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Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011

Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effec...

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Published in:Journal of diabetes and its complications 2015-07, Vol.29 (5), p.637-643
Main Authors: Schroeder, Emily B, Powers, J. David, O’Connor, Patrick J, Nichols, Gregory A, Xu, Stanley, Desai, Jay R, Karter, Andrew J, Morales, Leo S, Newton, Katherine M, Pathak, Ram D, Vazquez-Benitez, Gabriela, Raebel, Marsha A, Butler, Melissa G, Lafata, Jennifer Elston, Reynolds, Kristi, Thomas, Abraham, Waitzfelder, Beth E, Steiner, John F
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cited_by cdi_FETCH-LOGICAL-c532t-a4d5fc742b6cd097046201dfd3c2340cabc0d57613af738ba2bbe321df6fb2a83
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container_end_page 643
container_issue 5
container_start_page 637
container_title Journal of diabetes and its complications
container_volume 29
creator Schroeder, Emily B
Powers, J. David
O’Connor, Patrick J
Nichols, Gregory A
Xu, Stanley
Desai, Jay R
Karter, Andrew J
Morales, Leo S
Newton, Katherine M
Pathak, Ram D
Vazquez-Benitez, Gabriela
Raebel, Marsha A
Butler, Melissa G
Lafata, Jennifer Elston
Reynolds, Kristi
Thomas, Abraham
Waitzfelder, Beth E
Steiner, John F
description Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effect of different ascertainment methods on prevalence estimates. Methods We used the SUPREME-DM DataLink (n = 879,312) to estimate annual CKD prevalence. Methods of CKD ascertainment included: diagnosis codes alone, impaired estimated glomerular filtration rate (eGFR) alone (eGFR < 60 mL/min/1.73 m2 ), albuminuria alone (spot urine albumin creatinine ratio > 30 mg/g or equivalent), and combinations of these approaches. Results CKD prevalence was 20.0% using diagnosis codes, 17.7% using impaired eGFR, 11.9% using albuminuria, and 32.7% when one or more method suggested CKD. The criteria had poor concordance. After age- and sex-standardization to the 2010 U.S. Census population, prevalence using diagnosis codes increased from 10.7% in 2005 to 14.3% in 2011 ( P < 0.001). The prevalence using eGFR decreased from 9.7% in 2005 to 8.6% in 2011 ( P < 0.001). Conclusions Our data indicate that CKD prevalence and prevalence trends differ according to the CKD ascertainment method, highlighting the necessity for multiple sources of data to accurately estimate and track CKD prevalence.
doi_str_mv 10.1016/j.jdiacomp.2015.04.007
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David ; O’Connor, Patrick J ; Nichols, Gregory A ; Xu, Stanley ; Desai, Jay R ; Karter, Andrew J ; Morales, Leo S ; Newton, Katherine M ; Pathak, Ram D ; Vazquez-Benitez, Gabriela ; Raebel, Marsha A ; Butler, Melissa G ; Lafata, Jennifer Elston ; Reynolds, Kristi ; Thomas, Abraham ; Waitzfelder, Beth E ; Steiner, John F</creator><creatorcontrib>Schroeder, Emily B ; Powers, J. David ; O’Connor, Patrick J ; Nichols, Gregory A ; Xu, Stanley ; Desai, Jay R ; Karter, Andrew J ; Morales, Leo S ; Newton, Katherine M ; Pathak, Ram D ; Vazquez-Benitez, Gabriela ; Raebel, Marsha A ; Butler, Melissa G ; Lafata, Jennifer Elston ; Reynolds, Kristi ; Thomas, Abraham ; Waitzfelder, Beth E ; Steiner, John F ; SUPREME-DM Study Group</creatorcontrib><description>Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effect of different ascertainment methods on prevalence estimates. Methods We used the SUPREME-DM DataLink (n = 879,312) to estimate annual CKD prevalence. Methods of CKD ascertainment included: diagnosis codes alone, impaired estimated glomerular filtration rate (eGFR) alone (eGFR &lt; 60 mL/min/1.73 m2 ), albuminuria alone (spot urine albumin creatinine ratio &gt; 30 mg/g or equivalent), and combinations of these approaches. Results CKD prevalence was 20.0% using diagnosis codes, 17.7% using impaired eGFR, 11.9% using albuminuria, and 32.7% when one or more method suggested CKD. The criteria had poor concordance. After age- and sex-standardization to the 2010 U.S. Census population, prevalence using diagnosis codes increased from 10.7% in 2005 to 14.3% in 2011 ( P &lt; 0.001). The prevalence using eGFR decreased from 9.7% in 2005 to 8.6% in 2011 ( P &lt; 0.001). Conclusions Our data indicate that CKD prevalence and prevalence trends differ according to the CKD ascertainment method, highlighting the necessity for multiple sources of data to accurately estimate and track CKD prevalence.