Have Racial Disparities in Home Dialysis Utilization Changed Over Time?

Wang,Virginia;Zepel,Lindsay;Coffman,Cynthia;Diamantidis,Clarissa;Scholle,Sarah;Maciejewski,Matthew;

Objectives: The Medicare end-stage kidney disease (ESKD) prospective payment system (PPS) for maintenance dialysis, implemented in 2011, resulted in modestly increased access to both home-based peritoneal dialysis (PD) and home hemodialysis (HHD) treatment modalities, but it is unclear whether regional disparities in home dialysis (PD and HHD) were affected. We compared regional home dialysis use by White and non-White individuals over time.

Study Design: Retrospective cohort study of dialysis facilities offering home dialysis in 2006-2016 and of 1,098,579 patients with prevalent ESKD in 2006-2016.

Methods: We compared hospital referral region (HRR) utilization rates of home dialysis between White and non-White patients over time using a generalized estimating equation model with a negative binomial distribution adjusting for regional characteristics.

Results: The mean number of facilities offering home dialysis operating in each HRR increased from 15.6 in 2006 to 22.1 in 2016. Observed mean HRR home dialysis rates increased overall, but White patients maintained greater home dialysis use than non-White patients: 19.7% in 2006 and 26.2% in 2016 among White patients vs 13.0% in 2006 and 17.8% in 2016 among non-White patients. In adjusted analysis, there was no evidence of changes in White/non-White disparities in home dialysis use over time (P = .84) or after the Medicare ESKD PPS in 2011 (incidence rate ratio, 0.97; 95% CI, 0.92-1.02; P = .29).

Conclusions: Even after modest increases in dialysis facility availability and patient utilization after the implementation of the Medicare ESKD PPS in 2011, significant racial disparities in home dialysis remain.

Despite increased dialysis facility availability and patient utilization of home dialysis after implementation of the Medicare end-stage kidney disease (ESKD) prospective payment system (PPS) in 2011, racial disparities remain.

As Medicare is the largest payer of dialysis services in the United States, its policies can influence access to home dialysis in various ways. Over the years, Congress has enacted a number of changes to Medicare policy in an effort to improve the quality of dialysis services and control program costs. In 2011, Medicare implemented a bundled end-stage kidney disease (ESKD) prospective payment system (PPS) for maintenance dialysis. This PPS partly reflects Medicare’s attempt to better align patient preferences^1-3 and clinical appropriateness for home dialysis^4,5 with dialysis facilities, by reducing the relative profitability of in-center hemodialysis (ICHD).

Home dialysis modalities (ie, peritoneal dialysis [PD] and home hemodialysis [HHD]) were developed as an alternative form of dialysis in the 1970s and offer a clinically equivalent, convenient, and less expensive alternative to ICHD. Whereas ICHD is typically performed by clinical staff in dialysis facilities 3 times a week, home dialysis patients self-perform dialysis daily and visit dialysis facilities monthly for maintenance. Although home dialysis is not suitable for all patients, it is generally considered similar or complementary to ICHD. PD is the most common home dialysis modality; compared with ICHD, it has been associated with a similar risk of mortality^6,7 but greater patient-reported quality of life,^8-11 lower rates of complications,^12,13 and lower societal costs.^14-17 Nephrologists estimate that roughly half of patients with ESKD are good candidates for home dialysis,^2,5 and patients strongly prefer home dialysis.^10,11,18,19 Despite these advantages, only 9.4% of patients with ESKD were receiving home dialysis in 2010.¹⁷

The 2011 Medicare ESKD PPS altered payment for dialysis treatment by bundling dialysis, medications, and ancillary services into a single payment, adjusted for patient- and facility-level characteristics, and an additional payment for home dialysis training.²⁰ Because home dialysis is associated with lower costs than ICHD,^14-17 provider revenues under the PPS and therefore supply and use of home modalities were expected to increase. One study found that the operating margin of PD would increase from –$185 to $201 per patient-month after the implementation of the ESKD PPS,²¹ which would encourage greater provision and utilization of home dialysis (chiefly, PD). Prior research found that the 2011 ESKD bundled payment implementation resulted in a modest increase in dialysis facility provision of home dialysis services²² but little change in geographic variability in home dialysis²³ or incident utilization of home dialysis by patients with incident ESKD.²⁴ Despite these modest changes, it is possible that racial/ethnic disparities changed over time and place. In other words, modest increases in dialysis provision by dialysis facilities and dialysis utilization by patients could mask offsetting changes in different hospital referral regions (HRRs).

