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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Ethn Dis. Author manuscript; available in PMC 2013 May 11.
Published in final edited form as:
Ethn Dis. 2013 Spring; 23(2): 202–209.
PMCID: PMC3651651

Validation of the Kidney Disease Quality of Life Short Form 36 (KDQOL-36™) US Spanish and English Versions in a Cohort of Hispanics with Chronic Kidney Disease

Ana C. Ricardo, MD, MPH, Eileen Hacker, PhD, RN, Claudia M. Lora, MD, Lynn Ackerson, PhD, Karen B. DeSalvo, MD, MPH, MSc, Alan Go, MD, John W. Kusek, PhD, Lisa Nessel, MSS, MLSP, Akinlolu Ojo, MD, Raymond R. Townsend, MD, Dawei Xie, PhD, Carol E. Ferrans, PhD, RN, and James P. Lash, MD, on behalf of the CRIC Investigators



Evaluate the reliability and validity of the Kidney Disease Quality of Life Short Form 36 (KDQOL-36™) in Hispanics with mild-to-moderate chronic kidney disease (CKD).




Chronic Renal Insufficiency Cohort Study


420 Hispanic (150 English- and 270 Spanish-speakers), and 409 non-Hispanic White individuals, matched by age (mean 57 years), sex (60% male), kidney function (mean estimated glomerular filtration rate 36ml/min/1.73m2), and diabetes (70%).


To measure construct validity, we selected instruments, comorbidities, and laboratory tests related to at least one KDQOL-36™ subscale. Reliability was determined by calculating Cronbach’s alpha.


Reliability of each KDQOL-36™ subscale [SF-12 Physical Component Summary (PCS) and Mental Component Summary (MCS), Symptoms/Problems, Burden of Kidney Disease and Effects of Kidney Disease] was very good (Cronbach’s alpha >0.8). Construct validity was supported by expected negative correlation between MCS scores and the Beck Depression Inventory in all three subgroups (r= −0.56 to −0.61, P<.0001). There was inverse correlation between the Symptoms/Problems subscale and the Patient Symptom Form (r= −0.70 to −0.77, P<.0001). We also found significant, positive correlation between the PCS score and a physical activity survey (r= +0.29 to +0.38, P≤.003); and between the PCS and MCS scores and the Kansas City Questionnaire (r= +0.31 to +0.64, P<.0001). Reliability and validity were similar across all racial/ethnic groups analyzed separately.


Our findings support the use of the KDQOL-36™ as a measure of HRQOL in this cohort of US Hispanics with CKD.

Keywords: Validation, Quality of Life, Hispanics


Hispanics, the largest minority group in the United States,1 are more likely to progress to end-stage renal disease (ESRD) than non-Hispanic Whites,2 and experience a substantial psychosocial burden resulting from comorbidities (eg, diabetes), and the difficulties of living with a chronic disease.3 Despite the magnitude of this problem, there are no validated measures to assess health-related quality of life (HRQOL) for Hispanics with mild-to-moderate chronic kidney disease (CKD).

HRQOL has been increasingly recognized as an important medical outcome in patients with CKD.4,5 A commonly used measure is the Kidney Disease Quality of Life (KDQOL™), which is a 134-item instrument designed to assess generic and kidney-disease targeted aspects of quality of life for individuals on dialysis.6 An abbreviated version of the KDQOL™, KDQOL-36™, has been translated to Spanish and used in the United States;7 however, it has not been adequately validated. In addition, the English version of the KDQOL-36™ has not been validated in the US Hispanic population. We studied the validity and reliability of the US Spanish and English versions of the KDQOL-36™ among Hispanic individuals with CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study and the Hispanic CRIC (HCRIC) Study.


Study Design and Participants

We conducted a cross-sectional study of 150 English- and 270 Spanish-speaking Hispanic, and 409 non-Hispanic White adult participants in the CRIC and HCRIC Studies, frequency-matched by age, sex, kidney function, and diabetes mellitus. The design, methods and characteristics of the CRIC and HCRIC Study participants have been previously reported.810 In brief, the CRIC Study is a prospective cohort of 3612 individuals aged 21 to 74 years with mild-to-moderate CKD according to age-based estimated glomerular filtration rate (eGFR) inclusion criteria, recruited from seven clinical centers across the United States from May 2003 to March 2007. HCRIC is a parallel study to the CRIC Study that recruited 327 Hispanic individuals from the Chicago area between October 2005 and June 2008. Among Hispanic participants, 69% were Mexican American, 16% were Puerto Rican, and 15% had other Latin American ancestry. Protocols for both studies were approved by the Institutional Review Board of each participating Institution and are in accordance with the principles of the Declaration of Helsinki. All participants provided informed consent.

