Original ArticlesTransplantation
Open Access

Distance to Kidney Transplant Center and Access to Early Steps in the Kidney Transplantation Process in the Southeastern United States

Laura J. McPherson, Vaughn Barry, Jane Yackley, Jennifer C. Gander, Stephen O. Pastan, Laura C. Plantinga, Sudeshna Paul, Rachel E. Patzer and on behalf of the Southeastern Kidney Transplant Coalition
CJASN April 2020, 15 (4) 539-549; DOI: https://doi.org/10.2215/CJN.08530719

Abstract

Background and objectives Access to kidney transplantation requires a referral to a transplant center for medical evaluation. Prior research suggests that the distance that a person must travel to reach a center might be a barrier to referral. We examined whether a shorter distance from patients’ residence to a transplant center increased the likelihood of referral and initiating the transplant evaluation once referred.

Design, setting, participants, & measurements Adults who began treatment for ESKD at any Georgia, North Carolina, or South Carolina dialysis facility from 1/1/2012 to 12/31/2015 were identified from the US Renal Data System. Referral (within 1 year of dialysis initiation) and evaluation initiation (within 6 months of referral) data were collected from all nine transplant centers located in that region. Distance was categorized as <15, 15–30, 31–60, 61–90, and >90 miles from the center of a patient’s residential zip code to the nearest center. We used multilevel, multivariable-adjusted logistic regression to quantify the association between distance with referral and evaluation initiation.

Results Among 27,250 adult patients on incident dialysis, 9582 (35%) were referred. Among those referred, 58% initiated evaluation. Although patients who lived farther from a center were less likely to be referred, distance was not statistically significantly related to transplant referral: adjusted odds ratios of 1.08 (95% confidence interval, 0.96 to 1.22), 1.07 (95% confidence interval, 0.95 to 1.22), 0.96 (95% confidence interval, 0.84 to 1.10), and 0.87 (95% confidence interval, 0.74 to 1.03) for 15–30, 31–60, 61–90, and >90 miles, respectively, compared with <15 miles (P trend =0.05). There was no statistically significant association of distance and evaluation initiation among referred patients: adjusted odds ratios of 1.14 (95% confidence interval, 0.97 to 1.33), 1.12 (95% confidence interval, 0.94 to 1.35), 1.04 (95% confidence interval, 0.87 to 1.25), and 0.89 (95% confidence interval, 0.72 to 1.11) for 15–30, 31–60, 61–90, and >90 miles, respectively, compared with <15 miles (P trend =0.70).

Conclusions Distance from residence to transplant center among patients undergoing long-term dialysis in the southeastern United States was not associated with increased likelihood of referral and initiating transplant center evaluation.

Introduction

For most patients with ESKD on dialysis, a referral from a dialysis facility to a transplant center for a medical evaluation is required to initiate the kidney transplant process (1–3). The distance that patients with ESKD must travel to reach a transplant center may influence both the likelihood that they are referred by a provider and also, the likelihood that they present for the medical evaluation at a transplant center once referred. Patients and nephrologists, particularly those in rural locations, may view distance as a barrier (4,5). Concerns related to post-transplant medication cost and access to transportation were frequently reported by patients with ESKD when asked about barriers to initiating or completing the medical evaluation at a transplant center (6–8).

There is some indication that the 262 United States adult transplant centers may not be as geographically widespread as the >7000 dialysis facilities, and thus, they may be less accessible to referred patients on dialysis (9,10). Previous studies suggest the average one-way driving distance from a patient’s home to the nearest dialysis facility is 7.9 miles (11), whereas the mean distance between a transplant patient’s home and the nearest transplant center is 23 miles (12).

Unlike waitlisting and transplant receipt, early steps in the transplant process, like referral to a transplant center and initiation of the evaluation at that center, are not regularly studied outcomes because this information is not collected in national surveillance data. Examining the characteristics associated with referral and evaluation initiation is critical to the understanding of disparities in access to kidney transplant because completion of these early steps is necessary to move forward with transplant, and these characteristics may not be the same as factors associated with placement on the waitlist or receipt of a transplant (13). We are hypothesizing that distance may be a more apparent barrier in referral and evaluation initiation because patients may be more easily discouraged from pursuing the complex transplant process at these early steps. To our knowledge, no study has examined the effect of distance on early transplant steps. The purpose of this study was to measure the association of distance to transplant center with timely transplant center referral and evaluation initiation among incident patients with ESKD in the southeastern United States, a region with the nation’s lowest transplant rates (14).

