Ethnic and Geographic Disparities in Proteinuria and High Blood Pressure: A Cross-Sectional Study from Community Kidney Screenings in the Washington DC Area

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We conducted a cross-sectional analysis of 1,597 adults through community screenings in underserved areas of the Washington DC Metropolitan Area, assessing proteinuria and high blood pressure using urine dipstick tests and automated blood pressure cuffs. African Americans were the predominant group and exhibited the highest prevalence of proteinuria and high blood pressure, followed by Hispanic/Latino individuals, with Caucasians having the lowest rates. Adjusted analysis showed African Americans and Hispanic/Latino participants had significantly higher odds of significantly elevated blood pressure (>160/95) compared to Caucasians. Crude analysis revealed higher proteinuria rates among residents of DC Wards 1-4 and 7-8, and Maryland, compared to Virginia. Of 245 high-risk participants contacted post-screening, 92 (37.6%) responded; 48 (52.2%) had visited a physician and 31 (33.7%) intended to seek care. Awareness surveys indicated that 60% of respondents reported increased CKD awareness after the intervention. Our findings highlight significant ethnic and geographic disparities in CKD risk factors, particularly among African Americans and specific DC wards. Community outreach screenings can enhance early detection, improve healthcare engagement, and potentially reduce CKD progression, leading to better access to transplantation and post-transplant outcomes. “CKD” “Ethnic disparities” “Community screening” “Proteinuria” “Hypertension” “DC” “Kidney Transplantation” Figures Figure 1 Figure 2 Figure 3 Introduction Chronic kidney disease (CKD) is a significant but often overlooked public health issue. It affects 14% of U.S adults with an estimated 90% of them unaware of their condition ( 1 ). Recent estimates of patients over the age of 66 suggest that the adjusted mortality rate for those with CKD is more than double that of patients without the disease ( 2 ). CKD often presents with non-specific symptoms and progresses slowly until advanced stages. When left untreated, it greatly increases the risk for adverse health outcomes such as kidney failure, heart disease, and stroke ( 1 ). Furthermore, end stage renal disease (ESRD) management is limited to two options: dialysis or kidney transplant. National statistics revealed that approximately 44,187 adult candidates were added to the kidney waiting list in 2022. The list of patients waiting for a kidney transplant has grown steadily over the years, now at approximately 92,000 people ( 3 , 4 ). Beyond the public health implications, CKD is also a financial burden for our healthcare system. Treatment cost among Medicare beneficiaries alone reached $ 87.2 billion in 2019 ( 1 ). The need for early detection and management is apparent, particularly in populations that are at high risk for developing CKD. One way to address this need is by providing free community-based health screenings, targeted at areas where risk factors are prevalent ( 5 , 6 ). Differences in awareness rate among adults with CKD Stages 3–5 exist across ethnic groups with higher awareness seen in non-Hispanic Blacks compared to non-Hispanic Whites ( 7 ). This may be contributed to by factors including higher prevalence of disease within ethnic communities and families, higher rates of CKD comorbidities, and more advanced disease stage at time of diagnosis among ethnic groups ( 8 , 9 ). By improving awareness of CKD incidence and management, patients are empowered to seek necessary timely follow-up care, begin appropriate treatment, manage risk factors, lower long-term healthcare costs, and avoid progression and complications ( 10 ). Community health screenings have demonstrated some success in raising awareness and facilitating early detection of CKD, but the overall effectiveness remains uncertain without an objective measure of its direct impact ( 5 ). Many existing screening programs, such as the World Kidney Day (WKD) campaign and the Kidney Early Evaluation Program (KEEP), have reported modest increases in CKD awareness ( 11 , 12 ). Despite prior mixed results, the apparent benefit of kidney screening programs to detect early disease and improve awareness can outweigh the risk when utilizing non-complex screening tools such as single urine protein dipstick and sphygmomanometers. This may be especially effective in areas where the incidence of kidney disease is highest (13,14). The benefits of targeted health screenings may be most evident in diverse populations, such as those in the DC metropolitan area. This area has the highest CKD rate in the United States. In the greater Washington DC area alone 8,300 individuals are on the transplant waitlist and around 80% of these patients identify as ethnic minorities ( 15 ). From January 1, 2019 to July 1, 2024 we conducted over 100 kidney screenings events across the DC metropolitan area [Figure 3 ]. These were held at a variety of sites including community centers and health fairs. During these screenings we gathered comprehensive data including blood pressure measurements, proteinuria tests, demographic information, medical history, and health awareness levels via voluntary surveys. The aim of this study is to: ( 1 ) detect early markers of kidney disease; ( 2 ) identify demographic and clinical predictors of CKD risk; and ( 3 ) evaluate the utility of community screenings in promoting early awareness and healthcare engagement. By doing that, we hope to enhance public health knowledge, stratify the relative risk of CKD between local subpopulations, and contribute to reducing the growing burden of kidney disease on both individuals and the healthcare system. Methods The George Washington Ron and Joy Paul Kidney Center (in partnership with the National Minority Organ Tissue Transplant Education Program or MOTTEP) provides kidney screenings and education at health fairs across the Washington D.C. metropolitan area. The center participates in approximately 45 events annually. Events are held both independently and in partnership with local organizations. In 2024, 34 events were held in D.C and 12 were held in Maryland. Kidney screenings consist of: ( 1 ) completing the participant form and signing the consent, ( 2 ) a blood pressure test using automated cuff machines, ( 3 ) a proteinuria screening using dipstick urinalysis to measure the Albumin creatinine ratio (ACR), and ( 4 ) reviewing results with a team member and discussing relevant health education. Following the screening, participants are followed up via phone. The intention of this follow-up call is to gauge how effective the screening was and, for those who have abnormal results (blood pressure > 130/80) or albuminuria (more than 30 mg/g), to usher participants to see a provider. Ultimately, we want subjects with abnormal results to seek care and change their health trajectory. This study was approved by the Institutional Review Board (IRB) at The George Washington University (NCR245978), ensuring compliance with ethical guidelines. All data was anonymized prior to analysis to protect participant identities. Statistical Methods : Chi-squared and Analysis of Variance tests were used to evaluate the relationships between potential covariables including sex, age, history of type II diabetes, heart disease, previous stroke and history of hypertension, home zip code, and patient ethnicity. Univariant and multivariant logistic regression was used to elucidate the relationship between ethnicity and the likelihood of screened proteinuria. Univariable and multivariable logistic regression was also used to assess the relationship between ethnicity and screened high blood pressure (≥ 130/80). Logistic regression analysis was repeated for significantly elevated blood pressure (≥ 160/95). Subjects with missing values were dropped. Results A total of 1,597 participants were included in this cross-sectional study. The cohort predominantly consisted of African Americans (73.0%), with additional representation from Caucasians (10.1%), Hispanic/Latino (5.0%), Asian (4.6%), Alaskan (1.8%), and other ethnicities (5.6%). The mean age was 54.2 years (SD 16.1), with 52.3% female and 47.7% male participants. Significant comorbidities were prevalent, with 37.4% reporting Type II diabetes mellitus and 54.9% having a history of hypertension [Table1] Proteinuria Prevalence Proteinuria prevalence varied significantly across ethnic groups, with the highest observed among individuals identifying as “Other” (16.9%) and African Americans (16.1%), followed by Hispanic/Latino (12.7%), Alaskans (10.3%), Asians (9.6%), and Caucasians (8.0%).In the crude analysis, African Americans demonstrated higher odds of proteinuria compared to Caucasians (OR 2.21, 95% CI 1.19–4.08). However, after adjustment for potential confounders such as age, history of hypertension, sex, home address, and history of diabetes, this difference was not statistically significant (adjusted OR 1.35, 95% CI 0.65–2.82). [Table2] [Figure 1]. Additionally, residence location was associated with differences in proteinuria prevalence. Participants living in DC Wards 1-4 OR 2.63 (95% CI 1.19-5.82), DC Wards 7-8 OR 2.10 (95% CI 0.99-4.47), and Maryland OR 1.94 (95% CI 0.94-4.00), had higher odds of proteinuria compared to those living in Virginia. High Blood Pressure Prevalence High blood pressure prevalence also varied by ethnicity with the highest rates among African Americans (69.3%), followed by Hispanic/Latino (56.9%), Other ethnicities (57.0%), Caucasians (53.1%), Asians (47.9%), and Alaskans (34.5%). Crude analysis showed African Americans (OR 2.00, 95% CI 1.43-2.79) and Alaskans (OR 2.65, 95% CI 1.07-6.58) were more likely to have measured high blood pressure compared to Caucasians. After adjustment, African American participants remained significantly more likely to have hypertension (adjusted OR 1.68, 95% CI 1.05-2.70) [Table3] [Figure2] Significantly elevated blood pressure (≥160/95 mmHg) was most prevalent among Hispanic/Latino participants (24.1%), followed by those who identified as Other ethnicities (22.5%), African Americans (20.5%), Asians (10.9%), Caucasians (8.6%), and Alaskans (6.9%). In adjusted analysis African Americans were three times (OR 3.04, 95% CI 1.39-6.67), and Hispanic/Latinos were nearly four times (OR 3.91, 95% CI 1.35-11.30) as likely as Caucasians to have measured significantly elevated blood pressure [Table4] [Figure2] Insurance and Follow-Up Outcomes Regarding