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• W2165003884 abstract "Several studies have investigated geographical variations in access to renal transplant waiting lists, but none has assessed the impact on these variations of factors at both the patient and geographic levels. The objective of our study was to identify medical and non-medical factors at both these levels associated with these geographical variations in waiting-list placement in France. We included all incident patients aged 18–80 years in 11 French regions who started dialysis between January 1, 2006, and December 31, 2008. Both a multilevel Cox model with shared frailty and a competing risks model were used for the analyses. At the patient level, old age, comorbidities, diabetic nephropathy, non-autonomous first dialysis, and female gender were the major determinants of a lower probability of being waitlisted. At the regional level, the only factor associated with this probability was an increase in the number of patients on the waiting list from 2005 to 2009. This finding supports a slight but significant impact of a regional organ shortage on waitlisting practices. Our findings demonstrate that patients' age has a major impact on waitlisting practices, even for patients with no comorbidity or disability, whose survival would likely be improved by transplantation compared with dialysis. Several studies have investigated geographical variations in access to renal transplant waiting lists, but none has assessed the impact on these variations of factors at both the patient and geographic levels. The objective of our study was to identify medical and non-medical factors at both these levels associated with these geographical variations in waiting-list placement in France. We included all incident patients aged 18–80 years in 11 French regions who started dialysis between January 1, 2006, and December 31, 2008. Both a multilevel Cox model with shared frailty and a competing risks model were used for the analyses. At the patient level, old age, comorbidities, diabetic nephropathy, non-autonomous first dialysis, and female gender were the major determinants of a lower probability of being waitlisted. At the regional level, the only factor associated with this probability was an increase in the number of patients on the waiting list from 2005 to 2009. This finding supports a slight but significant impact of a regional organ shortage on waitlisting practices. Our findings demonstrate that patients' age has a major impact on waitlisting practices, even for patients with no comorbidity or disability, whose survival would likely be improved by transplantation compared with dialysis. Kidney transplantation (KTx) has been demonstrated to be the most effective renal replacement therapy (RRT) for patients with an end-stage renal disease (ESRD) in terms of life expectancy (1.Wolfe RA Ashby VB Milford EL Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant.N Engl J Med. 1999; 341 (et al): 1725-1730Crossref PubMed Scopus (4051) Google Scholar, 2.Rabbat CG Thorpe KE Russell JD Churchill DN. Comparison of mortality risk for dialysis patients and cadaveric first renal transplant recipients in Ontario, Canada.J Am Soc Nephrol. 2000; 11: 917-922Crossref PubMed Google Scholar, 3.Tonelli M Wiebe N Knoll G Systematic review: Kidney transplantation compared with dialysis in clinically relevant outcomes.Am J Transplant. 2011; 11 (et al): 2093-2109Abstract Full Text Full Text PDF PubMed Scopus (811) Google Scholar, quality of life (4.Maglakelidze N Pantsulaia T Tchokhonelidze I Managadze L Chkhotua A. Assessment of health-related quality of life in renal transplant recipients and dialysis patients.Transplant Proc. 2011; 43: 376-379Crossref PubMed Scopus (82) Google Scholar, 5.Franke GH Reimer J Philipp T Heemann U. Aspects of quality of life through end-stage renal disease.Qual Life Res. 2003; 12: 103-115Crossref PubMed Scopus (77) Google Scholar, 6.Beauger D Gentile S Jouve E Dussol B Jacquelinet C Briançon S. Analysis, evaluation and adaptation of the ReTransQoL: A specific quality of life questionnaire for renal transplant recipients.Health Qual Life Outcomes. 