Limited prior work has examined disparities in home dialysis utilization. Wallace and colleagues reported state-level variability in disparities of PD and HHD use in a cross-sectional cohort of Medicare fee-for-service patients with ESKD in 2012.²⁵ Turenne and colleagues’ 2008-2013 patient-level analysis found overall increased use of PD in the initial post-PPS years but no change in persistently lower use among Black and Hispanic patients.²⁶

We conducted analyses to evaluate the extent of racial disparities in home dialysis utilization (PD and HHD) over time and to determine whether regional disparities in home dialysis use changed after the introduction of the Medicare ESKD PPS in 2011. We describe time trends in racial disparities across HRRs and identify market characteristics associated with provision of home dialysis within HRRs. Assessment of racial disparities at the HRR level is particularly important as new policy efforts target ESKD provider organizations to improve disparities in care and utilization of home-based modalities.

We conducted a longitudinal retrospective cohort study of US patients and dialysis facilities at the Dartmouth Atlas HRR level²⁷ between 2006 and 2016. HRRs are used because they reflect home dialysis service areas better than municipal designations (eg, zip codes, counties) for patients receiving dialysis, who often travel outside county boundaries for monthly maintenance visits.^28-31

The primary source of data for this study was the US Renal Data System (USRDS).³² Dialysis facility characteristics were drawn from the CMS Annual Facility Survey (CMS-2744, USRDS FACILITY file) and supplemented with the Medicare Provider of Service file to account for facilities that opened or closed during the study period and for changes in facility ownership.^22,33 Facility-level characteristics of patients with prevalent ESKD were based on longitudinal records of their treatment history (USRDS-RXHIST file). Region-level characteristics of patients with prevalent ESKD (eg, age, gender, race and ethnicity, employment status) were drawn from the Medical Evidence Report (CMS-2728, USRDS-PATIENTS and USRDS-MEDEVID files). Such data are required to be reported for all patients, regardless of Medicare enrollment status. Patient race and Hispanic ethnicity were ascertained from the medical evidence form (primary) or documented in the overall USRDS-PATIENTS file (secondary). When Hispanic ethnicity information from all USRDS sources was missing, patient ethnicity was set to non-Hispanic. General region-level demographic characteristics (eg, urban population, per capita income) were drawn from the county-level Area Health Resources Files, which we converted to the zip code level using land-area weighting and then aggregated to HRRs.

For the dialysis facility inclusion, we identified a cohort of all nonfederal outpatient ESKD facilities that ever offered home dialysis services during the study period, which accounted for new facilities, closures, and changes in ownership.²² We excluded facilities that provided only transplant services, never reported home dialysis services, had an invalid zip code, or lacked a valid Medicare provider identifier. The patient cohort included all patients receiving dialysis who had prevalent or incident ESKD, had nonmissing demographic and zip code information, survived 90 days from dialysis initiation, did not receive a kidney transplant within 90 days or recover kidney function within 180 days after initiating dialysis, and had documented dialysis modality.^22,24 We generated annual records of the dialysis modality and treating dialysis facility for the resulting 1,098,579 patients with prevalent ESKD, which were then merged with longitudinal dialysis facility data sets (eAppendix Figure 1 [eAppendix available at ajmc.com]). All data for this study were available at the zip code level and then aggregated to the HRR market level for each year of the study period.

