Abstract
Rationale & Objective:
The effects of race, ethnicity, socioeconomic status, and disease severity on acute care utilization (ACU) in patients with glomerular disease (GD) are unknown.
Study Design:
A prospective cohort study.
Setting & Participants:
1,456 adults and 768 children with biopsy proven GD enrolled in the Cure Glomerulonephropathy cohort.
Exposure:
Race and ethnicity as a participant-reported social factor.
Outcome:
ACU defined as hospitalizations or emergency department visits.
Analytical Approach:
Multivariable recurrent event proportional rate models were used to estimate associations between race and ethnicity and ACU.
Results:
Black or Hispanic participants had lower socioeconomic status and more severe GD compared to White or Asian participants. ACU rates were 45.6, 29.5, 25.8, and 19.2 per 100 person-years in Black, Hispanic, White, and Asian adults, respectively, and 55.8, 42.5, 40.8, and 13.0, respectively, for children. Compared to White race (reference group): Black race was significantly associated with ACU in adults (rate ratio (RR) 1.76, 95% Confidence Interval (CI) 1.37–2.27), although this finding was attenuated after multivariable adjustment (RR 1.31, 95% CI 1.03–1.68). Black race was not significantly associated with ACU in children; Asian race was significantly associated with lower ACU in children (RR 0.32, 95% CI 0.14–0.70); no significant associations between Hispanic ethnicity and ACU were identified.
Limitations:
We used proxies for socioeconomic status and lacked direct information on income, household unemployment or disability.
Conclusions:
Significant differences in ACU rates were observed across racial and ethnic groups in persons with prevalent GD, although many of these difference were explained by differences in socioeconomic status and disease severity. Measures to combat socioeconomic disadvantage in Black patients, and more effectively prevent and treat glomerular disease, are needed to reduce disparities in ACU, improve patient wellbeing, and reduce healthcare costs.
Plain-Language Summary:
Racial and ethnic disparities have been described in many aspects of kidney disease, but little is known about disparities in glomerular disease. This large, multi-national, cohort study of the Cure Glomerulonephropathy Network describes differences in acute care utilization for patients with glomerular disease across racial and ethnic groups and explores potential underlying reasons for any observed differences. We found that Black race was associated with higher rates of acute care utilization, while Asian race was associated with lower rates of acute care utilization. These associations may be explained by differences in socioeconomic status and disease severity amongst these groups. Efforts to improve health equity should consider socioeconomic factors contributing to disease severity or reliance on hospital over ambulatory care.
Graphical Abstract
Introduction
Glomerular disease can result in acute medical complications (e.g. anasarca1, infections2, acute kidney injury3, thromboembolism4, and cardiovascular events5) requiring a visit to the emergency department (ED) or hospitalization, both referred to here as acute care utilization (ACU). Immunosuppressive therapies used to treat glomerular disease also cause complications, particularly infections, that can result in ACU.6 A study of the Kid’s Inpatient Database from the Healthcare Cost and Utilization Project (HCUP-KID) found that in the two years studied (2006, 2009), children hospitalized with nephrotic syndrome accounted for 48,700 inpatient days and $259 million in charges.7 This study did not include adults and race, ethnicity, socioeconomic status (SES) or disease severity were not examined.
Race, ethnicity, and SES may all affect ACU risk in patients with glomerular disease. Non-White children with nephrotic syndrome were reported to have a higher risk of acute kidney injury related8 and hospitalization9 compared to White children. Another study using HCUP-KID data identified more admissions for nephrotic syndrome covered by public compared to private insurance.10 The relative importance of race, ethnicity, and SES were not analyzed in any of these studies. Disease severity is also a determinant of ACU risk: in a study of adults with nephrotic syndrome, those with steroid-resistant versus steroid sensitive nephrotic syndrome had higher hospitalization rates.11
Accordingly, we aimed to compare rates of ACU across racial and ethnic groups in adults and children with glomerular disease enrolled in the Cure Glomerulonephropathy (CureGN) cohort, and to explore whether differences across racial and ethnic groups in disease severity or SES explained any observed findings. We hypothesized that Black or Hispanic patients would have increased rates of ACU but that lower SES and more severe glomerular disease among these groups would largely explain any observed differences.
Methods
Population Studied
CureGN is an ongoing National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)-funded longitudinal cohort study of children and adults with glomerular disease recruited from 70 centers from the United States, Canada, Italy, and Poland. Participants all had a kidney biopsy within 5 years of enrollment demonstrating minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), or immunoglobulin A nephropathy/vasculitis (IgAN/IgAV).12 All participants provided written informed consent, or assent (if a minor), to enroll in the study.
After enrollment, participants are followed prospectively every 6 months. Collected baseline and interval data include the following: demographics, symptoms, medications, physical exam findings, biospecimens, hospitalizations and ED visits. Details of exclusion criteria and visit schedule have been previously reported.12 This study was approved by the Institutional Review Board of Stanford University (#49739). Participants who had completed at least one follow-up visit and who self-reported as White, Black, or Asian race, with or without Hispanic ethnicity, were included (Figure 1).
Figure 1:
Patient enrollment to CureGN and selection for inclusion in this current study. Shown are the patient enrolment and study completion numbers for adults and children in the CureGN study, following STROBE recommendations for reporting on observational clinical studies
Exposures, Outcomes, and Covariates
Exposures:
The exposure of interest was participant-reported race and ethnicity. We categorized race and ethnicity as: White, which included non-Hispanic White; Black, which included participants of Black race and any ethnicity; Asian, which included participants of Asian race and any ethnicity; and Hispanic, which included Hispanic White. The small number of participants who reported any other race, mixed race, or had missing race or ethnicity were excluded.
Outcomes:
ACU was defined as a hospitalization or ED visit, either self-reported or captured by review of the health record, occurring between follow-up study visits. Event data included length of stay (LOS) and intensive care unit (ICU) admission. ACU rates are reported per 100 person years of follow-up for each racial and ethnic group.
Covariates:
We compared demographic, socioeconomic, and disease-related characteristics at enrollment across racial and ethnic groups and adjusted for these covariates when determining independent associations between race and ethnicity and ACU. Demographic variables included age and sex. Socioeconomic variables included a combined variable of insurance type and country (United States private=reference, United States public, International private (adults only), International public, none); participant or parental education (at least a college degree=reference, less than a college degree); and, for adults, student/employment status (employed or a student=reference vs. unemployed/disability/medical leave/other). Participants with multiple insurance types were categorized using the hierarchy of private, public, and no insurance. Parental education was categorized using the highest education level attained by either parent.
