste.2024.100643

M3 - Journal article

AN - SCOPUS:85185559964

VL - 49

JO - Spatial and Spatio-temporal Epidemiology

JF - Spatial and Spatio-temporal Epidemiology

SN - 1877-5845

M1 - 100643

ER -