The outcome of interest was the proportion of White (or non-White) patients who used home dialysis (PD or HHD) in each HRR in each year between 2006 and 2016. Given the small mean numbers of home dialysis patients per facility (17.3 in 2006 and 22.9 in 2016) and small or zero number of cases when stratified by racial and ethnic subgroups at the facility level, the home dialysis outcome was aggregated to the HRR level. When aggregating Hispanic, non-Hispanic Asian, or Pacific Islander groups to the HRR-level racial groups, the numbers who used home dialysis or received any type of dialysis numbers were still small or zero for some HRRs in each year. Grouping patients of non-Hispanic Black/African American, Hispanic, non-Hispanic Asian or Pacific Islander, or other race/ethnicity into a general category of non-White in this way reduced the number of HRRs that would have missing outcomes and be dropped from models. For each HRR/year combination, the count for the number of White and non-White home dialysis users and the count for the number of White and non-White users of any dialysis modality were generated.

We examined descriptive statistics of the cohorts and unadjusted outcomes for White patients and non-White patients, as well as White patients and Black patients. Choropleth maps illustrated changes in the regional distribution of home dialysis availability at the HRR level between 2006 and 2016. Informed by prior research on home dialysis utilization^24-26 and disparities measurement,^25,34 utilization rate ratios in each HRR were used to descriptively compare any home dialysis utilization rates between non-White patients and White patients, such that values greater than 1.00 indicate a non-White home dialysis utilization rate greater than the rate among White patients and values less than 1.00 indicate a non-White home dialysis utilization rate less than the rate among White patients.

A generalized estimating equations model³⁵ was used to account for the repeated HRR-level observations across years with a negative binomial distribution, a log link function, and an exchangeable covariance structure that best accounted for HRR-level repeated measures.³⁶ An offset for the number of White or non-White dialysis users in the HRR for the year was used. Explanatory variables of interest were an indicator for White or non-White dialysis users in the HRR, indicator variables for year, and indicator variables for the interactions between White and non-White dialysis users in the HRR and year.

Adjusted analysis controlled for time-varying composition of regions’ market characteristics (Table) to adjust for factors that may influence provider service strategies and patient utilization of home dialysis at the HRR level. We first determined whether there were differences in racial disparities in HRR home dialysis use over time, and then examined differences between the prepolicy (2006-2010) and postpolicy (2011-2016) periods. For HRR-level market characteristics, HRR-level dialysis facility composition included the proportion of facilities in HRRs that were for-profit owned, freestanding (vs being hospital based), affiliated with a chain organization, or newly opened or closed in the year; had ownership change in the prior year; and had an urban or rural location based on zip code. Dialysis market competition was calculated using the Herfindahl-Hirschman Index, which is equal to the sum of the square of each dialysis facility’s market share, based on the number of patients receiving dialysis unique to each facility. Facilities within a market under the same chain affiliation were treated as a single firm.^37,38 Regional composition of patients with ESKD included ESKD incidence and the proportion of patients with ESKD who were White, younger than 65 years, or employed. General population characteristics included per capita income and proportion of urban residents. Time-varying covariates contributed both cross-sectional and longitudinal sources of variation in our HRR-level home use rate outcomes and were decomposed into between-HRR and within-HRR components, respectively, for inclusion in models³⁹; linear effects were included for all between- and within-HRR components. This study was approved by the institutional review board of Duke University.

The mean number of dialysis facilities operating in each HRR increased from 15.6 in 2006 to 22.1 in 2016, and the proportion with for-profit ownership increased over time (79.8% in 2006 vs 87.1% in 2016). Chain affiliation of dialysis facilities (82.3% in 2006 vs 91.7% in 2016) also increased over time (Table). Mean ESKD incidence in HRRs grew more slowly (3.8 per 10,000 HRR general population in 2006 vs 4.0 per 10,000 HRR general population in 2016) and the mean percentage of White patients with ESKD in an HRR decreased from 53.1 in 2006 to 50.2 in 2016. The prevalence of PD, the most common form of home dialysis, within HRRs increased over time, from a mean of 75.1 patients receiving PD per 1000 in the 2006 ESKD patient population to 91.0 per 1000 ESKD population in 2016. Overall (2006-2016), 51.2% of patients receiving dialysis were White, 31.2% were Black, 12% were Hispanic, 4% were Asian or Pacific Islander, and 1.6% were other/unknown race (results available from authors).

