Abstract
Background:
In response to a longstanding Federal mandate to minimize the role of geography in access to transplant in the United States, we assessed whether patient travel distance was associated with lung transplant outcomes. We focused on the posttransplant time period, when the majority of patient visits to a transplant center occur.
Methods:
We present a cohort study of lung transplants in the U.S. between January 1, 2006 through May 31, 2017. Travel distance was measured from the patient’s permanent home zip code to the transplant center using SAS URL access to GoogleMaps. We leveraged data from the U.S. Census, U.S. Department of Agriculture, and the Economic Innovations Group to assess socioeconomic status. Multivariable Cox models were used to assess graft survival.
Results:
18 128 patients met inclusion criteria. Median distance was 69.6 miles. Among patients who traveled >60 miles to reach a transplant center, 41.8% bypassed a closer center and sought care at a more distant center. Patients traveling longer distances sought care at centers with a higher annual transplant volume. In the adjusted Cox Model, patients who traveled >360 miles had a slightly higher risk for posttransplant graft failure than patients traveling ≤60 miles (Hazard Ratio 1.09, 95% CI 1.01–1.18), and a higher risk for treated acute rejection (HR 1.63, 95% CI 1.43–1.86).
Conclusion:
Travel distance was significantly associated with post lung transplant survival. However, this effect was relatively modest. Patient travel distance is an important component of access to lung transplant care.
Introduction
Lung transplant is the definitive treatment for end stage lung disease and provides a survival benefit.1,2 However, from 2000–2017 there were only 75 lung transplant centers across the United States,3 which indicates a wide spectrum of patient travel distances to access care. We studied the association between patient travel distance and post lung transplant survival for 2 reasons. First, as a response to a longstanding Federal mandate to minimize the role of geography in access to organ transplant in the United States,4 and second, to identify existing access to care disparities which may change after recent modifications to the national donor lung allocation policy.5 We focused on the posttransplant timeframe when patients encounter more travel from home to transplant center for clinic visits as compared to the pretransplant timeframe. In 2017 the median waitlist time for a lung transplant in the United States was 2.5 months, whereas the 3-year posttransplant survival was 68.8%.6
There has been a growing number of contemporary reports in liver and renal transplantation using patient zip codes in large registries to calculate travel distance.7,8 Based on our previous single-center work,9 we hypothesized that despite delivery of care challenges associated with travel, distance was not associated with post lung transplant graft survival in a national cohort. Our approach was fourfold: 1) measure patient driving distance from permanent residential zip code to lung transplant center with the route mapping site GoogleMaps, which would provide more granularity over traditional linear travel measurements, 2) calculate the frequency patients bypassed the nearest center to seek care at a more distant center, 3) assess zip code level socioeconomic indicators by linking the transplant registry with data from the U.S. Census, U.S. Department of Agriculture, and the Economic Innovations Group, and 4) evaluate the association between travel distance and posttransplant patient survival.
Methods
We performed a retrospective cohort study of all adult (≥18 years) lung only transplants in the United States listed and transplanted between January 1, 2006 through May 31, 2017 in the Scientific Registry of Transplant Recipients (SRTR). Patients undergoing retransplant, lobar transplant, missing a residential zip code, or traveling from outside the United States for transplant were excluded. This study was declared exempt by the Cleveland Clinic Institutional Review Board.
Travel distance was determined from the patient’s permanent residential zip code recorded at the time of transplant to the transplant center using SAS URL access to GoogleMaps,10 Distance was categorized a priori as ≤60, >60–180, >180–360, and >360 road miles. <60 miles was chosen to represent approximately 1 hour of driving, >60–180 miles approximated 1 to 3 hours of driving, >180–360 miles approximated 3–6 hours of driving, and >360 miles represented a full day or more of driving. For patients traveling from Hawaii or Puerto Rico, we calculated the linear distance inflated by the average percent increase from linear to driving distance observed in all cases with available data, and ultimately all these patients were in the >360 mile category.
