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

    Author(s)
    Bagheri, N
    Pearce, S
    Mazumdar, S
    Sturgiss, E
    Haxhimolla, H
    Harley, D
    Griffith University Author(s)
    Harley, David
    Year published
    2021
    Metadata
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    Abstract
    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 ...
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    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.
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    Journal Title
    Internal Medicine Journal
    Volume
    51
    Issue
    8
    DOI
    https://doi.org/10.1111/imj.14924
    Subject
    Nephrology and urology
    Clinical sciences
    Public health
    Health geography
    chronic kidney disease
    geographical information system
    primary care
    Publication URI
    http://hdl.handle.net/10072/409621
    Collection
    • Journal articles

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