</description><identifier>ISSN: 1056-8727</identifier><identifier>EISSN: 1873-460X</identifier><identifier>DOI: 10.1016/j.jdiacomp.2015.04.007</identifier><identifier>PMID: 25936953</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Age ; Aged ; Aged, 80 and over ; Albuminuria - etiology ; Cardiovascular disease ; Chronic renal insufficiency ; Codes ; Cohort Studies ; Delivery of Health Care, Integrated ; Diabetes ; Diabetes mellitus ; Diabetic Nephropathies - epidemiology ; Diabetic Nephropathies - physiopathology ; Diabetic Nephropathies - urine ; Disease control ; Disease prevention ; Electronic Health Records ; Endocrinology &amp; Metabolism ; Epidemiological Monitoring ; Epidemiology ; Female ; Follow-Up Studies ; Glomerular Filtration Rate ; Humans ; Information Storage and Retrieval ; International Classification of Diseases ; Kidney diseases ; Laboratories ; Male ; Medicare ; Methods ; Middle Aged ; Mortality ; Population ; Prevalence ; Registries ; Renal Insufficiency, Chronic - complications ; Renal Insufficiency, Chronic - epidemiology ; Renal Insufficiency, Chronic - physiopathology ; Renal Insufficiency, Chronic - urine ; Surveillance ; United States - epidemiology ; Young Adult</subject><ispartof>Journal of diabetes and its complications, 2015-07, Vol.29 (5), p.637-643</ispartof><rights>Elsevier Inc.</rights><rights>2015 Elsevier Inc.</rights><rights>Copyright © 2015 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited Jul 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c532t-a4d5fc742b6cd097046201dfd3c2340cabc0d57613af738ba2bbe321df6fb2a83</citedby><cites>FETCH-LOGICAL-c532t-a4d5fc742b6cd097046201dfd3c2340cabc0d57613af738ba2bbe321df6fb2a83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25936953$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schroeder, Emily B</creatorcontrib><creatorcontrib>Powers, J. David</creatorcontrib><creatorcontrib>O’Connor, Patrick J</creatorcontrib><creatorcontrib>Nichols, Gregory A</creatorcontrib><creatorcontrib>Xu, Stanley</creatorcontrib><creatorcontrib>Desai, Jay R</creatorcontrib><creatorcontrib>Karter, Andrew J</creatorcontrib><creatorcontrib>Morales, Leo S</creatorcontrib><creatorcontrib>Newton, Katherine M</creatorcontrib><creatorcontrib>Pathak, Ram D</creatorcontrib><creatorcontrib>Vazquez-Benitez, Gabriela</creatorcontrib><creatorcontrib>Raebel, Marsha A</creatorcontrib><creatorcontrib>Butler, Melissa G</creatorcontrib><creatorcontrib>Lafata, Jennifer Elston</creatorcontrib><creatorcontrib>Reynolds, Kristi</creatorcontrib><creatorcontrib>Thomas, Abraham</creatorcontrib><creatorcontrib>Waitzfelder, Beth E</creatorcontrib><creatorcontrib>Steiner, John F</creatorcontrib><creatorcontrib>SUPREME-DM Study Group</creatorcontrib><title>Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011</title><title>Journal of diabetes and its complications</title><addtitle>J Diabetes Complications</addtitle><description>Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effect of different ascertainment methods on prevalence estimates. Methods We used the SUPREME-DM DataLink (n = 879,312) to estimate annual CKD prevalence. Methods of CKD ascertainment included: diagnosis codes alone, impaired estimated glomerular filtration rate (eGFR) alone (eGFR &lt; 60 mL/min/1.73 m2 ), albuminuria alone (spot urine albumin creatinine ratio &gt; 30 mg/g or equivalent), and combinations of these approaches. Results CKD prevalence was 20.0% using diagnosis codes, 17.7% using impaired eGFR, 11.9% using albuminuria, and 32.7% when one or more method suggested CKD. The criteria had poor concordance. After age- and sex-standardization to the 2010 U.S. Census population, prevalence using diagnosis codes increased from 10.7% in 2005 to 14.3% in 2011 ( P &lt; 0.001). The prevalence using eGFR decreased from 9.7% in 2005 to 8.6% in 2011 ( P &lt; 0.001). Conclusions Our data indicate that CKD prevalence and prevalence trends differ according to the CKD ascertainment method, highlighting the necessity for multiple sources of data to accurately estimate and track CKD prevalence.</description><subject>Adult</subject><subject>Age</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Albuminuria - etiology</subject><subject>Cardiovascular disease</subject><subject>Chronic renal insufficiency</subject><subject>Codes</subject><subject>Cohort Studies</subject><subject>Delivery of Health Care, Integrated</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetic Nephropathies - epidemiology</subject><subject>Diabetic Nephropathies - physiopathology</subject><subject>Diabetic Nephropathies - urine</subject><subject>Disease control</subject><subject>Disease prevention</subject><subject>Electronic Health Records</subject><subject>Endocrinology &amp; Metabolism</subject><subject>Epidemiological Monitoring</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>Glomerular Filtration Rate</subject><subject>Humans</subject><subject>Information Storage and Retrieval</subject><subject>International Classification of Diseases</subject><subject>Kidney diseases</subject><subject>Laboratories</subject><subject>Male</subject><subject>Medicare</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Mortality</subject><subject>Population</subject><subject>Prevalence</subject><subject>Registries</subject><subject>Renal Insufficiency, Chronic - complications</subject><subject>Renal Insufficiency, Chronic - epidemiology</subject><subject>Renal Insufficiency, Chronic - physiopathology</subject><subject>Renal Insufficiency, Chronic - urine</subject><subject>Surveillance</subject><subject>United States - epidemiology</subject><subject>Young Adult</subject><issn>1056-8727</issn><issn>1873-460X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqFksFu1DAQhiMEoqXwCpUlLhxIGNuxk1wQqGwBqRUrSiVulmNPWGezyWIni_bWd-ANeRIcbQtSLz3Z0nz_2P_8kySnFDIKVL5ps9Y6bYbNNmNARQZ5BlA8So5pWfA0l_D9cbyDkGlZsOIoeRZCCwBSCPo0OWKi4rIS_DhZLz3udIe9QTI0xKz80DtD1s72uCfWBdQBid4M_Q_ieut2zk66C-SXG1exrGscMcQKGVdIrq6XXxeXi_TDJVn6oUUzviYMQPy5-R0_SZ8nT5qoxRe350lyfb74dvYpvfjy8fPZ-4vUCM7GVOdWNKbIWS2NhaqAXEa1bSw3jOdgdG3AikJSrpuCl7VmdY2cRUI2NdMlP0leHfpu_fBzwjCqjQsGu073OExB0ZIVFQNeiIdRWcqcioqyiL68h7bD5PtoZKZEHG6Vy0jJA2X8EILHRm2922i_VxTUnJxq1V1yak5OQa5iclF4ett-qjdo_8nuoorAuwOAcXQ7h14F4-bcrPNx0soO7uE33t5rYToX49bdGvcY_vtRgSlQV_P-zOtDozsqo7u_WozAhA</recordid><startdate>20150701</startdate><enddate>20150701</enddate><creator>Schroeder, Emily B</creator><creator>Powers, J. 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David ; O’Connor, Patrick J ; Nichols, Gregory A ; Xu, Stanley ; Desai, Jay R ; Karter, Andrew J ; Morales, Leo S ; Newton, Katherine M ; Pathak, Ram D ; Vazquez-Benitez, Gabriela ; Raebel, Marsha A ; Butler, Melissa G ; Lafata, Jennifer Elston ; Reynolds, Kristi ; Thomas, Abraham ; Waitzfelder, Beth E ; Steiner, John F</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c532t-a4d5fc742b6cd097046201dfd3c2340cabc0d57613af738ba2bbe321df6fb2a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Adult</topic><topic>Age</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Albuminuria - etiology</topic><topic>Cardiovascular disease</topic><topic>Chronic renal insufficiency</topic><topic>Codes</topic><topic>Cohort Studies</topic><topic>Delivery of Health Care, Integrated</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetic Nephropathies - epidemiology</topic><topic>Diabetic Nephropathies - physiopathology</topic><topic>Diabetic Nephropathies - urine</topic><topic>Disease control</topic><topic>Disease prevention</topic><topic>Electronic Health Records</topic><topic>Endocrinology &amp; Metabolism</topic><topic>Epidemiological Monitoring</topic><topic>Epidemiology</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>Glomerular Filtration Rate</topic><topic>Humans</topic><topic>Information Storage and Retrieval</topic><topic>International Classification of