Other variables included glomerular disease subtype (IgAN/IgAV=reference, MCD, FSGS, MN), family history of kidney disease (no=reference, yes), glomerular disease duration (time from initial kidney biopsy to enrollment), hypertension status (normal=reference, pre-hypertension, or hypertension)13,14, and weight status (underweight [BMI<18.5 or <5th percentile for pediatric participants] or normal [BMI 18.5–25 or 5th to 85th percentile for pediatric participants]=reference, overweight [BMI 25–30 or 85th to 95th percentile for pediatric participants], obese [BMI ≥30 or >95th percentile for pediatric participants]). Disease severity included presence of self-reported edema (less than moderate=reference, moderate or greater), urine protein/creatinine ratio (UPCR in g/g), estimated glomerular filtration rate (eGFR expressed as ml/min/1.73m2), serum albumin (in g/dL), use of glucocorticoids (no=reference, yes) and use of other immunosuppression (no=reference, yes) at enrollment. eGFR was determined using the race adjusted Chronic Kidney Disease Epidemiology Collaboration formula15 for participants 26 years or older, and the bedside Schwartz formula16 for participants < 18 years old. For participants between 18 and 26 years old, values derived from both equations were averaged.17 Longitudinally collected eGFR was also examined as a time-dependent covariate.
Statistical Analysis
Separate analyses were performed for adults and children. Participant characteristics are reported as median and interquartile range for continuous variables and as number and percent for categorical variables. Demographic, socioeconomic, disease-related characteristics, as well as rates of ACU were compared across racial and ethnic groups using Chi-square tests for categorical variables and Kruskal-Wallis tests for continuous variables. Multiple imputation using sequential regression techniques was performed using IVEware version 0.1.18
We used recurrent event proportional rate models19 to determine the associations between rate ratios of ACU and race and ethnicity. We serially adjusted for confounders using six sequential models to explore underlying explanations for observed differences. The first model examined unadjusted associations between race and ethnicity and ACU; the second model additionally adjusted for demographics; the third model additionally adjusted for socioeconomic factors; the fourth model additionally adjusted for chronic disease indicators; the fifth model additionally adjusted for disease severity and activity markers (at enrollment); and in the sixth model eGFR was incorporated as a time-varying covariate to account for disease progression. All variables were retained in the model. Modeling was performed separately for each of 10 imputed datasets, and parameter estimates were combined using Rubin’s rules to account for between and within imputation variation.20 A sensitivity analysis was performed restricting to US participants given the relatively small number of Black participants from international sites, and to eliminate the socioeconomic effects of international differences in healthcare delivery on study findings.
Results
Racial and ethnic differences in demographic, socioeconomic and disease characteristics: Adults
Of the 1,456 adults in this study, 950 (65%) were non-Hispanic White, 233 (16%) were Black (of which 14 were Hispanic), 154 (11%) were Asian (of which none were Hispanic), and 119 (8%) were Hispanic White. Black or Hispanic adults were less likely to be college-educated, more likely to have US public insurance, and were more likely to be on medical leave, disabled or unemployed, compared to White or Asian adults (Table 1a).
Table 1a.
Characteristics of adult study participants at enrollment
| Variable | Overall (n=1456) | White (n=950) | Black (n=233)a | Asian (n=154) | Hispanic (n=119) | p-value |
|---|---|---|---|---|---|---|
|
| ||||||
| Age at consent (years), median (IQR) | 47.0 (33.0–60.0) | 49.0 (34.0–62.0) | 46.0 (32.0–57.0) | 42.0 (32.0–55.0) | 40.0 (32.0–51.0) | <0.001 |
| Male, n(%) | 823 (57) | 579 (61) | 94 (40) | 85 (55) | 65 (55) | <0.001 |
| Disease Duration, in months, median (IQR) | 15.0 (5.0–38.0) | 14.0 (5.0–36.0) | 16.0 (6.0–41.0) | 21.0 (6.0–42.0) | 13.0 (4.0–44.0) | 0.2 |
| Disease, n(%) | <0.001 | |||||
| MCD | 213 (15) | 144 (15) | 32 (14) | 27 (18) | 10 (8) | |
| FSGS | 376 (26) | 212 (22) | 104 (45) | 23 (15) | 37 (31) | |
| MN | 472 (32) | 328 (35) | 77 (33) | 42 (27) | 25 (21) | |
| IgAN/IgAV | 395 (27) | 266 (28) | 20 (9) | 62 (40) | 47 (39) | |
| eGFR (mL/min/1.73m2), median (IQR) | 68.0 (42.0–93.0) | 69.0 (44.0–93.0) | 63.0 (38.0–91.0) | 75.0 (42.0–97.0) | 57.0 (37.0–95.0) | 0.2 |
| UPCR, median (IQR) | 1.60 (0.40–4.40) | 1.50 (0.40–4.10) | 2.50 (0.80–5.90) | 1.45 (0.40–3.80) | 1.80 (0.70–4.00) | 0.004 |
| Serum albumin (g/dL), median (IQR) | 3.7 (3.0–4.1) | 3.8 (3.1–4.2) | 3.5 (2.7–3.69) | 3.8 (3.2–4.3) | 3.8 (3.2–4.1) | <0.001 |
| Weight Status, n(%) | <0.001 | |||||
| Normal/Underweight | 419 (30) | 268 (29) | 42 (19) | 78 (53) | 31 (27) | |
| Overweight | 452 (32) | 302 (33) | 58 (26) | 55 (38) | 37 (32) | |
| Obese | 539 (38) | 351 (38) | 127 (56) | 13 (9) | 48 (41) | |
| Hypertension Status, n(%) | <0.001 | |||||
| Normal | 480 (35) | 292 (33) | 65 (30) | 76 (51) | 47 (41) | |
| Pre-Hypertensive | 553 (40) | 380 (42) | 79 (36) | 50 (33) | 44 (38) | |
| Hypertensive | 348 (25) | 226 (25) | 74 (34) | 24 (16) | 24 (21) | |
| Current edema: moderate or greater, n(%) | 388 (30) | 233 (27) | 87 (44) | 32 (23) | 36 (34) | <0.001 |
| Current corticosteroids, n(%) | 413 (28) | 281 (30) | 68 (29) | 34 (22) | 30 (25) | 0.2 |
| Current Other Immunosuppression, n(%) | 420 (29) | 278 (29) | 70 (30) | 41 (27) | 31 (26) | 0.8 |
