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

BACKGROUND Chronic kidney disease (CKD) causes a significant health burden in Australia and up to 50% of Australians with CKD remain undiagnosed. AIMS The objective of this study was to estimate the five-year risk for chronic kidney disease (CKD) from general practice clinical records, and to investigate the spatial variation and hot spots of CKD risk in an Australian community. METHOD A cross-sectional study designed using de-identified general practice clinical data recorded from 2010 to 2015. 16 general practices participated in this study from West Adelaide, Australia. We used health record of 36,565 patients aged 35-74, with no prior history of CKD. The five-year estimated CKD risk was calculated using the QKidney® algorithm. Individuals' risk score was aggregated to Statistical Area Level 1 to predict community CKD risk. A spatial hotspots analysis was applied to identify the communities with greater risk. RESULTS The mean estimated five-year risk for CKD in the sample population was 0·95% (0·93 to 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. This article is protected by copyright. All rights reserved.