The unadjusted mean HRR home dialysis (PD or HHD) utilization rates were higher in 2016 (26.2%) than in 2006 (19.7%) for White patients with ESKD (Figure 1) and for non-White patients with ESKD (17.8% in 2016 and 13.0% in 2006). In every year, the unadjusted mean utilization rate of HRR home dialysis was approximately one-third lower for non-White patients than White patients. Black patients had similar trends to non-White patients (eAppendix Figure 2). In most HRRs, home dialysis utilization rates in 2006 and 2016 were higher for White patients than non-White patients (utilization ratios < 1.0). Exceptions in 2006 where non-White patients had higher home dialysis utilization rates than White patients were in the upper Midwest, upper New England, and selected parts of California, Oregon, and Washington (utilization ratios > 1.0). However, in 2016, non-White patients had lower home dialysis utilization rates than White patients in most of those regions (eAppendix Figures 3 and 4).

In adjusted analysis, there was no evidence of changes in observed disparities between White and non-White patients in HRR home dialysis utilization over time (χ²₁₀ = 5.71; P = .84) (Figure 2 and eAppendix Table). Similarly, there was no difference in HRR-level disparities in HRR home dialysis use before vs after the implementation of the Medicare ESKD PPS in 2011 (incidence rate ratio, 0.97; 95% CI, 0.92-1.02; P = .29).

Several market characteristics were associated with HRR home dialysis utilization. HRR home dialysis utilization was positively associated with the proportion of new dialysis facilities in HRRs (P = .02), less competitive markets (P = .02), percentage of facilities in HRR in an urban location (P = .04), percentage of the general population in HRR who are urban residents (P = .02), and percent of patients with ESKD who are employed (P = .02); it was negatively associated with facility closures in HRRs (P = .04) (eAppendix Table).

This study described regional trends in White vs non-White disparities in home dialysis utilization between 2006 and 2016. Overall, home dialysis utilization increased for White patients and non-White patients but remained roughly one-third lower among non-White patients before and after the implementation of the Medicare ESKD PPS. In other words, the regional disparity in home dialysis utilization for White patients and non-White patients did not change over time. We also found that less competitive markets and markets with more new dialysis facility openings were associated with greater overall home dialysis utilization.

Despite the fact that just under half of all patients with ESKD are non-White, the 2011 Medicare dialysis PPS had little impact on existing disparities in home dialysis use between White and non-White patients. As noted above, home dialysis utilization increased for both White and non-White dialysis patients with ESKD after the implementation of the ESKD PPS in 2011, but the historic disparities remained. On the other hand, disparities in home dialysis utilization did not worsen. This small observed increase in improved non-White home dialysis utilization could be due to an increasing number of HRRs in which non-White patients have greater utilization than White patients over time, or due to HRRs with lower non-White home dialysis utilization getting better over time, or both. Most likely, the increase is due to non-White utilization increasing in most HRRs and regions, with higher rates of non-White patients receiving home dialysis translating into more equitable White and non-White home dialysis use. Findings from our 2006-2016 region-level analysis are consistent with Turenne and colleagues’ patient-level analysis of disparities in PD use from 2008 to 2013; they found growth in PD among racially diverse subgroups of patients after Medicare payment reform but no change in differences in rates of use across race and ethnicity.²⁶ Thus, more than a decade of utilization trends converge on the conclusion that even after modest increases in dialysis facility availability²² and patient utilization²⁴ after the implementation of the Medicare ESKD PPS in 2011, disparities in home dialysis use remain.

