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dc.contributor.authorBagheri, N
dc.contributor.authorPearce, S
dc.contributor.authorMazumdar, S
dc.contributor.authorSturgiss, E
dc.contributor.authorHaxhimolla, H
dc.contributor.authorHarley, D
dc.date.accessioned2021-11-02T00:24:48Z
dc.date.available2021-11-02T00:24:48Z
dc.date.issued2021
dc.identifier.issn1444-0903en_US
dc.identifier.doi10.1111/imj.14924en_US
dc.identifier.urihttp://hdl.handle.net/10072/409621
dc.description.abstractBackground: 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.en_US
dc.description.peerreviewedYesen_US
dc.languageengen_US
dc.publisherWileyen_US
dc.relation.ispartofpagefrom1278en_US
dc.relation.ispartofpageto1285en_US
dc.relation.ispartofissue8en_US
dc.relation.ispartofjournalInternal Medicine Journalen_US
dc.relation.ispartofvolume51en_US
dc.subject.fieldofresearchNephrology and urologyen_US
dc.subject.fieldofresearchClinical sciencesen_US
dc.subject.fieldofresearchPublic healthen_US
dc.subject.fieldofresearchHealth geographyen_US
dc.subject.fieldofresearchcode320214en_US
dc.subject.fieldofresearchcode3202en_US
dc.subject.fieldofresearchcode4206en_US
dc.subject.fieldofresearchcode440605en_US
dc.subject.keywordschronic kidney diseaseen_US
dc.subject.keywordsgeographical information systemen_US
dc.subject.keywordsprimary careen_US
dc.titleIdentifying community chronic kidney disease risk profile utilising general practice clinical records and spatial analysis: approach to inform policy and practiceen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationBagheri, N; Pearce, S; Mazumdar, S; Sturgiss, E; Haxhimolla, H; Harley, D, Identifying community chronic kidney disease risk profile utilising general practice clinical records and spatial analysis: approach to inform policy and practice, Internal Medicine Journal, 2021, 51 (8), pp. 1278-1285en_US
dcterms.dateAccepted2020-05-16
dc.date.updated2021-10-22T00:37:06Z
gro.hasfulltextNo Full Text
gro.griffith.authorHarley, David


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