To identify the closest center for each patient, we selected the 5 transplant center zip codes closest to the patient’s residential zip code using linear distance, and calculated driving distances to those 5 centers. The shortest driving distance was considered the patient’s nearest transplant center. A transplant center bypass was defined as: driving distance from permanent residential zip code to the patient’s transplant center was greater than the driving distance to the nearest center, and the selected center was ≥60 miles from the permanent residential zip code.
We drew upon additional registries to build a multi-dimensional portrait of a patient’s socioeconomic background. The SRTR reports patient insurance type, education level, and permanent residential zip code. We included the median annual income for each zip code captured in the United States census data,11 the metropolitan population size from the United States Department of Agriculture,12 and the Distressed Communities Index (DCI). The DCI is calculated by the Economic Innovation Group13 and uses measures such as unemployment, poverty rate, and business growth to rank each zip code from 0 (no distress) to 100 (severe distress).
The data have been supplied by SRTR as the contractor for the Organ Procurement and Transplantation Network in September 2018 and have a censoring date of May 31, 2018. A full explanation of the clinical variables is in the Supplemental Digital Content.
Outcomes
The primary outcome was posttransplant graft survival, defined as time from transplant until death or retransplant. A secondary outcome was the risk for treated acute rejection on either the 6 or 12 month posttransplant follow up form.
Statistical Analysis
We evaluated patient characteristics across travel distance groups including counts and percentages for categorical variables, and means and standard deviations or medians and interquartile ranges for continuous variables. ANOVA, Kruskal-Wallis, and Pearson’s chi-square tests were used where appropriate. Similarly, we evaluated the characteristics of the subset of patients that traveled >360 miles. We categorized this subgroup into those having their closest center ≤60, >60–180, >180–360, and >360 miles from their permanent zip code, and compared the characteristics across groups. We created maps of the United States showing the percent of patients that received lung transplants in each state living at different distances from the transplant center used. We also created a map showing the median distance from the recipients’ home zip code to the closest center by state. We plotted lung transplant centers as circles with size proportional to the number of lung transplants at that center during the study period.
Kaplan-Meier survival analysis and adjusted Cox proportional hazard models were used to assess the relationship between patient travel distance group and graft survival. The following recipient transplantation characteristics were chosen a priori for inclusion in the model: sex, recipient age, race, Body Mass Index (BMI), native lung disease, insurance status, education level, ABO blood group, transplantation procedure, donor age, and distressed community index. Logistic regression analysis was used to assess the relationship between patient distance group and 1-year treated acute rejection separately. The logistic model included the same adjustment variables as the Cox model.
Data were missing for the following variables: BMI (0.3%), distressed community index (2%), and education (6%). We used multiple imputation (SAS proc MI) with the Markov Chain Monte Carlo method and a single chain to impute 5 datasets with complete data. The multiple imputation included the following variables: sex, age, race, BMI, native lung disease, insurance status, education level, ABO blood group, transplantation procedure, DCI quintile, and Lung Allocation Score (LAS) at the time of transplant. All models were fitted on each of the 5 imputed datasets, and parameter estimates were combined using SAS MIanalyze.
Two-sided P values of 0.05 or less were significant. All data were analyzed with Linux SAS, version 9.4 (SAS Institute, Cary, North Carolina). Graphics were produced with R version 3.5.1 (The R Foundation for Statistical Computing, Vienna, Austria).
Results
There were 18 128 patients that met inclusion criteria (Figure 1). There were 74 unique transplant centers. The median patient travel distance was 69.6 miles. Among patients who traveled >60 miles from home, 41.8% bypassed the nearest center. A bypass was more likely with increased travel distance and occurred in 903 (17.0%), 1 558 (57.6%), and 1 613 (92.3%) of patients in the >60–180, >180–360, and >360 mile distance categories, respectively (Table 1). Figure 2 shows the location of each transplant center on a state map of the United States, with circle size proportional to the number of lung transplants at that center included in our study. Concentric circles indicate more than 1 center in that city. Each state was shaded to indicate the percentage of patients who lived >360 driving miles from the center used for transplant. Additional maps representing patient travel patterns are in the (supplement Figures S1–S3).
