Introduction
The incidence of ESKD over the last 20 years (1) has increased. According to the United States Renal Data System (USRDS; a national data system about CKD and ESKD in the United States), there were approximately 750,000 patients with prevalent ESKD in the United States in 2017 (1), of which a majority are on dialysis (in-center hemodialysis [iHD], peritoneal dialysis [PD], or home HD). In 2016, the rate of iHD and PD utilization in patients with incident ESKD in the United States was 87% and 10%, respectively (1). Although most nephrologists acknowledge that iHD is the least desirable modality (1), 88% of the prevalent ESKD patient population on dialysis use iHD and only 11% use PD. Although this is similar to the PD utilization rate worldwide (approximately 11%), there are countries where PD utilization rates are far higher—Hong Kong (72%), Iceland (34%), and New Zealand (32%). With the signing of the Advancing Kidney Health Initiative in 2019, there has been a renewed enthusiasm for home dialysis in the United States, with a goal of 80% of new patients with kidney failure receiving dialysis at home or receiving a kidney transplant by 2025 (2).
The causes for this underutilization are many, ranging from medical factors/contraindications, limited resource availability, social factors, and patient preference (3–5). Practical factors that play a central role when deciding on a patient’s long-term dialysis modality include accessibility to dialysis facilities and the modality options they offer. A survey of American nephrologists found that distances of >50 km between a patient’s residence and a dialysis facility had a significant effect on modality selection (3). With the ability to remotely monitor treatments and have monthly clinic visits, home dialysis is a convenient option for patients who do not live in close proximity to their dialysis-providing unit. This is especially relevant in rural areas. Using Rural-Urban Commuting Area codes to determine the degree of rurality of patients’ residences, O’Hare et al. (6) found that patients on PD lived in more rural areas.
There have been conflicting results regarding the association of distance from patients’ homes to dialysis facilities and the uptake of PD. One study found that the rate of PD utilization decreased as the distance to the nearest iHD unit increased (7). Moreover, PD utilization increased the farther the nearest home dialysis unit was from patients’ homes. On the other hand, a recent study by Wang et al. (8) showed that, with increasing distances from the closest iHD facility, PD utilization increased. Furthermore, the odds of PD use was even greater if the distance to the closest PD facility was less than or equal to that of the iHD facility. A limitation of this study, however, was that the straight-line distance between the patients’ residence zip codes and the dialysis-providing facilities’ zip codes was used for the analysis. Travel distance, however, is not interchangeable with straight-line distance. It has been previously shown that the ratio of travel distance to straight-line distance (detour index) in the United States is approximately 1.4 (9). This difference can be substantial in terms of travel time and might alter a patient’s decision about dialysis modality choice.
Using USRDS data, we set out to further investigate the association between patients’ dialysis modalities and both the driving and straight-line distances to their dialysis units. Furthermore, we assessed the correlation between patient demographics, employment status, pre-ESKD nephrology care, comorbid conditions, area of residence (urban versus rural), and the dialysis modality.
Materials and Methods
Participants
Inclusion Criteria
All patients with ESKD in the USRDS (10) database who initiated chronic dialysis treatments (iHD, continuous ambulatory PD, continuous cycler-assisted PD, and other PD) in 2017, who were 18–90 years old, and were on dialysis for ≥30 days were included in the analysis.
Exclusion Criteria
Patients who were <18 or >90 years of age or those who died or received a kidney transplant within 30 days after initiating dialysis were excluded. Those patients who resided in residence zip codes in nonconterminous United States (Alaska, Hawaii, American Samoa, Guam, Northern Mariana Islands, Puerto Rico, Virgin Islands, foreign [according to social security administration code]) were excluded from the analysis because it was not possible to calculate driving distance using zip code data. Moreover, patients who resided >90 miles from the nearest HD-providing unit were excluded from the analysis because this was thought to be highly unlikely and the result of data entry error.