Variables and Measurements

Sociodemographic characteristics, medical history and medications were self-reported at the baseline study visit. Blood pressure (BP) and anthropometric measurements were obtained using standard methods. The CRIC Study definitions of hypertension, diabetes mellitus and history of cardiovascular disease (CVD) have been published elsewhere.9 Glomerular filtration rate was estimated using the four-variable Modification of Diet in Renal Disease (MDRD) equation.11

Participants self-administered the KDQOL-36™ in their language of preference (Spanish or English) at study entry. The KDQOL-36™ is a measure of kidney disease-related quality of life that comprises four subscales: Generic core [Physical Component Summary (PCS, 12 items) and Mental Component Summary (MCS, 12 items)]; Symptoms/Problems (12 items); Burden of Kidney Disease (4 items), and Effects of Kidney Disease (8 items).6,7 Scores of the different subscales were calculated according to the KDQOL-36™ scoring program.12,13 Raw, precoded numeric values for each item were transformed linearly to a 0 to 100 range, with higher scores reflecting better quality of life.13 Subsequently, the scores for the PCS and MCS were converted to T-scores with a mean of 50 and a standard deviation of 10. Most questions in the KDQOL-36™ are focused on the underlying health status during the preceding four weeks. Two items regarding problems with access to dialysis site were not answered because none of the participants were on dialysis at study entry. The KDQOL-36™ US Spanish version was adapted from an existing Spain Spanish version by FACITtrans (affiliate of the Functional Assessment of Chronic Illness Therapy (FACIT) Measurement System).7 This process involved: harmonization of the Spain Spanish version with the existing US Spanish RAND-36; review and suggested modifications by two native Spanish-speaking translators; reconciliation of suggestions by a Consensus Committee; back-translation of modifications into US English by one native English-speaker fluent in Spanish; comparison to the source US English version of the KDQOL-36™ and rating of equivalence by a KDQOL Working Group project coordinator (Benjamin Arnold,, personal communication).

Statistical Methods

Summary statistics for demographic and KDQOL-36™ subscales were calculated for all three subgroups (Spanish- and English-speaking Hispanics, and non-Hispanic Whites) as a whole and then separately. We used Chi-squared tests for dichotomous variables and analysis of variance (ANOVA) for continuous variables to test differences between groups. When the overall ANOVA was statistically significant (P<.05), it was followed by a Tukey’s multiple comparisons procedure to determine which pairwise comparisons were statistically significant. In addition, we conducted multiple adjusted comparisons on the least squares means of each KDQOL-36™ subscale score across the three groups. Spearman correlations were used to capture the strength of the monotonic relationship between variables without confining the shape of the relationship to be linear.

Reliability and Validity of the KDQOL-36™

Internal consistency reliability was estimated using Cronbach’s alpha for each subscale of the KDQOL-36™. A Cronbach’s alpha of >0.7 was considered high internal consistency. For analysis of construct validity we selected instruments, comorbidities, and laboratory tests that were expected to be correlated with at least one of the KDQOL-36 ™ subscales. The first measure of construct validity was the correlation between the overall health rating score (the first item of the KDQOL-36™) and each of the KDQOL-36™ subscales score. Second, we calculated the correlations between the generic core of the KDQOL-36™ and selected measures expected to be correlated with the PCS or MCS including the Beck Depression Inventory (BDI), a 21-item instrument to measure depression;14 the Multi-Ethnic Study of Atherosclerosis (MESA) Typical Week Physical Activity Survey (TWPAS), which measures how much physical activity of different intensities is undertaken by the study participant summarized as the metabolic equivalent (MET) score for all intentional exercise;15 and the Kansas City Cardiomyopathy Questionnaire (KCCQ), which focuses on HRQOL in patients with congestive heart failure.16 Third, we selected several measures that were expected to be correlated with the kidney-disease specific subscales of the KDQOL-36™ including the Patient Symptom Form derived from a review of the MDRD Study database for symptoms commonly reported in that study,17 and the Davies comorbidity score.18 We did not have sufficient data to calculate the Charlson Index used in other validation studies.19 However, the Davies comorbidity score has similar prognostic value20 and has been used by other studies of HRQOL in ESRD patients.21,22 This index (possible range from 0 to 7) comprises seven domains of active comorbid disease including malignancy, ischemic heart disease, peripheral vascular disease, left ventricular dysfunction, diabetes mellitus, systemic collagen vascular disease and other significant pathology (a condition severe enough to have an impact on survival in the general population, such as severe chronic obstructive pulmonary disease).18 Lower hemoglobin and albumin levels were also evaluated and expected to be associated with lower HRQOL.