Our findings highlight an important and attainable goal in improving US kidney care: increasing uptake of home dialysis for all patients and reducing disparities in home dialysis for non-White patients. Disparate and low utilization of home dialysis modalities belie the promise of home dialysis for patients. Prior research found that nephrologists consider a greater share of their patients clinically appropriate for home dialysis than actual utilization reflects.^5,40 The continued racial disparities observed in home dialysis use over time^24-26,41,42 suggest the presence of clinical as well as nonclinical factors that may be driving differences in uptake of home dialysis. Access to predialysis services, comprehensive education, and counseling around renal replacement options are important for raising awareness of home dialysis for patient-informed decision-making; these factors may also drive disparities. This suggests that interventions to increase home dialysis should occur prior to ESKD onset, and that nephrologists and dialysis providers should play critical roles in patient selection of home-based treatment modalities. There has been no empirical examination of the source of racial differences in home dialysis. Investigating structural, systemic, or provider bias in home dialysis in future research will be critical for understanding and reducing racial and socioeconomic disparities in dialysis care. Equitable education and geographic access to home dialysis modalities, offered in roughly half of all US dialysis facilities and in regions with higher average income and proportions of White ESKD patient populations, may also significantly influence patient selection and disparities in home dialysis.^23,31

To date, disparities in ESKD care have largely remained unchanged, and as noted earlier, the implementation of the ESKD PPS in 2011 did not specifically target equity. However, implementation of Medicare alternative payment models may be a promising solution. The Kidney Care Choices model, launched on January 1, 2022, includes financial incentives for health care providers to improve clinical management of Medicare beneficiaries with advanced kidney disease and ESKD, to delay their progression to kidney failure, and to promote kidney transplantation. The 2021 ESKD Treatment Choices (ETC) Model is designed to encourage greater use of home dialysis and kidney transplants for Medicare beneficiaries with ESKD, reduce Medicare expenditures, and enhance the quality of ESKD care. In 2022, the ETC Model was revised to also encourage dialysis facilities and health care providers to decrease disparities in rates of home dialysis and kidney transplants among patients with ESKD of lower socioeconomic status. Our regional findings, assessed at the same HRR level as the ETC Model’s geographic unit of provider selection, are a useful foundation for future evaluation of these policies. Continued monitoring of dialysis facility home dialysis utilization across racial and ethnic groups, as well as case studies of dialysis facility strategies for equitable and effective home dialysis patient identification, education, and adoption, will be important future directions for research to inform improvements and disparities in the care of patients with kidney failure.

There are several analytic limitations to acknowledge. Sample sizes of patients receiving home dialysis were small in many facilities (mean was 17 in 2006, 22 in 2016), preventing analysis of facility-level attributes of racial disparities in home dialysis utilization. Aggregating the dialysis use data to the regional level may mask important facility-level variability and patterns in disparity of use. To retain as many patients as possible in the sample, those with missing Hispanic ethnicity data were recoded to non-Hispanic, which may skew results. However, only 0.5% of patients during the observation period had missing ethnicity data, so this was unlikely to alter our findings. In addition, there are several factors potentially associated with changes in home dialysis utilization that we could not assess, such as regional supply of providers with home dialysis training and experience. Finally, our region-level analysis did not adjust for patient-level covariates including the suitability of the patient’s residence for home dialysis, patient educational background, family support, and acuity of dialysis initiation (inpatient vs outpatient) that could affect home dialysis use.^43-45

Significant racial disparities in home dialysis use that existed years before the 2011 dialysis payment reform did not improve in the 5 years that followed this reform. The continued racial disparities observed in home dialysis use over time suggest that the presence of clinical as well as nonclinical factors may be driving differences in uptake of home dialysis.

Author Affiliations: Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System (VW, CJC, MLM), Durham, NC; Department of Population Health Sciences (VW, LZ, CJD, MLM) and Department of Biostatistics and Bioinformatics (CJC), Duke University School of Medicine, Durham, NC; Division of General Internal Medicine (VW, CJD, MLM) and Division of Nephrology (CJD), Department of Medicine, Duke University School of Medicine, Durham, NC; National Committee for Quality Assurance (SHS), Washington, DC.

