• Nasser Bagheri, Scott Pearce, Soumya Mazumdar, Elizabeth Sturgiss, Hodo Haxhimolla, and David Harley.
• Visual and Data Analytics Lab, Research School of Population Health, Australian National University, Canberra, Australian Capital Territory, Australia.
• Intern Med J. 2021 Aug 1; 51 (8): 1278-1285.

BackgroundChronic kidney disease (CKD) causes a significant health burden in Australia, and up to 50% of Australians with CKD remain undiagnosed.AimsTo estimate the 5-year risk for CKD from general practice (GP) clinical records and to investigate the spatial variation and hot spots of CKD risk in an Australian community.MethodA cross-sectional study was designed using de-identified GP clinical data recorded from 2010 to 2015. A total of 16 GP participated in this study from West Adelaide, Australia. We used health records of 36 565 patients aged 35-74 years, with no prior history of CKD. The 5-year estimated CKD risk was calculated using the QKidney algorithm. Individuals' risk score was aggregated to Statistical Area Level 1 to predict the community CKD risk. A spatial hotspot analysis was applied to identify the communities with greater risk.ResultsThe mean estimated 5-year risk for CKD in the sample population was 0.95% (0.93-0.97). Overall, 2.4% of the study population was at high risk of CKD. Significant hot spots and cold spots of CKD risk were identified within the study region. Hot spots were associated with lower socioeconomic status.ConclusionsThis study demonstrated a new approach to explore the spatial variation of CKD risk at a community level, and implementation of a risk prediction model into a clinical setting may aid in early detection and increase disease awareness in regions of unmet CKD care.© 2020 Royal Australasian College of Physicians.

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