2013; 11: 148Crossref PubMed Scopus (17) Google Scholar, 7.Gentile S Beauger D Speyer E Factors associated with health-related quality of life in renal transplant recipients: Results of a national survey in France.Health Qual Life Outcomes. 2013; 11 (et al): 88Crossref PubMed Scopus (55) Google Scholar, 8.Mellerio H Alberti C Labèguerie M The French working group on the long-term outcome of transplanted children. Adult social and professional outcomes of pediatric renal transplant recipients.Transplantation. 2014; 97 (et al): 196-205Crossref PubMed Scopus (34) Google Scholar, and cost (9.Karopadi AN Mason G Rettore E Ronco C. Cost of peritoneal dialysis and haemodialysis across the world.Nephrol Dial Transplant. 2013; 28: 2553-2569Crossref PubMed Scopus (204) Google Scholar, 10.http://www.agence-biomedecine.fr/About-us, Last consulted on 16 Dec 2013.Google Scholar, 11.Blotière MO Tuppin P Weil A Ricordeau A Allemand H. The cost of dialysis and kidney transplantation in France in 2007, impact of an increase of peritoneal dialysis and transplantation.Nephrol Ther. 2010; 6: 240-247Crossref PubMed Scopus (54) Google Scholar). Despite sustained efforts to improve organ donation and retrieval, supply still fails to cover ever-increasing needs for organs for transplantation in many countries (12.Hauptman J O’Connor K. Procurement and allocation of solid organs for transplantation.New Engl J Med. 1997; 336: 422-431Crossref PubMed Scopus (117) Google Scholar, 13.Third WHO Global Consultation on Organ Donation and Transplantation: Striving to achieve self-sufficiency, March 23–25, 2010, Madrid, Spain. WHO; Transplantation Society (TTS); Organizatión Nacional de Transplantes (ONT). Transplantation 2011; 91 Suppl 11: S27–8.Google Scholar). In this context, the selection of patients for KTx, a process that includes evaluation of eligible patients (indications/contraindications), placement on the waiting list, and organ allocation, is crucial. In France, the Agence de la Biomédecine (AB) is responsible for waiting-list management and organ allocation (14.http://www.agence-biomedecine.fr/Google Scholar). All transplant candidates must be placed on the national waiting list, regardless of whether the donor is expected to be living, deceased, or both. Since 2004, AB has used a patient-based kidney allocation score, which replaced the previous center-based allocation system within each region (15.Jacquelinet C Houssin D. Principles and practice of cadaver organ allocation in France.in: Touraine JL Organ allocation. Kluver, Academic Publishers, 1998: 3-28Crossref Google Scholar, 16.Hourmant M Jacquelinet C Antoine C Hiesse C. The new French rules for the attribution of renal transplants.Nephrol Ther. 2005; 1: 7-13Crossref PubMed Scopus (5) Google Scholar, 17.Jacquelinet C Audry B Golbreich C Changing kidney allocation policy in France: The value of simulation.AMIA Annu Symp Proc. 2006; (et al): 374-378PubMed Google Scholar). The new allocation policy decreased intercenter disparities within allocation regions: when no priority cases exist, one kidney is allocated within the region according to the kidney allocation score (no exception allowed). Nevertheless, the geographical model still allows local priority for the other kidney (with the allocation score used for decision support but exceptions allowed). This geographical model limits national kidney exchanges to national priorities for urgent or hyper-sensitized patients and pediatric recipients. This type of organization is likely to maintain inter-regional disparity in access to kidney transplantation, because of the major geographical variations in ESRD incidence and prevalence, organ recovery, and center practices related to acceptance of extended criteria donors, living donations, and waitlisting. Periodic evaluation of our allocation policy and on-going simulation studies indicate that a new national scoring system would significantly improve the allocation of deceased donor kidneys. But the large regional differences in the prevalence of waitlisted patients prevent any profound change in allocation policy without first assessing waitlisting practices. This prompted us to extend the scope of a previous study of medical and nonmedical determinants of placement on a waiting list for kidney transplantation in the region of Lorraine (23.Bayat S Frimat L Thilly N Loos C Briançon S Kessler M. Medical and non-medical determinants of access to renal transplant waiting list in a French community-based network of care.Nephrol Dial transplant. 2006; 21: 2900-2907Crossref PubMed Scopus (62) Google Scholar). Here we examine these determinants from a multiregional perspective and include additional patient-level and region-level factors. We hypothesize that individual medical and nonmedical factors influence patient waitlisting, but also that regional disparities may be explained by variations in socioeconomic situations, healthcare supply, healthcare needs, and organ shortages. Several studies have shown that selection criteria for potential kidney transplant candidates vary significantly between countries and also between centers in the same country (18.Stel VS van Dijk PC van Manen JG Prevalence of co-morbidity in different European RRT populations and its effect on access to renal transplantation.Nephrol Dial Transplant. 2005; 20 (et al): 2803-2811Crossref PubMed Scopus (51) Google Scholar, 19.Ravanan R Udayaraj U Ansell D Variation between centres in access to renal transplantation in UK: Longitudinal cohort study.BMJ. 2010; 341 (et al): 3451Crossref PubMed Scopus (61) Google Scholar, 20.Fritsche L Vanrenterghem Y Nordal KP Practice variations in the evaluation of adult candidates for cadaveric kidney transplantation: A survey of the European Transplant Centers.Transplantation. 2000; 70 (et al): 1492-1497Crossref PubMed Scopus (35) Google Scholar, 21.Ramos EL Kasiske BL Alexander SR The evaluation of candidates for renal transplantation: the current practice of US transplant centers.Transplantation. 1994; 57 (et al): 490-497Crossref PubMed Scopus (122) Google Scholar, 22.Jeffrey R Akbani H Scally A Peel R. Comparison of transplant listing strategy in two renal dialysis centers within a regional transplant alliance.Clin Nephrol. 2005; 64: 438-443Crossref PubMed Google Scholar). Similarly, a wide variety of patient-level factors have been associated with access to renal transplant waiting lists, including not only medical factors (diabetes, cardiovascular disease and other comorbidities, and causes of ESRD), but also nonmedical factors such as gender and race/ethnicity (23.Bayat S Frimat L Thilly N Loos C Briançon S Kessler M. Medical and non-medical determinants of access to renal transplant waiting list in a French community-based network of care.Nephrol Dial transplant. 2006; 21: 2900-2907Crossref PubMed Scopus (62) Google Scholar, 24.Couchoud C Bayat S Villar E Jacquelinet C Ecochard R. On behalf of the REIN registry. A new approach for measuring gender disparity in access to renal transplantation waiting lists.Transplantation. 2012; 94: 513-519Crossref PubMed Scopus (78) Google Scholar, 25.Dudley CR Johnson RJ Thomas HL Ravanan R Ansell D. Factors that influence access to the national renal transplant waiting list.Transplantation. 