CKD Prevalence in the Military Health System: Coded Versus Uncoded CKDPlain-Language Summary
Rationale & Objective: Chronic kidney disease (CKD) is common but often goes unrecorded. Study Design: Cross-sectional. Setting & Participants: Military Health System (MHS) beneficiaries aged 18 to 64 years who received care during fiscal years 2016 to 2018. Predictors: Age, sex, act...
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doaj-9fe2a907d22b4a54a4667023408c9bc82021-08-04T04:20:36ZengElsevierKidney Medicine2590-05952021-07-0134586595.e1CKD Prevalence in the Military Health System: Coded Versus Uncoded CKDPlain-Language SummaryJenna M. Norton0Lindsay Grunwald1Amanda Banaag2Cara Olsen3Andrew S. Narva4Eric Marks5Tracey P. Koehlmoos6Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD; Address for Correspondence: Jenna M. Norton, PhD, MPH, F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20184.Henry M Jackson Foundation for the Advancement of Military Medicine, Bethesda, MDHenry M Jackson Foundation for the Advancement of Military Medicine, Bethesda, MDDepartment of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MDCollege of Agriculture, Urban Sustainability & Environmental Sciences, University of the District of Columbia, Washington, DCDepartment of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD; Division of Nephrology, Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MDDepartment of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MDRationale & Objective: Chronic kidney disease (CKD) is common but often goes unrecorded. Study Design: Cross-sectional. Setting & Participants: Military Health System (MHS) beneficiaries aged 18 to 64 years who received care during fiscal years 2016 to 2018. Predictors: Age, sex, active duty status, race, diabetes, hypertension, and numbers of kidney test results. Outcomes: We defined CKD by International Classification of Diseases, Tenth Revision (ICD-10) code and/or a positive result on a validated electronic phenotype that uses estimated glomerular filtration rate and measures of proteinuria with evidence of chronicity. We defined coded CKD by the presence of an ICD-10 code. We defined uncoded CKD by a positive e-phenotype result without an ICD-10 code. Analytical Approach: We compared coded and uncoded populations using 2-tailed t tests (continuous variables) and Pearson χ2 test for independence (categorical variables). Results: The MHS population included 3,330,893 beneficiaries. Prevalence of CKD was 3.2%, based on ICD code and/or positive e-phenotype result. Of those identified with CKD, 63% were uncoded. Compared with beneficiaries with coded CKD, those with uncoded CKD were younger (aged 45 ± 13 vs 52 ± 11 years), more often women (54.4% vs 37.6%) and active duty (20.2% vs 12.5%), and less often of Black race (18.5% vs 31.5%) or with diabetes (23.5% vs 43.5%) or hypertension (46.6% vs 77.1%; P < 0.001). Beneficiaries with coded (vs uncoded) CKD had greater numbers of kidney test results (P < 0.001). Limitations: Use of cross-sectional administrative data prevents inferences about causality. The CKD e-phenotype may fail to capture CKD in individuals without laboratory data and may underestimate CKD. Conclusions: The prevalence of CKD in the MHS is ~3.2%. Beneficiaries with well-known CKD risk factors, such as older age, male sex, Black race, diabetes, and hypertension, were more likely to be coded, suggesting that clinicians may be missing CKD in groups traditionally considered lower risk, potentially resulting in suboptimal care.http://www.sciencedirect.com/science/article/pii/S2590059521001059Chronic kidney diseasechronic renal diseasechronic renal insufficiencyelectronic health recordmedical recordestimated glomerular filtration rate |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jenna M. Norton Lindsay Grunwald Amanda Banaag Cara Olsen Andrew S. Narva Eric Marks Tracey P. Koehlmoos |
spellingShingle |
Jenna M. Norton Lindsay Grunwald Amanda Banaag Cara Olsen Andrew S. Narva Eric Marks Tracey P. Koehlmoos CKD Prevalence in the Military Health System: Coded Versus Uncoded CKDPlain-Language Summary Kidney Medicine Chronic kidney disease chronic renal disease chronic renal insufficiency electronic health record medical record estimated glomerular filtration rate |
author_facet |
Jenna M. Norton Lindsay Grunwald Amanda Banaag Cara Olsen Andrew S. Narva Eric Marks Tracey P. Koehlmoos |
author_sort |
Jenna M. Norton |
title |
CKD Prevalence in the Military Health System: Coded Versus Uncoded CKDPlain-Language Summary |
title_short |
CKD Prevalence in the Military Health System: Coded Versus Uncoded CKDPlain-Language Summary |
title_full |
CKD Prevalence in the Military Health System: Coded Versus Uncoded CKDPlain-Language Summary |
title_fullStr |
CKD Prevalence in the Military Health System: Coded Versus Uncoded CKDPlain-Language Summary |
title_full_unstemmed |
CKD Prevalence in the Military Health System: Coded Versus Uncoded CKDPlain-Language Summary |
title_sort |
ckd prevalence in the military health system: coded versus uncoded ckdplain-language summary |
publisher |
Elsevier |
series |
Kidney Medicine |
issn |
2590-0595 |
publishDate |
2021-07-01 |
description |
Rationale & Objective: Chronic kidney disease (CKD) is common but often goes unrecorded. Study Design: Cross-sectional. Setting & Participants: Military Health System (MHS) beneficiaries aged 18 to 64 years who received care during fiscal years 2016 to 2018. Predictors: Age, sex, active duty status, race, diabetes, hypertension, and numbers of kidney test results. Outcomes: We defined CKD by International Classification of Diseases, Tenth Revision (ICD-10) code and/or a positive result on a validated electronic phenotype that uses estimated glomerular filtration rate and measures of proteinuria with evidence of chronicity. We defined coded CKD by the presence of an ICD-10 code. We defined uncoded CKD by a positive e-phenotype result without an ICD-10 code. Analytical Approach: We compared coded and uncoded populations using 2-tailed t tests (continuous variables) and Pearson χ2 test for independence (categorical variables). Results: The MHS population included 3,330,893 beneficiaries. Prevalence of CKD was 3.2%, based on ICD code and/or positive e-phenotype result. Of those identified with CKD, 63% were uncoded. Compared with beneficiaries with coded CKD, those with uncoded CKD were younger (aged 45 ± 13 vs 52 ± 11 years), more often women (54.4% vs 37.6%) and active duty (20.2% vs 12.5%), and less often of Black race (18.5% vs 31.5%) or with diabetes (23.5% vs 43.5%) or hypertension (46.6% vs 77.1%; P < 0.001). Beneficiaries with coded (vs uncoded) CKD had greater numbers of kidney test results (P < 0.001). Limitations: Use of cross-sectional administrative data prevents inferences about causality. The CKD e-phenotype may fail to capture CKD in individuals without laboratory data and may underestimate CKD. Conclusions: The prevalence of CKD in the MHS is ~3.2%. Beneficiaries with well-known CKD risk factors, such as older age, male sex, Black race, diabetes, and hypertension, were more likely to be coded, suggesting that clinicians may be missing CKD in groups traditionally considered lower risk, potentially resulting in suboptimal care. |
topic |
Chronic kidney disease chronic renal disease chronic renal insufficiency electronic health record medical record estimated glomerular filtration rate |
url |
http://www.sciencedirect.com/science/article/pii/S2590059521001059 |
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