insurance status, only 27.0% (n=321) of participants were insured, 7.5% (n=89) were uninsured, and insurance status was missing for 65.5% of the cohort. Crude analysis showed that insured participants were 91% more likely to have screened proteinuria than uninsured, though it was not statistically significant (95% CI 0.90-4.06; p=0.091). Follow-up results (n=245 high-risk participants): Among 245 high-risk participants identified during screening, follow-up outcomes showed a response rate of 37.6% (n=92), with the majority (n=84) being African American. Of these, 52.2% (n=48) reported already having seen a healthcare provider, and 33.7% (n=31) planned to do so; 19 of those who followed up and 16 of those who planned were insured. Of the 60 respondents who answered CKD awareness questions, 36 (60%) reported increased awareness post-screening, while six were already aware prior to screening. Discussion According to the National Kidney Foundation there is a 5-step plan for CKD evaluation and referral including knowing the criteria for CKD, recognizing risk factors, screening for CKD, classifying CKD to guide testing and treatment, and implementing a clinical action plan based on patient's CKD classification ( 16 ). In our project, we focused on the screening and risk factor recognition phases. CKD screening typically involves two simple tests: a spot urine albumin-to-creatinine ratio (ACR) to detect albuminuria and/or a serum creatinine test to estimate glomerular filtration rate (GFR). Identifying risk factors requires obtaining a thorough patient’s medical history, of chronic diseases such as diabetes and hypertension, social history and family history of kidney disease. Our study reinforces the same goal of WKD campaign, and the KEEP screening programs ( 11 , 12 ) by: Targeting underserved communities with limited healthcare access echoing WKD’s 2024 theme of “Kidney Health for All.” Using screening to promote early detection of CKD risk factors, such as proteinuria and hypertension, in minority groups disproportionately affected by kidney disease. Advocating for community-based interventions that can bridge healthcare gaps and reduce long-term disease burden. Our study also in Alignment with the Kidney Early Evaluation Program (KEEP) by providing free screening to individuals at risk for CKD, with a focus on early intervention. We complement KEEP by: Emphasizing portable, community-based screening, mirroring KEEP’s outreach model. Collecting data on self-reported history, blood pressure, and urine protein, directly paralleling KEEP’s approach to risk stratification. Highlighting racial disparities in KEEP, African Americans and Hispanics consistently showed higher CKD risks, a pattern that our findings reinforce, particularly with adjusted odds for hypertension and proteinuria. On the other hand, Unique Contributions of our Study: Geographic Focus: Our research adds value by focusing on the Washington DC Metropolitan Area, a region with marked health disparities. Our dataset provides contemporary insight, particularly valuable post-COVID-19, as pandemic-related barriers may have worsened chronic disease outcomes in some specific communities. Specific Ethnic Associations: While KEEP and WKD broadly categorize racial risk, our study adds more details by examining adjusted associations between ethnicity and both high blood pressure and proteinuria; offering a more refined understanding of disease dynamics in real world settings. CKD prevalence and access to care disproportionately affect various ethnic groups in the U.S ( 17 – 19 ). Approximately 20% of African Americans adults and 14% of Hispanics adults are affected compared to 12% of non-Hispanic White adults ( 1 ). Moreover, the incidence of progressing to ESRD among African Americans is four times greater than non-Hispanic Whites, and two times greater in Hispanic and Native Americans ( 2 ). The impact of this is magnified by the fact that African Americans comprise almost one-third of the transplant list and are less likely to receive a living donor kidney, thereby contributing to a higher mortality rate due to ESRD ( 8 ). Our study’s results were in line with national statistics; African Americans were more than twice as likely as the Caucasian population to have proteinuria (OR 2.21, 95% CI 1.19–4.08). However, when considering covariables such as age, history of risk factors, sex, zip codes, and history of diabetes this association was no longer significant (OR 1.35, 95%, CI 0.65–2.82). As proteinuria is both a risk factor and indicator of CKD, these findings highlight persistent ethnic disparities in renal disease seen among African Americans populations in the U.S ( 1 , 2 ), ( 11 , 12 ). While our adjusted results suggest that proteinuria may be partially explained by comorbidities, literature consistently points to the trend that African Americans have systemically faced barriers that contribute to higher disease prevalence within this population ( 14 ). Among these persistent barriers are understanding of CKD and its risk factors, low trust in healthcare systems, and financial burden. Our findings also revealed geographic disparities in prevalence of proteinuria. Residents in DC Wards 1–4, 7–8, and Maryland, demonstrated higher odds of proteinuria than Virginia residents. End-stage renal disease in the 20019-zip code alone is 44 times the national average ( 15 ). These findings underscore the importance of socioeconomic and geographical factors particularly in developing health strategies targeting CKD screening. Our analysis highlights the need for incorporating adequate geographical context in future health strategies targeting CKD screening. This trend has been seen in numerous other health conditions such as Diabetes with Ward 8 reporting at a 15.2% prevalence whereas Ward 3 reports a 2.2% prevalence ( 20 ). We found that the prevalence of high blood pressure (> 130/80) and significantly elevated blood pressure (> 160/95) were notably higher among African American and Hispanic/Latino participants, further illustrating the disproportionate burden of CKD risk factors in these groups. African Americans were twice as likely to have high blood pressure compared to Caucasians (OR 2.00, 95% CI 1.43–2.79), and this association remained significant after adjustment for covariables (OR 1.68, 95% CI 1.05–2.70). These disparities are partially attributed to the greater prevalence of CKD risk factors and comorbidities such as hypertension, diabetes, proteinuria, and cardiovascular disease within these minority populations ( 9 , 21 ) Hypertension among the African American population is believed to contribute to almost a third of all CKD cases (22). Our finding in African Americans is well-supported by existing literature, which highlights their increased risk of proteinuria compared to White population at similar levels of elevated blood pressure (23). This is particularly troublesome in African American men ages 24–44, who have a 15-fold greater risk than White men of the same age group (23). Poor blood pressure control among African Americans participants, particularly men, was also observed through the Kidney Early Evaluation Program (KEEP) screening initiative (13). Addressing hypertensive CKD among African Americans requires a multidisciplinary approach that involves early detection of disease, lifestyle changes, addressing social determinants, patient education, pharmacological intervention, and frequent follow-up (23). In our study, we employed healthcare screenings within communities comprised of a large African American population; in doing so we aimed at addressing such barriers. To better address medical mistrust among future screening efforts, studies have suggested benefit in recruiting community members to work in conjunction with healthcare providers to conduct community screenings ( 6 ). Further, enlisting the help of community facilitators such as church health leaders to bridge gaps in knowledge and prompt screening among peers (22). Future kidney screening efforts should consider employing these community-centered approaches to better address the implicit barriers in screening within the African American population. Among the 245 subjects contacted for follow-up in our study, 92 (37.6%) were successfully reached, which is considered a low response rate. 84 (91.3%) of them were African American. Among respondents, 79 (85.9%) reported either having visited a doctor or planned to do so, which is considered a high follow-up rate. Additionally. 60% of respondents reported an increase in CKD awareness following their screening experience. Our findings align with previous studies showing that African Americans tend to have higher awareness of CKD. This could be due to factors such as a higher prevalence of CKD within ethnic communities and families, increased rates of CKD-related health conditions, and more advanced stages of CKD at the time of diagnosis in these populations ( 8 , 9 ). However, despite our respondents expressing intent to seek follow-up care and increased awareness, more systemic challenges in follow-up care of chronic disease are highlighted by the low follow-up response seen in our study. This limitation may be due to differences in participants’ understanding of healthcare and technology, as well as unequal access to technology among those surveyed (24). Further, shared barriers between providers and patients alike include lack of time to coordinate care and a relative non-prioritization of CKD when considering numerous other comorbidities (24). Innovative strategies are needed to improve CKD follow-up care in populations who have these barriers. This could include community partnership initiatives and utilizing patient navigation services that can ensure that patients are not lost during the follow-up period. For providers, there is a need for greater supportive technology embedded in the EMR and clinical guideline suggestions in ensuring that kidney disease is properly addressed during visits and followed up appropriately with the patient (24). By improving follow-up rates, the largely asymptomatic progression of CKD can be mitigated, thereby reducing morbidity and mortality. Limitations This study focuses on diverse populations. Its strength is found in the sample size and in integrating both crude and adjusted analyses for disparities. However, the study has several notable limitations that should be acknowledged. The cross-sectional design precludes conclusions about causality. While the overall sample size was large, representation from certain ethnic