2009; 88: 96-102Crossref PubMed Scopus (71) Google Scholar, 26.Wolfe RA Ashby VB Milford EL Differences in access to cadaveric renal transplantation in the United States.Am J Kidney Dis. 2000; 36 (et al): 1025-1033Abstract Full Text Full Text PDF PubMed Scopus (169) Google Scholar, 27.Alexander GC Sehgal AR. Barriers to cadaveric renal transplantation among blacks, women, and the poor.JAMA. 1998; 280: 1148-1152Crossref PubMed Scopus (403) Google Scholar, 28.Sequist TD Narva AS Stiles SK Karp SK Cass A Ayanian JZ. Access to renal transplantation among American Indians and Hispanics.Am J Kidney Dis. 2004; 44: 344-352Abstract Full Text Full Text PDF PubMed Scopus (109) Google Scholar, 29.Satayathum S Pisoni RL McCullough KP Kidney transplantation and wait-listing rates from the international Dialysis Outcomes and Practice Patterns Study (DOPPS).Kidney Int. 2005; 68 (et al): 330-337Abstract Full Text Full Text PDF PubMed Scopus (78) Google Scholar, 30.Oniscu GC Schalkwijk AAH Johnson RJ Brown H Forsythe JLR. Equity of access to renal transplant waiting list and renal transplantation in Scotland: Cohort study.Br Med J. 2003; 327: 1261-1270Crossref PubMed Google Scholar, 31.Villar E Rabilloud M Berthoux F Vialtel P Labeeuw M Pouteil-Noble C. A multicentre study of registration on renal transplantation waiting list of the elderly and patients with type 2 diabetes.Nephrol Dial Transplant. 2004; 19 (Jan): 207-214Crossref PubMed Scopus (30) Google Scholar). Studies have also shown center-level factors (private ownership of dialysis facilities, size of renal unit, and listing practice for living donor transplantation) to be related to access to waiting lists (25.Dudley CR Johnson RJ Thomas HL Ravanan R Ansell D. Factors that influence access to the national renal transplant waiting list.Transplantation. 2009; 88: 96-102Crossref PubMed Scopus (71) Google Scholar, 32.Garg P Frick K Diener-West M Powe NR. Effect of the ownership of dialysis facilities on patients’ survival and referral for transplantation.N Engl J Med. 1999; 341: 1653-1660Crossref PubMed Scopus (194) Google Scholar). Some countries have also observed geographical variations in access to waiting lists (33.Mathur AK Ashby VB Sands RL Wolfe RA. Geographic variation in end-stage renal disease incidence and access to deceased donor kidney transplantation.Am J Transplant. 2010; 10: 1069-1080Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar, 34.Ashby VB Kalbfleisch JD Wolfe RA Lin MJ Port FK Leichtman AB. Geographic variability in access to primary kidney transplantation in the United States, 1996-2005.Am J Transplant. 2007; 7: 1412-1423Abstract Full Text Full Text PDF PubMed Scopus (98) Google Scholar, 35.Chapman J Russ G. Geographic variance in access to renal transplantation in Australia.Transplantation. 2003; 76: 1403-1406Crossref PubMed Scopus (8) Google Scholar). In the United States (US), for example, living in areas with a high incidence of ESRD is associated with poor waiting-list access (33.Mathur AK Ashby VB Sands RL Wolfe RA. Geographic variation in end-stage renal disease incidence and access to deceased donor kidney transplantation.Am J Transplant. 2010; 10: 1069-1080Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar). However, none of these studies has assessed the simultaneous effect of both patient-level and geographic-level factors specifically on access to renal transplant waiting lists. The objectives of this study are (1.Wolfe RA Ashby VB Milford EL Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant.N Engl J Med. 1999; 341 (et al): 1725-1730Crossref PubMed Scopus (4051) Google Scholar to describe geographical (regional) variations in access to the renal transplant waiting list in France; (2.Rabbat CG Thorpe KE Russell JD Churchill DN. Comparison of mortality risk for dialysis patients and cadaveric first renal transplant recipients in Ontario, Canada.J Am Soc Nephrol. 