ORCID
- Clive E. Sabel: 0000-0001-9180-4861
Abstract
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.
DOI
10.1016/j.sste.2024.100643
Publication Date
2024-01-01
Publication Title
Spatial and Spatio-temporal Epidemiology
Volume
49
ISSN
1877-5845
Keywords
Bayesian spatial modelling, Contextual factors, Dementia, Socioeconomic factors, Stochastic partial differential equation (SPDE)
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Amegbor, P., Sabel, C., Mortensen, L., & Mehta, A. (2024) 'Modelling the spatial risk pattern of dementia in Denmark using residential location data: A registry-based national cohort', Spatial and Spatio-temporal Epidemiology, 49. Available at: https://doi.org/10.1016/j.sste.2024.100643