groups, such as Alaskan and Hispanic/Latino populations, was limited, reducing the statistical power to detect differences. Many variables, including hypertension and diabetes status, were self-reported and may be subject to recall or reporting bias. Proteinuria was assessed using a dipstick-based estimation of the ACR, which, although practical for screening purposes, is less accurate than laboratory-based methods. Additionally, possible selection bias may exist, as individuals attending health screenings could be more health-conscious or have better access to healthcare than the general population. The low proportion of responses to the follow-up calls and the absence of objective outcome data to evaluate the long-term impact of increased awareness also highlight areas for future improvement ( 5 ). Future Implications: Despite these limitations, the findings underscore the value of community-based screening programs in identifying at-risk populations. These results could help inform local public health strategies by guiding the allocation of resources, such as targeting mobile clinics or health education campaigns to underserved zip codes populations with high prevalence of risk factors. Additionally, the data could support efforts to integrate routine screenings into community health initiatives, ultimately contributing to earlier detection and intervention for chronic kidney disease. Conclusion Our finding highlights the persistent disparities in CKD risk factors, particularly among African American and Hispanic/Latino populations in the Washington DC metropolitan area. Community-based kidney screenings serve as a valuable tool for early detection and awareness, especially in underserved and high-risk communities. Expanding such efforts and incorporating strategies to improve follow-up and evaluate long-term outcomes could significantly contribute to reducing the burden of CKD and advancing health equity. Abbreviations ACR – Albumin/Creatinine Ratio CKD – chronic kidney disease DC – District of Columbia DM – Diabetes Mellitus ESRD – End Stage Renal Disease GFR – Glomerular Filtration Rate KEEP – Kidney Early Evaluation Program WKD – World Kidney Day Declarations Ethics approval and consent to participate: The study was approved by the IRB committee at George Washington University under IRB number of NCR245978. Written informed consent was obtained from all the participants of the study. Clinical trial number: Not applicable Consent for publication: Yes, obtained from the head of the Institute . Availability of data and materials : The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests : The authors of this manuscript have no conflicts of interest to disclose. Funding : This study was not funded by any external sources Authors’ contributions: All the authors were involved in conception, design, and execution of the study. The first author has prepared the manuscript, first and second authors were involved in the analysis of data. All others critically reviewed the manuscript and helped with the final draft. Acknowledgments: The authors wish to thank Muxin (Anna) Han, Omar Moharram, Rinna Talwar, and GW Kidney Club members for their contributions in the screening events, data collection, and phone calls. This contribution was invaluable in ensuring the success of this research. References Centers for Disease Control and Prevention. Chronic Kidney Disease in the United States, 2023. 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Washington (DC); Center for Planning, Policy, and Epidemiology (CPPE) [cited 2012 Apr 25]. Available from: http://www.cdc.gov/brfss Toto RD. Proteinuria and hypertensive nephrosclerosis in African Americans. Kidney Int Suppl. 2004;(92):S102-104. doi:10.1111/j.1523-1755.2004.09224.x Umeukeje EM, Wild MG, Maripuri S, Davidson T, Rutherford M, Abdel-Kader K, Lewis J, Wilkins CH, Cavanaugh K. Black Americans’ Perspectives of Barriers and Facilitators of Community Screening for Kidney Disease. Clinical Journal of the American Society of Nephrology. 2018;13(4):551–559. MARTINS D, AGODOA L, NORRIS KC. Hypertensive chronic kidney disease in African Americans: Strategies for improving care. Cleve Clin J Med. 2012;79(10):726-734. doi:10.3949/ccjm.79a.11109 Neale EP, Middleton J, Lambert K. Barriers and enablers to detection and management of chronic kidney disease in primary healthcare: a systematic review. BMC Nephrol. 2020;21:83. doi:10.1186/s12882-020-01731-x Tables Table 1. Comparison of covariates based on ethnicity for patients screened for Chronic Kidney Disease (CKD) risk factors Characteristic African American (n=1166) Alaskan (n=29) Asian (n=73) Caucasian (n=161) Hispanic/Latino (n=79) Other (n=89) p-value AGE (years), Mean (SD) 59.2 (16.3) 48.4 (19.5) 51.7 (14.7) 51.8 (16.8) 46.0 (15.9) 53.9 (17.2) <0.001 TYPE II DIABETES MELLITUS - Yes 483 (49%) - 22 (42%) 41 (30%) 29 (45%) 23 (34%) <0.001 TYPE II DIABETES MELLITUS - No 512 (51%) - 31 (58%) 95 (70%) 35 (55%) 45 (66%) <0.001 HISTORY OF CKD - Yes 137 (14%) - - - - - 0.372 HISTORY OF CKD - No 848 (86%) - - - - - 0.372 HISTORY OF HEART DISEASE - Yes 200 (20%) - 11 (20%) 39 (28%) - 11 (16%) 0.082 HISTORY OF HEART DISEASE - No 787 (80%) - 44 (80%) 98 (72%) - 57 (84%) 0.082 HISTORY OF STROKE - Yes 190 (19%) - 11 (20%) 26 (19%) 11 (16%) - 0.906 HISTORY OF STROKE - No 797 (81%) - 44 (80%) 109 (81%) 52 (84%) - 0.906 GENDER - Female 650 (69%) - 32 (65%) 72 (54%) 38 (64%) 43 (72%) 0.017 GENDER - Male 287 (31%) - 17 (35%) 61 (46%) 21 (36%) 17 (28%) 0.017 HISTORY OF HYPERTENSION - Yes 711 (72%) - 31 (56%) 64 (47%) 32 (50%) 39 (57%) <0.001 HISTORY OF HYPERTENSION - No 283 (28%) - 24 (44%) 71 (53%) 32 (50%) 29 (43%) <0.001 HOME ADDRESS - DC Wards 1-4 128 - 15 27 - - <0.001 HOME ADDRESS - DC Wards 7-8 316 - 15 - - 12 <0.001 HOME ADDRESS - Virginia 37 16 49 - - - <0.001 HOME ADDRESS - Maryland 397 17 35 35 35 30 <0.001 Table 2. The Relationship between Ethnicity and Protein in Urinalysis Characteristic Crude Model* ( Odds Ratio , 95% CI); p-value Adjusted Model** ( Odds ratio , 95% CI); p-value Ethnicity African American Alaskan Asian Caucasian Hispanic/Latino Other Covariables Gender (male) Age History of Diabetes (yes) History of Hypertension (yes) Home Address DC Wards 1-4 DC Wards 7-8 Virginia Maryland 2.21 (1.19-4.08); 0.012 1.82 (0.47-7.03); 0.388 1.72 (0.69-4.31); 0.243 1 1.72 (0.69-4.31); 0.243 1.69 (0.70-4.11); 0.246 1.02 (0.72-1.44); 0.771 1 (0.99-1.01); 0.626 1.08 (0.78-1.49); 0.649 1.12 (0.79-1.58); 0.525 2.63 (1.19-5.82); 0.017 2.10 (0.99-4.47); 0.055 1 1.94 (0.94-4.00); 0.075 1.35 (0.65-2.82); 0.425 1.31 (0.25-7.01); 0.749 1.31 (0.41-4.18); 0.649 1 1.19 (0.34-4.16); 0.787 1.31 (0.41-4.23); 0.650 1.06 (0.69-1.61); 0.796 1 (0.99-1.01); 0.978 0.90 (0.60-1.37); 0.635 1.20 (0.76-1.90); 0.425 2.07 (0.81-5.27); 0.129 1.99 (0.81-4.89); 0.134 1 1.72 (0.73-4.07); 0.216 *Univariable logistic regressions analysis performed. **Multivariable logistic regressions analysis performed. Table 3. The Relationship between Ethnicity and High Blood Pressure Characteristic Crude Model* ( Odds Ratio , 95% CI); p-value Adjusted Model** ( Odds Ratio , 95% CI); p-value Ethnicity African American Alaskan Asian Caucasian Hispanic/Latino Other Covariables Gender (male) Age History of Diabetes (yes) History of Hypertension (yes) Home Address DC Wards 1-4 DC Wards 7-8 Virginia Maryland 2.00 (1.43-2.79); <0.001 2.65 (1.07-6.58); 0.036 1.36 (0.77-2.39); 0.291 1 1.06 (0.62-1.83); 0.833 1.97 (1.13-3.44); 0.017 1.08 (0.84-1.38); 0.644 1.03 (1.02-1.04); <0.001 1.23 (0.98-1.55); 0.077 2.09 (1.65-2.65); <0.001 1.25 (0.78-2.01); 0.362 1.21 (0.79-1.87); 0.386 1 1.41 (0.93-2.14); 0.107 1.68 (1.05-2.70); 0.031 2.88 (0.81-10.19); 0.101 1.32 (0.61-2.89); 0.480 1 0.99 (0.46-2.13); 0.978 2.35 (0.99-5.60); 0.054 1.47 (1.08-2.01); 0.016 1.03 (1.02-1.04); <0.001 1.06 (0.78-1.44); 0.694 1.62 (1.18-2.23); 0.003 0.69 (0.38-1.24); 0.217 0.67 (0.38-1.18); 0.166 1 1.04 (0.61-1.75); 0.896 *Univariable logistic regressions analysis performed. **Multivariable logistic regressions analysis performed. Table 4. The Relationship between Ethnicity and Significantly Elevated Blood Pressure Characteristic Crude Model* ( Odds Ratio 95% CI); p-value Adjusted Model** ( Odds Ratio, 95% CI); p-value Ethnicity African American Alaskan Asian Caucasian Hispanic/Latino Other Covariables Gender (male) Age History of Diabetes (yes) History of Hypertension (yes) Home Address DC Wards 1-4 DC Wards 7-8 Virginia Maryland 2.70 (1.53-4.77); 0.001 2.28 (0.75-6.94); 0.146 1.72 (0.73-4.09); 0.218 1 3.38 (1.59-7.18); 0.002 2.28 (1.04-4.99); 0.039 0.87 (0.64-1.18); 0.361 1.02 (1.01-1.03); <0.001 1.10 (0.84-1.45); 0.482 1.62 (1.19-2.20); 0.002 1.33 (0.70-2.54); 0.382 1.30 (0.72-2.36); 0.386 1 1.83 (1.04-3.23); 0.036 3.04 (1.39-6.67); 0.006 3.54 (0.79-15.82); 0.098 2.20 (0.70-6.92); 0.176 1 3.91 (1.35-11.30); 0.012 2.38 (0.74-7.64); 0.143 1.16 (0.81-1.67); 0.418 1.02 (1.01-1.03); <0.001 1.09 (0.78-1.54); 0.605 1.23 (0.83-1.83); 0.297 0.72 (0.33-1.57); 0.408 0.71 (0.34-1.47); 0.357 1 1.17 (0.59-2.30); 0.651 *Univariable logistic regressions analysis performed. **Multivariable logistic regressions analysis performed. Significantly Elevated Pressure was defined as systolic at or above 160 or diastolic at or above 95 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2025 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 14 Nov, 2025 Reviews received at journal 27 Oct, 2025 Reviews received at journal 24 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor invited by journal 03 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 30 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7496653","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530824704,"identity":"58036d7a-da67-4118-b2fb-d2dcf612b8a6","order_by":0,"name":"Abdelrhman Refaey","email":"","orcid":"","institution":"Holy Name Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Abdelrhman","middleName":"","lastName":"Refaey","suffix":""},{"id":530824705,"identity":"482a82ff-fc22-4629-9aa5-acca4032f785","order_by":1,"name":"Alexander Bunce","email":"","orcid":"","institution":"The George Washington University School of Medicine and 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1","display":"","copyAsset":false,"role":"figure","size":324983,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart showing the prevalence of proteinuria across different ethnicities.