TY - JOUR
T1 - General Practice Clinical Data Help Identify Dementia Hotspots
T2 - A Novel Geospatial Analysis Approach
AU - Bagheri, Nasser
AU - Wangdi, Kinley
AU - Cherbuin, Nicolas
AU - Anstey, Kaarin J.
PY - 2018
Y1 - 2018
N2 - We have a poor understanding of whether dementia clusters geographically, how this occurs, and how dementia may relate to socio-demographic factors. To shed light on these important questions, this study aimed to compute a dementia risk score for individuals to assess spatial variation of dementia risk, identify significant clusters (hotspots), and explore their association with socioeconomic status. We used clinical records from 16 general practices (468 Statistical Area level 1 s, N = 14,746) from the city of west Adelaide, Australia for the duration of 1 January 2012 to 31 December 2014. Dementia risk was estimated using The Australian National University-Alzheimer's Disease Risk Index. Hotspot analyses were applied to examine potential clusters in dementia risk at small area level. Significant hotspots were observed in eastern and southern areas while coldspots were observed in the western area within the study perimeter. Additionally, significant hotspots were observed in low socio-economic communities. We found dementia risk scores increased with age, sex (female), high cholesterol, no physical activity, living alone (widow, divorced, separated, or never married), and co-morbidities such as diabetes and depression. Similarly, smoking was associated with a lower dementia risk score. The identification of dementia risk clusters may provide insight into possible geographical variations in risk factors for dementia and quantify these risks at the community level. As such, this research may enable policy makers to tailor early prevention strategies to the correct individuals within their precise locations.
AB - We have a poor understanding of whether dementia clusters geographically, how this occurs, and how dementia may relate to socio-demographic factors. To shed light on these important questions, this study aimed to compute a dementia risk score for individuals to assess spatial variation of dementia risk, identify significant clusters (hotspots), and explore their association with socioeconomic status. We used clinical records from 16 general practices (468 Statistical Area level 1 s, N = 14,746) from the city of west Adelaide, Australia for the duration of 1 January 2012 to 31 December 2014. Dementia risk was estimated using The Australian National University-Alzheimer's Disease Risk Index. Hotspot analyses were applied to examine potential clusters in dementia risk at small area level. Significant hotspots were observed in eastern and southern areas while coldspots were observed in the western area within the study perimeter. Additionally, significant hotspots were observed in low socio-economic communities. We found dementia risk scores increased with age, sex (female), high cholesterol, no physical activity, living alone (widow, divorced, separated, or never married), and co-morbidities such as diabetes and depression. Similarly, smoking was associated with a lower dementia risk score. The identification of dementia risk clusters may provide insight into possible geographical variations in risk factors for dementia and quantify these risks at the community level. As such, this research may enable policy makers to tailor early prevention strategies to the correct individuals within their precise locations.
KW - Dementia
KW - dementia risk score tools
KW - general practice data
KW - geospatial analysis
KW - hotspots
KW - spatial variation
UR - http://www.scopus.com/inward/record.url?scp=85036546633&partnerID=8YFLogxK
U2 - 10.3233/JAD-170079
DO - 10.3233/JAD-170079
M3 - Article
SN - 1387-2877
VL - 61
SP - 125
EP - 134
JO - Journal of Alzheimer's Disease
JF - Journal of Alzheimer's Disease
IS - 1
ER -