2000; 11: 917-922Crossref PubMed Google Scholar to identify medical and nonmedical patient-level factors and region-level factors associated with geographical variations in waiting-list placement. The renal epidemiology and information network (REIN) registry provides a comprehensive view of ESRD epidemiology in France. It relies on a network of nephrologists, epidemiologists, patients, and public health representatives, coordinated regionally and nationally. REIN is used as a decision support system for public health policy-making, evaluation, and epidemiological research related to ESRD in France. In 2002, it began recording data about ESRD patients treated by dialysis or KTx (36.Couchoud C Stengel B Landais P The renal epidemiology and information network (REIN): A new registry for end-stage renal disease in France.Nephrol Dial Transplant. 2006; 21 (et al): 411-418Crossref PubMed Scopus (207) Google Scholar, 37.Strang WN Tuppin P Atinault A Jacquelinet C. The French organ transplant data system.Stud Health Technol Inform. 2005; 116: 77-82PubMed Google Scholar in each administrative region where all hospitals and dialysis centers treating ESRD agreed to participate. As agreements increased, the registry progressively included more regions and finally reached complete national coverage (France and overseas) in 2011 (Figure 1): 26 administrative regions and 64 million inhabitants. Annual reports from REIN show that placement of dialyzed patients on the waiting list in France is a long process: waiting list access rates reach a plateau only 4 years after dialysis starts (38.Hourmant M de Cornelissen F Brunet P Access to the waiting list and renal transplantation; registre du REIN.Nephrol Ther. 2013; 9 (et al): S139-66Crossref PubMed Scopus (6) Google Scholar). To have a minimum follow-up period of 4 years in a sufficient number of contributing regions, our cohort included all incident patients aged between 18 and 80 years who started dialysis between January 1, 2006 and December 31, 2008 (inclusion period), and the follow-up ran from date they started dialysis to the date they were placed on the waiting list, date of death, or December 31, 2012 (follow-up period). All patients were living in 11 French regions contributing to REIN and exhaustively recorded ESRD patients before 2006: Auvergne, Basse-Normandie, Bourgogne, Bretagne, Champagne-Ardenne, Haute-Normandie, Languedoc Roussillon, Limousin, Midi-Pyrénées, Provence-Alpes-Côte-d'Azur (PACA), and Rhône-Alpes. These regions account for 45% of the French population and are representative of metropolitan (European) France. From the 2006, 2007, and 2008 REIN annual reports, we know that dialysis prevalence was significantly lower than the mean standardized prevalence in seven regions and higher in two regions (Figure 2), while the standardized prevalence ratio did not differ significantly from the mean in the two remaining regions. Regional variations were also observed in the number of donors providing at least one organ (from 15.7 to 39.9 donors per million population (pmp)) (Figure 3), in the number of patients added on the waiting list (from 29.7 to 67.6 patients pmp) and in the number of KTx (from 27.5 to 50.7 pmp) (Table 1).Figure 3Geographic variation of organ recovery from deceased donors in 2007.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Table 1Characteristics of regions included in the studyAdministrative regionTransplant centersPopulation 31/12/2006Incident ESRD dialyzed patients (2006–2008) Diabetics (%)Incident ESRD dialyzed patients (2006–2008) Age (median)Dialysis prevalence pmp 12/31/2007Waitlisting incidence pmp 2007Waitlisting prevalence pmp 12/31/2007KTx incidence pmp 2007Living donor KTx (%) 2007Preemptive KTx (%) 2007AuvergneClermont-Ferrand1 299 11343.367.650852.365.433.711.115.6Basse-NormandieCaen1 446 72027.564.743344.954.642.06.613.1BourgogneDijon1 614 30843.768.248429.744.043.51014.1BretagneRennes, Brest3 006 66227.267.640643.657.938.93.36.7Champagne-ArdenneReims1 330 64742.168.054350.452.630.8207.3Haute-NormandieRouen1 822 06438.365.750036.247.727.5820Languedoc-RoussillonMontpellier2 499 32435.668.469550.8121.645.87.711.1LimousinLimoges724 99839.368.849267.667.649.55.68.3Midi-PyrénéesToulouse2 754 