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7496653/v1/21fb93094ef6e19b1c60bedf.jpeg"},{"id":93774341,"identity":"c420d87a-2db7-4d07-819f-acdb445ea73e","added_by":"auto","created_at":"2025-10-17 12:24:20","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":238135,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart showing the prevalence of high blood pressure and significantly elevated blood pressure across ethnicities.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7496653/v1/468aebb08c93cb077a5f9961.jpeg"},{"id":93774345,"identity":"726add4a-459b-4a02-9665-cb60401da13b","added_by":"auto","created_at":"2025-10-17 12:24:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":552450,"visible":true,"origin":"","legend":"\u003cp\u003eA heat map showing the geographic distribution study participants’ addresses.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7496653/v1/527576f2f8649ff482021209.png"},{"id":99172351,"identity":"b5e3424a-7878-4121-94a5-b242a90a6a75","added_by":"auto","created_at":"2025-12-29 16:08:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2063464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7496653/v1/147ead1d-cb44-495c-8083-da2c26e07947.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ethnic and Geographic Disparities in Proteinuria and High Blood Pressure: A Cross-Sectional Study from Community Kidney Screenings in the Washington DC Area","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is a significant but often overlooked public health issue. It affects 14% of U.S adults with an estimated 90% of them unaware of their condition (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Recent estimates of patients over the age of 66 suggest that the adjusted mortality rate for those with CKD is more than double that of patients without the disease (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). CKD often presents with non-specific symptoms and progresses slowly until advanced stages. When left untreated, it greatly increases the risk for adverse health outcomes such as kidney failure, heart disease, and stroke (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Furthermore, end stage renal disease (ESRD) management is limited to two options: dialysis or kidney transplant. National statistics revealed that approximately 44,187 adult candidates were added to the kidney waiting list in 2022. The list of patients waiting for a kidney transplant has grown steadily over the years, now at approximately 92,000 people (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Beyond the public health implications, CKD is also a financial burden for our healthcare system. Treatment cost among Medicare beneficiaries alone reached \u003cspan\u003e$\u003c/span\u003e87.2\u0026nbsp;billion in 2019 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe need for early detection and management is apparent, particularly in populations that are at high risk for developing CKD. One way to address this need is by providing free community-based health screenings, targeted at areas where risk factors are prevalent (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Differences in awareness rate among adults with CKD Stages 3\u0026ndash;5 exist across ethnic groups with higher awareness seen in non-Hispanic Blacks compared to non-Hispanic Whites (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This may be contributed to by factors including higher prevalence of disease within ethnic communities and families, higher rates of CKD comorbidities, and more advanced disease stage at time of diagnosis among ethnic groups (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). By improving awareness of CKD incidence and management, patients are empowered to seek necessary timely follow-up care, begin appropriate treatment, manage risk factors, lower long-term healthcare costs, and avoid progression and complications (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Community health screenings have demonstrated some success in raising awareness and facilitating early detection of CKD, but the overall effectiveness remains uncertain without an objective measure of its direct impact (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Many existing screening programs, such as the World Kidney Day (WKD) campaign and the Kidney Early Evaluation Program (KEEP), have reported modest increases in CKD awareness (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite prior mixed results, the apparent benefit of kidney screening programs to detect early disease and improve awareness can outweigh the risk when utilizing non-complex screening tools such as single urine protein dipstick and sphygmomanometers. This may be especially effective in areas where the incidence of kidney disease is highest (13,14). The benefits of targeted health screenings may be most evident in diverse populations, such as those in the DC metropolitan area. This area has the highest CKD rate in the United States. In the greater Washington DC area alone 8,300 individuals are on the transplant waitlist and around 80% of these patients identify as ethnic minorities (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom January 1, 2019 to July 1, 2024 we conducted over 100 kidney screenings events across the DC metropolitan area [Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e]. These were held at a variety of sites including community centers and health fairs. During these screenings we gathered comprehensive data including blood pressure measurements, proteinuria tests, demographic information, medical history, and health awareness levels via voluntary surveys. The aim of this study is to: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) detect early markers of kidney disease; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) identify demographic and clinical predictors of CKD risk; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) evaluate the utility of community screenings in promoting early awareness and healthcare engagement. By doing that, we hope to enhance public health knowledge, stratify the relative risk of CKD between local subpopulations, and contribute to reducing the growing burden of kidney disease on both individuals and the healthcare system.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe George Washington Ron and Joy Paul Kidney Center (in partnership with the National Minority Organ Tissue Transplant Education Program or MOTTEP) provides kidney screenings and education at health fairs across the Washington D.C. metropolitan area. The center participates in approximately 45 events annually. Events are held both independently and in partnership with local organizations. In 2024, 34 events were held in D.C and 12 were held in Maryland. Kidney screenings consist of: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) completing the participant form and signing the consent, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a blood pressure test using automated cuff machines, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a proteinuria screening using dipstick urinalysis to measure the Albumin creatinine ratio (ACR), and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) reviewing results with a team member and discussing relevant health education. Following the screening, participants are followed up via phone. The intention of this follow-up call is to gauge how effective the screening was and, for those who have abnormal results (blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;130/80) or albuminuria (more than 30 mg/g), to usher participants to see a provider. Ultimately, we want subjects with abnormal results to seek care and change their health trajectory.\u003c/p\u003e\u003cp\u003eThis study was approved by the Institutional Review Board (IRB) at The George Washington University (NCR245978), ensuring compliance with ethical guidelines. All data was anonymized prior to analysis to protect participant identities.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eStatistical Methods\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eChi-squared and Analysis of Variance tests were used to evaluate the relationships between potential covariables including sex, age, history of type II diabetes, heart disease, previous stroke and history of hypertension, home zip code, and patient ethnicity. Univariant and multivariant logistic regression was used to elucidate the relationship between ethnicity and the likelihood of screened proteinuria. Univariable and multivariable logistic regression was also used to assess the relationship between ethnicity and screened high blood pressure (\u0026ge;\u0026thinsp;130/80). Logistic regression analysis was repeated for significantly elevated blood pressure (\u0026ge;\u0026thinsp;160/95). Subjects with missing values were dropped.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1,597 participants were included in this cross-sectional study. The cohort predominantly consisted of African Americans (73.0%), with additional representation from Caucasians (10.1%), Hispanic/Latino (5.0%), Asian (4.6%), Alaskan (1.8%), and other ethnicities (5.6%). The mean age was 54.2 years (SD 16.1), with 52.3% female and 47.7% male participants. Significant comorbidities were prevalent, with 37.4% reporting Type II diabetes mellitus and 54.9% having a history of hypertension [Table1]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteinuria Prevalence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProteinuria prevalence varied significantly across ethnic groups, with the highest observed among individuals identifying as \u0026ldquo;Other\u0026rdquo; (16.9%) and African Americans (16.1%), followed by Hispanic/Latino (12.7%), Alaskans (10.3%), Asians (9.6%), and Caucasians (8.0%).In the crude analysis, African Americans demonstrated higher odds of proteinuria compared to Caucasians (OR 2.21, 95% CI 1.19\u0026ndash;4.08). However, after adjustment for potential confounders such as age, history of hypertension, sex, home address, and history of diabetes, this difference was not statistically significant (adjusted OR 1.35, 95% CI 0.65\u0026ndash;2.82). [Table2] [Figure 1].