Source of Funding: The research in this article was supported by CMS under contract No. HHSM-500-2014-00442G with the National Committee for Quality Assurance. This work was also supported by the Office of Research and Development, Health Services Research and Development Service, Department of Veterans Affairs. Dr Maciejewski was supported by a Research Career Scientist award from the Department of Veterans Affairs (RCS 10-391).

Author Disclosures: Dr Diamantidis reports a consultancy with Optum Labs, unrelated to this manuscript. Dr Maciejewski has received grant funding via a contract with CMS (HHSM-500-2016-00097G) and owns Amgen stock due to his spouse’s employment. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (VW, LZ, CJC, CJD, SHS, MLM); acquisition of data (VW, SHS, MLM); analysis and interpretation of data (VW, LZ, CJC, CJD, MLM); drafting of the manuscript (VW, LZ, CJD, MLM); critical revision of the manuscript for important intellectual content (VW, LZ, CJC, CJD, SHS, MLM); statistical analysis (LZ, CJC, MLM); provision of patients or study materials (VW); obtaining funding (SHS, MLM); administrative, technical, or logistic support (SHS); and supervision (VW, SHS).

Address Correspondence to: Virginia Wang, PhD, Department of Population Health Sciences, Duke University School of Medicine, 215 Morris St, Campus Box 104427, Durham, NC 27710. Email: virginia.wang@duke.edu.

1. Ahlmén J, Carlsson L, Schönborg C. Well-informed patients with end-stage renal disease prefer peritoneal dialysis to hemodialysis. Perit Dial Int. 1993;13(suppl 2):S196-S198.

2. Stack AG. Determinants of modality selection among incident US dialysis patients: results from a national study. J Am Soc Nephrol. 2002;13(5):1279-1287. doi:10.1681/ASN.V1351279

3. Thodis E, Passadakis P, Vargemezis V, Oreopoulos DG. Peritoneal dialysis: better than, equal to, or worse than hemodialysis? data worth knowing before choosing a dialysis modality. Perit Dial Int. 2001;21(1):25-35.

4. Charest AF, Mendelssohn DC. Are North American nephrologists biased against peritoneal dialysis? Perit Dial Int. 2001;21(4):335-337.

5. Mendelssohn DC, Mullaney SR, Jung B, Blake PG, Mehta RL. What do American nephrologists think about dialysis modality selection? Am J Kidney Dis. 2001;37(1):22-29. doi:10.1053/ajkd.2001.20635

6. Wang V, Coffman CJ, Sanders LL, et al. Comparing mortality of peritoneal and hemodialysis patients in an era of Medicare payment reform. Med Care. 2021;59(2):155-162. doi:10.1097/MLR.0000000000001457

7. Wong B, Ravani P, Oliver MJ, et al. Comparison of patient survival between hemodialysis and peritoneal dialysis among patients eligible for both modalities. Am J Kidney Dis. 2018;71(3):344-351. doi:10.1053/j.ajkd.2017.08.028

8. Queeley GL, Campbell ES. Comparing treatment modalities for end-stage renal disease: a meta-analysis. Am Health Drug Benefits. 2018;11(3):118-127.

9. Tong A, Lesmana B, Johnson DW, Wong G, Campbell D, Craig JC. The perspectives of adults living with peritoneal dialysis: thematic synthesis of qualitative studies. Am J Kidney Dis. 2013;61(6):873-888. doi:10.1053/j.ajkd.2012.08.045

10. Walker RC, Hanson CS, Palmer SC, et al. Patient and caregiver perspectives on home hemodialysis: a systematic review. Am J Kidney Dis. 2015;65(3):451-463. doi:10.1053/j.ajkd.2014.10.020

11. Walker RC, Morton RL, Palmer SC, Marshall MR, Tong A, Howard K. A discrete choice study of patient preferences for dialysis modalities. Clin J Am Soc Nephrol. 2018;13(1):100-108. doi:10.2215/CJN.06830617