99232.468.955152.386.850.79.25.7Provence-Alpes-Côtes d’AzurMarseille, Nice4 774 19835.168.268842.567.441.37.110.1Rhône-AlpesLyon, Grenoble, Saint-Etienne5 939 32639.867.248958.9114.746.95.610.2pmp: per million population Open table in a new tab pmp: per million population Our study focused on access to the renal transplant waiting list in a cohort of incident dialyzed patients; it included all dialyzed patients, including those who were placed on the waiting list preemptively. It did not include patients with either preemptive or previous KTx and those listed for multiorgan transplant. Data were collected at 2 levels: patient and region. At the patient level, we studied three categories of variables. The first category included demographic data: age (grouped as 18–39, 40–59, 60–69, ≥70 years), gender, and region of residence at first dialysis. The second category covered clinical data at first dialysis. Primary renal disease was categorized into six groups: glomerulonephritis, pyelonephritis, diabetic nephropathy, hypertensive and vascular nephropathy, polycystic kidney disease, and other (other causes and unknown). Comorbidities of interest were cardiovascular disease (coronary artery disease, peripheral vascular disease, congestive heart failure, arrhythmia, aneurysm, and cerebrovascular disease), diabetes, respiratory disease, hepatic disease, active malignancy, and physical or psychiatric disabilities (physical impairment of ambulation, para- or hemi-plegia, blindness, member amputations, and/or psychiatric disorder). The third category included data related to medical follow-up in nephrology centers: ownership of the nephrology facility where the first dialysis was performed (public university center, public non-university center, private not-for-profit, and private for-profit centers), date of first dialysis, autonomous (home and out-center hemodialysis and non-assisted peritoneal dialysis) vs non-autonomous first dialysis, and date of placement on the waiting list. The patient level data were extracted from REIN registry. At the region level, we studied five categories of variables. The first category comprised three socioeconomic indicators, extracted from National Institute of Statistics and Economic Studies (INSEE) (39.http://www.insee.fr/.Google Scholar databases: number of cities with more than 200 000 inhabitants, gross domestic product per capita (€ per inhabitant/year) and disposable household income per capita in 2007. The second category covered health care supply indicators: the numbers of general practitioners and specialists per 100 000 inhabitants in 2007 were provided by the Health Ministry, Department of Research, Studies, Evaluation and Statistics (DREES) (40.http://www.drees.sante.gouv.fr.Google Scholar, and the number of nephrologists per 100 000 inhabitants in 2007 came from statistics obtained from the National College of Physicians (Ordre National des Médecins) (41.www.conseil-national.medecin.fr/.Google Scholar). The number of dialysis and transplantation centers in 2009 and the number of intensive care beds in hospitals with organ procurement activity per 100 000 inhabitants in 2007 were extracted from AB databases. The third category comprised factors related to health care needs: mean prevalence of dialyzed ESRD patients and mean incidence of ESRD patients pmp during the 2005–2009 period. We also included factors likely to influence ESRD incidence: age-adjusted cardiovascular and diabetes mortality rate per 100 000 inhabitants from the 2006–2008 period, obtained from the National Institute of Health and Medical Research (Inserm) statistics (42.http://www.cepidc.inserm.fr/.Google Scholar). The fourth category comprised the mean preemptive KTx incidence pmp during 2006–2008 period. Finally, the fifth category comprised factors related to trends in waiting list changes. To study the course of waiting list from 2005 to 2009, we used a linear regression