\u003c/p\u003e\n\u003cp\u003eAdditionally, residence location was associated with differences in proteinuria prevalence. Participants living in DC Wards 1-4 OR 2.63 (95% CI 1.19-5.82), DC Wards 7-8 OR 2.10 (95% CI 0.99-4.47), and Maryland OR 1.94 (95% CI 0.94-4.00), had higher odds of proteinuria compared to those living in Virginia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh Blood Pressure Prevalence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh blood pressure prevalence also varied by ethnicity with the highest rates among African Americans (69.3%), followed by Hispanic/Latino (56.9%), Other ethnicities (57.0%), Caucasians (53.1%), Asians (47.9%), and Alaskans (34.5%). Crude analysis showed African Americans (OR 2.00, 95% CI 1.43-2.79) and Alaskans (OR 2.65, 95% CI 1.07-6.58) were more likely to have measured high blood pressure compared to Caucasians. After adjustment, African American participants remained significantly more likely to have hypertension (adjusted OR 1.68, 95% CI 1.05-2.70) [Table3] [Figure2]\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Significantly elevated blood pressure (\u0026ge;160/95 mmHg) was most prevalent among \u0026nbsp;Hispanic/Latino participants (24.1%), followed by those who identified as Other ethnicities (22.5%), African Americans (20.5%), Asians (10.9%), Caucasians (8.6%), and Alaskans (6.9%). In adjusted analysis African Americans were three times (OR 3.04, 95% CI 1.39-6.67), and Hispanic/Latinos were nearly four times (OR 3.91, 95% CI 1.35-11.30) as likely as Caucasians to have measured significantly elevated blood pressure [Table4] [Figure2]\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsurance and Follow-Up Outcomes\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Regarding insurance status, only 27.0% (n=321) of participants were insured, 7.5% (n=89) were uninsured, and insurance status was missing for 65.5% of the cohort. Crude analysis showed that insured participants were 91% more likely to have screened proteinuria than uninsured, though it was not statistically significant (95% CI 0.90-4.06; p=0.091).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFollow-up results (n=245 high-risk participants):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003eAmong 245 high-risk participants identified during screening, follow-up outcomes showed a response rate of 37.6% (n=92), with the majority (n=84) being African American. Of these, 52.2% (n=48) reported already having seen a healthcare provider, and 33.7% (n=31) planned to do so; 19 of those who followed up and 16 of those who planned were insured. Of the 60 respondents who answered CKD awareness questions, 36 (60%) reported increased awareness post-screening, while six were already aware prior to screening.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccording to the National Kidney Foundation there is a 5-step plan for CKD evaluation and referral including \u003cem\u003eknowing\u003c/em\u003e the criteria for CKD, \u003cem\u003erecognizing\u003c/em\u003e risk factors, \u003cem\u003escreening\u003c/em\u003e for CKD, \u003cem\u003eclassifying\u003c/em\u003e CKD to guide testing and treatment, and \u003cem\u003eimplementing\u003c/em\u003e a clinical action plan based on patient's CKD classification (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our project, we focused on the screening and risk factor recognition phases. CKD screening typically involves two simple tests: a spot urine albumin-to-creatinine ratio (ACR) to detect albuminuria and/or a serum creatinine test to estimate glomerular filtration rate (GFR). Identifying risk factors requires obtaining a thorough patient\u0026rsquo;s medical history, of chronic diseases such as diabetes and hypertension, social history and family history of kidney disease. Our study reinforces the same goal of WKD campaign, and the KEEP screening programs (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) by:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTargeting underserved communities with limited healthcare access echoing WKD\u0026rsquo;s 2024 theme of \u0026ldquo;Kidney Health for All.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUsing screening to promote early detection of CKD risk factors, such as proteinuria and hypertension, in minority groups disproportionately affected by kidney disease.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAdvocating for community-based interventions that can bridge healthcare gaps and reduce long-term disease burden. Our study also in Alignment with the Kidney Early Evaluation Program (KEEP)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eby providing free screening to individuals at risk for CKD, with a focus on early intervention. We complement KEEP by: Emphasizing portable, community-based screening, mirroring KEEP\u0026rsquo;s outreach model.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCollecting data on self-reported history, blood pressure, and urine protein, directly paralleling KEEP\u0026rsquo;s approach to risk stratification.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHighlighting racial disparities in KEEP, African Americans and Hispanics consistently showed higher CKD risks, a pattern that our findings reinforce, particularly with adjusted odds for hypertension and proteinuria.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eOn the other hand, Unique Contributions of our Study:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGeographic Focus: Our research adds value by focusing on the Washington DC Metropolitan Area, a region with marked health disparities.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOur dataset provides contemporary insight, particularly valuable post-COVID-19, as pandemic-related barriers may have worsened chronic disease outcomes in some specific communities.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSpecific Ethnic Associations: While KEEP and WKD broadly categorize racial risk, our study adds more details by examining adjusted associations between ethnicity and both high blood pressure and proteinuria; offering a more refined understanding of disease dynamics in real world settings.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eCKD prevalence and access to care disproportionately affect various ethnic groups in the U.S (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Approximately 20% of African Americans adults and 14% of Hispanics adults are affected compared to 12% of non-Hispanic White adults (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Moreover, the incidence of progressing to ESRD among African Americans is four times greater than non-Hispanic Whites, and two times greater in Hispanic and Native Americans (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The impact of this is magnified by the fact that African Americans comprise almost one-third of the transplant list and are less likely to receive a living donor kidney, thereby contributing to a higher mortality rate due to ESRD (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur study\u0026rsquo;s results were in line with national statistics; African Americans were more than twice as likely as the Caucasian population to have proteinuria (OR 2.21, 95% CI 1.19\u0026ndash;4.08). However, when considering covariables such as age, history of risk factors, sex, zip codes, and history of diabetes this association was no longer significant (OR 1.35, 95%, CI 0.65\u0026ndash;2.82). As proteinuria is both a risk factor and indicator of CKD, these findings highlight persistent ethnic disparities in renal disease seen among African Americans populations in the U.S (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile our adjusted results suggest that proteinuria may be partially explained by comorbidities, literature consistently points to the trend that African Americans have systemically faced barriers that contribute to higher disease prevalence within this population (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Among these persistent barriers are understanding of CKD and its risk factors, low trust in healthcare systems, and financial burden.\u003c/p\u003e\u003cp\u003eOur findings also revealed geographic disparities in prevalence of proteinuria. Residents in DC Wards 1\u0026ndash;4, 7\u0026ndash;8, and Maryland, demonstrated higher odds of proteinuria than Virginia residents. End-stage renal disease in the 20019-zip code alone is 44 times the national average (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These findings underscore the importance of socioeconomic and geographical factors particularly in developing health strategies targeting CKD screening. Our analysis highlights the need for incorporating adequate geographical\u003c/p\u003e\u003cp\u003econtext in future health strategies targeting CKD screening. This trend has been seen in numerous other\u003c/p\u003e\u003cp\u003ehealth conditions such as Diabetes with Ward 8 reporting at a 15.2% prevalence whereas Ward 3 reports a 2.2% prevalence (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe found that the prevalence of high blood pressure (\u0026gt;\u0026thinsp;130/80) and significantly elevated blood pressure (\u0026gt;\u0026thinsp;160/95) were notably higher among African American and Hispanic/Latino participants, further illustrating the disproportionate burden of CKD risk factors in these groups. African Americans were twice as likely to have high blood pressure compared to Caucasians (OR 2.00, 95% CI 1.43\u0026ndash;2.79), and this association remained significant after adjustment for covariables (OR 1.68, 95% CI 1.05\u0026ndash;2.70). These disparities are partially attributed to the greater prevalence of CKD risk factors and comorbidities such as hypertension, diabetes, proteinuria, and cardiovascular disease within these minority populations (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eHypertension among the African American population is believed to contribute to almost a third of all CKD cases (22). Our finding in African Americans is well-supported by existing literature, which highlights their increased risk of proteinuria compared to White population at similar levels of elevated blood pressure (23). This is particularly troublesome in African American men ages 24\u0026ndash;44, who have a 15-fold greater risk than White men of the same age group (23). Poor blood pressure control among African Americans participants, particularly men, was also observed through the Kidney Early Evaluation Program (KEEP) screening initiative (13). Addressing hypertensive CKD among African Americans requires a multidisciplinary approach that involves early detection of disease, lifestyle changes, addressing social determinants, patient education, pharmacological intervention, and frequent follow-up (23).