Modelling the spatial risk pattern of dementia in Denmark using residential location data: A registry-based national cohort
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Modelling the spatial risk pattern of dementia in Denmark using residential location data : A registry-based national cohort. / Amegbor, Prince M.; Sabel, Clive E.; Mortensen, Laust H.; Mehta, Amar J.
In: Spatial and Spatio-temporal Epidemiology, Vol. 49, 100643, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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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 -
ID: 386718106