Data and Variables
The USRDS database was the primary source of patient and dialysis unit data collection. We examined patients’ date of dialysis initiation, dialysis modality, and number of days on dialysis. Moreover, data on the individual dialysis unit managing patients’ treatments were obtained, including the unit’s zip code. Patient characteristics, such as demographics, employment status, pre-ESKD nephrologist care, and comorbid conditions, were also collected. Patients’ residence zip codes at day 30 after dialysis initiation were also recorded.
The 2010 Rural-Urban Commuting Area codes (11) were used to determine the degree of rurality of patients’ residences on the basis of the zip codes obtained. The classification of rurality was defined by the Economic Research Service from the US Department of Agriculture, which is based on population density, urbanization, and daily commuting flow in each area. Moreover, individual state population census and ESKD prevalence were obtained from the US Census Bureau and the USRDS website, respectively.
We determined the dialysis modalities provided in each zip code using the USRDS database and 2017 Annual Files from the Dialysis Facility Compare data archive from the Centers for Medicare and Medicaid (CMS) website (12). The median household income in 2017 in each zip code was obtained from the CMS website (13).
We calculated the travel distance (D) and time (T) from each patient’s residence zip code at day 30 after dialysis initiation to (1) the closest HD-providing unit zip code (DHD, THD), (2) the closest PD-providing unit zip code (DPD, TPD), and (3) the dialysis unit zip code that the patient was actually going to at day 30 after dialysis initiation (DA, TA). A SAS macro was used to calculate the driving distances and times between zip codes; the macro uses the URL access method of Google Maps and extracts times and distances (14).
We also calculated the straight-line distance from each patient’s residence zip code to (1) the closest HD-providing unit zip code (DHDS), (2) the closest PD-providing unit zip code (DPDS), and (3) the dialysis unit zip code that the patient was actually going to at day 30 after dialysis initiation (DAS).
The relative distance of the nearest PD-providing unit and the nearest HD-providing unit from each patient’s residence (DR) was then calculated (DPD−DHD). The DR was recorded as both a continuous variable (where the actual DR values were recorded) and as a dichotomous variable (with values being split into two groups: (1) DR less than or equal to zero, and (2) DR greater than zero; Figure 1).
;)
Figure 1.: Distances from patients’ homes to respective dialysis units. Distances from patient’s residence zip code to the (A) closest hemodialysis (HD)-providing unit zip code (blue), (B) closest peritoneal dialysis (PD)-providing unit zip code (red), and (C) dialysis unit zip code that patients went to at day 30 after dialysis initiation (green) were calculated by using a SAS macro and Google Maps. The driving distances and straight-line distances are represented by the solid and broken line, respectively. The relative distance (DR) was calculated as DPD minus by DHD, and the DR was categorized into two groups, greater than zero and less than or equal to zero. DA, driving distance to actual dialysis unit used; DAS, straight-line distance to the actual dialysis unit used; DHD, driving distance to the closest HD-providing unit zip code; DHDS, straight-line distance to the closest HD unit; DPD, driving distance to the closest PD-providing unit zip code; DPDS, straight-line distance to the closest PD unit.
Statistical Analyses
Categoric and continuous data were presented in number (%) and median (interquartile range), respectively. The Fisher exact test and Wilcoxon rank-sum test were used to compare the categoric and continuous data, respectively.
Logistic regression analyses were performed to determine the relationship between PD utilization versus the DHD, DHDS, and the dichotomized DR. The analyses were adjusted by patient demographics (age, sex, race, ethnicity, employment status at onset of ESKD, median household income, urban residence, pre-ESKD nephrologist care, diabetes as the causes of ESKD, body mass index), comorbidities (diabetes, hypertension, coronary artery disease, cerebrovascular disease, peripheral vascular disease, congestive heart failure, other cardiac disease, chronic obstructive pulmonary disease, malignant neoplasm, inability to ambulate), number of patients receiving PD per 1000 of the ESKD prevalent population in 2016, and the census population in each state in 2017.