| College Educated, n(%) | 664 (46) | 456 (48) | 66 (28) | 102 (66) | 40 (34) | <0.001 |
| Country/Insurance Status, n(%) b | <0.001 | |||||
| US Private | 960 (67) | 669 (72) | 144 (64) | 92 (61) | 55 (49) | |
| US Public | 218 (15) | 106 (11) | 65 (29) | 17 (11) | 30 (27) | |
| International Private | 75 (5) | 42 (4) | 2 (1) | 23 (15) | 8 (7) | |
| International Public | 143 (10) | 112 (12) | 8 (4) | 20 (13) | 3 (3) | |
| None | 30 (2) | 6 (1) | 7 (3) | 0 (0) | 17 (15) | |
| Current Student: Full Time, n(%) | 141 (10) | 93 (10) | 21 (9) | 15 (10) | 12 (10) | 0.9 |
| Current Employment Status, n(%) | <0.001 | |||||
| Employed Full or Part Time | 952 (69) | 617 (68) | 143 (64) | 116 (79) | 76 (66) | |
| Medical Leave or Disabled | 85 (6) | 50 (6) | 22 (10) | 0 (0) | 13 (11) | |
| Unemployed | 105 (8) | 58 (6) | 28 (13) | 7 (5) | 12 (10) | |
| Otherb | 247 (18) | 180 (20) | 30 (13) | 23 (16) | 14 (12) | |
| Family History of ESKD, n(%) | 471 (33) | 287 (31) | 101 (44) | 51 (34) | 32 (28) | 0.002 |
MCD, minimal change disease; FSGS, focal segmental glomerulosclerosis; MN, membranous nephropathy; IgAN, IgA nephropathy; IgAV, IgA vasculitis; eGFR, estimated glomerular filtration rate; UPCR, urine protein-to-creatinine ratio; ESKD, end stage kidney disease
N=14 (6%) Black Hispanic
Insurance is missing for 30 (2%) of adults
Black adults had the highest frequency of FSGS (45%) while Asian or Hispanic adults had the highest frequency of IgAN/IgAV (39–40%). Hispanic adults had the lowest eGFR while Black adults had the highest UPCR. Black adults were most likely, and Asian adults were least likely, to have obesity or hypertension. There were no differences in frequency of glucocorticoid or other immunosuppression use at enrollment across racial and ethnic groups. Black adults were most likely to have a family history of kidney disease (Table 1a).
Racial and ethnic differences in demographic, socioeconomic and disease characteristics: Children
Of the 768 children in this study, 507 (66%) were non-Hispanic White, 141 (18%) were Black (of which 4 were Hispanic), 48 (6%) were Asian (of which 1 was Hispanic), and 72 (10%) were Hispanic White. Black or Hispanic (34% each) children were least likely to have US private insurance compared to White (59%) or Asian (50%) children. The parents of Black or Hispanic children were least likely to be college educated compared to White or Asian children (Table 1b).
Table 1b.
Characteristics of pediatric study participants at enrollment
| Variable | Overall (n=768) | White (n=507) | Black (n=141)a | Asian (n=48) | Hispanic (n=72) | p-value |
|---|---|---|---|---|---|---|
|
| ||||||
| Age at consent (years), median (IQR) | 11.0 (7.0–15.0) | 11.0 (7.0–15.0) | 12.0 (8.0–15.0) | 11.0 (7.0–13.0) | 11.0 (7.0–15.0) | 0.1 |
| Male, n(%) | 444 (58) | 303 (60) | 72 (51) | 31 (65) | 38 (53) | 0.2 |
| Disease Duration, in months, median (IQR) | 15.0 (5.0–38.0) | 14.0 (5.0–36.0) | 15.0 (5.0–43.0) | 23.0 (8.0–41.5) | 17.0 (5.0–39.0) | 0.6 |
| Disease, n(%) | <0.001 | |||||
| MCD | 299 (39) | 185 (36) | 65 (46) | 23 (48) | 26 (36) | |
| FSGS | 178 (23) | 98 (19) | 58 (41) | 10 (21) | 12 (17) | |
| MN | 40 (5) | 22 (4) | 11 (8) | 1 (2) | 6 (8) | |
| IgAN/IgAV | 251 (33) | 202 (40) | 7 (5) | 14 (29) | 28 (39) | |
| eGFR (mL/min/1.73m2), median (IQR) | 102.0 (85.0–124.0) | 102.0 (87.0–124.0) | 95.0 (73.0–114.0) | 111.0 (98.0–134.0) | 110.5 (93.0–132.0) | <0.001 |
| UPCR, median (IQR) | 0.40 (0.10–2.40) | 0.30 (0.10–1.90) | 1.10 (0.15–3.35) | 0.40 (0.10–2.50) | 0.50 (0.10–3.10) | 0.2 |
| Serum albumin (g/dL), median (IQR) | 3.8 (2.9–4.2) | 3.8 (2.9–4.2) | 3.5 (2.8–4.0) | 4.0 (3.6–4.3) | 3.9 (3.1–4.3) | 0.003 |
| Weight Status, n(%) | 0.01 | |||||
| Normal/Underweight | 368 (48) | 255 (51) | 58 (42) | 27 (56) | 28 (39) | |
| Overweight | 155 (20) | 112 (22) | 26 (19) | 5 (10) | 12 (17) | |
| Obese | 237 (31) | 135 (27) | 55 (40) | 16 (33) | 31 (44) | |
| Hypertension Status, n(%) | 0.1 | |||||
| Normal | 462 (63) | 321 (66) | 72 (54) | 31 (67) | 38 (55) | |
| Pre-Hypertensive | 101 (14) | 64 (13) | 23 (17) | 4 (9) | 10 (14) | |
| Hypertensive | 170 (23) | 100 (21) | 38 (29) | 11 (24) | 21 (30) | |
| Current edema: moderate or greater, n(%) | 91 (14) | 53 (12) | 25 (22) | 5 (12) | 8 (13) | 0.06 |
| Current glucocorticoid, n(%) | 311 (40) | 209 (41) | 54 (38) | 23 (48) | 25 (35) | 0.5 |
| Current Other Immunosuppression, n(%) | 373 (49) | 249 (49) | 66 (47) | 25 (52) | 33 (46) | 0.9 |
| Country/Insurance Status, n(%) b | <0.001 | |||||
| US Private | 376 (52) | 283 (60) | 46 (35) | 23 (50) | 24 (34) | |
| US Public | 254 (35) | 124 (26) | 79 (60) | 9 (20) | 42 (60) | |
| International Public | 84 (12) | 63 (13) | 4 (3) | 13 (28) | 4 (6) | |
| None | 8 (1) | 5 (1) | 2 (2) | 1 (2) | 0 (0) | |
| Current Student: Full Time, n(%) | 649 (85) | 418 (82) | 125 (89) | 40 (83) | 66 (92) | 0.1 |
| Family History of ESKD, n(%) | 238 (32) | 137 (28) | 58 (42) | 15 (35) | 28 (39) | 0.01 |
| Either Parent College Educated, n(%) | 276 (38) | 205 (43) | 30 (23) | 28 (65) | 13 (19) | <0.001 |
MCD, minimal change disease; FSGS, focal segmental glomerulosclerosis; MN, membranous nephropathy; IgAN, IgA nephropathy; IgAV, IgA vasculitis; eGFR, estimated glomerular filtration rate; UPCR, urine protein-to-creatinine ratio; ESKD, end stage kidney disease
N=4 (3%) Black Hispanic
Insurance is missing for 46 (6%) of children
MCD was the most frequent diagnosis overall, but Black children were most likely to have FSGS. Black children had the lowest eGFR and highest UPCR. Black or Hispanic children were most likely to have obesity, hypertension, or a family history of kidney disease. There were no differences in the frequencies of glucocorticoid or other immunosuppression use at enrollment across racial and ethnic groups (Table 1b).