model to study the slopes for each region of the annual number of patients on the waiting list on each January 1, of grafts implanted, and of patients withdrawn from the list. The outcome of interest was placement on the renal transplant waiting list, regardless of the donor type (living and/or deceased). Death before waitlisting was considered a competing event. Patients preemptively placed on the waiting list before starting dialysis start were considered to be waitlisted at first dialysis. Times to outcomes (death, waiting-list placement) were calculated from the date of first dialysis. Non-waitlisted living patients were censored at the end of the follow-up: December 31, 2012. Regional cumulative incidence rates of placement on the waiting list were assessed with a competing risk model. A first model (M1), which fit a Cox proportional hazards model that defined death as a censored failure, was used to evaluate cause-specific hazard functions. M1 was used to study the impact of patient-level baseline characteristics on waitlisting. A complementary table showing sub-distribution hazards for each competing event is provided in the supplementary material. A second model (M2) used multilevel Cox analyses with shared frailty to study the impact of patient-level and region-level characteristics on waitlisting. Missing data were systematically analyzed first as a modality and kept in the final model if significantly associated with registration probability. A p-value of <0.05 (two-sided) was considered statistically significant. Results are reported as cause-specific hazard ratios (HR) with 95% CIs and p values. Statistical analyses were performed with STATA 11.2 software. The study included 8447 adult patients, aged 18–80 years, who lived and started dialysis in the 11 study regions during the inclusion period (2006–2008). By the end of 2012, 2498 patients (29.6%) had been placed on the renal transplant waiting list, and 3669 (43.4%) censored due to death before waitlisting. Due to the exhaustiveness of the REIN registry, no patient was lost to follow-up. Table 2 shows the cumulative incidence of waiting-list placement at 0, 6, 12, 24, 36, and 48 months after first dialysis in our 11 regions. The rates of patients already on the waiting list when they started dialysis (preemptive registration) varied from 1.4% in Limousin to 6.9% in Bretagne. The probability of being waitlisted 6 months after first dialysis ranged from 5.9% in Auvergne to 15.7% in Bretagne, while at 4 years after first dialysis, it ranged from 24.8% in Provence-Alpes-Côte-d'Azur to 33.7% in Bretagne.Table 2Unadjusted cumulative incidence of waiting-list placement after first dialysis, by regionRegion of residenceCumulative incidence of waiting-list placement (95% CI)M*Month0M6M12M24M36M48Provence Alpes Côte d'Azur2.16.914.721.323.424.8(1.5–2.9)(5.8–8.2)(13.1–16.5)(19.4–23.3)(21.4–25.5)(22.8–26.9)Haute–Normandie2.48.213.519.222.825.2(1.3–3.9)(6.1–10.7)(10.8–16.5)(16.0–22.6)(19.4–26.4)(21.6–28.9)Languedoc-Roussillon4.910.417.523.124.525.6(3.7–6.3)(8.6–12.4)(15.2–19.9)(20.5–25.7)(21.8–27.2)(22.9–28.4)Limousin1.46.617.124.625.126.1(0.4–3.8)(3.8–10.5)(12.3–22.5)(19.0–30.6)(19.5–31.1)(20.3–32.2)Auvergne2.85.913.722.925.726.7(1.5–4.8)(3.8–8.5)(10.6–17.4)(18.9–27.2)(21.5–30.1)(22.4–31.2)Bourgogne3.010.518.624.726.927.5(1.8–4.8)(8.0–13.4)(15.3–22.2)(21.0–28.6)(23.1–30.9)(23.7–31.5)Champagne Ardenne3.29.914.922.326.727.6(1.8–5.2)(7.3–12.9)(11.8–18.5)(18.5–26.3)(22.6–30.9)(23.5–31.9)Basse-Normandie5.411.820.326.528.930.8(3.4–8.0)(8.7–15.3)(16.4–24.5)(22.1–31.0)(24.4–33.5)(26.1–35.5)Midi-Pyrenees3.013.222.327.529.331.4(2.0–4.3)(10.9–15.6)(19.5–25.3)(24.5–30.7)(26.2–32.5)(28.2–34.6)Rhône-Alpes6.613.120.829.131.732.8(5.5–7.9)(11.6–14.8)(18.9–22.7)(27.0–31.2)(29.5–33.9)(30.6–35.0)Bretagne6.915.724.230.633.033.7(5.3–8.8)(13.3–18.4)(21.2–27.3)(27.3–33.8)(29.7–36.4)(30.4–37.0)* Month Open table in a new tab The mean age