A P value <0.05 was considered statistically significant for all comparisons.
This study was approved by Icahn School of Medicine Program for the Protection of Human Subject Institutional Review Board (reference 18-01300). The analysis was done using Stata/IC 15.1 software (StataCorp, College Station, TX) and SAS version 9.4 (SAS Institute Inc., Cary, NC).
Results
A total of 102,247 patients in the United States initiated iHD and PD in 2017. Of those, 75,213 patients (66,856 on iHD, 8357 on PD) met the study inclusion/exclusion criteria. Accounting for modality changes by day 30 of dialysis initiation, there were 66,676 patients (89%) on iHD and 8537 patients (11%) on PD (Figure 2).
Figure 2.: Study flow. A total of 507 patients had no closest HD zip code, 18,661 had no closest PD zip code, and 2753 had neither closest HD nor PD zip code. USRDS, United States Renal Data System.
The baseline characteristics of the patients is shown in Table 1. Patients on PD were younger and more likely to be male, White, employed, have received pre-ESKD nephrologist care, and have a higher median household income. Eighty-three percent of patients lived in urban areas and 50% had diabetes as the underlying etiology of their ESKD. Patients on PD were less likely to have comorbid conditions, with the exception of hypertension—which was similar between the two groups.
Table 1. -
Characteristics of patients included in the analysis according to dialysis modalities at day 30 after dialysis initiation
| Characteristics |
Hemodialysis |
Peritoneal Dialysis |
Total |
| Number, n (%) |
66,676 (89) |
8537 (11) |
75,213 (100) |
| Age, yr, median (IQR) |
65.7 (55.8–74.4) |
62.0 (50.9–71.0) |
65.3 (55.2–74.1)
a
|
| Male, n (%) |
38,964 (58) |
5101 (60) |
44,065 (59)
a
|
|
Race, n (%)
|
|
|
|
| White |
44,140 (66) |
5996 (70) |
50,136 (67)
a
|
| Black |
18,264 (27) |
1820 (21) |
20,084 (27)
a
|
| Other |
4272 (6) |
721 (8) |
4993 (7)
a
|
| Hispanic ethnicity, n (%) |
9057 (14) |
1077 (13) |
10,134 (14)
a
|
| Employed (full time or part time), n (%) |
6621 (10) |
2422 (28) |
9043 (12)
a
|
| Median household income, $, median (IQR) |
63,473 (50,164–82,027) |
67,303 (53,671–87,813) |
63,919 (50,577–82,678)
a
|
| Urban residence, n (%) |
55,535 (83) |
7002 (82) |
62,537 (83)
a
|
| Pre-ESKD nephrologist care, n (%) |
42,918 (76) |
7351 (92) |
50,269 (78)
a
|
| Cause of ESKD: diabetes, n (%) |
33,411 (50) |
3985 (47) |
37,396 (50)
a
|
| BMI, kg/m2, median (IQR) |
28.5 (24.2–34.2) |
29.0 (24.9–33.7) |
28.6 (24.3–34.1)
a
|
|
Comorbid conditions, n (%)
|
|
|
|
| Diabetes (required medication) |
36,066 (55) |
4025 (48) |
40,091 (54)
a
|
| Hypertension |
58,356 (89) |
7542 (89) |
65,898 (89) |
| Coronary artery disease |
9026 (14) |
779 (9) |
9805 (13)
a
|
| Cerebrovascular disease |
5912 (9) |
438 (5) |
6350 (9)
a
|
| Peripheral arterial disease |
6403 (10) |
444 (5) |
6847 (9)
a
|
| Other cardiac disease |
13,741 (21) |
999 (12) |
14,740 (20)
a
|
| Congestive heart failure |
19,673 (30) |
1268 (15) |
20,941 (28)
a
|
| Chronic obstructive pulmonary disease |
6485 (10) |
322 (4) |
6807 (9)
a
|
| Cancer |
4640 (7) |
449 (5) |
5089 (7)
a
|
| Inability to ambulate |
4737 (7) |
77 (1) |
4814 (7)
a
|
|
Driving distance to closest dialysis unit, miles, median (IQR)