Acute Care Utilization: Adults
Black adults had the highest, and Asian adults the lowest, rates of ACU (45.6 ACU events for 100 person years for Black adults, compared to 29.5 for Hispanic, 25.8 for White, and 19.2 for Asian adults, p<0.001). Median LOS was highest for Black or Hispanic adults (Table 2).
Table 2.
Unadjusted rates of Acute Care Utilization (ACU)
| White | Black | Asian | Hispanic | p-value | ||
|---|---|---|---|---|---|---|
|
| ||||||
| Adults | n | 950 | 233 | 154 | 119 | |
| Follow-up time (years, median [IQR]) | 3.6 (2.4,5.1) | 3.1 (1.9,4.6) | 3.8 (2.7,4.8) | 4.3 (2.3,5.5) | 0.01 | |
| ACU Rate per 100 person-years (95% CI) | 25.8 (24.1–27.6) | 45.6 (41.0–50.7) | 19.2 (15.9–23.2) | 29.5 (24.9–35.0) | <0.001 | |
| ER Rate per 100 person-years (95% CI) | 10.9 (9.9–12.1) | 19.9 (16.9–23.3) | 8.4 (6.3–11.1) | 17.0 (13.6–21.3) | <0.001 | |
| Hospitalization Rate per 100 person-years (95% CI) | 14.9 (13.6–16.2) | 25.7 (22.3–29.6) | 10.9 (8.4–14.0) | 12.5 (9.6–16.3) | <0.001 | |
| Hospital length of stay (days, median [IQR])* | 3.0 (1.0,5.0) | 4.0 (2.0,7.0) | 3.0 (1.0,5.0) | 4.0 (2.0,8.0) | 0.001 | |
| ICU admission rate per 100 person-years (95% CI) | 0.5 (0.3–0.8) | 1.3 (0.7–2.5) | 0.4 (0.1–1.4) | 0.9 (0.3–2.4) | 0.1 | |
|
| ||||||
| Children | n | 507 | 141 | 48 | 72 | |
| Follow-up time (years, median [IQR]) | 4.1 (3.1,5.2) | 3.5 (2.5,4.7) | 3.8 (3.0,5.1) | 3.9 (3.0,5.0) | 0.03 | |
| ACU Rate per 100 person-years (95% CI) | 40.8 (38.1–43.7) | 55.8 (49.5–62.9) | 13.0 (8.6–19.6) | 42.5 (35.4–51.0) | <0.001 | |
| ER Rate per 100 person-years (95% CI) | 13.6 (12.1–15.3) | 22.6 (18.8–27.3) | 2.8 (1.2–6.8) | 11.8 (8.4–16.7) | <0.001 | |
| Hospitalization Rate per 100 person-years (95% CI) | 27.2 (25.0–29.6) | 33.2 (28.4–38.8) | 10.2 (6.4–16.2) | 30.7 (24.7–38.0) | <0.001 | |
| Hospital length of stay (days, median [IQR])* | 3.0 (1.0,5.0) | 3.0 (1.0,5.0) | 3.0 (2.0,8.0) | 3.0 (2.0,7.0) | 0.02 | |
| Rate of ACU with ICU stay per 100 person-years (95% CI) | 1.2 (0.8–1.8) | 3.5 (2.2–5.7) | 1.1 (0.3–4.5) | 3.3 (1.7–6.4) | 0.004 | |
|
|
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Legend:
For those hospitalized
ER, emergency room; ICU: intensive care unit
Black race was associated with a higher rate of ACU compared to White race in unadjusted analysis [rate ratio (RR) 1.76, 95% Confidence Interval (CI) 1.37–2.25]. After multivariable adjustment, this effect was attenuated but remained statistically significant (RR=1.31 95% CI 1.03–1.68). Both before (RR 0.74, 95% CI 0.55–1.01) and after (RR 0.78, 95% CI 0.57–1.07) multivariable adjustment, a lower rate of ACU in Asian compared to White adults was observed by point estimate, but this difference did not reach statistical significance. Rates of ACU were not significantly different comparing Hispanic to non-Hispanic White race (Figure 2 and Table S1a). Several markers of SES and disease severity were independently associated with ACU in the fully adjusted model: having US public insurance, less than college education, lower eGFR (at enrollment and time-dependent), or higher UPCR were associated with a higher rate of ACU in adult participants (Table S1a). Results of sensitivity analyses including US participants only were similar, with the following exceptions: adjusted relationship between Black race and ACU was no longer statistically significant (RR=1.25, 95% CI=0.96–1.62); unadjusted and adjusted relationship between Asian race and ACU was statistically significant (unadjusted RR=0.63, 95% CI=0.43–0.93, adjusted RR=0.67, 95% CI=0.46–0.99, Table S2a).
Figure 2.