Demographic and clinical characteristics of study participants are summarized in Table 1. Compared with non-Hispanic Whites (W), Spanish-speaking Hispanics (SH) were more likely to have 6th grade education or less, lower hemoglobin, and less likely to have a self-reported history of CVD. Compared with non-Hispanic Whites, English-speaking Hispanics (EH) were more likely to be younger, have lower hemoglobin, and greater self-reported hypertension; and less likely to have a high school diploma.

Table 1
Demographics and KDQOL-36 subscale scores overall and by participants’ group

Statistical Properties and Reliability of the KDQOL-36™

The mean scores for each KDQOL-36™ subscale ranged from 45.0 (MCS) to 83.0 (effects of kidney disease) among Spanish-Speaking Hispanics; from 37.8 (PCS) to 87.1 (effects of kidney disease) among English-Speaking Hispanics; and from 41.2 (PCS) to 88.8 (effects of kidney disease) among non-Hispanic whites (Table 2). The majority of the dimensions did not suffer from ceiling effects. However, burden of kidney disease and effects of kidney disease had high percentages of floor effects. Internal consistency reliability for all the subscales was very good with Cronbach’s alpha values ranging from 0.80 to 0.87 (Table 2). The largest pairwise difference in Cronbach’s alpha across ethnicity/language subgroups within any KDQOL-36™ subscale was 0.03.

Table 2
Statistical properties and internal consistency reliability of each KDQOL-36™ subscale by participants’ group

Construct Validity of the KDQOL-36™

The overall health rating score correlated inversely with all of the KDQOL-36™ subscales among all three subgroups (Tables 3). Of the generic domains, the strongest correlation with the overall health rating scale was seen with the PCS (r= −0.58 [EH], −0.54 [SH] and −0.65 [W]). Of the kidney disease-specific domains, the strongest correlation was found for the Symptoms/problems subscale (r= −0.46 [EH], −0.50 [SH] and −0.54 [W]). We also found significant negative correlation between the BDI and MCS scores (r= −0.56 [EH], −0.59 [SH], and −0.61 [W]). There was significant negative correlation between the Davies comorbidity score and the PCS scores (r= −0.32 [EH], −0.30 [SH], and −0.37 [W]). We also found significant positive correlation between the KCCQ clinical summary and the PCS scores (r= +0.48 [EH], +0.38 [SH], and +0.64 [W]). The Patient Symptom Form score had a significant, negative correlation with the Symptoms/problems subscale (r= −0.71 [EH], −0.70 [SH], and −0.77 [W]).

Table 3
Spearman correlations between KDQOL-36™ subscales and independent measures by participants’ group

KDQOL-36™ Scores by Language and Ethnic/Racial Group

After adjustment for clinical and demographic characteristics, the scores of three KDQOL-36™ subscales (Burden of Kidney Disease, Effects of Kidney Disease and MCS) were significantly lower in Spanish-speaking Hispanics than in non-Hispanic Whites (P<.01) (Table 4).