Figure 1.
STROBE diagram: The number of patients meeting inclusion and exclusion criteria are displayed.
Table 1.
Characteristics of study cohort by travel distance.
| ≤60 miles | >60–180 miles | >180–360 miles | >360 miles | p-value | |
|---|---|---|---|---|---|
| Travel variables | |||||
| Median drive distance in miles | 23 [13,37] | 106 [80,137] | 241[207,283] | 586 [443,897] | |
| Bypassed nearest center | ~ | 903 (17.0) | 1,558 (57.6) | 1,613 (92.3) | <0.001c |
| Demographic variables | |||||
| Women | 3 490 (41.7) | 2 031 (38.3) | 1 058 (39.1) | 643 (36.8) | <0.001c |
| Age at transplant in years | 56.2±12.6 | 56.0±12.6 | 55.3±13.3 | 55.6±14.0 | 0.007a |
| Race | <0.001c | ||||
| White | 6 483 (77.5) | 4 655 (87.7) | 2 376 (87.9) | 1 497 (85.6) | |
| Black | 1 016 (12.1) | 322 (6.1) | 169 (6.3) | 106 (6.1) | |
| Other | 871 (10.4) | 329 (6.2) | 159 (5.9) | 145 (8.3) | |
| Clinical variables | |||||
| Lung allocation score at transplant* | 47.4±17.6 | 46.7±17.2 | 47.5±17.5 | 49.8±18.4 | <0.001a |
| Body mass index | 25.4±4.7 | 25.2±4.5 | 25.0±4.6 | 24.9±4.4 | <0.001a |
| Bilateral lung transplant | 5 653 (67.5) | 3 660 (69.0) | 1 928 (71.3) | 1 193 (68.2) | 0.003c |
| Native lung disease | <0.001c | ||||
| A – obstructive lung disease | 2 512 (30.0) | 1 722 (32.5) | 797 (29.5) | 394 (22.5) | |
| B – pulmonary vascular disease | 366 (4.4) | 165 (3.1) | 112 (4.1) | 76 (4.3) | |
| C – cystic lung disease | 891 (10.6) | 610 (11.5) | 352 (13.0) | 258 (14.8) | |
| D – fibrotic lung disease | 4 601 (55.0) | 2 809 (52.9) | 1 443 (53.4) | 1 020 (58.4) | |
| Blood type | 0.14c | ||||
| A | 3 275 (39.1) | 2 173 (41.0) | 1 133 (41.9) | 710 (40.6) | |
| AB | 330 (3.9) | 192 (3.6) | 96 (3.6) | 78 (4.5) | |
| B | 966 (11.5) | 560 (10.6) | 286 (10.6) | 195 (11.2) | |
| O | 3 799 (45.4) | 2 381 (44.9) | 1 189 (44.0) | 765 (43.8) | |
| Median waitlist days | 63 [19,179] | 59 [17,178] | 51 [17,167] | 36 [11,124] | <0.001b |
| Not hospitalized at time of transplant | 6 734 (80.5) | 4 387 (82.7) | 2 183 (80.7) | 1 360 (77.8) | <0.001c |
| Simultaneously listed at >1 center | 182 (2.2) | 170 (3.2) | 74 (2.7) | 136 (7.8) | <0.001c |
| Median annual transplant center volume from 2006–2017 | 39.0 [22.5,59.5] | 38.5 22.5,59.5] | 56.0 [33.0,100.0] | 62.0 [38.5,102.0] | <0.001b |
| Socioeconomic variables | |||||
| Median zip code income in U.S. dollars | 64 370 [49825,83860] | 51 349 [42406.5,65053] | 50 845 [41208,63848] | 54 894 [43567,71231] | <0.001b |
| Some college or higher education | 4 688 (56.0) | 2 694 (50.8) | 1 449 (53.6) | 1 111 (63.6) | <0.001c |
| Metro area population of 1 million or more | 7 199 (86.0) | 1 492 (28.1) | 821 (30.4) | 625 (35.8) | <0.001c |
| Distressed Community Index | 35.1 ±27.8 | 48.4 ±28.0 | 48.1 ±29.1 | 42.2 ±28.4 | <0.001a |