Diseases</topic><topic>Kidney diseases</topic><topic>Laboratories</topic><topic>Male</topic><topic>Medicare</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Mortality</topic><topic>Population</topic><topic>Prevalence</topic><topic>Registries</topic><topic>Renal Insufficiency, Chronic - complications</topic><topic>Renal Insufficiency, Chronic - epidemiology</topic><topic>Renal Insufficiency, Chronic - physiopathology</topic><topic>Renal Insufficiency, Chronic - urine</topic><topic>Surveillance</topic><topic>United States - epidemiology</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schroeder, Emily B</creatorcontrib><creatorcontrib>Powers, J. 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David</au><au>O’Connor, Patrick J</au><au>Nichols, Gregory A</au><au>Xu, Stanley</au><au>Desai, Jay R</au><au>Karter, Andrew J</au><au>Morales, Leo S</au><au>Newton, Katherine M</au><au>Pathak, Ram D</au><au>Vazquez-Benitez, Gabriela</au><au>Raebel, Marsha A</au><au>Butler, Melissa G</au><au>Lafata, Jennifer Elston</au><au>Reynolds, Kristi</au><au>Thomas, Abraham</au><au>Waitzfelder, Beth E</au><au>Steiner, John F</au><aucorp>SUPREME-DM Study Group</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011</atitle><jtitle>Journal of diabetes and its complications</jtitle><addtitle>J Diabetes Complications</addtitle><date>2015-07-01</date><risdate>2015</risdate><volume>29</volume><issue>5</issue><spage>637</spage><epage>643</epage><pages>637-643</pages><issn>1056-8727</issn><eissn>1873-460X</eissn><abstract>Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effect of different ascertainment methods on prevalence estimates. Methods We used the SUPREME-DM DataLink (n = 879,312) to estimate annual CKD prevalence. Methods of CKD ascertainment included: diagnosis codes alone, impaired estimated glomerular filtration rate (eGFR) alone (eGFR &lt; 60 mL/min/1.73 m2 ), albuminuria alone (spot urine albumin creatinine ratio &gt; 30 mg/g or equivalent), and combinations of these approaches. Results CKD prevalence was 20.0% using diagnosis codes, 17.7% using impaired eGFR, 11.9% using albuminuria, and 32.7% when one or more method suggested CKD. The criteria had poor concordance. After age- and sex-standardization to the 2010 U.S. Census population, prevalence using diagnosis codes increased from 10.7% in 2005 to 14.3% in 2011 ( P &lt; 0.001). The prevalence using eGFR decreased from 9.7% in 2005 to 8.6% in 2011 ( P &lt; 0.001). Conclusions Our data indicate that CKD prevalence and prevalence trends differ according to the CKD ascertainment method, highlighting the necessity for multiple sources of data to accurately estimate and track CKD prevalence.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>25936953</pmid><doi>10.1016/j.jdiacomp.2015.04.007</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record>
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identifier ISSN: 1056-8727
ispartof Journal of diabetes and its complications, 2015-07, Vol.29 (5), p.637-643
issn 1056-8727
1873-460X
language eng
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subjects Adult
Age
Aged
Aged, 80 and over
Albuminuria - etiology
Cardiovascular disease
Chronic renal insufficiency
Codes
Cohort Studies
Delivery of Health Care, Integrated
Diabetes
Diabetes mellitus
Diabetic Nephropathies - epidemiology
Diabetic Nephropathies - physiopathology
Diabetic Nephropathies - urine
Disease control
Disease prevention
Electronic Health Records
Endocrinology & Metabolism
Epidemiological Monitoring
Epidemiology
Female
Follow-Up Studies
Glomerular Filtration Rate
Humans
Information Storage and Retrieval
International Classification of Diseases
Kidney diseases
Laboratories
Male
Medicare
Methods
Middle Aged
Mortality
Population
Prevalence
Registries
Renal Insufficiency, Chronic - complications
Renal Insufficiency, Chronic - epidemiology
Renal Insufficiency, Chronic - physiopathology
Renal Insufficiency, Chronic - urine
Surveillance
United States - epidemiology
Young Adult
title Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011
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