Table 4
Adjusteda regression models for KDQOL-36 subscales scores comparing Spanish- and English-Speaking Hispanics vs Whites


In these cohorts of US Spanish- and English-speaking Hispanics with mild-to-moderate CKD, we found that the KDQOL-36™ is a reliable and valid tool to assess HRQOL. Consistent with results from studies validating other language versions of the KDQOL™ instrument, we found significant correlation between each KDQOL-36™ subscale and the overall health rating score.2326 Similar to other studies,23 we found that individuals with depressive symptoms tend to have lower HRQOL as measured by the mental component summary of the KDQOL-36™. Furthermore, the correlation between the Patient Symptom Form score and the Symptoms/Problems subscale of the KDQOL-36™ was strong and in the anticipated direction.

A secondary objective of this study was to evaluate HRQOL in Hispanic and non-Hispanic individuals with mild-to-moderate CKD. Overall, the KDQOL-36™ scores were similar to those reported in other US studies of non-dialysis CKD and kidney transplant recipients,27,28 and higher than in dialysis patients.29 Similar to findings from the Dialysis Outcomes and Practice Patterns Study,29 we observed that Hispanics with CKD had lower HRQOL than non-Hispanics. Hispanics in the United States are known to be at socioeconomic disadvantage,30 and this is evident in our study cohort by the significant disparities in educational attainment. However, differences in HRQOL between ethnic groups were not fully explained by differences in age, education or clinical factors. The lower HRQOL in Hispanics may also be related to differences in disease burden, which were not measured, or to reporting bias, which is supported by a study by Marin et al31 suggesting that Hispanics are more likely to choose extreme categories in a response scale.

Our study had several limitations. First, the majority of Hispanics in our study were recruited from a single clinical center perhaps limiting the generalizability of findings. However, the characteristics of Hispanics in CRIC and HCRIC are reflective of the heterogeneity of the US Hispanic population.1,32,33 Second, the KDQOL-36™ was originally developed for patients with ESRD; however, it has been previously used in non-dialysis CKD individuals.27,28 Third, the KDQOL-36™ was administered once and we could not evaluate test-retest reliability. Nonetheless, we were able to demonstrate good internal consistency reliability within three different ethnic/language subgroups.

In conclusion, based on our study findings, the KDQOL-36™ can be used to assess HRQOL in US Hispanics with CKD. Future research is needed to evaluate HRQOL as a predictor for adverse health outcomes and responsiveness to interventions aimed at improving HRQOL in Hispanics with CKD.

We studied the validity and reliability of the US Spanish and English versions of the KDQOL-36™ among Hispanic individuals with CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study and the Hispanic CRIC (HCRIC) Study.

Based on our study findings, the KDQOL-36™ can be used to assess HRQOL in US Hispanics with CKD.


Funding for the CRIC Study was obtained under a cooperative agreement from National Institute of Diabetes and Digestive and Kidney Diseases (U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK 061028, U01DK060980, U01DK060963, and U01DK060902). In addition, this work was supported in part by: the University of Pennsylvania CTRC CTSA UL1 RR-024134, Johns Hopkins University UL1 RR-025005, University of Maryland GCRC M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research, University of Michigan GCRC grant number M01 RR-000042 CTSA grant number UL1 RR-024986, University of Illinois at Chicago CTSA UL1RR029879, The Clinical and Translational Research, Education, and Commercialization Project (CTRECP), Kaiser NIH/NCRR UCSF-CTSI UL1 RR-024131.

APPENDIX A. Chronic Renal Insufficiency Cohort (CRIC) Study

University of Pennsylvania Scientific & Data Coordinating Center

Harold I. Feldman, MD, MSCE (PI)

J. Richard Landis, PhD (Co-PI)

Amanda Hyre Anderson, PhD, MPH

Shawn Ballard, MS

Boyang Chai, MS

Laura M. Dember, MD

Jennifer Dickson

Marie Durborow

Chuck Girard

Melanie Glenn, MPH

Elizabeth S. Helker, RN

Peter A. Kanetsky, PhD, MPH

Scott Kasner, MD, MSCE, FAHA

Stephen E. Kimmel, MD, MSCE

Carissa Mazurick, MS

Steven R. Messe, MD

Lisa Nessel, MSS, MLSP

Qiang Pan, MA

Nancy Robinson, PhD

Jason Roy, PhD

Kaixiang (Kelvin) Tao, PhD, MS

Valerie L. Teal, MS

Krista Whitehead, MS

Dawei Xie, PhD

Wei (Peter) Yang, PhD

Xiaoming Zhang, MS

University of Pennsylvania Medical Center

Raymond R. Townsend, MD (PI)