|
|
|
|
| Home to HD |
2.9 (0–8.5) |
3.9 (0–10.4) |
3.0 (0–8.7)
a
|
| Home to PD |
3.4 (0–9.8) |
4.4 (0–12.0) |
3.5 (0–10.0)
a
|
|
Straight-line distance to closest dialysis unit, miles, median (IQR)
|
|
|
|
| Home to HD |
2.0 (0–5.4) |
2.5 (0–6.9) |
2.1 (0–5.6)
a
|
| Home to PD |
2.3 (0–6.3) |
2.9 (0–8.0) |
2.3 (0–6.5)
a
|
|
Time to closest dialysis unit, min, median (IQR)
|
|
|
|
| Home to HD |
9 (0–16) |
10 (0–18) |
9 (0–16)
a
|
| Home to PD |
10 (0–17) |
11 (0–20) |
10 (0–18)
a
|
| Driving distance to actual dialysis unit, miles, median (IQR) |
6.8 (0–14.4) |
11.9 (4.9–22.4) |
7.3 (1.5–15.2)
a
|
| Straight-line distance to actual dialysis unit, miles, median (IQR) |
4.1 (0–9.1) |
7.5 (2.9–15.6) |
4.4 (0.5–9.7)
a
|
| Time to actual dialysis unit, min, median (IQR) |
15 (0–23) |
21 (12–32) |
15 (6–24)
a
|
| Relative distance: closest PD or HD, miles, median (IQR) |
0 (0–0) |
0 (0–0) |
0 (0–0)
b
|
| Distance to the closest PD is equal/closer than HD unit, n (%) |
51,449 (77) |
6559 (77) |
58,008 (77) |
| Relative time: closest PD or HD, min, median (IQR) |
0 (0–0) |
0 (0–0) |
0 (0–0)
b
|
| Time to the closest PD is equal to/less than that to HD unit, n (%) |
53,294 (80) |
6765 (79) |
60,059 (80) |
|
Market characteristics
|
|
|
|
| PD prevalence in 2016 (cases per 1000 ESKD prevalent population), median (IQR) |
71.6 (64.2–81.3) |
74.1 (64.5–81.3) |
71.6 (64.2–81.3)
a
|
| Population density (patients×106), median (IQR) |
10.4 (6.1–21.0) |
10.4 (6.0–21.0) |
10.4 (6.1–21.0) |
HD, hemodialysis; PD, peritoneal dialysis; IQR, interquartile range; BMI, body mass index.
aP<0.05 for comparison between HD and PD group.
b77% (6559 out of 8537 of total patients on PD) of patients on PD had a distance to the closest PD that was equal to/closer than HD unit.
The median driving distance to the closest HD-providing unit was greater for patients on PD than those on HD (3.9 versus 2.9 miles, respectively; P<0.001). The same was true for the median straight-line distances (2.5 versus 2.0 miles, respectively; P<0.001). Interestingly, the median driving distance to the closest PD-providing unit was greater for patients on PD than those on HD (4.4 versus 3.4 miles, respectively; P<0.001). Again, this remained the case when assessing straight-line distances (2.9 versus 2.3 miles, respectively; P<0.001). Moreover, the distance to the closest PD-providing unit was equal to or less than the closest HD-providing unit in 77% of both patients on HD and those on PD. Most of the dialysis units provide both HD and PD modalities, and only a small percentage of the dialysis units provide solely HD or PD. The travel time from patients’ residences to both HD- and PD-providing units for the patients on PD was greater than that for the patients on HD (10 versus 9 minutes and 11 versus 10 minutes, respectively).
There was no difference in the proportion of patients who were on PD, regardless of whether the PD unit was farther from or closer than their nearest HD-providing unit (12% versus 11%; Table 2). As shown in Figure 3, patients who resided ≥30 miles from their nearest HD-providing unit were more likely to be on PD if the distance to the PD-providing unit was equal to or less than the distance to the nearest HD-providing unit.