\u003c/p\u003e\u003cp\u003eIn our study, we employed healthcare screenings within communities comprised of a large African American population; in doing so we aimed at addressing such barriers. To better address medical mistrust among future screening efforts, studies have suggested benefit in recruiting community members to work in conjunction with healthcare providers to conduct community screenings (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurther, enlisting the help of community facilitators such as church health leaders to bridge gaps in knowledge and prompt screening among peers (22). Future kidney screening efforts should consider employing these community-centered approaches to better address the implicit barriers in screening within the African American population.\u003c/p\u003e\u003cp\u003eAmong the 245 subjects contacted for follow-up in our study, 92 (37.6%) were successfully reached, which is considered a low response rate. 84 (91.3%) of them were African American. Among respondents, 79 (85.9%) reported either having visited a doctor or planned to do so, which is considered a high follow-up rate. Additionally. 60% of respondents reported an increase in CKD awareness following their screening experience.\u003c/p\u003e\u003cp\u003eOur findings align with previous studies showing that African Americans tend to have higher awareness of CKD. This could be due to factors such as a higher prevalence of CKD within ethnic communities and families, increased rates of CKD-related health conditions, and more advanced stages of CKD at the time of diagnosis in these populations (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, despite our respondents expressing intent to seek follow-up care and increased awareness, more systemic challenges in follow-up care of chronic disease are highlighted by the low follow-up response seen in our study. This limitation may be due to differences in participants\u0026rsquo; understanding of healthcare and technology, as well as unequal access to technology among those surveyed (24).\u003c/p\u003e\u003cp\u003eFurther, shared barriers between providers and patients alike include lack of time to coordinate care and a relative non-prioritization of CKD when considering numerous other comorbidities (24). Innovative strategies are needed to improve CKD follow-up care in populations who have these barriers. This could include community partnership initiatives and utilizing patient navigation services that can ensure that patients are not lost during the follow-up period. For providers, there is a need for greater supportive technology embedded in the EMR and clinical guideline suggestions in ensuring that kidney disease is properly addressed during visits and followed up appropriately with the patient (24).\u003c/p\u003e\u003cp\u003eBy improving follow-up rates, the largely asymptomatic progression of CKD can be mitigated, thereby reducing morbidity and mortality.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study focuses on diverse populations. Its strength is found in the sample size and in integrating both crude and adjusted analyses for disparities. However, the study has several notable limitations that should be acknowledged. The cross-sectional design precludes conclusions about causality. While the overall sample size was large, representation from certain ethnic groups, such as Alaskan and Hispanic/Latino populations, was limited, reducing the statistical power to detect differences. Many variables, including hypertension and diabetes status, were self-reported and may be subject to recall or reporting bias. Proteinuria was assessed using a dipstick-based estimation of the ACR, which, although practical for screening purposes, is less accurate than laboratory-based methods. Additionally, possible selection bias may exist, as individuals attending health screenings could be more health-conscious or have better access to healthcare than the general population. The low proportion of responses to the follow-up calls and the absence of objective outcome data to evaluate the long-term impact of increased awareness also highlight areas for future improvement (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFuture Implications:\u003c/h2\u003e\u003cp\u003eDespite these limitations, the findings underscore the value of community-based screening programs in identifying at-risk populations. These results could help inform local public health strategies by guiding the allocation of resources, such as targeting mobile clinics or health education campaigns to underserved zip codes populations with high prevalence of risk factors. Additionally, the data could support efforts to integrate routine screenings into community health initiatives, ultimately contributing to earlier detection and intervention for chronic kidney disease.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur finding highlights the persistent disparities in CKD risk factors, particularly among African American and Hispanic/Latino populations in the Washington DC metropolitan area. Community-based kidney screenings serve as a valuable tool for early detection and awareness, especially in underserved and high-risk communities. Expanding such efforts and incorporating strategies to improve follow-up and evaluate long-term outcomes could significantly contribute to reducing the burden of CKD and advancing health equity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACR \u0026ndash; Albumin/Creatinine Ratio\u003cbr\u003e\u0026nbsp;CKD \u0026ndash; chronic kidney disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDC \u0026ndash; District of Columbia\u003c/p\u003e\n\u003cp\u003eDM \u0026ndash; Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eESRD \u0026ndash; End Stage Renal Disease\u003c/p\u003e\n\u003cp\u003eGFR \u0026ndash; Glomerular Filtration Rate\u003c/p\u003e\n\u003cp\u003eKEEP \u0026ndash; Kidney Early Evaluation Program\u003c/p\u003e\n\u003cp\u003eWKD \u0026ndash; World Kidney Day\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study was approved by the IRB committee at George Washington University under IRB number of NCR245978. Written informed consent was obtained from all the participants of the study.\u0026nbsp;\u003cbr\u003e\u0026nbsp;Clinical trial number: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eYes, obtained from the head of the Institute\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors of this manuscript have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study was not funded by any external sources\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eAll the authors were involved in conception, design, and execution of the study. The first author has prepared the manuscript, first and second authors were involved in the analysis of data. All others critically reviewed the manuscript and helped with the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors wish to thank Muxin (Anna) Han, Omar Moharram, Rinna Talwar, and GW Kidney Club members for their contributions in the screening events, data collection, and phone calls. This contribution was invaluable in ensuring the success of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Chronic Kidney Disease in the United States, 2023. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2023\u003c/li\u003e\n\u003cli\u003eNational Institute of Diabetes and Digestive and Kidney Diseases. Kidney disease statistics. U.S. Department of Health and Human Services. Accessed December 18, 2024. \u003c/li\u003e\n\u003cli\u003eAmerican Kidney Fund. Kidney donation and transplant. Accessed December 18, 2024. https://www.kidneyfund.org/kidney-donation-and-transplant\u003c/li\u003e\n\u003cli\u003eLentine KL, Smith JM, Lyden GR, Miller JM, Dolan TG, Bradbrook K, Larkin L, Temple K, Handarova DK, Weiss S, Israni AK, Snyder JJ. OPTN/SRTR 2022 Annual Data Report: Kidney. Am J Transplant. 2024 Feb;24(2S1):S19-S118. doi: 10.1016/j.ajt.2024.01.012. PMID: 38431360\u003c/li\u003e\n\u003cli\u003ePowe NR, Boulware LE. Population-Based Screening for CKD. Am J Kidney Dis. 2009;53(3 Suppl 3):S64-S70. doi:10.1053/j.ajkd.2008.07.050\u003c/li\u003e\n\u003cli\u003eShah KM, Hsiao LL. Leveraging Resources Effectively at the Community Level: Lessons Learned from the Kidney Disease Screening and Awareness Program. Kidney Int Rep. 2022;7(12):2551-2554. doi:10.1016/j.ekir.2022.09.028\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Chronic kidney disease surveillance system: incidence of end-stage kidney disease by race/ethnicity. Accessed December 18, 2024. https://nccd.cdc.gov/CKD/detail.aspx?Qnum=Q98\u0026amp;Strat=Race%2fEthnicity#refreshPosition\u003c/li\u003e\n\u003cli\u003eOrgan Procurement and Transplantation Network (OPTN) and Scientific Registry of Transplant Recipients (SRTR). OPTN/SRTR 2022 Annual Data Report. U.S. Department of Health and Human Services, Health Resources and Services Administration; 2024. Accessed December 18, 2024. http://srtr.transplant.hrsa.gov/annual_reports/Default.aspx\u003c/li\u003e\n\u003cli\u003eOgunniyi MO, Commodore -Mensah Yvonne, Ferdinand KC. Race, Ethnicity, Hypertension, and Heart Disease. \u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e. 2021;78(24):2460-2470. doi:10.1016/j.jacc.2021.06.017\u003c/li\u003e\n\u003cli\u003ePlantinga LC, Tuot DS, Powe NR. Awareness of Chronic Kidney Disease among Patients and Providers. Adv Chronic Kidney Dis. 2010;17(3):225-236. doi:10.1053/j.ackd.2010.03.002\u003c/li\u003e\n\u003cli\u003eQui\u0026ntilde;ones J, Hammad Z. Social Determinants of Health and Chronic Kidney Disease. Cureus. 12(9):e10266. doi:10.7759/cureus.10266 \u003c/li\u003e\n\u003cli\u003eLaster M, Shen JI, Norris KC. Kidney Disease Among African Americans: A Population Perspective. Am J Kidney Dis. 2018;72(5 Suppl 1):S3-S7. doi:10.1053/j.ajkd.2018.06.021 \u003c/li\u003e\n\u003cli\u003eMcCullough PA, Brown WW, Gannon MR, et al. Sustainable Community-Based CKD\u003cbr\u003e \u003cbr\u003e Screening Methods Employed by the National Kidney Foundation\u0026rsquo;s Kidney Early Evaluation \u003cbr\u003e \u003cbr\u003e Program (KEEP). American Journal of Kidney Diseases. 2011;57(3):S4-S8. \u003cbr\u003e \u003cbr\u003e doi:10.1053/j.ajkd.2010.11.010 \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eChin HJ, Ahn JM, Na KY, et al. The effect of the World Kidney Day campaign on the \u003cbr\u003e \u003cbr\u003e awareness of chronic kidney disease and the status of risk factors for cardiovascular disease \u003cbr\u003e \u003cbr\u003e and renal progression. Nephrol Dial Transplant. 