Debbie Cohen, MD

Magdalena M. Cuevas, MT

Mark J. Duckworth

Virginia Ford, MSN, CRNP

Yonghong Huan, MD

Radhakrishna R. Kallem, MD, MPH

Juliet Leshner

Stephanie McDowell

Emile R. Mohler, III, MD

Wanda M. Seamon

Angie Sheridan, MPH

Jillian Strelsin

Karen Teff, PhD

Johns Hopkins University

Lawrence J. Appel, MD, MPH (PI)

Cheryl Anderson, PhD, MPH

Teresa Chan, MD, MHS

Alexander Chang, MD

Jeanne Charleston, RN

Tara Harrison

Bernard Jaar, MD, MPH

Carla Martin

Edgar “Pete” Miller, MD, PhD

Patience Ngoh

Steve Sozio, MD, MHS

Sharon Turban, MD, MHS

University of Maryland

Jeffrey Fink, MD, MS (Co-PI)

Clarissa Diamantidis, MD

Wanda Fink, RN, MS

Lisa Lucas

Tiffany Page

Afshin Parsa, MD, MPH

Beth Scism

Stephen Seliger, MD, MS

Matthew Weir, MD

University Hospitals of Cleveland Case Medical Center

Mahboob Rahman, MD (PI)

Mirela A. Dobre, MD, MPH (Co-PI)

Kathryn Clark, RA

Valori Corrigan RN

Genya Kisin, CTC

Radhika Kanthety, MD, MSHS

Louise Strauss, RN

Jackson T. Wright, Jr., MD, PhD

MetroHealth Medical Center

Jeffrey Schelling, MD (Co-PI)

Ed Horwitz, MD (Co-PI)

Lori Guillon, RN

Alicia O’Brien, RN, BSN

Noreen O’Malley, RN, BSN

Suma Prakesh, MD

John R. Sedor, MD

Mary Ann Shella, RN,BSN

Anne Slaven, MSSA

Jacqueline Theurer, MBA

J. Daryl Thornton, MD, MPH

Cleveland Clinic Foundation

Sankar D. Navaneethan, MD, MPH (PI)

Martin J. Schreiber, MD (Co-PI)

Jon Taliercio, DO (Co-PI)

Martha Coleman, RN, BSN

Kim Hopkins, RN

Lara Danziger-Isakov, MD MPH

Teresa Markle

Melanie Ramos, RN

Annette Russo

Stephanie Slattery, RN

Kay Stelmach, RN

Velma Stephens, LPN

University of Michigan at Ann Arbor

Akinlolu Ojo, MD, PhD (PI)

Baskaran Sundaram, MD

Jeff Briesmiester

Denise Cornish-Zirker, RN, BSN

Nancy Hill

Kenneth Jamerson, MD

Rajiv Saran, MD

Bonnie Welliver, BSN, CNN

Jillian Wilson, MHA

Eric Young, MD, MS

Julie Wright, MD

St. John’s Health System

Susan P. Steigerwalt, MD, FACP (Co-PI)

Keith Bellovich, DO

Jennifer DeLuca

Gail Makos, RN, MSN

Kathleen Walls

Wayne State University

John M. Flack, MD, MPH (Co-PI)

James Sondheimer, MD

Jennifer Mahn

Mary Maysura

Stephen Migdal, MD

M. Jena Mohanty, MD

Carol Muzyk, CCRP

Yanni Zhuang

University of Illinois at Chicago

James P. Lash, MD (PI)

Carolyn Brecklin, MD

Eunice Carmona

Janet Cohan, MSN

Michael Fischer, MD, MSPH

Anne Frydrych, MS, RD

Amada Lopez

Claudia Lora, MD

Monica Martinez

Alejandro Mercado

Brenda Moreno

Patricia Meslar, MSN

Ana Ricardo, MD, MPH

Tulane University Health Science Center

Jiang He, MD, PhD (PI)