of the patients was 63.9 ± 14.7 years, and 5358 (63.4%) were men. Among the 8447 patients, 4142 (49%) had cardiovascular comorbidities, 3072 (36.4%) diabetes, 891 (10.6%) an active malignancy, and 819 (9.7%) respiratory disease. About 8.4% of them had physical impairments, and 3.5% psychiatric disorders. Table 3 presents the baseline characteristics of patients included in the study.Table 3Patients' baseline characteristics and association with waiting-list placement (univariate Cox analyses)ChracteristicsNumber (%)% of waitlisted patientsUnivariate Cox analyses8447HR (95% CI)p-valueGenderMale5358 (63.4)29.30.98 (0.9–1.1)0.560Female3089 (36.6)30.01Age (years)18–39585 (6.9)87.7140–592173 (25.7)61.20.49 (0.4–0.5)<0.000160–692026 (24.0)27.90.18 (0.16 -0.2)<0.000170–803663 (43.4)2.50.01 (0.01–0.02)<0.0001DiabetesYes3072 (36.4)14.30.33 (0.3–0.4)<0.0001No5375 (63.6)38.31Number of cardiovascular diseases04305 (51.0)46.08.59 (6.7–11.0)<0.000111795 (21.2)18.32.95 (2.2–3.8)<0.000121190 (14.1)10.31.69 (1.3–2.3)<0.001≥ 31157 (13.7)5.81Respiratory diseaseYes819 (9.7)9.40.28 (0.2–0.4)<0.0001No7628 (90.3)31.71Active malignancyYes891 (10.6)8.60.27 (0.2–0.3)<0.0001No7556 (89.4)32.01Hepatic diseaseYes184 (2.2)13.00.44 (0.3–0.7)<0.0001No8263 (97.8)29.91Number of physical or psychiatric disabilities06653 (78.8)34.83.90 (3.3–4.7)<0.0001≥ 11507 (17.8)8.41Missing287 (3.4)20.92.36 (1.7–3.2)<0.0001Primary renal diseasePolycystic kidney disease658 (7.8)71.61Diabetic1925 (22.8)15.70.15 (0.1–0.2)<0.0001Pyelonephritis382 (4.5)38.50.43 (0.4–0.5)<0.0001Others2445 (28.9)28.90.31 (0.3–0.4)<0.0001Hypertensive and vascular1967 (23.3)15.50.15(0.1–0.2)<0.0001Glomerulonephritis1070 (12.7)53.00.64 (0.6–0.7)<0.0001Ownership of nephrology facilityPublic non-university center2531 (30.0)26.51Public university center (performing KTx)1978 (23.4)26.31.04 (0.9–1.2)0.482Private for-profit center2032 (24.0)24.50.90 (0.8–1.0)0.082Private not-for- profit center1906 (22.6)42.41.75 (1.6–1.9)<0.0001First dialysis sessionNon" @default.
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• W2165003884 date "2015-04-01" @default.
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• W2165003884 title "Individual and Regional Factors of Access to the Renal Transplant Waiting List in France in a Cohort of Dialyzed Patients" @default.
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• W2165003884 cites W1983772379 @default.
• W2165003884 cites W1984190380 @default.
• W2165003884 cites W1992682240 @default.
• W2165003884 cites W1998418254 @default.
• W2165003884 cites W2002955575 @default.
• W2165003884 cites W2006229210 @default.
• W2165003884 cites W2025323510 @default.
• W2165003884 cites W2026399893 @default.
• W2165003884 cites W2034772080 @default.
• W2165003884 cites W2039905279 @default.
• W2165003884 cites W2044403270 @default.
• W2165003884 cites W2049695163 @default.
• W2165003884 cites W2052055638 @default.
• W2165003884 cites W2056770992 @default.
• W2165003884 cites W2075082665 @default.
• W2165003884 cites W2075789341 @default.
• W2165003884 cites W2081872797 @default.
• W2165003884 cites W2089407538 @default.
• W2165003884 cites W2093758449 @default.
• W2165003884 cites W2099490854 @default.
• W2165003884 cites W2104656551 @default.
• W2165003884 cites W2109968192 @default.
• W2165003884 cites W2134583363 @default.
• W2165003884 cites W2135354390 @default.
• W2165003884 cites W2136451458 @default.
• W2165003884 cites W2142815242 @default.
• W2165003884 cites W2146888586 @default.
• W2165003884 cites W2149490397 @default.
• W2165003884 cites W2165557575 @default.
• W2165003884 cites W2167319953 @default.
• W2165003884 cites W2171720784 @default.
• W2165003884 cites W2186000797 @default.
• W2165003884 cites W2321773089 @default.
• W2165003884 cites W2323824031 @default.
• W2165003884 cites W2336715442 @default.
• W2165003884 cites W2338016958 @default.
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