| Distressed Community Index Quintile | <0.001c | ||||
| 1 – Prosperous | 3,365 (40.7) | 1,084 (21.0) | 570 (21.8) | 487 (28.9) | |
| 2 – Comfortable | 1,785 (21.6) | 1,056 (20.4) | 565 (21.7) | 394 (23.4) | |
| 3 – Midtier | 1,351 (16.3) | 1,079 (20.9) | 483 (18.5) | 290 (17.2) | |
| 4 – At risk | 985 (11.9) | 1,072 (20.7) | 506 (19.4) | 284 (16.9) | |
| 5 - Distressed | 787 (9.5) | 882 (17.1) | 485 (18.6) | 230 (13.6) | |
| Insurance status | <0.001c | ||||
| Private | 4,484 (53.6) | 2,612 (49.2) | 1,361 (50.3) | 910 (52.1) | |
| Medicare/Public | 3,184 (38.0) | 2,255 (42.5) | 1,157 (42.8) | 628 (35.9) | |
| Medicaid | 602 (7.2) | 369 (7.0) | 148 (5.5) | 61 (3.5) | |
| Veterans Affairs | 62 (0.74) | 44 (0.83) | 33 (1.2) | 144 (8.2) | |
| Other | 38 (0.45) | 26 (0.49) | 5 (0.18) | 5 (0.29) | |
| Donor variables | |||||
| Donor age in years | 34.6±14.0 | 34.4±14.0 | 34.6±14.5 | 35.1±14.3 | 0.43a |
| Maximum PaO2 (mmHg) on 100% FiO2 | 373.4±147.5 | 378.7±149.9 | 381.8±148.3 | 369.5±148.6 | 0.008a |
| Maximum organ ischemic time in minutes | 302.4±103.9 | 311.8±103.5 | 320.4±107.7 | 325.0±108.2 | <0.001a |
| Donation after circulatory death | 170 (2.0) | 93 (1.8) | 74 (2.7) | 28 (1.6) | 0.015c |
| Increased risk donor | 1 128 (13.5) | 732 (13.8) | 312 (11.5) | 238 (13.6) | 0.031c |
Statistics presented as Mean ± SD, Median [P25, P75], or N (column %).
p-values:
=ANOVA,
=Kruskal-Wallis test,
=Pearson’s chi-square test.
N Missing data: BMI 50, LAS 1, median zip income 253, education 1165, Distressed Community Index 388, maximum PaO2 91, maximum ischemic time 376.
The clinical algorithm used to calculate the LAS was revised and went into effect February 20, 2015.
Figure 2.
Map of United States with shading color of state representing the percentage of patients who traveled >360 miles to the transplant center used. Circles represent location of transplant centers and size is proportional to the total number of transplants during the study period. Source: SAS gmap procedure.
Patients in the furthest distance category when compared to the shortest distance category were less likely to be female (36.8% in >360 miles vs 41.7% in ≤60 miles), Black (6.1% in >360 vs 12.1% in ≤60), from a large metropolitan area (35.8% in >360 vs 86.0% in ≤60), or have Medicaid insurance (3.5% in >360 vs 7.2% in ≤60). They were more likely to have a higher LAS (49.8 in >360 vs 47.4 in ≤60), shorter waitlist days (36 in >360 vs 63 in ≤60), be simultaneously listed at multiple centers (7.8% in >360 vs 2.2% in ≤60), be at a center with higher median annual transplant volume (62.0 in >360 vs 39.0 in ≤60), and have a college education (63.6 in >360 vs 56.0 in ≤60). Donor age was not significantly different by distance category.