Materials and Methods

Cohort Creation

The underlying cohort was identified from the US Renal Data System (USRDS) and consisted of all adults (ages 18–80 years old) with incident ESKD who began dialysis at any Georgia, North Carolina, or South Carolina dialysis facility between January 1, 2012 and December 31, 2015, with follow-up through June 30, 2017. Because patients can be referred prior to ESKD diagnosis, more than once, and to multiple centers, we restricted our population to patients’ first nonpreemptive referral event during the study period. A more complete description of cohort exclusion criteria is available in Figure 1. Each transplant center sent referral and evaluation initiation data securely to ESRD Network 6, which served as the data coordinating center (15). This study was approved by the Emory Institutional Review Board (IRB79596).

Figure 1.
Figure 1.

Data merge and cohort selection to examine the relationship between distance from a patient’s residence to the nearest transplant center and access to kidney transplantation resulted in 27,250 incident dialysis patients in the southeastern United States (GA, NC, and SC). GA, Georgia; NC, North Carolina; SC, South Carolina; USRDS, US Renal Data System.

Referral and Transplant Evaluation Initiation

Referral for transplant and transplant evaluation initiation were determined by transplant center–reported referral or evaluation date, respectively, to any of the nine transplant centers in Georgia, North Carolina, and South Carolina (13,15). Patient’s transplant referral and evaluation date were linked with USRDS data, and referral was defined as a referral date occurring within 12 months of dialysis initiation. Among referred patients, patients were considered to initiate transplant evaluation if they presented for evaluation at a transplant center within 6 months of referral. Patient referral and evaluation start data were collected after June 30, 2017 to ensure no loss to follow-up. Specifically, patients had a minimum of 1 year following their dialysis initiation date and 6 months following their referral date to receive a referral or initiate the transplant evaluation, respectively.

Distance

Distance from the patient’s residential zip code tabulation area (ZCTA) centroid to the nearest transplant center was estimated in miles as a direct route approximation. We converted each patient’s residential zip code at the time of starting dialysis to ZCTA using a zip-ZCTA crosswalk file (16). The coordinates corresponding to the centroid of each ZCTA were taken from the US Census 2014 data file “mapping ZCTAs to centroids” (17). The US Census Geocoder (18) provided the coordinates associated with the addresses of each transplant center. We also identified coordinates for transplant centers located in neighboring states (Alabama, Tennessee, Florida, Virginia, West Virginia, and Kentucky) and used the SAS “geodist” function to calculate distance in miles from the patient’s residential ZCTA centroid to each transplant center. Patients who lived closer to a transplant center outside Georgia, North Carolina, or South Carolina were excluded (n=2872) because it was possible that they could have been referred to one of these centers. Because distance was estimated and could not be assumed to be linear, distance was categorized as <15, 15–30, 31–60, 61–90, and >90 miles. Figure 2 shows the geographic distribution of southeastern dialysis facilities and transplant centers.

Figure 2.
Figure 2.

Georgia, North Carolina, and South Carolina transplant centers (n=9) are not as geographically widespread as dialysis facilities (n=745) in the same southeastern United States region. Of note, transplant center–associated satellite clinics are not shown here.

Covariates

Patient- and dialysis facility–level characteristics potentially associated with transplant center referral, evaluation, or distance were extracted from the USRDS. Patient-level demographic and clinical characteristics corresponded to the USRDS date on which the patient began dialysis and included age in years, sex, race/ethnicity, attributed cause of ESKD, comorbidities, and primary health insurance type. Facility-level characteristics included for-profit status, hospital based versus free standing, facility size, and number of social workers employed at the facility. The 2014 American Community Survey provided neighborhood-specific sociodemographic/economic information (19). This included the percentage of people in a patient’s neighborhood who lived in poverty, had a high school diploma, and identified as black race. A measure of residential remoteness was calculated for each patient using rural-urban commuting area codes that were assigned to zip codes (20). We classified patients as residing in one of three areas: metropolitan, micropolitan, and rural (21).

Statistical Analyses

Cohort characteristics were described overall, by distance, and by referral and evaluation initiation status. Two multivariable multilevel logistic regression models assessed the effect of distance on (1) referral (yes/no) and (2) evaluation initiation (yes/no) among those referred. The multilevel regression models used facility-specific random effects with an exchangeable correlation structure and robust SEMs to account for correlations between patients from the same dialysis facility. Both models were adjusted for any variable that was significantly associated with distance and the respective outcome in unadjusted analyses. Adjusted odds ratios (aORs) and corresponding 95% confidence intervals (95% CIs) were reported.