Rate ratios with 95% CI bars compared to White participants with sequential adjustment for (II) demographics, (III) socioeconomic factors, (IV) health status, (V) disease severity at enrollment, (VI) time dependent eGFR
Acute Care Utilization: Children
Rates of ACU were overall higher in children than in adults. Black children had the highest, and Asian children the lowest, rates of ACU (55.8 ACU events per 100 person years for Black children, compared to 42.5 for Hispanic, 40.8 for White, and 13.0 for Asian children p<0.001). Rates of ICU utilization were higher in Black or Hispanic compared to White or Asian children (Table 2).
A borderline significant association between Black (vs White) race and higher rates of ACU in children by unadjusted analysis (RR 1.32, 95% CI 1.00–1.73) disappeared after multivariable adjustment (RR 1.10, 95% CI 0.81–1.49). Asian race was associated with significantly lower ACU rates compared to White race both before (RR 0.32, 95% CI 0.13–0.78) and after (RR 0.32, 95% CI 0.14–0.70) multivariable adjustment. Rates of ACU were not significantly different comparing Hispanic to non-Hispanic White race (Figure 2 and Table S1b). In the fully adjusted model, a number of markers of SES and disease characteristics were independently associated with ACU in children: having public or no insurance, FSGS, lower serum albumin at enrollment, or immunosuppression use at enrollment were associated with higher rates of ACU (Table S1b). Results of sensitivity analyses among US participants were similar except that immunosuppression use at enrollment was no longer significantly associated with ACU (Table S2b).
Missing Data
Except for UPCR (19%) and serum albumin (22%), missingness was ≤10% for all other covariates. Demographic (including racial and ethnic distributions), socioeconomic, and clinical characteristics of adults and children with complete data for all variables were largely similar to those of participants with missing data for one or more variables, with some exceptions that suggested that participants with milder disease were more likely to have missing data: disease duration was longer in participants with missing data; children with missing data were less likely to be using glucocorticoids or be a full time student; and adults with missing data had less proteinuria, were less likely to have edema, were less likely to be using glucocorticoids or other immunosuppression, and were more likely to be a full time student (Table S3a and S3b).
Discussion
In this international multicenter cohort study of adults and children with glomerular disease, we determined that race and ethnicity, socioeconomic status, and disease severity were all important determinants of ACU. ACU rates were highest in Black, and lowest in Asian participants. After multivariable adjustment and compared to White race: 1. Black race remained associated with higher ACU rates in adults but not in children; 2. Asian race was associated with lower ACU rates in children only; and 3. there was no significant association between Hispanic ethnicity and ACU rates in either adults or children. Health insurance status and markers of glomerular disease severity (eGFR and uPCR in adults, serum albumin and immunosuppressive medication use in children), were other important determinants of ACU.
These findings are consistent with previously reported racial and ethnic disparities in ACU in children with glomerular disease. In a retrospective single center study of 87 children with nephrotic syndrome, Black race was associated with a higher risk for hospitalization.9 We confirm these findings in a larger multicenter cohort of children with glomerular disease and that a similar disparity exists in adults with glomerular disease.
Asian race was associated with lower rates of ACU in children and a similar trend was observed in adults. Prior cohort studies of children with nephrotic syndrome identified a lower odds of frequently relapsing or steroid dependent nephrotic syndrome in Asian children,21,22 but a threefold higher likelihood of steroid-resistant nephrotic syndrome in Black children.23 Higher rates of FSGS are observed among individuals of African ancestry, particularly those with two high-risk APOL1 variants,24 while IgAN is more frequent in persons of Asian ancestry.25 Although we accounted for differences in disease severity and glomerular disease subtype in our multivariable models, it may be that residual confounding by differences in glomerular disease phenotype explained some of the racial and ethnic differences in ACU rates we observed.
In this study, race and ethnicity represent social rather than biological constructs, as they were self-reported without knowledge of ancestry. The relative contribution of social versus biological factors on disease severity and ACU could not be discerned from our data. However, we hypothesize that our findings reflect an interplay between genetic factors and social determinants of health, stemming from the social construct of race and the consequences of cumulative social factors on health over time. To support this hypothesis, we determined that the observed association between Black race and higher rates of ACU was attenuated after adjusting for markers of SES, suggesting that at least some of the racial and ethnic differences in ACU rates were related to differences in social factors rather than from race and ethnicity itself.
We hope these data might inform the development of solutions to prevent excess hospitalizations in Black patients, perhaps offsetting the effects from race and associated social factors, such as racism and economic disparities, on patient outcomes.26, 27 For example, a “model medical home” to improve chronic illness management in the primary care setting includes decision support, community resources, clinical information systems, and delivery system redesign,28 which could serve as a foundation to improve care delivery for glomerular disease patients.
Another modifiable social factor to minimize disparities in ACU is access to care limited by insurance. Black or Hispanic children had higher rates of ICU stay and were more likely to be uninsured, while lack of insurance was associated with higher ACU in children. Conversely, in adults, lack of insurance was associated with lower ACU. It may be that adults without health insurance are reluctant to seek medical care for themselves, but lack of insurance does not pose the same barrier when seeking care for children. However, illness might be allowed to progress in severity before children are brought to the hospital, perhaps compounded by reduced access to ambulatory care, as we observed particularly high rates of ICU care among Black and Hispanic children. Similarly, Hispanic adults had particularly high rates of ED use, that did not translate into higher rates of hospitalization, suggesting that this group might disproportionately rely on the ED over primary or ambulatory care services for their acute care needs. We also found differences in ACU for those with public insurance from the US as opposed to international sites (Canada, Italy, and Poland), likely representing differences in the implications of public health insurance under different health care organizational structures.
The present study has some limitations. First, we lacked complete data for all participants. Second, we used indirect measures of SES including education and insurance status without direct information on household income, household unemployment or disability. Third, our study population comprised participants with access to healthcare, who agreed to participate in a clinical study, and underwent kidney biopsy. Therefore, our findings may not be generalizable to patients with more limited access to health care. Fourth, we only included eGFR as a time-updated variable in our final model, as disentangling the influence of disease activity from the influence of immunosuppression rendered handling of these variables challenging. Fifth, except for infection related ACU, diagnoses associated with ACU events have not yet been validated in the CureGN cohort; thus, we elected not to report this information. Sixth, utilization of the ED for acute care may reflect limited clinic hours of specialty care, particularly in the university setting. This may not reflect a choice by the patient but instead is an aspect of where patient care is received. Finally, APOL1 genotype data are not yet available for the entire CureGN cohort; therefore, the full impact of genetic predisposition to more severe disease was not explored in this study but will be the focus of future work.