12. Johansen KL, Zhang R, Huang Y, et al. Survival and hospitalization among patients using nocturnal and short daily compared to conventional hemodialysis: a USRDS study. Kidney Int. 2009;76(9):984-990. doi:10.1038/ki.2009.291

13. Ramar P, Ahmed AT, Wang Z, et al. Effects of different models of dialysis care on patient-important outcomes: a systematic review and meta-analysis. Popul Health Manag. 2017;20(6):495-505. doi:10.1089/pop.2016.0157

14. Liu FX, Walton SM, Leipold R, Isbell D, Golper TA. Financial implications to Medicare from changing the dialysis modality mix under the bundled prospective payment system. Perit Dial Int. 2014;34(7):749-757. doi:10.3747/pdi.2013.00305

15. Sennfält K, Magnusson M, Carlsson P. Comparison of hemodialysis and peritoneal dialysis—a cost-utility analysis. Perit Dial Int. 2002;22(1):39-47.

16. Shih YC, Guo A, Just PM, Mujais S. Impact of initial dialysis modality and modality switches on Medicare expenditures of end-stage renal disease patients. Kidney Int. 2005;68(1):319-329. doi:10.1111/j.1523-1755.2005.00413.x

17. USRDS 2018 Annual Data Report: Atlas of End-stage Renal Disease in the United States. US Renal Data System; 2018. Accessed February 12, 2023. https://www.niddk.nih.gov/about-niddk/strategic-plans-reports/usrds/prior-data-reports/2018

18. Devoe DJ, Wong B, James MT, et al. Patient education and peritoneal dialysis modality selection: a systematic review and meta-analysis. Am J Kidney Dis. 2016;68(3):422-433. doi:10.1053/j.ajkd.2016.02.053

19. Mehrotra R, Marsh D, Vonesh E, Peters V, Nissenson A. Patient education and access of ESRD patients to renal replacement therapies beyond in-center hemodialysis. Kidney Int. 2005;68(1):378-390. doi:10.1111/j.1523-1755.2005.00453.x

20. End stage renal disease (ESRD) prospective payment system (PPS). CMS. Updated October 31, 2022. Accessed February 12, 2023. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ESRDpayment/index.html

21. Hornberger J, Hirth RA. Financial implications of choice of dialysis type of the revised Medicare payment system: an economic analysis. Am J Kidney Dis. 2012;60(2):280-287. doi:10.1053/j.ajkd.2012.03.010

22. Wang V, Coffman CJ, Sanders LL, Lee SYD, Hirth RA, Maciejewski ML. Medicare’s new prospective payment system on facility provision of peritoneal dialysis. Clin J Am Soc Nephrol. 2018;13(12):1833-1841. doi:10.2215/CJN.05680518

23. Sloan CE, Coffman CJ, Sanders LL, et al. Trends in regional supply of peritoneal dialysis in an era of health reform, 2006 to 2013. Med Care Res Rev. 2021;78(3):281-290. doi:10.1177/1077558720910633

24. Sloan CE, Coffman CJ, Sanders LL, et al. Trends in peritoneal dialysis use in the United States after Medicare payment reform. Clin J Am Soc Nephrol. 2019;14(12):1763-1772. doi:10.2215/CJN.05910519

25. Wallace EL, Lea J, Chaudhary NS, et al. Home dialysis utilization among racial and ethnic minorities in the United States at the national, regional, and state level. Perit Dial Int. 2017;37(1):21-29. doi:10.3747/pdi.2016.00025

26. Turenne M, Baker R, Pearson J, Cogan C, Mukhopadhyay P, Cope E. Payment reform and health disparities: changes in dialysis modality under the new Medicare dialysis payment system. Health Serv Res. 2018;53(3):1430-1457. doi:10.1111/1475-6773.12713

27. Wennberg JE, Cooper MM. The Dartmouth Atlas of Health Care in the United States. American Hospital Publishing; 1996.

28. O’Hare AM, Johansen KL, Rodriguez RA. Dialysis and kidney transplantation among patients living in rural areas of the United States. Kidney Int. 2006;69(2):343-349. doi:10.1038/sj.ki.5000044

