CKD Prevalence in the Military Health System: Coded Versus Uncoded CKD

Jenna M Norton; Lindsay Grunwald; Amanda Banaag; Cara Olsen; Andrew S Narva; Eric Marks; Tracey P Koehlmoos

doi:10.1016/j.xkme.2021.03.015

CKD Prevalence in the Military Health System: Coded Versus Uncoded CKD

Kidney Med. 2021 Jun 2;3(4):586-595.e1. doi: 10.1016/j.xkme.2021.03.015. eCollection 2021 Jul-Aug.

Authors

Jenna M Norton¹, Lindsay Grunwald², Amanda Banaag², Cara Olsen¹, Andrew S Narva³, Eric Marks^{1

4}, Tracey P Koehlmoos¹

Affiliations

¹ Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD.
² Henry M Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD.
³ College of Agriculture, Urban Sustainability & Environmental Sciences, University of the District of Columbia, Washington, DC.
⁴ Division of Nephrology, Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD.

Abstract

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.

Keywords: Chronic kidney disease; chronic renal disease; chronic renal insufficiency; electronic health record; electronic phenotype; estimated glomerular filtration rate; medical record; proteinuria.