In conclusion, in this large multinational cohort study of adults and children with glomerular disease, Black race was associated with higher rates of ACU in adults, while Asian race was associated with lower rates of ACU in children, even after adjusting for racial and ethnic differences in SES and disease characteristics. Addressing socioeconomic barriers to accessing and adhering to treatment and determining the influence of race and ethnicity on treatment effectiveness might help to minimize healthcare disparities.
Supplementary Material
Table S1a: Unadjusted and multivariable adjusted rate ratios for Acute Care Utilization (ACU), with White participants as the reference group: adults
Table S1b: Unadjusted and multivariable adjusted rate ratios for Acute Care Utilization (ACU), with White participants as the reference group: children
Table S2a: Unadjusted and multivariable adjusted rate ratios for Acute Care Utilization (ACU), with White participants as the reference group: adults (US only)
Table S2b: Unadjusted and multivariable adjusted rate ratios for Acute Care Utilization (ACU), with White participants as the reference group: children (US only)
Table S3a: Adult Sample Characteristics at Enrollment by Data Availability
Table S3b: Pediatric Sample Characteristics at Enrollment by Data Availability
Acknowledgements:
We acknowledge the efforts of all CureGN participants, their parents, investigators and staff.
Support:
Funding for the CureGN consortium is provided by U24DK100845 (formerly UM1DK100845), U01DK100846 (formerly UM1DK100846), U01DK100876 (formerly UM1DK100876), U01DK100866 (formerly UM1DK100866), and U01DK100867 (formerly UM1DK100867) from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Patient recruitment is supported by NephCure Kidney International. Dates of funding for first phase of CureGN was 9/16/2013–5/31/2019. Dr. Krissberg was a Tashia and John Morgridge Endowed Postdoctoral Fellow of the Stanford Maternal and Child Health Research Institute at the time this study was completed. Dr. Kaye Brathwaite is supported by the NIDDK T32 Diversity Supplement Award T32DK007110–47S1. Dr. Jordan Nestor is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant Number KL2TR001874. The funders had no role in study design; data collection, analysis, or reporting; or the decision to submit for publication.
Article Information
CureGN Consortium: Members listed by CureGN Participating Clinical Center network or as part of the Data Coordinating Center. Columbia University network: Wooin Ahn, Gerald Appel, Paul Appelbaum, Revekka Babayev, Andrew Bomback, Eric Brown, Pietro Canetta, Lucrezia Carlassara, Brenda Chan, Vivette Denise D’Agati, Samitri Dogra, Hilda Fernandez, Ali Gharavi**, William Hines, Syed Ali Husain, Namrata Jain, Krzysztof Kiryluk, Fangming Lin, Maddalena Marasa#, Glen Markowitz, Hila Milo Rasouly, Sumit Mohan, Nicola Mongera, Jordan Nestor, Thomas Nickolas, Jai Radhakrishnan, Maya Rao, Simone Sanna-Cherchi, Shayan Shirazian, Michael Barry Stokes, Natalie Uy, Anthony Valeri, Natalie Vena (Columbia University, New York); Bartosz Foroncewicz, Barbara Moszczuk, Krzysztof Mucha*, and Agnieszka Perkowska-Ptasińska (University of Warsaw, Warszawa, Poland); Gian Marco Ghiggeri*, Francesca Lugani (Gaslini Children’s Hospital, Genoa, Italy). Midwest Pediatric Nephrology Consortium: Josephine Ambruzs, Helen Liapis (Arkana Laboratories, Little Rock); Rossana Baracco, Amrish Jain* (Children’s Hospital of Michigan, Detroit); Isa Ashoor, Diego Aviles* (Children’s Hospital of New Orleans/LSU Health, New Orleans); Tarak Srivastava* (Children’s Mercy Hospital, Kansas City); Sun-Young Ahn* (Children’s National Medical Center, Washington, DC); Prasad Devarajan, Elif Erkan*, Donna Claes, Hillarey Stone (Cincinnati Children’s Hospital, Cincinnati); Sherene Mason*, Cynthia Silva (Connecticut Children’s Medical Center, Hartford); Rasheed Gbadegesin* (Duke Children’s Hospital Medical Center, Durham); Liliana Gomez-Mendez* (East Carolina University Brody School of Medicine, Greenville); Larry Greenbaum**, Chia-shi Wang, Hong (Julie) Yin (Emory University, Atlanta); Yi Cai*, Goebel Jens, Julia Steinke (Helen DeVos Children’s Hospital, Grand Rapids), Donald Weaver* (Levine Children’s Hospital/ Carolinas Medical Center, Charlotte); Jerome Lane* (Lurie Children’s Hospital, Chicago); Carl Cramer* (Mayo Clinic, Rochester); Cindy Pan, Rajasree Sreedharan* (Medical College of Wisconsin, Milwaukee); David Selewski, Katherine Twombley* (Medical University of South Carolina, Charleston); Corinna Bowers#, Mary Dreher# Mahmoud Kallash*, John Mahan, Samantha Sharpe#, William Smoyer** (Nationwide Children’s Hospital, Columbus); Amira Al-Uzri*, Sandra Iragorri (Oregon Health and Science University, Portland); Myda Khalid* (Riley Children’s Hospital, Indianapolis); Craig Belsha* (Cardinal Glennon Children’s Medical Center/ St. Louis University, St. Louis); Michael Braun, AC Gomez, Scott Wenderfer* (Texas Children’s Hospital, Houston); Tetyana Vasylyeva* (Texas Tech Health Sciences Center, Amarillo); Daniel Feig* (Children’s of Alabama, University of Alabama, Birmingham); Gabriel Cara Fuentes, Melisha Hannah* (University of Colorado Children’s Hospital, Colorado, Aurora); Carla Nester* (University of Iowa Children’s