Among patients in the shortest distance category, 40.7% were from the lowest quintile of the community distress index (no distress) whereas 9.5% were from the highest quintile (most distress). Among patients in the longest distance category, 28.9% were from a low distress community whereas 13.6% were from a high distressed community. Patients in the shortest distance category had the highest median annual income ($64 370) and more likely to live in a metro area with a population of 1 million or more.
We further assessed patients in the >360 mile distance category. Within this group 36% (629/1,748) bypassed a transplant center which was ≤60 miles from their home zip code. These patients were more likely to be from a large metro area (75.8%) and sought care at centers with a high median annual transplant volume (100/year). (Table S1)
Driving vs Linear Distance
We assessed the impact of using Googlemaps, and found that up to 18% of patients were recategorized into a longer travel distance by using driving instead of linear distance (Table S2).
Primary outcome: Survival
During a median follow up of 3.1 years, 8 359 patients experienced graft failure. Figure 3 shows the Kaplan-Meier estimates of graft survival by travel distance group (P = 0.07). In the adjusted Cox model of graft survival, patients who traveled >360 miles were found to have a slightly higher mortality than patients who traveled ≤60 miles (Hazard Ratio: 1.09, 95% Confidence Interval: 1.01, 1.18, Table 2).
Figure 3.
Kaplan-Meier estimates of posttransplant graft survival. Graft survival by travel distance group was not significantly different.
Table 2.
Model of graft failure
| Driving distance in miles | Cox Model Graft Fail/Death* HR (95%CI) |
|---|---|
| ≤60 | Ref |
| >60–180 | 0.99 (0.94, 1.05) |
| >180–360 | 1.06 (0.99, 1.13) |
| >360 | 1.09 (1.01, 1.18) |
Model adjusted for age at transplant, sex, BMI, race, insurance (Private, Medicare, VA, Medicaid or other), education (some college vs. less), ABO group, native lung disease (group A obstructive disease, group B vascular disease, group C cystic disease, and group D fibrotic disease), community distress score, bilateral transplant (versus single lung transplant) and donor age.
CI: Confidence Interval
HR: Hazard Ratio
OR: Odds Ratio
Ref:Reference value
Acute Rejection
Table 3 shows results from the logistic model evaluating acute rejection for those with 1 year follow up. Among 15,316 patients with rejection status at 1 year, 3,486 had treated acute rejection; 20.4%, 22.4%, 26.5% and 29.2% among patients traveling ≤60, >60–180, >180–360 and >360 miles respectively (p < 0.001). In the adjusted multivariable model, when compared to the patients traveling ≤60 miles, those traveling >180–360 (Odds Ratio: 1.37, 95% CI: 1.22, 1.53) and >360 (OR: 1.63, 95% CI 1.43, 1.86) had significantly increased odds of treated acute rejection at 1 year.
Table 3.
Model of acute rejection
| Driving distance in miles | Logistic Model Treated Acute Rejection 1 year* OR (95%CI) |
|---|---|
| ≤60 | Ref |
| >60–180 | 1.10 (0.996, 1.21) |
| >180–360 | 1.37 (1.22, 1.53) |
| >360 | 1.63 (1.43, 1.86) |
Model adjusted for age at transplant, sex, BMI, race, insurance (Private, Medicare, VA, Medicaid or other), education (some college vs. less), ABO group, native lung disease (group A obstructive disease, group B vascular disease, group C cystic disease, and group D fibrotic disease), community distress score, bilateral transplant (versus single lung transplant) and donor age.