We conducted a complete patient analysis; 1937 (7%) and 405 (4%) were excluded for missing covariates from referral and evaluation initiation models, respectively. Missing data for each covariate can be found in Tables 1 and 2. P values testing the trend of effect estimates across distance categories were calculated by assigning each person the midpoint/median distance in miles of each category. We assessed interactions of distance by race/ethnicity, socioeconomic status, neighborhood poverty, and rural/urban status. Sensitivity analyses examined distance continuously and categorized using different cut points, and the main association among a cohort including patients who lived closest to a transplant center outside of the tristate area, excluding patients who died within 12 months of beginning dialysis and including patients representative of an ideal kidney transplant candidate (excluding patients age >65 years old; diagnosed with peripheral vascular disease, coronary heart failure, or cerebrovascular disease; or reported by dialysis facility staff not to be assessed for transplantation due to medical reasons).

View this table:
Table 1.

Demographic and clinical characteristics of southeastern patients on incident dialysis at baseline by patient distance to a transplant center, 2012–2015

View this table:
Table 2.

Demographic, clinical, and dialysis facility characteristics of patients with ESKD who started dialysis at a Georgia, North Carolina, or South Carolina dialysis facility during 2012–2015, whether they were referred to a transplant center in any of the three states within 1 year of beginning dialysis, and whether they initiated a transplant evaluation at a transplant center within 6 months of referral

Results

Cohort Description

There were 37,052 patients identified by the USRDS as beginning dialysis in a Georgia, North Carolina, or South Carolina dialysis facility between 1/1/2012 and 12/31/2015 (Figure 1). Our final analytic cohort contained 27,250 patients, and over half were men (55%) and black (57%) (Table 1). Average age at dialysis start was 60 years old (SD: 13). Two thirds of the cohort resided in a metropolitan area (68%). The median distance from patient residence to the nearest transplant center was 38 miles (interquartile range [IQR], 18–69). One fifth of patients (22%) lived <15 miles from a transplant center, whereas one tenth (10%) lived >90 miles away. Among the cohort, n=9582 (35%) were referred to a Georgia, North Carolina, or South Carolina transplant center within 1 year of beginning dialysis (Table 2). Among those referred, n=5536 (58%) initiated a transplant evaluation within 6 months of being referred.

Unadjusted Analyses

In unadjusted analyses, patient characteristics varied by distance (Table 1). Over two thirds of patients living <15 miles of a transplant center were black (69%); 26% were white. In general, the percentage of patients with comorbidities was higher with farther distance (e.g., 56% of patients living <15 miles from a center had diabetes versus 60% of patients living >90 miles away).

Similarly, compared with not referred patients, referred patients were more likely to be younger (average age 54 [SD: 13] versus 62 [SD: 12] years old), be men (59% versus 53%), be black (63% versus 54%), have private health insurance (25% versus 14%), and live closer to a transplant center (median distance, 36 [IQR, 17–67] versus 40 [IQR, 19–70] miles) (Table 2). Referred patients were less likely to have certain comorbidities, including heart and cerebrovascular disease–related issues, diabetes, cancer, and chronic obstructive pulmonary disease. Among referred patients, those who initiated a transplant evaluation within 6 months of referral tended to be younger (52 [SD: 13] versus 56 [SD: 13] years) and live closer to a transplant center (median distance, 32 [IQR, 15–64] versus 41 [IQR, 19–73] miles). Patients living <15 miles from a transplant center had higher proportions of referral compared with those who lived >90 miles away (38% versus 33%). There is a more pronounced decrease in the proportion of referred patients initiating evaluation living <15 miles from a center compared with those who live >90 miles away (62% versus 51%) (Table 3).

View this table:
Table 3.

Results from adjusted models showing effect of distance on patient with ESKD referral and transplant center evaluation initiation, 2012–2015

Multivariable Analyses

In adjusted analyses, the distance that a patient lived from the nearest transplant center was not statistically significantly associated with referral or evaluation initiation; trends for each of these early steps were also not statistically significant (referral: aORs of 1.08 [95% CI, 0.96 to 1.22], 1.07 [95% CI, 0.95 to 1.22], 0.96 [95% CI, 0.84 to 1.10], and 0.87 [95% CI, 0.74 to 1.03] for 15–30, 31–60, 61–90, and >90 miles, respectively, compared with <15 miles; P trend =0.05; evaluation initiation: aORs of 1.14 [95% CI, 0.97 to 1.33], 1.12 [95% CI, 0.94 to 1.35], 1.04 [95% CI, 0.87 to 1.25], and 0.89 [95% CI, 0.72 to 1.11] for 15–30, 31–60, 61–90, and >90 miles, respectively, compared with <15 miles; P trend =0.70]. The association of distance did not vary by patient race/ethnicity, insurance type, poverty level of the patient’s neighborhood, or urban/rural classification.