Brent Alper, MD

Vecihi Batuman, MD

Lydia A. Bazzano, MD, PhD

Bernadette Borja

Jing Chen, MD, MSc

Catherine Cooke

Patrice Delafontaine, MD

Karen B. DeSalvo, MD, MPH, MSc

Jacquelyn Dolan

Lee Hamm, MD

Eva Lustigova, MPH

Erin Mahone, RN, BSN, MPH

Lanie Sansing

Claire Starcke

Kaiser Permanente of Northern California

Alan S. Go, MD (PI)

Arthur Choi

Pete Dorin, MPA

Nancy G. Jensvold, MPH

Joan C. Lo, MD

Liliana Metzger

Elisa Frances Nasol

Juan D. Ordonez, MD, MPH

Rachel Perloff

Nina Sasso

Daphne Thompson

Jingrong Yang, MA

University of California, San Francisco

Chi-yuan Hsu, MD, MSc (Co-PI)

Glenn M. Chertow, MD, MPH (Stanford



John W. Kusek, PhD

Andrew S. Narva, MD

External Expert Panel

Linda Fried, MD, MPH

David T. Gilbertson, PhD

William McClellan, MD, MPH

Peter A. McCullough, MD, MPH

David Nathan, MD

Ann M. O’Hare, MD

Paul M. Palevsky, MD

Stephen S. Rich, PhD

John B. Stokes, MD

Gina S. Wei, MD, MPH

Peter W. Wilson, MD

Translational Core Lab-University of Pennsylvania

Megan Donovan

Steve Master, MD, PhD

Ted Mifflin

Linda Morrell

GFR Lab-Cleveland Clinic

Phillip Hall, MD

Henry Rolin

Sue Saunders

EBT Reading Center- UCLA

Mathew Budoff, MD

Chris Dailing

ECG Reading Center- Wake Forest

Elsayed Z. Soliman, MD, MSc, MS

Zhu-Ming Zhang, MD

Echo Reading Center- University of Pennsylvania

Martin St. John Sutton, MBBS

Martin G. Keane, MD, FACC, FAHA

University of Pennsylvania CTRC CTSA

UL1 RR-024134

The Johns Hopkins University

UL1 RR-025005

University of Maryland GCRC

M01 RR-16500

Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research

Michigan Institute for Clinical and Health Research (MICHR) UL1RR024986

University of Illinois at Chicago CTSA


The Clinical and Translational Research, Education, and Commercialization Project (CTRECP)


UL1 RR-024131


Author ContributionsDesign and concept of study: Ricardo, Hacker, Ackerson, Go, Ojo, Ferrans, Lash Acquisition of data: Ricardo, DeSalvo, Go, Ojo, Townsend, Ferrans, Lash Data analysis and interpretation: Ricardo, Hacker, Lora, Ackerson, Go, Kusek, Nessel, Ojo, Xie, Ferrans, Lash Manuscript draft: Ricardo, Hacker, Lora, Ackerson, DeSalvo, Go, Kusek, Nessel, Ojo, Townsend, Xie, Ferrans, Lash Statistical expertise: Ricardo, Ackerson, Go, Xie, Ferrans, Lash Acquisition of funding: Ricardo, Ackerson, Kusek, Ojo, Townsend Administrative: Lora, Kusek, Nessel, Townsend Supervision: Hacker, Townsend

Contributor Information

Ana C. Ricardo, Department of Medicine, Section of Nephrology, University of Illinois at Chicago.

Eileen Hacker, College of Nursing, University of Illinois at Chicago.

Claudia M. Lora, Department of Medicine, Section of Nephrology, University of Illinois at Chicago.

Lynn Ackerson, Division of Research, Kaiser Permanente of Northern California.

Karen B. DeSalvo, Section of General Internal Medicine and Geriatrics, Tulane University.

Alan Go, Division of Research, Kaiser Permanente of Northern California.

John W. Kusek, National Institute of Diabetes and Digestive and Kidney Diseases.

Lisa Nessel, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania.

Akinlolu Ojo, Department of Medicine, University of Michigan.

Raymond R. Townsend, Department of Medicine, University of Pennsylvania.

Dawei Xie, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania.

Carol E. Ferrans, College of Nursing, University of Illinois at Chicago.

James P. Lash, Department of Medicine, Section of Nephrology, University of Illinois at Chicago.


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