29. Osterlund K, Mendelssohn D, Clase C, Guyatt G, Nesrallah G. Identification of facilitators and barriers to home dialysis selection by Canadian adults with ESRD. Semin Dial. 2014;27(2):160-172. doi:10.1111/sdi.12183

30. Prakash S, Coffin R, Schold J, et al. Travel distance and home dialysis rates in the United States. Perit Dial Int. 2014;34(1):24-32. doi:10.3747/pdi.2012.00234

31. Wang V, Maciejewski ML, Coffman CJ, et al. Impacts of geographic distance on peritoneal dialysis utilization: refining models of treatment selection. Health Serv Res. 2017;52(1):35-55. doi:10.1111/1475-6773.12489

32. 2017 Researcher’s Guide to the USRDS Database. US Renal Data System; 2017. Accessed February 12, 2023. https://www.niddk.nih.gov/-/media/Files/USRDS/For-Researchers/Researchers-Guide/2017/2017_usrds_researcher_guide.pdf

33. Wang V, Lee SY, Patel UD, Maciejewski ML, Ricketts TC. Longitudinal analysis of market factors associated with provision of peritoneal dialysis services. Med Care Res Rev. 2011;68(5):537-558. doi:10.1177/1077558711399768

34. Beyene J, Moineddin R. Methods for confidence interval estimation of a ratio parameter with application to location quotients. BMC Med Res Methodol. 2005;5:32. doi:10.1186/1471-2288-5-32

35. Diggle PJ, Heagerty P, Liang KY, Zeger SL. Analysis of Longitudinal Data. Oxford University Press; 2002.

36. Cui J. QIC program and model selection in GEE analyses. Stata J. 2007;7(2):209-220. doi:10.1177/1536867X0700700205

37. Hirth RA, Held PJ, Orzol SM, Dor A. Practice patterns, case mix, Medicare payment policy, and dialysis facility costs. Health Serv Res. 1999;33(6):1567-1592.

38. Pozniak AS, Hirth RA, Banaszak-Holl J, Wheeler JR. Predictors of chain acquisition among independent dialysis facilities. Health Serv Res. 2010;45(2):476-496. doi:10.1111/j.1475-6773.2010.01081.x

39. Fitzmaurice GM, Laird NM, Ware JH. Applied Longitudinal Analysis. 2nd ed. Wiley; 2011.

40. Mendelssohn DC. PD and the future: the role of PD in the overall management of ESRD. Blood Purif. 2003;21(1):24-28. doi:10.1159/000067853

41. Mehrotra R, Soohoo M, Rivara MB, et al. Racial and ethnic disparities in use of and outcomes with home dialysis in the United States. J Am Soc Nephrol. 2016;27(7):2123-2134. doi:10.1681/ASN.2015050472

42. Shen JI, Erickson KF, Chen L, et al. Expanded prospective payment system and use of and outcomes with home dialysis by race and ethnicity in the United States. Clin J Am Soc Nephrol. 2019;14(8):1200-1212. doi:10.2215/CJN.00290119

43. Furth SL, Hwang W, Yang C, Neu AM, Fivush BA, Powe NR. Relation between pediatric experience and treatment recommendations for children and adolescents with kidney failure. JAMA. 2001;285(8):1027-1033. doi:10.1001/jama.285.8.1027

44. Mehrotra R, Blake P, Berman N, Nolph KD. An analysis of dialysis training in the United States and Canada. Am J Kidney Dis. 2002;40(1):152-160. doi:10.1053/ajkd.2002.33924

45. Thamer M, Hwang W, Fink NE, et al. US nephrologists’ recommendation of dialysis modality: results of a national survey. Am J Kidney Dis. 2000;36(6):1155-1165. doi:10.1053/ajkd.2000.19829

Have Racial Disparities in Home Dialysis Utilization Changed Over Time?

Related Content