Sensitivity Analyses

Results from adjusted models that used different distance categorizations were similar to reported results. Cut-point selections for sensitivity models included (1) <20, 20–45, and >45 miles; (2) <15, 15–30, 31–60, and >60 miles; and (3) <20, 20–50, 51–90, and >90 miles. When adjusted models were run with distance as a continuous variable, results were similar to the reported trend test P values found by using the midpoint/median of each distance category (per 10-mile increment: aOR, 0.88 per 10-mile increment; 95% CI, 0.76 to 1.00; P value =0.08 for referral and for evaluation initiation: aOR, 0.99 per 10-mile increment; 95% CI, 0.97 to 1.01; P value =0.23). The association of distance on referral and evaluation was also similar after excluding patients who died (n=1993) within 12 months of beginning dialysis from the analysis and excluding patients who did not meet ideal kidney transplant criteria (n=15,265). Within the subset of nonpreemptively referred patients who were excluded for living closer to a transplant center outside of the tristate area (n=2705), the majority of the patients lived >90 miles from the nearest Georgia, North Carolina, or South Carolina transplant center (<15 miles: 112 [4%]; 16–30 miles 160 [6%]; 31–60 miles 582 [22%]; 61–90 638 [24%]; and >90 miles 1213 [45%]). The association of distance on referral and evaluation was also similar to reported results.

Discussion

In this study of southeastern United States patients on dialysis, we did not find an association between distance from patient zip code to the nearest transplant center and referral for kidney transplant evaluation or evaluation initiation at a transplant center. Although the farthest (>90 miles) compared with the shortest (<15 miles) distances were associated with 13% and 11% lower likelihood of referral and evaluation initiation, respectively, the associations were not statistically significant, and the trend analyses did not support a dose-response relationship between distance and these early steps.

We found these results surprising given the previous qualitative studies that cited distance as a barrier to referral (4,5) for patients with ESKD. Our findings also generally contradict other literature demonstrating associations between distance and reduced access to both primary care and specialized medical services for chronic conditions (22–25). Transportation cost and access are more important barriers to referral and evaluation initiation compared with distance. Populations of patients with ESKD report transportation concerns as barriers to transplant access (6–8), and other studies among general populations have shown that transportation barriers (including lack of transportation in the form of car or mass transit and transportation cost) are important impediments to health care access in the United States (26–28). Furthermore, studies have shown that, although transportation is often more expensive for people living farther from clinics compared with those living closer, transportation is often reported as a concern equally among both rural and urban residents (28,29), suggesting that transportation barriers affect patients regardless of distance. Similarly, it could be that travel time rather than distance is a more important measure when examining disparities in kidney transplant access (30). For example, a person living in an urban area <15 miles from a transplant center who must coordinate bus schedules with medical appointment times may spend more time in transit than a person living 40 miles away who is transported in a car. Alternatively, other factors, like access to childcare or elderly care and time off from work, may be more important predictors of referral or evaluation initiation than distance.

Previous research has examined patient travel distance in relation to waitlist placement and kidney transplantation access. However, associations of distance with these later steps in the transplantation process across prior studies are inconsistent, and it is unclear whether this could be due to differences in study populations (i.e., pediatric versus adult patients with ESKD), time periods, geographic locations (Canada versus the United States), or other factors. Two studies reported that patients living farther from the nearest transplant center had a reduced likelihood of receiving a transplant (31,32), whereas two other studies reported no association of distance with transplantation or placement on the waitlist (33,34), and one study indicated that adults living farther from a transplant center were more likely to receive a transplant than adults living closer (35). One study analyzed the association between distance to transplant center and waitlist placement in the same three states as our study (Georgia, North Carolina, and South Carolina) but during the years 1998–2002, and found no association with distance (33). A separate but similar study also used USRDS data but examined the entire United States adult population with incident ESKD during 1995–2007 (n=699,751) (35). Receipt of a kidney transplant was the primary outcome. A study using USRDS data from 1995 to 2007 examined the association of receipt of a kidney transplant and longer distances (0–15, 16–50, 51–100, 101–136, 137–231, 232–310, and >310 miles) compared with our study (<15, 15–30, 31–60, 61–90, and >90 miles), and it found that patients living farther (>15 miles) from a transplant center were more likely to receive a transplant compared with patients living within 15 miles of a transplant center (35). The authors speculated that this could happen if patients with ESKD living farther from a transplant center were (1) more motivated to pursue transplantation, (2) less likely to originally initiate dialysis, or (3) completed transplant evaluation faster with the assistance of physicians who may be aware of the potential logistical barriers incurred by living farther from a center.