Table 2. -
Numbers of peritoneal dialysis patients, total patients, and percentages of peritoneal dialysis utilization categorized by distance from patient’s residence to the closest hemodialysis unit and if distance to closest hemodialysis unit is equal/closer or farther than distance to hemodialysis unit
| Distance, miles |
Total |
Closest Peritoneal Dialysis Is Equal/ Closer than Hemodialysis Unit |
Closest Peritoneal Dialysis Is Farther than Hemodialysis Unit |
| Peritoneal Dialysis |
Total |
Peritoneal Dialysis Utilization, % |
Peritoneal Dialysis |
Total |
Peritoneal Dialysis Utilization, % |
Peritoneal Dialysis |
Total |
Peritoneal Dialysis Utilization, % |
| 0 |
3123 |
29,052 |
11 |
3123 |
29,052 |
11 |
0 |
0 |
0 |
| 0.1–5.0 |
1737 |
18,085 |
10 |
1102 |
11,194 |
10 |
635 |
6891 |
9 |
| 5.1–10.0 |
1496 |
11,711 |
13 |
973 |
7761 |
13 |
523 |
3950 |
13 |
| 10.1–15.0 |
830 |
6788 |
12 |
544 |
4400 |
12 |
286 |
2388 |
12 |
| 15.1–20.0 |
570 |
4105 |
14
a
|
374 |
2460 |
15 |
196 |
1645 |
12 |
| 20.1–25.0 |
295 |
2185 |
14
a
|
157 |
1295 |
12 |
138 |
890 |
16 |
| 25.1–30.0 |
154 |
1194 |
13 |
87 |
679 |
13 |
67 |
515 |
13 |
| 30.1–40.0 |
163 |
1119 |
15 |
97 |
632 |
15 |
66 |
487 |
14 |
| 40.1–50.0 |
76 |
472 |
16 |
41 |
239 |
17 |
35 |
233 |
15 |
| 50.1–60.0 |
44 |
247 |
18 |
28 |
134 |
21 |
16 |
113 |
14 |
| >60.0 |
49 |
255 |
19 |
33 |
162 |
20 |
16 |
93 |
17 |
| Total |
8537 |
75,213 |
11 |
6559 |
58,008 |
11 |
1978 |
17,205 |
12 |
Figure 3.: Comparing PD utilization based on whether the closest PD unit is closer or farther than the closest HD unit. *P<0.05.
Logistic Regression Analyses
Unadjusted Analysis
For every 10-mile increase in distance from patients’ homes to the nearest HD unit, the odds of PD utilization increased by 13% (odds ratio [OR], 1.13; 95% CI, 1.11 to 1.16), whereas there was a 5% increase for every 10-mile increase to the nearest PD unit (OR, 1.05; 95% CI, 1.04 to 1.06). The relative distance to the closest dialysis unit was not significantly associated with PD utilization (OR, 0.999; 95% CI, 0.996 to 1.002).
When grouped by whether the PD unit was closer to or farther from the HD unit, the odds of PD utilization increased as the distance to the closest HD unit was >5 miles, regardless of whether the PD unit was closer or further than the HD unit (Supplemental Figure 1). There was also a trend of increased ORs of PD utilization with increasing distances from the nearest HD-providing unit in both groups.
Adjusted Analysis
After adjusting for age, demographics, and comorbidities, the driving distance to the closest HD unit remained significantly associated with increased PD utilization (especially if the nearest HD unit was >30 miles away from the patient’s home). The ORs of PD utilization were slightly greater in patients whose closest PD unit was farther than the closest HD unit (Figure 4A, Supplemental Table 1).
