Screening for Chronic Kidney Disease by Mobile Health Unit Outreach

TL;DR

This study used mobile health units to screen for chronic kidney disease and identify risk factors in the Detroit area.

Contribution

The study provides population-wide screening data and risk factors for chronic kidney disease via mobile health units.

Findings

Mobile health units effectively reached a broad population for chronic kidney disease screening.

Common risk factors for chronic kidney disease were identified among the screened individuals.

Abstract

This cross-sectional study reports population-wide screening results and risk factors for chronic kidney disease among individuals who received screening from mobile health units in the Detroit, Michigan, area.

Tables

Table 1.. Characteristics of Patients at Mobile Health Unit Encountersa

Kidney disease screening cohort	Unique patients (N = 5128)b	Total encounters (N = 5973)
Age, median (IQR), y [range, 18-103 y]	56 (41-67)	57 (42-68)
Race and ethnicity, No. (%)c
American Indian or Alaska Native	11 (0.2)	17 (0.3)
Asian	126 (2.5)	159 (2.7)
Black or African American	3560 (69.4)	4175 (69.9)
Middle Eastern or North African	10 (0.2)	10 (0.2)
White	732 (14.3)	845 (14.2)
Other not specified	393 (7.7)	427 (7.1)
Patient declined	293 (5.7)	337 (5.6)
Sex, No. (%)
Female	3150 (61.4)	3684 (61.7)
Male	1978 (38.6)	2289 (38.3)
Systolic BP, median (IQR), mm Hg (range, 80-231 mm Hg)	121 (111-133)	121 (111-133)
Diastolic BP, median (IQR), mm Hg (range, 34-146 mm Hg)	75 (67-83)	75 (67-83)
Systolic BP ≥130 mm Hg, No. (%)	1380 (30.9)	1634 (31.5)
Laboratory test results
HbA1c, median (IQR), % (range, 4.2%-15.4%)	5.8 (5.4-6.2)	5.8 (5.5-6.2)
HbA1c ≥6.5%, No. (%)	811 (16.2)	965 (16.5)
Creatinine, median (IQR), mg/dL (range, 0.4-12. mg/dL)	0.9 (0.8-1.1)	0.9 (0.8-1.1)
eGFR, median (IQR), mL/min/1.73 m2 (range, 4-142 mL/min/1.73 m2)	86 (71-100)	84 (70-99)
eGFR threshold, mL/min/1.73 m2, No. (%)
≥90	2222 (43.3)	2453 (41.1)
60-89	2293 (44.7)	2767 (46.3)
Stage G3 (30-59)	579 (11.3)	717 (12.0)
G3a (45-59)	455 (8.9)	581 (9.7)
G3b (30-44)	124 (2.4)	136 (2.3)
Stage G4 (15-29)	29 (0.6)	31 (0.5)
Stage G5 (<15)	5 (0.1)	5 (<0.1)

Table 2.. Associations Between Patient Characteristics and Chronic Kidney Disease

Characteristic	Unadjusted		Adjusteda
	RR (95% CI)	P value	RR (95% CI)	P value
eGFR <60 mL/min/1.73 m2
Ageb
Q2 vs Q1	4.71 (2.88-7.69)	<.001c	3.99 (2.40-6.64)	<.001c
Q3 vs Q1	11.33 (7.11-18.06)	<.001c	9.32 (5.76-15.06)	<.001c
Q4 vs Q1	21.95 (13.91-34.64)	<.001c	18.62 (11.64-29.78)	<.001c
Sex (male vs female)	0.71 (0.60-0.83)	<.001c	0.78 (0.67-0.92)	.002c
Race (Black vs race other than Black)	1.63 (1.36-1.95)	<.001c	1.73 (1.44-2.08)	<.001c
HbA1c ≥6.5%	1.34 (0.73-2.47)	.35	1.18 (1.02-1.38)	.03c
Systolic BP ≥130 mm Hg	0.76 (0.51-1.14)	.18	0.95 (0.82-1.09)	.43
eGFR <45 mL/min/1.73 m2
Ageb
Q2 vs Q1	9.94 (2.33-42.35)	.002c	8.07 (1.87-34.76)	.005c
Q3 vs Q1	24.68 (6.05-100.69)	<.001c	17.62 (4.25-73.00)	<.001c
Q4 vs Q1	49.69 (12.37-199.57)	<.001c	35.35 (8.66-144.29)	<.001c
Sex (male vs female)	0.87 (0.63-1.21)	.42	1.08 (0.77-1.51)	.65
Race (Black vs race other than Black)	1.70 (1.16-2.49)	.006c	2.01 (1.31-3.09)	.002c
HbA1c ≥6.5%	2.53 (1.82-3.51)	<.001c	1.74 (1.25-2.43)	.001c
Systolic BP ≥130 mm Hg	1.32 (0.93-1.87)	.12	1.04 (0.75-1.43)	.82
eGFR <30 mL/min/1.73 m2
Ageb
Q2 vs Q1	3.08 (0.32-29.39)	.33	1.47 (0.31-16.37)	.75
Q3 vs Q1	15.77 (2.08-119.73)	.008c	8.66 (1.11-67.44)	.04c
Q4 vs Q1	17.53 (2.33-131.91)	.005c	11.68 (1.52-89.95)	.02c
Sex (male vs female)	1.76 (0.89-3.47)	.10	2.07 (1.04-4.12)	.04c
Race (Black vs race other than Black)	2.10 (0.87-5.08)	.10	2.29 (0.89-5.91)	.09
HbA1c ≥6.5%	4.17 (2.17-8.02)	<.001c	2.58 (1.30-5.14)	.007c
Systolic BP ≥130 mm Hg	2.58 (1.33-5.03)	.005c	1.86 (0.94-3.69)	.07