Distance to a transplant center may be more likely to have an effect on earlier steps in the kidney transplant process, indicating a need to examine the outcomes of referral and evaluation initiation. For example, one study found that black patients with ESKD were more likely to be referred to a transplant center compared with white patients but were less likely placed on the waitlist (13). Our results suggested a slightly more notable effect of distance and a decreased likelihood of referral compared with the lack of association between distance and evaluation initiation among patients referred, indicating that factors associated with referral may differ from those associated with evaluation. The decision to refer a patient may be directed more by the nephrologist’s perceptions, whereas evaluation initiation may be more dependent on a patient’s resources, perceptions, and motivations. Explaining the role that distance may play on timely referral and evaluation initiation can elucidate and inform intervention efforts required to improve equity in access to transplantation, particularly with respect to geographic disparities.

Our study has limitations. First, our results may not be generalizable to other United States regions where a higher proportion of patients live >100 miles from a transplant center because 95% of our cohort lived within 100 miles of a transplant center. Second, we may have misclassified distance estimates due to the use of the centroid of patients’ residential ZCTA; however, we broadly categorized distance in our analyses to minimize any effect of this imprecision. Third, residential zip code was defined by the patient’s home residence location at the time of beginning dialysis, and patients could have moved after beginning dialysis. However, any resulting distance misclassification may not be severe because only 11% of Americans moved between 2015 and 2016, and of Americans who moved, over two thirds stayed within the same county (36). Fourth, our data did not capture facility referrals or center evaluations that occurred outside the three states. We thus excluded patients who lived closest to a transplant center outside the tristate region because they may have been more likely referred to one of these outside transplant centers. Because these excluded patients were more likely to live the farthest from a Georgia, North Carolina, or South Carolina transplant center, including these patients in the study cohort would have made it impossible to discern whether any lack of referral was due to distance or instead, because a referral occurred at an outside transplant center, and this exclusion may have attenuated the effects that we saw. Fifth, four of the nine centers in the southeastern United States offer at least one satellite location where patients living far away from the center can be evaluated. We are unable to discern whether a patient was evaluated at a transplant center or an associated satellite clinic in our transplant center–reported data, which also may have tempered the examined associations.

We found that distance from patient zip code to the nearest transplant center was not associated with the likelihood of referral or transplant evaluation initiation in Georgia, North Carolina, or South Carolina. Results suggest that distance to a transplant center may be a barrier for a subset of patients living >90 miles from the nearest transplant center in the southeastern United States, and those patients may benefit from additional transplant center–associated satellite locations or a more streamlined evaluation process that reduces the number of visits that a patient needs to make to a center to complete the evaluation. However, these results suggest that distance is not the primary driving force in accessing these early steps in the transplant process. Other unmeasured factors in the same genre as distance, such as travel time or transportation options, may have a larger effect on these early steps and should be more robustly explored. Future studies outside of the southeastern United States examining the association between distance and access to early steps in the kidney transplant process should be conducted to observe whether there is heterogeneity in this association across geographic regions.

Disclosures

Dr. Barry, Dr. Gander, Ms. McPherson, Dr. Pastan, Dr. Paul, Dr. Patzer, Dr. Plantinga, and Ms. Yackley have nothing to disclose.

Funding

This project and The Reducing Disparities in Access to kidNey Transplantation Regional Study was funded in part by the National Institute on Minority Health and Health Disparities award U01MD010611. Support for the preparation of this document was funded by Centers for Medicare and Medicaid Services (an agency of the US Department of Health and Human Services) ESRD Network 6 contract HHSM-500-2013-NW006C.

Acknowledgments

The authors acknowledge the assistance of the Southeastern Kidney Transplant Coalition and the Centers for Medicare and Medicaid Services (CMS) in providing data, which made this research possible.