Figure 4.: Adjusted odds ratios (ORs) for PD utilization categorized by the driving distance from patient’s residence to the closest HD-providing unit and distance to closest PD-providing unit (equal/closer or farther than distance to HD-providing unit). (A) The model was adjusted by distance to closest HD unit, demographics (age, sex, race, ethnicity, body mass index, employment status at onset of ESKD, median household income, urban residence, pre-ESKD nephrologist care, diabetes as the causes of ESKD), comorbidities, PD prevalence in 2016, and number of census population in 2017. (B) Adjusted odds ratios for PD utilization categorized by the straight-line distance from patient’s residence to the closest HD-providing unit and distance to closest PD-providing unit (equal/closer or farther than distance to HD-providing unit). The model was adjusted by distance to closest HD unit, demographics (age, sex, race, ethnicity, body mass index, employment status at onset of ESKD, median household income, urban residence, pre-ESKD nephrologist care, diabetes as the causes of ESKD), comorbidities, PD prevalence in 2016, and number of census population in 2017.
Using straight-line distance, there was only a significant association in patients whose PD unit was equal in distance or closer than the HD unit, but not if the PD unit was farther than the HD unit. In contrast, the ORs for PD utilization were slightly lower in patients whose closest PD unit was farther than the closest HD unit (Figure 4B).
Employment status at dialysis initiation, pre-ESKD nephrologist care, PD prevalence, and census population was associated with increased odds of PD utilization. Black race, Hispanic ethnicity, diabetes diagnosis requiring treatment, peripheral arterial disease, other cardiac disease, congestive heart failure, chronic obstructive pulmonary disorder, malignancy, and inability to ambulate were associated with lower odds of PD utilization.
For every 10-mile increase in distance from patients’ homes to the nearest HD unit, the odds of PD utilization increased by 11% (adjusted OR, 1.11; 95% CI, 1.08 to 1.14), whereas there was a 4% increase for every 10-mile increase to the nearest PD unit (adjusted OR, 1.04; 95% CI, 1.02 to 1.05). The relative distance to the closest dialysis unit was not significantly associated with PD utilization (adjusted OR, 0.998; 95% CI, 0.99 to 1.00).
The median driving distances were greater than the median straight-line distances, with median detour indexes of 1.46, 1.37, and 1.38 from the patients’ homes to their actual dialysis unit, nearest HD unit, and nearest PD unit, respectively (Supplemental Table 2). Adjusting for dialysis modality, only 31,551 (42%) patients received their dialysis care at their nearest unit.
Discussion
In this study, we looked at the association between patients’ driving distances to their closest PD and HD units, and PD utilization. Compared with patients on HD, patients undergoing PD lived farther from both HD- and PD-providing units. The farther patients lived from an HD unit, the more likely the patients were to be on PD. This was regardless of whether the closest PD unit was closer or further than the closest HD unit.
Many factors contribute to the dialysis modality patients ultimately choose. Although some factors are not modifiable, such as age and comorbidities, systemic factors, including proximity to a PD unit, for example, are potentially modifiable. Prior studies have found that rural areas tend to have higher PD utilization than their urban counterparts (6,15). Our results are similar to other studies where the greater the distance from a patient’s home to an HD-providing unit, the greater the likelihood of PD use.
Analyzing the relationship between patients’ actual driving distances and dialysis modality choices was previously conducted by Prakash et al. (7). Others have used straight-line distance, which may not be reflective of the true distance traveled (time consumed by patients), and, here, we show that driving distance is consistently greater than its straight-line counterpart. This is the first study comparing dialysis modality choices using both driving distance and straight-line distance in the same cohort. To highlight the difference between using straight-line and driving distances, we calculated adjusted ORs using both metrics. Using straight-line distances, we only see a significant increase in PD utilization if the patient’s nearest PD unit is equal/closer than an HD unit. Whereas, using actual driving distance analysis, we see increased PD utilization regardless of whether the nearest PD unit is equal/closer or farther than the nearest HD unit. This is an important and striking difference.