All adjusted models used 5 datasets created with multiple imputation and MI analyze to obtain estimates
CI: Confidence Interval
HR: Hazard Ratio
OR: Odds Ratio
Ref: Reference value
Discussion
Our primary findings were: 1) increased travel distance was associated with decreased posttransplant graft survival in the adjusted model, though the effect was small. In addition, increased travel distance was associated with an increased risk for treated acute rejection during the first posttransplant year. 2) Among patients traveling >60 miles, 41.8% bypassed the nearest transplant center and sought care at a further center; 46.2% of patients traveled ≤60 miles to the transplant center. 3) patients in the longest distance category (>360 miles) were a higher proportion of males, Whites, higher LAS, college level education, and traveled to a center with higher transplant volume and shorter wait time when compared to the shortest distance (≤60 miles). Patients in the shortest distance category had the highest median zip code income, lowest Distressed Community Index score, and were more likely to live in a large metropolitan area with a population greater than 1 million.
Primary outcome: Survival
As the median lung transplant survival remains among the lowest of all solid organ transplants, we felt that evaluating survival as the primary outcome would further inform the lung transplantation field. In our Cox model we found that although travel distance was significantly associated with posttransplant graft survival, this effect was relatively modest with a 9% absolute difference between the shortest and longest distance categories. The survival difference may reflect the increased logistical follow-up needs of patients traveling long distances as compared to shorter distances, that patients who travel longer distances have co-morbidities beyond those codified in SRTR data (such as concomitant complex heart disease or esophageal reflux), or that patients from longer travel distances, in urgent situations, may be adversely impacted by care fragmentation when accessing local care at a nontransplant center. Also, over time patient social support networks, which typically are robust in the peritransplant period, may wane in the years afterwards and make follow up visits more difficult, which is exacerbated by longer travel distances. The SRTR registry does not capture the frequency with which patients follow up in outpatient clinic, thus an area of future study may be the relationship between travel distance, frequency of outpatient visits, and survival. Despite the challenges associated with long distance travel, we feel the survival difference was modest and that patients and transplant centers should not be deterred from considering lung transplant based on patient travel distance alone. A prior report by Vock et al. found that 73% of lung transplant recipients were predicted to achieve a survival benefit,1 and that access to a high volume transplant center was an important contributor to the survival benefit.
Secondary outcome: Acute rejection
Increased travel distance was also associated with increased risk for treated acute rejection in the first year after transplant (not necessarily biopsy proven), an association which likely has multi-factorial causes. As there are varying transplant center surveillance protocols to diagnose and treat acute rejection, there may be a lower threshold to empirically treat rejection for patients from a longer travel distance. Additional comorbidities known to increase the risk of rejection, such as antibody sensitization, may be more prevalent in patients who have traveled longer distances. The diagnosis of acute rejection is disruptive to patients as it adds additional tests and treatments in order to manage the rejection, and also portends a higher risk for chronic rejection.14 This finding warrants further investigation.
Center Bypass
We felt that measuring travel distance alone did not fully capture a patient’s travel burden without considering whether patients bypassed a closer transplant center. For example, patients in a rural area may travel hundreds of miles to reach the nearest transplant center. In another example, patients in a large city may choose to bypass a local lung transplant center and travel a further distance to reach another center. While both these patients have traveled long distances, the explanation for the longer distances are distinct.
For patients who traveled >60 miles, we found that 41.8% bypassed the nearest transplant center. When we focused on patients in the >360 mile distance category, 36% bypassed a transplant center which was ≤60 miles of their permanent zip code. Patients bypassing a closer center were more likely to have group D fibrotic lung disease and come from a large metropolitan area when compared to patients who did not bypass a center and traveled >360 miles. Bypass of a closer center may occur for several reasons, which include patient self-referral or referral by a local clinician or insurance network. Also, a patient may be declined or ‘turned down’ for transplant at a nearby center due to center specific selection criteria, at which point patients may seek care at a more distant center. Patient preferences in seeking lung transplant care will require further study.
Patients in the longest travel distance category (>360 miles) were more likely to seek care at high volume transplant centers. The association between increased travel distance and higher transplant center volume is noteworthy as prior reports have shown that increased transplant volume is associated with improved posttransplant survival.1,3,15 Patients able to travel >360 miles were from zip codes with higher median income, favorable distressed community scores, and larger metropolitan population as compared to patients traveling >60–180 and >180–360 miles.