The conclusions presented are solely those of the authors and do not represent those of the Southeastern Kidney Transplant Coalition or the CMS. The content of this publication does not necessarily reflect the policies or positions of the Department of Health and Human Services, and mention of trade names, commercial products, or organizations does not imply endorsement by the US Government. The authors assume full responsibility for the accuracy and completeness of the ideas presented. A portion of the data reported here has been supplied by the US Renal Data System. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as official policy or interpretation of the US Government. The study funder/sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

The Southeastern Kidney Transplant Coalition participated in data collection, and the authors specifically acknowledge the coalition members who heavily contributed to data collection: Randy Detwiler (University of North Carolina Center for Transplant), Derek DuBay (Medical University of South Carolina), Matthew Ellis (Duke University Transplant Center), Chris Fotiadis (Carolinas Health), Joseph Gulotta (Island Peer Review Organization ESRD Network of the South Atlantic), Erica Hartmann (Piedmont Transplant Institute), Heather Jones (Vidant Medical Center), Laura Mulloy (Augusta University Health Transplant Center), Amber Reeves-Daniel (Wake Forest Health), Brenda Thrasher (Carolinas Health), Al Wagner (University of North Carolina Center for Transplant), Shannon Wright (Island Peer Review Organization ESRD Network of the South Atlantic), and Carlos Zayas (Augusta University Health Transplant Center).

Footnotes

  • Published online ahead of print. Publication date available at www.cjasn.org.

  • See related Patient Voice, “Distance from a Transplant Center and Getting Listed for a Transplant,” and editorial, “The Long Road to Kidney Transplantation: Does Center Distance Impact Transplant Referral and Evaluation?” on pages 439–440 and 453–454, respectively.

  • Received July 22, 2019.
  • Accepted December 23, 2019.