Hospital, Iowa City); Aftab Chishti* (University of Kentucky, Lexington); Jon Klein** (University of Louisville, Louisville); Chryso Katsoufis, Wacharee Seeherunvong* (Holtz Medical Center, University of Miami, Miami); Michelle Rheault* (University of Minnesota Children’s Hospital, Minneapolis); Craig Wong* (University of New Mexico Health Sciences Center, Albuquerque); Nisha Mathews* (University of Oklahoma Health Sciences Center, Oklahoma City); John Barcia*, Agnes Swiatecka-Urban (University of Virginia, Charlottesville); Sharon Bartosh* (University of Wisconsin, Madison); Tracy Hunley* (Vanderbilt Children’s Hospital, Nashville); Vikas Dharnidharka*, Joseph Gaut (Washington University in St. Louis, St. Louis). University of North Carolina network: Louis-Philippe Laurin*, Virginie Royal (Hôpital Maisonneuve-Rosemont, Montreal, Canada); Anand Achanti, Milos Budisavljevic*, Sally Self (Medical University of South Carolina, Charleston); Cybele Ghossein, Shikha Wadhwani* (Northwestern University, Chicago); Salem Almaani, Isabelle Ayoub, Tibor Nadasdy, Samir, Parikh, Brad Rovin* (Ohio State University, Columbus); Anthony Chang (University of Chicago); Huma Fatima, Jan Novak, Matthew Renfrow, Dana Rizk* (University of Alabama at Birmingham); Dhruti Chen, Vimal Derebail, Ronald Falk**, Keisha Gibson, Susan Hogan, Koyal Jain, J. Charles Jennette, Amy Mottl*, Caroline Poulton#, Manish Kanti Saha (University of North Carolina Kidney Center, Chapel Hill); Agnes Fogo, Neil Sanghani* (Vanderbilt University, Nashville); Jason Kidd*, Hugh Massey, Selvaraj Muthusamy (Virginia Commonwealth University, Richmond). University of Pennsylvania network: Santhi Ganesan, Agustin Gonzalez-Vicente, Jeffrey Schelling* (MetroHealth Medical Center/Case Western Reserve University, Cleveland); Jean Hou (Cedars-Sinai Health System, Los Angeles); Kevin Lemley*, Warren Mika, Pierre Russo (Children’s Hospital of LA, Los Angeles); Michelle Denburg, Amy Kogon, Kevin Meyers*, Madhura Pradhan (Children’s Hospital of Philadelphia, Philadelphia); Raed Bou Matar*, John O’Toole*, John Sedor* (Cleveland Clinic, Cleveland); Christine Sethna* (Cohen Children’s Medical Center, New Hyde Park); Serena Bagnasco, Alicia Neu, John Sperati* (Johns Hopkins University, Baltimore); Sharon Adler*, Tiane Dai, Ram Dukkipati (Lundquist Institute at Harbor-UCLA Medical Center, Torrance); Fernando Fervenza*, Sanjeev Sethi (Mayo Clinic, Rochester); Frederick Kaskel, Kaye Brathwaite, Kimberly Reidy* (Montefiore Medical Center, New York); Suzanne Vento #, Joseph Weisstuch, Ming Wu, Olga Zhdanova (New York University, New York); Jurgen Heymann, Jeffrey Kopp*, Meryl Waldman, Cheryl Winkler (NIDDK, Bethesda); Katherine Tuttle* (Spokane Providence Medical Center, Spokane); Jill Krissberg, Richard Lafayette* (Stanford University, Palo Alto); Michelle Hladunewich* (Sunnybrook Health Sciences Centre, Toronto, Canada); Rulan Parekh* (The Hospital for Sick Children, Toronto, Canada); Carmen Avila-Casado, Daniel Cattran*, Reich Heather, Philip Boll (University Health Network, Toronto, Canada); Yelena Drexler, Alessia Fornoni* (University of Miami, Miami); Patrick Gipson*, Jeffrey Hodgin, Andrew Oliverio (University of Michigan, Ann Arbor); Jon Hogan, Lawrence Holzman**, Matthew Palmer (University of Pennsylvania, Philadelphia); Blaise Abromovitz*, Michael Mortiz* (University of Pittsburgh School of Medicine, Pittsburgh); Charles Alpers, J. Ashley Jefferson* (University of Washington, Seattle); Elizabeth Brown, Kamal Sambandam* (UT Southwestern, Dallas). Data Coordinating Center: Bruce Robinson**, Abigail Smith (Arbor Research Collaborative for Health, Ann Arbor); Cynthia Nast (Cedar Sinai Medical Center, Los Angeles); Laura Barisoni (Duke University, Durham); Brenda Gillespie**, Deb Gipson**, Maggie Hicken, Matthias Kretzler**, Laura Mariani (University of Michigan, Ann Arbor). Steering Committee Chair: Lisa M. Guay-Woodford (Children’s National Hospital, Washington DC). *CureGN Site Principal Investigators; **CureGN Principal Investigators; #CureGN Lead Coordinators.
Footnotes
Financial Disclosure: Salem Almaani has received research support from Gilead Sciences. He has received personal fees for Aurinia and Kezar Life Sciences. Larry A. Greenbaum has received research support from Vertex Pharmaceuticals and Reata Pharmaceuticals. He has served as a consultant for Aurinia, Roche Pharmaceuticals and Novartis. David T. Selewski has served as a consultant for Travere. Keisha Gibson has served as a consultant for Aurinia Inc, Travere Inc (formally Retrophin) and Reata Inc. Drs Krissberg, Brathwaite, and Nestor declare that they have no other relevant financial interests. The remaining authors declare that they have no relevant financial interests.
Peer Review: Received January 28, 2022. Evaluated by 2 external peer reviewers, with direct editorial input from a Statistics/Methods Editor and an Associate Editor who served as Acting Editor-in-Chief. Accepted in revised form August 3, 2022. The involvement of an Acting Editor-in-Chief was to comply with AJKD’s procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Data Sharing:
Data are available in a repository at the NIDDK central repository at https://repository.niddk.nih.gov/home/ and can be accessed using the unique identifier CureGN.