2010;25(2):413-419. doi:10.1093/ndt/gfp512 \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eFacts: Kidney Disease. GW Kidney. Accessed December 21, 2024. \u003cbr\u003e \u003cbr\u003ehttps://gwkidney.org/facts-kidney-disease \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003ehttps://www.kidney.org/quick-reference-guide-kidney-disease-screening \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eVart P, Powe NR, McCulloch CE, et al. National Trends in the Prevalence of Chronic Kidney \u003cbr\u003e \u003cbr\u003e Disease Among Racial/Ethnic and Socioeconomic Status Groups, 1988-2016. JAMA \u003cbr\u003e \u003cbr\u003e Network Open. 2020;3(7):e207932. doi:10.1001/jamanetworkopen.2020.7932 \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eChu CD, Powe NR, McCulloch CE, et al. Trends in Chronic Kidney Disease Care in the US \u003cbr\u003e \u003cbr\u003e by Race and Ethnicity, 2012-2019. JAMA Network Open. 2021;4(9):e2127014. \u003cbr\u003e \u003cbr\u003e doi:10.1001/jamanetworkopen.2021.27014 \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eEvans K, Coresh J, Bash LD, et al. Race differences in access to health care and disparities in\u003cbr\u003e \u003cbr\u003e incident chronic kidney disease in the US. Nephrology Dialysis Transplantation. \u003cbr\u003e \u003cbr\u003e 2011;26(3):899-908. doi:10.1093/ndt/gfq473 \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eDistrict of Columbia Department of Health (DC DOH). Behavioral Risk Factors Surveillance \u003cbr\u003e \u003cbr\u003e System (BRFSS); 2009 and 2010 data [Internet]. Washington (DC); Center for Planning, \u003cbr\u003e \u003cbr\u003e Policy, and Epidemiology (CPPE) [cited 2012 Apr 25]. Available from: \u003cbr\u003e \u003cbr\u003ehttp://www.cdc.gov/brfss \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eToto RD. Proteinuria and hypertensive nephrosclerosis in African Americans. Kidney Int \u003cbr\u003e \u003cbr\u003e Suppl. 2004;(92):S102-104. doi:10.1111/j.1523-1755.2004.09224.x \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eUmeukeje EM, Wild MG, Maripuri S, Davidson T, Rutherford M, Abdel-Kader \u003cbr\u003e \u003cbr\u003e K, Lewis J, Wilkins CH, Cavanaugh K. Black Americans\u0026rsquo; Perspectives of \u003cbr\u003e \u003cbr\u003e Barriers and Facilitators of Community Screening for Kidney Disease. Clinical \u003cbr\u003e \u003cbr\u003e Journal of the American Society of Nephrology. 2018;13(4):551\u0026ndash;559. \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eMARTINS D, AGODOA L, NORRIS KC. Hypertensive chronic kidney disease in African \u003cbr\u003e \u003cbr\u003e Americans: Strategies for improving care. Cleve Clin J Med. 2012;79(10):726-734. \u003cbr\u003e \u003cbr\u003e doi:10.3949/ccjm.79a.11109 \u003cbr\u003e \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eNeale EP, Middleton J, Lambert K. Barriers and enablers to detection and management of \u003cbr\u003e \u003cbr\u003e chronic kidney disease in primary healthcare: a systematic review. BMC Nephrol. \u003cbr\u003e \u003cbr\u003e 2020;21:83. doi:10.1186/s12882-020-01731-x \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Comparison of covariates based on ethnicity for patients screened for Chronic Kidney Disease (CKD) risk factors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eAfrican American (n=1166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAlaskan (n=29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eAsian (n=73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eCaucasian (n=161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eHispanic/Latino (n=79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOther (n=89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAGE (years), Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e59.2 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e48.4 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e51.7 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e51.8 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e46.0 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e53.9 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eTYPE II DIABETES MELLITUS - Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e483 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e22 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e41 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e29 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e23 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eTYPE II DIABETES MELLITUS - No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e512 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e31 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e95 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e35 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e45 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHISTORY OF CKD - Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e137 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHISTORY OF CKD - No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e848 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHISTORY OF HEART DISEASE - Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e200 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e11 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e39 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e11 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHISTORY OF HEART DISEASE - No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e787 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e44 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e98 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e57 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHISTORY OF STROKE - Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e190 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e11 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e26 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e11 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHISTORY OF STROKE - No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e797 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e44 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e109 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e52 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eGENDER - Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e650 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e32 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e72 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e38 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e43 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eGENDER - Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e287 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e17 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e61 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e21 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e17 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHISTORY OF HYPERTENSION - Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e711 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e31 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e64 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e32 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e39 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHISTORY OF HYPERTENSION - No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e283 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e24 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e71 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e32 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e29 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHOME ADDRESS - DC Wards 1-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHOME ADDRESS - DC Wards 7-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHOME ADDRESS - Virginia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHOME ADDRESS - Maryland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003eTable 2. The Relationship between Ethnicity and Protein in Urinalysis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude Model*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eOdds Ratio\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;95% CI); p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted Model**\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cu\u003eOdds ratio\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e, 95% CI); p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;African American\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Alaskan\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Asian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Caucasian\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Hispanic/Latino\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCovariables\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGender (male)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of Diabetes (yes)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of Hypertension (yes)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHome Address\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;DC Wards 1-4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;DC Wards 7-8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Virginia\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Maryland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.21 (1.19-4.08); 0.012\u003c/p\u003e\n \u003cp\u003e1.82 (0.47-7.03); 0.388\u003c/p\u003e\n \u003cp\u003e1.72 (0.69-4.31); 0.243\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.72 (0.69-4.31); 0.243\u003c/p\u003e\n \u003cp\u003e1.69 (0.70-4.11); 0.246\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e1.02 (0.72-1.44); 0.771\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (0.99-1.01); 0.626\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.08 (0.78-1.49); 0.649\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.12 (0.79-1.58); 0.525\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e2.63 (1.19-5.82); 0.017\u003c/p\u003e\n \u003cp\u003e2.10 (0.99-4.47); 0.055\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.94 (0.94-4.00); 0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.35 (0.65-2.82); 0.425\u003c/p\u003e\n \u003cp\u003e1.31 (0.25-7.01); 0.749\u003c/p\u003e\n \u003cp\u003e1.31 (0.41-4.18); 0.649\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.19 (0.34-4.16); 0.787\u003c/p\u003e\n \u003cp\u003e1.31 (0.41-4.23); 0.650\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e1.06 (0.69-1.61); 0.796\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e1 (0.99-1.01); 0.978\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.90 (0.60-1.37); 0.635\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.20 (0.76-1.90); 0.425\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e2.07 (0.81-5.27); 0.129\u003c/p\u003e\n \u003cp\u003e1.99 (0.81-4.89); 0.134\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.72 (0.73-4.07); 0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Univariable logistic regressions analysis performed.