Additional studies
Travel distance and posttransplant care has been explored in liver and renal transplantation. Recent work by Webb et al. assessed driving distances for liver transplant patients in the United Kingdom and found that 25% of patients did not seek care at the nearest center. Additionally, there was no association between driving distance and retransplant free survival16. There have been conflicting reports in the United States on the association between travel distance and post liver transplant survival.17–19 In a large renal transplant registry study, living farther from the transplant center was associated with inferior posttransplant survival.8
In lung transplant, our prior single-center report demonstrated no association between linear patient travel distance and posttransplant graft survival.9 Mooney et al. evaluated pretransplant outcomes on patients in the SRTR registry waitlisted at multiple lung transplant centers. Multi-listed patients comprised 2.3% of the waitlist and were more likely to seek listing at a geographically more distant center with a higher transplant rate, a pattern similar to our cohort where patients traveling longer distances sought care at higher volume centers.20 A study by Thabut et al. included patients from 2001–2009 in the national transplant registry and measured linear distance from patient residential zip code to the closest lung transplant center. No relationship between distance to nearest transplant center and posttransplant survival was identified.21 Our study builds on this important work by including patients from 2006 through 2017, and measuring patient driving distance from residential zip code to the center where the transplant occurred.
Limitations
The limitations to our study include the self-reported nature of the registry. Patients may move to other zip codes during the posttransplant time period, or may have transferred their care to another transplant center closer to home after a requisite follow-up period, which is in many cases 1 year post transplant, a requirement used at our center. Based on our center experience this occurs in <2% of patients. Some transplant programs may have temporary patient relocation policies so that patients temporarily reside closer to the transplant center in the peritransplant period; though to avoid misinterpretation the transplant registry forms request the patient’s permanent zip code in the data field. Zip codes are limited in that there can be a wide range of socioeconomic levels in a given zip code. Our approach of using zip codes as a marker of geographic location is consistent with other large registry studies assessing patient travel in organ transplantation,7,8 and we took the additional step of leveraging multiple registries to better characterize the patient cohort. Finally, as our methodology focused on driving distances, at a certain threshold some patients may elect to travel by air for clinic visits after considering out of pocket costs. This choice would also be influenced by socioeconomic status.
Implications
In November 2017, the donor lung allocation algorithm in the United States shifted towards sharing donor lungs across a broader geographic region with a goal of decreasing waitlist mortality5. One year since this change some lung transplant centers have experienced an increased number of transplants while others have seen a decrease.22 Our observation that increased patient travel distance followed increased center volume emphasizes the need to continuously evaluate patient travel patterns, which may shift in response to changes in national donor lung allocation policy.
Conclusion
This study used a novel approach in a national cohort of lung transplant patients by measuring patient driving distance from permanent home zip code to lung transplant center, and by linking the transplant registry with other registries to better demonstrate the socioeconomic background of patients. Further work is needed to assess the relationship between travel distance on outcomes such as patient quality of life and financial health. There are also opportunities to leverage the growth of tele-health to reduce patient travel burdens.23 Understanding whether certain populations have disadvantages to accessing organ transplant can inform policies to address disparities. We highlight the role of patient travel distance and its impact on post lung transplant care.
Supplementary Material
Acknowledgments
The data reported here have been supplied by SRTR as the contractor for the Organ Procurement and Transplantation Network. Data use was in accordance with the SRTR data-use agreement. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy of or interpretation by the SRTR.
Dr. Tsuang is funded by a Career Development Award from the National Heart, Lung, and Blood Institute (K23 HL138191-02). The authors have no additional funding sources or relevant disclosures.
Abbreviations:
- BMI
Body Mass Index
- CI
Confidence Interval
- HR
Hazard Ratio
- LAS
Lung Allocation Score
- OR
Odds Ratio
- Ref
Reference value
- SRTR
Scientific Registry of Transplant Recipients
Footnotes
Disclosure
The authors declare no conflicts of interest.
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