Identifying community chronic kidney disease risk profile utilising general practice clinical records and spatial analysis: approach to inform policy and practice

Nasser Bagheri, Scott Pearce, Soumya Mazumdar, Elizabeth Sturgiss, Hodo Haxhimolla, David Harley

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


Background: Chronic kidney disease (CKD) causes a significant health burden in Australia, and up to 50% of Australians with CKD remain undiagnosed. Aims: To 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. Method: A 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. Results: The 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. Conclusions: This 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.

Original languageEnglish
Pages (from-to)1278-1285
Number of pages8
JournalInternal Medicine Journal
Issue number8
Publication statusPublished - Aug 2021


  • chronic kidney disease
  • geographical information system
  • primary care

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