In our analysis, although greater distance to the closest HD unit was significantly associated with PD utilization, employment at dialysis initiation and pre-ESKD nephrology care were the features with the highest ORs of PD utilization. There have been several studies that have shown that patient education and pre-ESKD nephrology care are associated with increased home dialysis utilization (16). Overall, only 23%–24% of patients were employed at the start of dialysis, with 38% of patients stopping employment 6 months into dialysis initiation (17). PD has previously been demonstrated to be associated with increased probability of employment compared with iHD (18). This suggests that ability to continue employment may be a driving force for patients to choose PD over iHD. On the other hand, this may be an indicator that patients on PD are more independent and have better self-care at baseline. As expected, several comorbidities, including congestive heart failure and peripheral artery disease, were associated with a decreased likelihood of PD utilization. Patients on HD were older and had more comorbidities than patients on PD.
Interestingly, we also found that, for both patients on HD and those on PD, the actual dialysis units used were not the ones providing their modality of choice closest to their homes. We hypothesize this is likely due to patients opting to continue their dialysis care with their existing nephrologist or providers recommended by them.
We recognize there are many factors that influence a patient’s decision when choosing a dialysis modality. Although we tried to account for some social determinants of health (e.g., race, employment status, and household income), there are likely more social determinants of health for which we have not accounted. Availability of transportation likely affects the patient’s modality selection, however, these data were not available. Driving distances were calculated using zip code centroids instead of addresses, which may not be an accurate representation of true driving distance. We recognize this is a retrospective study and a randomized study would be needed to confirm our results.
In conclusion, we found that patients’ odds of PD utilization increased with increasing relative driving distance of their nearest HD or PD unit. Using driving distance rather than straight-line distance affects data analysis and outcomes. Although increasing the number of home dialysis units may improve PD availability, it may have a limited effect on increasing PD utilization. Focus should instead be on improving pre-ESKD care, where nephrologists can review dialysis modality selection with patients.
Disclosures
L. Chan reports receiving honoraria from Fresenius Medical Care, receiving research funding from the National Institutes of Health (K23DK124645), and having consultancy agreements with Vifor Pharma. O. El Shamy reports having other interests in/relationships with Home Dialysis Academy of Excellence, receiving honoraria from Home Dialysis University and UpToDate, and serving as a scientific advisor for, or member of, the International Journal of Nephrology. All remaining authors have nothing to disclose.
Funding
None.
Acknowledgments
The data reported here has been supplied by the USRDS. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US Government.
Author Contributions
L. Chan, K. Chauhan, O. El Shamy, P. Pattharanitima, S. Sharma, and J. Uribarri were responsible for methodology; L. Chan, K. Chauhan, P. Pattharanitima, A. Saha, and H.H. Wen were responsible for formal analysis; L. Chan, O. El Shamy, and P. Pattharanitima were responsible for data curation; L. Chan, O. El Shamy, S. Sharma, and J. Uribarri conceptualized the study and were responsible for investigation; O. El Shamy and P. Pattharanitima wrote the original draft; and all authors reviewed and edited the manuscript.
Supplemental Material
This article contains supplemental material online at http://kidney360.asnjournals.org/lookup/suppl/doi:10.34067/KID.0004762021/-/DCSupplemental.
Supplemental Figure 1. Unadjusted odds ratios for PD utilization categorized by distance from patient’s residence to the closest HD unit in all patients and distance to closest PD unit if it is equal/closer or farther than distance to HD unit.
Supplemental Table 1. Adjusted odds ratios for PD utilization categorized by driving distance from patient’s residence to the closest HD unit and distance to closest PD unit if it is equal/closer or farther than distance to HD unit. The model was adjusted by distance to closest PD unit, demographics, comorbidities, PD prevalence in 2016, and number of census population in 2017.
Supplemental Table 2. Median (IQR) of driving distance, straight-line distance, and driving time from patient’s residence to actual dialysis unit that patients received dialysis treatment, to closest HD- and PD-provided unit in all available data and in patients whom had all data.
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