Full text

Introduction

Guidelines advocate chronic kidney disease (CKD) screening approaches that target individuals with risk factors (eg, diabetes).^1^ However, recent studies suggest that population-wide screening for CKD may improve public health and could be cost-effective with the advent of effective preventive treatments (eg, sodium-glucose transport-2 inhibitors).^2,3^ Greater benefits, particularly at younger ages, were shown for non-Hispanic Black populations with increased CKD-related burden.^2^ Nonetheless, proactive large-scale screening systems are uncommon, even in high-risk socially disadvantaged communities such as Detroit, Michigan.^3^ To improve earlier detection of cardiovascular-kidney-metabolic risk factors, we developed a population health program that deploys mobile health units (MHUs) across metropolitan Detroit.^4^ The goal is to decrease area-wide morbidity and mortality and reduce health disparities. We report program findings regarding population-wide CKD screening results and risk factors.

Methods

This cross-sectional study, conducted from July 7, 2022, to August 24, 2025, was approved by Wayne State University’s institutional review board and is reported in accordance with the STROBE reporting guideline. The institutional review board determined that the study met criteria for waiver of informed consent because the research involved minimal risk to participants and could not practicably be conducted without the waiver. We analyzed a deidentified limited use dataset provided under an agreement between Wayne State University and Wayne Health. The MHU program has been previously described.^4^ Briefly, 5 to 7 MHUs deploy with nonphysician staff 5 to 6 days per week to partner locations across metropolitan Detroit. After a brief medical history was taken, adults 18 years or older were offered screenings for high blood pressure (BP), diabetes (ie, hemoglobin A_1c_ [HbA_1c_] measurement), and CKD (ie, estimated glomerular filtration rate [eGFR] calculated from a comprehensive metabolic panel). Seated blood pressure was measured onsite after 5 minutes of rest using the mean of 3 upper arm readings (1-minute intervals) determined by an Omron 907XL monitor (Omron Healthcare).^5^ Venous blood samples were obtained onsite and shipped to a partner laboratory (Quest Diagnostics or Labcorp). We analyzed results from July 7, 2022, to August 24, 2025, after the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 creatinine equation for calculating eGFR using creatinine (eliminating race-based adjustments to enhance equity) was adopted.

We estimated relative risk with 95% CIs for levels of CKD using log-binomial generalized estimating equations models that accounted for repeated measurements by clustering on patient identification numbers using an exchangeable working correlation matrix and robust (sandwich) standard errors. Multivariable adjusted models included patient characteristics selected a priori. Type I error was limited to 5% (2-sided α = .05), and analyses were performed using SAS, version 9.4.

Results

Results from our cohort of 5128 unique patients with 5973 encounters are shown in Table 1; 2293 patients (44.7%) had eGFRs of 60 to 89 mL/min/1.73 m^2^, whereas 579 (11.3%) had CDK stage 3 or higher (eGFR <60 mL/min/1.73 m^2^). Older age, Black race, and diabetes (HbA_1c_ ≥6.5%) were associated with higher risks for CKD stage 3 or higher (Table 2).

Discussion

This is the first report of results from a population-wide screening program using community outreach by MHUs. Findings are similar to national statistics using the CKD-EPI 2021 eGFR creatinine formula showing higher CKD rates among at-risk subgroups—especially older individuals and Black adults.^5^ However, prevalence of CKD stage 3 or higher (based on eGFR alone) in our cohort (11%-13%) was double that of the general US population in the National Health and Nutrition Examination Survey (6.5%) and closer to that in the Veteran’s Affairs cohort (19.6%).^5,6^ This difference is most likely due to our population residing in socially vulnerable communities enriched with CKD risk factors. A limitation was our inability to measure urine albumin to creatinine ratio, which, when added in the future, will result in higher rates of CKD detection. Follow-up eGFR measurements were also not performed to confirm CKD stages, which may result in some misclassification of patients. Altogether, these novel findings suggest significant potential for public health benefits associated with population-wide CKD screening in at-risk communities using MHU platforms.