Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice
Nasser Bagheri A G , Paul Konings B , Kinley Wangdi C , Anne Parkinson B , Soumya Mazumdar D , Elizabeth Sturgiss E , Aparna Lal F , Kirsty Douglas E and Nicholas Glasgow B
+ Author Affiliations
- Author Affiliations
A Centre for Mental Health Research, Research School of Population Health, Australian National University, 63 Eggleston Road, Acton 2601, Australia.
B Department of Health Services Research and Policy, Research School of Population Health, Australian National University, 62 Eggleston Road, Acton, ACT 2601, Australia.
C Department of Global Health, Research School of Population Health, Australian National University, 62 Eggleston Road, Acton, ACT 2601, Australia.
D Healthy People and Place Unit, Population Health, Liverpool Hospital, South West Sydney Local Health District, New South Wales Health, 52 Scrivener Street, Warwick Farm, NSW 2170, Australia.
E Department of General Practice, Monash University, 270 Ferntree Gully Road, Notting Hill, Vic. 3168, Australia.
F National Centre for Epidemiology and Population Health, Research School of Population Health, Australian National University, 62 Eggleston Road, Acton, ACT 2601, Australia.
G Corresponding author. Email: nasser.bagheri@anu.edu.au
Australian Journal of Primary Health 26(1) 43-51 https://doi.org/10.1071/PY19043
Submitted: 28 February 2019 Accepted: 23 August 2019 Published: 22 November 2019
Abstract
The prevalence of type 2 diabetes (T2D) is increasing worldwide and there is a need to identify communities with a high-risk profile and to develop appropriate primary care interventions. This study aimed to predict future T2D risk and identify community-level geographic variations using general practices data. The Australian T2D risk assessment (AUSDRISK) tool was used to calculate the individual T2D risk scores using 55 693 clinical records from 16 general practices in west Adelaide, South Australia, Australia. Spatial clusters and potential ‘hotspots’ of T2D risk were examined using Local Moran’s I and the Getis-Ord Gi* techniques. Further, the correlation between T2D risk and the socioeconomic status of communities were mapped. Individual risk scores were categorised into three groups: low risk (34.0% of participants), moderate risk (35.2% of participants) and high risk (30.8% of participants). Spatial analysis showed heterogeneity in T2D risk across communities, with significant clusters in the central part of the study area. These study results suggest that routinely collected data from general practices offer a rich source of data that may be a useful and efficient approach for identifying T2D hotspots across communities. Mapping aggregated T2D risk offers a novel approach to identifying areas of unmet need.
Additional keywords: geographical variation, primary health care, spatial clusters, T2D risk.
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