References
- 1.Siddall EC, Radhakrishnan J. The pathophysiology of edema formation in the nephrotic syndrome. Kidney Int. 2012;82(6):635–642. doi: 10.1038/ki.2012.180 [DOI] [PubMed] [Google Scholar]
- 2.Gulati S, Kher V, Gupta A, Arora P, Rai PK, Sharma RK. Spectrum of infections in Indian children with nephrotic syndrome. Pediatr Nephrol Berl Ger. 1995;9(4):431–434. [DOI] [PubMed] [Google Scholar]
- 3.Rheault MN, Wei CC, Hains DS, Wang W, Kerlin BA, Smoyer WE. Increasing frequency of acute kidney injury amongst children hospitalized with nephrotic syndrome. Pediatr Nephrol Berl Ger. 2014;29(1):139–147. doi: 10.1007/s00467-013-2607-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kerlin BA, Haworth K, Smoyer WE. Venous thromboembolism in pediatric nephrotic syndrome. Pediatr Nephrol Berl Ger. 2014;29(6):989–997. doi: 10.1007/s00467-013-2525-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Sethna CB, Merchant K, Reyes A. Cardiovascular Disease Risk in Children With Kidney Disease. Semin Nephrol. 2018;38(3):298–313. doi: 10.1016/j.semnephrol.2018.02.009 [DOI] [PubMed] [Google Scholar]
- 6.Glenn DA, Henderson CD, O’Shaughnessy M, et al. Infection-Related Acute Care Events among Patients with Glomerular Disease. Clin J Am Soc Nephrol. 2020;15(12):1749–1761. doi: 10.2215/CJN.05900420 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Gipson DS, Messer KL, Tran CL, et al. Inpatient health care utilization in the United States among children, adolescents, and young adults with nephrotic syndrome. Am J Kidney Dis Off J Natl Kidney Found. 2013;61(6):910–917. doi: 10.1053/j.ajkd.2012.12.025 [DOI] [PubMed] [Google Scholar]
- 8.Rheault MN, Zhang L, Selewski DT, et al. AKI in Children Hospitalized with Nephrotic Syndrome. Clin J Am Soc Nephrol. 2015;10(12):2110–2118. doi: 10.2215/CJN.06620615 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wang CS, Yan J, Palmer R, Bost J, Wolf MF, Greenbaum LA. Childhood Nephrotic Syndrome Management and Outcome: A Single Center Retrospective Analysis. Int J Nephrol. 2017;2017:2029583. doi: 10.1155/2017/2029583 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ayoob RM, Hains DS, Smoyer WE. Trends in hospitalization characteristics for pediatric nephrotic syndrome in the USA. Clin Nephrol. 2012;78(08):106–111. doi: 10.5414/CN107303 [DOI] [PubMed] [Google Scholar]
- 11.Fenton A, Smith SW, Hewins P. Adult minimal-change disease: observational data from a UK centre on patient characteristics, therapies, and outcomes. BMC Nephrol. 2018;19(1):207. doi: 10.1186/s12882-018-0999-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Mariani LH, Bomback AS, Canetta PA, et al. CureGN Study Rationale, Design, and Methods: Establishing a Large Prospective Observational Study of Glomerular Disease. Am J Kidney Dis. 2019;73(2):218–229. doi: 10.1053/j.ajkd.2018.07.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Flynn JT, Kaelber DC, Baker-Smith CM, et al. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics. 2017;140(3). doi: 10.1542/peds.2017-1904 [DOI] [PubMed] [Google Scholar]
- 14.Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2018;71(19):e127–e248. doi: 10.1016/j.jacc.2017.11.006 [DOI] [PubMed] [Google Scholar]
- 15.Levey AS, Stevens LA, Schmid CH, et al. A New Equation to Estimate Glomerular Filtration Rate. Ann Intern Med. 2009;150(9):604–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Schwartz GJ, Muñoz A, Schneider MF, et al. New Equations to Estimate GFR in Children with CKD. J Am Soc Nephrol. 2009;20(3):629–637. doi: 10.1681/ASN.2008030287 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ng DK, Schwartz GJ, Schneider MF, Furth SL, Warady BA. Combination of pediatric and adult formulas yield valid glomerular filtration rate estimates in young adults with a history of pediatric chronic kidney disease. Kidney Int. 2018;94(1):170–177. doi: 10.1016/j.kint.2018.01.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Raghunathan T, Lepkowski J, Hoewyk JV, Solenberger P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Methodol. 2001;27:85–89. [Google Scholar]
- 19.Lin D, Wei L, Yang I, Ying Z. Semiparametric Regression for the Mean and Rate Functions of Recurrent Events. J R Stat Soc Ser B Stat Methodol. 2000;62(4):711–730. [Google Scholar]
- 20.Frontmatter Rubin D.. In: Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons, Ltd; 1987:i–xxix. doi: 10.1002/9780470316696.fmatter [DOI] [Google Scholar]
- 21.Feehally J, Kendell NP, Swift PG, Walls J. High incidence of minimal change nephrotic syndrome in Asians. Arch Dis Child. 1985;60(11):1018–1020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Banh THM, Hussain-Shamsy N, Patel V, et al. Ethnic Differences in Incidence and Outcomes of Childhood Nephrotic Syndrome. Clin J Am Soc Nephrol. 2016;11(10):1760–1768. doi: 10.2215/CJN.00380116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kim JS, Bellew CA, Silverstein DM, Aviles DH, Boineau FG, Vehaskari VM. High incidence of initial and late steroid resistance in childhood nephrotic syndrome. Kidney Int. 2005;68(3):1275–1281. doi: 10.1111/j.1523-1755.2005.00524.x [DOI] [PubMed] [Google Scholar]
- 24.Freedman BI, Limou S, Ma L, Kopp JB. APOL1-Associated Nephropathy: A Key Contributor to Racial Disparities in CKD. Am J Kidney Dis Off J Natl Kidney Found. 2018;72(5S1):S8–S16. doi: 10.1053/j.ajkd.2018.06.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Gharavi AG, Kiryluk K, Choi M, et al. Genome-wide association study identifies susceptibility loci for IgA nephropathy. Nat Genet. 2011;43(4):321–327. doi: 10.1038/ng.787 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Mohottige D, Gibson K. Staying on track to achieve racial justice in kidney care. Nat Rev Nephrol. 2022;18(2):72–73. doi: 10.1038/s41581-021-00520-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Bignall ONR, Drews DC. Stony the road we trod: towards racial justice in kiney care. Nat Rev Nephrol. 2021;17(2):79–80. doi: 10.1038/s41581-020-00389-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness. JAMA. 2002;288(14):1775–1779. doi: 10.1001/jama.288.14.1775 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1a: Unadjusted and multivariable adjusted rate ratios for Acute Care Utilization (ACU), with White participants as the reference group: adults
Table S1b: Unadjusted and multivariable adjusted rate ratios for Acute Care Utilization (ACU), with White participants as the reference group: children
Table S2a: Unadjusted and multivariable adjusted rate ratios for Acute Care Utilization (ACU), with White participants as the reference group: adults (US only)
Table S2b: Unadjusted and multivariable adjusted rate ratios for Acute Care Utilization (ACU), with White participants as the reference group: children (US only)
Table S3a: Adult Sample Characteristics at Enrollment by Data Availability
Table S3b: Pediatric Sample Characteristics at Enrollment by Data Availability
Data Availability Statement
Data are available in a repository at the NIDDK central repository at https://repository.niddk.nih.gov/home/ and can be accessed using the unique identifier CureGN.