References

  1. ↵
    1. Alexander GC,
    2. Sehgal AR
    : Barriers to cadaveric renal transplantation among blacks, women, and the poor. JAMA 280: 1148–1152, 1998 pmid:9777814
    OpenUrlCrossRefPubMed
    1. Gallon LG,
    2. Leventhal JR,
    3. Kaufman DB
    : Pretransplant evaluation of renal transplant candidates. Semin Nephrol 22: 515–525, 2002 pmid:12430096
    OpenUrlCrossRefPubMed
  3. ↵
    1. Alexander GC,
    2. Sehgal AR
    : Why hemodialysis patients fail to complete the transplantation process. Am J Kidney Dis 37: 321–328, 2001 pmid:11157373
    OpenUrlCrossRefPubMed
  4. ↵
    1. Tandon A,
    2. Wang M,
    3. Roe KC,
    4. Patel S,
    5. Ghahramani N
    : Nephrologists’ likelihood of referring patients for kidney transplant based on hypothetical patient scenarios. Clin Kidney J 9: 611–615, 2016 pmid:27478607
    OpenUrlCrossRefPubMed
  5. ↵
    1. Ghahramani N,
    2. Sanati-Mehrizy A,
    3. Wang C
    : Perceptions of patient candidacy for kidney transplant in the United States: A qualitative study comparing rural and urban nephrologists. Exp Clin Transplant 12: 9–14, 2014
    OpenUrlCrossRefPubMed
  6. ↵
    1. Dageforde LA,
    2. Box A,
    3. Feurer ID,
    4. Cavanaugh KL
    : Understanding patient barriers to kidney transplant evaluation. Transplantation 99: 1463–1469, 2015 pmid:25606794
    OpenUrlCrossRefPubMed
    1. Kazley AS,
    2. Simpson KN,
    3. Chavin KD,
    4. Baliga P
    : Barriers facing patients referred for kidney transplant cause loss to follow-up. Kidney Int 82: 1018–1023, 2012 pmid:22832516
    OpenUrlCrossRefPubMed
  8. ↵
    1. Lockwood MB,
    2. Saunders MR,
    3. Nass R,
    4. McGivern CL,
    5. Cunningham PN,
    6. Chon WJ,
    7. Josephson MA,
    8. Becker YT,
    9. Lee CS
    : Patient-reported barriers to the prekidney transplant evaluation in an at-risk population in the United States. Prog Transplant 27: 131–138, 2017 pmid:28617167
    OpenUrlPubMed
  9. ↵
    1. USRDS
    : US Renal Data System 2018 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States, Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2018
  10. ↵
    1. Scientific Registry of Transplant Recipients
    : Compare transplant programs. 2019. Available at: http://www.srtr.org. Accessed April 29, 2019
  11. ↵
    1. Stephens JM,
    2. Brotherton S,
    3. Dunning SC,
    4. Emerson LC,
    5. Gilbertson DT,
    6. Harrison DJ,
    7. Kochevar JJ,
    8. McClellan AC,
    9. McClellan WM,
    10. Wan S,
    11. Gitlin M
    : Geographic disparities in patient travel for dialysis in the United States. J Rural Health 29: 339–348, 2013
    OpenUrlPubMed
  12. ↵
    1. Kasiske BL,
    2. Snyder JJ,
    3. Skeans MA,
    4. Tuomari AV,
    5. Maclean JR,
    6. Israni AK
    : The geography of kidney transplantation in the United States. Am J Transplant 8: 647–657, 2008 pmid:18294161
    OpenUrlCrossRefPubMed
  13. ↵
    1. Patzer RE,
    2. Plantinga LC,
    3. Paul S,
    4. Gander J,
    5. Krisher J,
    6. Sauls L,
    7. Gibney EM,
    8. Mulloy L,
    9. Pastan SO
    : Variation in dialysis facility referral for kidney transplantation among patients with end-stage renal disease in Georgia. JAMA 314: 582–594, 2015 pmid:26262796
    OpenUrlCrossRefPubMed
  14. ↵
    1. Patzer RE,
    2. Pastan SO
    : Kidney transplant access in the Southeast: View from the bottom. Am J Transplant 14: 1499–1505, 2014 pmid:24891223
    OpenUrlCrossRefPubMed
  15. ↵
    1. Patzer RE,
    2. Gander J,
    3. Sauls L,
    4. Amamoo MA,
    5. Krisher J,
    6. Mulloy LL,
    7. Gibney E,
    8. Browne T,
    9. Plantinga L,
    10. Pastan SO; Southeastern Kidney Transplant Coalition
    : The RaDIANT community study protocol: Community-based participatory research for reducing disparities in access to kidney transplantation. BMC Nephrol 15: 171, 2014 pmid:25348614
    OpenUrlCrossRefPubMed
  16. ↵
    1. Mapper UDS
    : ZIP code to ZCTA crosswalk, 2018. Available at: https://www.udsmapper.org/zcta-crosswalk.cfm. Accessed January 14, 2019
  17. ↵
    1. US Census Bureau
    : Geography program, 2018. Available at: https://www.census.gov/geographies/reference-files/time-series/geo/gazetteer-files.2010.html. Accessed January 14, 2019
  18. ↵
    1. US Census Bureau
    : United State census geocoder, 2018. Available at: https://geocoding.geo.census.gov/geocoder/geographies/addressbatch?form. Accessed January 14, 2019
  19. ↵
    1. US Census Bureau
    : American community survey, 2014. Available at: https://www.census.gov/programs-surveys/acs/. Accessed January 14, 2019
  20. ↵
    1. US Department of Agriculture
    : Economic research service: Rural-Urban Commuting Area (RUCA) codes, 2010. Available at: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/documentation/. Accessed January 14, 2019
  21. ↵
    1. Hall SA,
    2. Kaufman JS,
    3. Ricketts TC
    : Defining urban and rural areas in U.S. epidemiologic studies. J Urban Health 83: 162–175, 2006 pmid:16736366
    OpenUrlCrossRefPubMed
  22. ↵
    1. Arcury TA,