\u003c/p\u003e\n\u003cp\u003e**Multivariable logistic regressions analysis performed.\u003cstrong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003c/strong\u003eTable 3. The Relationship between Ethnicity and High Blood Pressure\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude Model*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cu\u003eOdds Ratio\u003c/u\u003e, 95% CI); p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted Model**\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cu\u003eOdds Ratio\u003c/u\u003e, 95% CI); p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;African American\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Alaskan\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Asian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Caucasian\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Hispanic/Latino\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCovariables\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGender (male)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of Diabetes (yes)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of Hypertension (yes)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003cstrong\u003eHome Address\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003eDC Wards 1-4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; DC Wards 7-8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Virginia\u003c/p\u003e\n \u003cp\u003eMaryland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.00 (1.43-2.79); \u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e2.65 (1.07-6.58); 0.036\u003c/p\u003e\n \u003cp\u003e1.36 (0.77-2.39); 0.291\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.06 (0.62-1.83); 0.833\u003c/p\u003e\n \u003cp\u003e1.97 (1.13-3.44); 0.017\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.08 (0.84-1.38); 0.644\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.03 (1.02-1.04); \u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.23 (0.98-1.55); 0.077\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.09 (1.65-2.65); \u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e1.25 (0.78-2.01); 0.362\u003c/p\u003e\n \u003cp\u003e1.21 (0.79-1.87); 0.386\u003cbr\u003e1\u003c/p\u003e\n \u003cp\u003e1.41 (0.93-2.14); 0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.68 (1.05-2.70); 0.031\u003c/p\u003e\n \u003cp\u003e2.88 (0.81-10.19); 0.101\u003c/p\u003e\n \u003cp\u003e1.32 (0.61-2.89); 0.480\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.99 (0.46-2.13); 0.978\u003c/p\u003e\n \u003cp\u003e2.35 (0.99-5.60); 0.054\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.47 (1.08-2.01); 0.016\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.03 (1.02-1.04); \u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.06 (0.78-1.44); 0.694\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.62 (1.18-2.23); 0.003\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e0.69 (0.38-1.24); 0.217\u003c/p\u003e\n \u003cp\u003e0.67 (0.38-1.18); 0.166\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.04 (0.61-1.75); 0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Univariable logistic regressions analysis performed.\u003c/p\u003e\n\u003cp\u003e**Multivariable logistic regressions analysis performed.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Table 4. The Relationship between Ethnicity and Significantly Elevated Blood Pressure\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude Model*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cu\u003eOdds Ratio\u003c/u\u003e 95% CI); p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 225px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted Model**\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cu\u003eOdds Ratio,\u003c/u\u003e 95% CI); p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;African American\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Alaskan\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Asian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Caucasian\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Hispanic/Latino\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCovariables\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGender (male)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of Diabetes (yes)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of Hypertension (yes)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHome Address\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;DC Wards 1-4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;DC Wards 7-8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Virginia\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Maryland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.70 (1.53-4.77); 0.001\u003c/p\u003e\n \u003cp\u003e2.28 (0.75-6.94); 0.146\u003c/p\u003e\n \u003cp\u003e1.72 (0.73-4.09); 0.218\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e3.38 (1.59-7.18); 0.002\u003c/p\u003e\n \u003cp\u003e2.28 (1.04-4.99); 0.039\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.87 (0.64-1.18); 0.361 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.02 (1.01-1.03); \u0026lt;0.001 \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.10 (0.84-1.45); 0.482\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.62 (1.19-2.20); 0.002\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.33 (0.70-2.54); 0.382\u003c/p\u003e\n \u003cp\u003e1.30 (0.72-2.36); 0.386\u003cbr\u003e1\u003c/p\u003e\n \u003cp\u003e1.83 (1.04-3.23); 0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.04 (1.39-6.67); 0.006\u003c/p\u003e\n \u003cp\u003e3.54 (0.79-15.82); 0.098\u003c/p\u003e\n \u003cp\u003e2.20 (0.70-6.92); 0.176\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e3.91 (1.35-11.30); 0.012\u003c/p\u003e\n \u003cp\u003e2.38 (0.74-7.64); 0.143\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.16 (0.81-1.67); 0.418\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.02 (1.01-1.03); \u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.09 (0.78-1.54); 0.605\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.23 (0.83-1.83); 0.297\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.72 (0.33-1.57); 0.408\u003c/p\u003e\n \u003cp\u003e0.71 (0.34-1.47); 0.357\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.17 (0.59-2.30); 0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Univariable logistic regressions analysis performed.\u003c/p\u003e\n\u003cp\u003e**Multivariable logistic regressions analysis performed.\u003c/p\u003e\n\u003cp\u003eSignificantly Elevated Pressure was defined as systolic at or above 160 or diastolic at or above 95\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"“CKD”, “Ethnic disparities”, “Community screening”, “Proteinuria”, “Hypertension”, “DC”, “Kidney Transplantation”","lastPublishedDoi":"10.21203/rs.3.rs-7496653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7496653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003echronic kidney disease (CKD) affects 14% of U.S adults, with 90% unaware of their condition. We conducted a cross-sectional analysis of 1,597 adults through community screenings in underserved areas of the Washington DC Metropolitan Area, assessing proteinuria and high blood pressure using urine dipstick tests and automated blood pressure cuffs. African Americans were the predominant group and exhibited the highest prevalence of proteinuria and high blood pressure, followed by Hispanic/Latino individuals, with Caucasians having the lowest rates. Adjusted analysis showed African Americans and Hispanic/Latino participants had significantly higher odds of significantly elevated blood pressure (\u0026gt;160/95) compared to Caucasians. Crude analysis revealed higher proteinuria rates among residents of DC Wards 1-4 and 7-8, and Maryland, compared to Virginia. Of 245 high-risk participants contacted post-screening, 92 (37.6%) responded; 48 (52.2%) had visited a physician and 31 (33.7%) intended to seek care. Awareness surveys indicated that 60% of respondents reported increased CKD awareness after the intervention. Our findings highlight significant ethnic and geographic disparities in CKD risk factors, particularly among African Americans and specific DC wards. Community outreach screenings can enhance early detection, improve healthcare engagement, and potentially reduce CKD progression, leading to better access to transplantation and post-transplant outcomes.\u003c/p\u003e","manuscriptTitle":"Ethnic and Geographic Disparities in Proteinuria and High Blood Pressure: A Cross-Sectional Study from Community Kidney Screenings in the Washington DC Area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 12:24:16","doi":"10.21203/rs.3.rs-7496653/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-14T18:55:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T16:44:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T08:27:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T04:38:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151680143702608083767720492216574576749","date":"2025-10-17T13:48:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292810731937408688876569584188584226272","date":"2025-10-17T01:19:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18742620515412136238679263031376581714","date":"2025-10-08T07:48:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T07:05:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-03T11:54:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T11:00:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T10:59:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-08-30T16:57:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"826d5386-dba1-494e-b28f-c9a5a1574cf7","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:02:34+00:00","versionOfRecord":{"articleIdentity":"rs-7496653","link":"https://doi.org/10.1186/s12882-025-04703-1","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2025-12-23 15:58:08","publishedOnDateReadable":"December 23rd, 2025"},"versionCreatedAt":"2025-10-17 12:24:16","video":"","vorDoi":"10.1186/s12882-025-04703-1","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04703-1","workflowStages":[]},"version":"v1","identity":"rs-7496653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7496653","identity":"rs-7496653","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}