    2. Gesler WM,
    3. Preisser JS,
    4. Sherman J,
    5. Spencer J,
    6. Perin J
    : The effects of geography and spatial behavior on health care utilization among the residents of a rural region. Health Serv Res 40: 135–155, 2005 pmid:15663706
    OpenUrlCrossRefPubMed
    1. Kelly C,
    2. Hulme C,
    3. Farragher T,
    4. Clarke G
    : Are differences in travel time or distance to healthcare for adults in global north countries associated with an impact on health outcomes? A systematic review. BMJ open 6: e013059, 2016
    OpenUrlAbstract/FREE Full Text
    1. Lin CC,
    2. Bruinooge SS,
    3. Kirkwood MK,
    4. Olsen C,
    5. Jemal A,
    6. Bajorin D,
    7. Giordano SH,
    8. Goldstein M,
    9. Guadagnolo BA,
    10. Kosty M,
    11. Hopkins S,
    12. Yu JB,
    13. Arnone A,
    14. Hanley A,
    15. Stevens S,
    16. Hershman DL
    : Association between geographic access to cancer care, insurance, and receipt of chemotherapy: Geographic distribution of oncologists and travel distance. J Clin Oncol 33: 3177–3185, 2015 pmid:26304878
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Napravnik S,
    2. Eron JJ Jr.,
    3. McKaig RG,
    4. Heine AD,
    5. Menezes P,
    6. Quinlivan E
    : Factors associated with fewer visits for HIV primary care at a tertiary care center in the Southeastern U.S. AIDS Care 18[Suppl 1]: S45–S50, 2006 pmid:16938674
    OpenUrlCrossRefPubMed
  26. ↵
    1. Smith ML,
    2. Prohaska TR,
    3. MacLeod KE,
    4. Ory MG,
    5. Eisenstein AR,
    6. Ragland DR,
    7. Irmiter C,
    8. Towne SD,
    9. Satariano WA
    : Non-emergency medical transportation needs of middle-aged and older adults: A rural-urban comparison in Delaware, USA. Int J Environ Res Public Health 14: E174, 2017 pmid:2820861
    OpenUrlPubMed
    1. Syed ST,
    2. Gerber BS,
    3. Sharp LK
    : Traveling towards disease: Transportation barriers to health care access. J Community Health 38: 976–993, 2013 pmid:23543372
    OpenUrlCrossRefPubMed
  28. ↵
    1. Wallace R,
    2. Hughes-Cromwick P,
    3. Mull H,
    4. Khasnabis S
    : Access to health care and nonemergency medical transportation: Two missing links. Transp Res Rec 76–84, 2005
  29. ↵
    1. Blazer DG,
    2. Landerman LR,
    3. Fillenbaum G,
    4. Horner R
    : Health services access and use among older adults in North Carolina: Urban vs rural residents. Am J Public Health 85: 1384–1390, 1995 pmid:7573622
    OpenUrlCrossRefPubMed
  30. ↵
    1. Shahid R,
    2. Bertazzon S,
    3. Knudtson ML,
    4. Ghali WA
    : Comparison of distance measures in spatial analytical modeling for health service planning. BMC Health Serv Res 9: 200, 2009 pmid:19895692
    OpenUrlPubMed
  31. ↵
    1. Axelrod DA,
    2. Dzebisashvili N,
    3. Schnitzler MA,
    4. Salvalaggio PR,
    5. Segev DL,
    6. Gentry SE,
    7. Tuttle-Newhall J,
    8. Lentine KL
    : The interplay of socioeconomic status, distance to center, and interdonor service area travel on kidney transplant access and outcomes. Clin J Am Soc Nephrol 5: 2276–2288, 2010 pmid:20798250
    OpenUrlAbstract/FREE Full Text
  32. ↵
    1. Samuel SM,
    2. Hemmelgarn B,
    3. Nettel-Aguirre A,
    4. Foster B,
    5. Soo A,
    6. Alexander RT,
    7. Tonelli M; Pediatric Renal Outcomes Canada Group
    : Association between residence location and likelihood of transplantation among pediatric dialysis patients. Pediatr Transplant 16: 735–741, 2012 pmid:22489932
    OpenUrlCrossRefPubMed
  33. ↵
    1. Patzer RE,
    2. Amaral S,
    3. Wasse H,
    4. Volkova N,
    5. Kleinbaum D,
    6. McClellan WM
    : Neighborhood poverty and racial disparities in kidney transplant waitlisting. J Am Soc Nephrol 20: 1333–1340, 2009 pmid:19339381
    OpenUrlAbstract/FREE Full Text
  34. ↵
    1. Tonelli M,
    2. Klarenbach S,
    3. Manns B,
    4. Culleton B,
    5. Hemmelgarn B,
    6. Bertazzon S,
    7. Wiebe N,
    8. Gill JS
    ; Alberta Kidney Disease Network: Residence location and likelihood of kidney transplantation. CMAJ 175: 478–482, 2006
  35. ↵
    1. Tonelli M,
    2. Klarenbach S,
    3. Rose C,
    4. Wiebe N,
    5. Gill J
    : Access to kidney transplantation among remote- and rural-dwelling patients with kidney failure in the United States. JAMA 301: 1681–1690, 2009 pmid:19383959
    OpenUrlCrossRefPubMed
  36. ↵
    1. Ihrke D
    : United States mover rate at a new record low, 2017 Available at: https://www.census.gov/newsroom/blogs/random-samplings/2017/01/mover-rate.html?cid=17mover-rate. Accessed January 23, 2017
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Distance to Kidney Transplant Center and Access to Early Steps in the Kidney Transplantation Process in the Southeastern United States
Laura J. McPherson, Vaughn Barry, Jane Yackley, Jennifer C. Gander, Stephen O. Pastan, Laura C. Plantinga, Sudeshna Paul, Rachel E. Patzer, on behalf of the Southeastern Kidney Transplant Coalition
CJASN Apr 2020, 15 (4) 539-549; DOI: 10.2215/CJN.08530719
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