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PLOS ONE  2012 

The Impact of Travel Time on Geographic Distribution of Dialysis Patients

DOI: 10.1371/journal.pone.0047753

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Backgrounds The geographic disparity of prevalence rates among dialysis patients is unclear. We evaluate the association between travel time to dialysis facilities and prevalence rates of dialysis patients living in 1,867 census areas of Hiroshima, Japan. Furthermore, we study the effects of geographic features (mainland or island) on the prevalence rates and assess if these effects modify the association between travel time and prevalence. Methods The study subjects were all 7,374 people that were certified as the “renal disabled” by local governments in 2011. The travel time from each patient to the nearest available dialysis facility was calculated by incorporating both travel time and the capacity of all 98 facilities. The effect of travel time on the age- and sex-adjusted standard prevalence rate (SPR) and 95% confidence intervals (CIs) at each census area was evaluated in two-level Poisson regression models with 1,867 census areas (level 1) nested within 35 towns or cities (level 2). The results were adjusted for area-based parameters of socioeconomic status, urbanity, and land type. Furthermore, the SPR of dialysis patients was calculated in each specific subgroup of population for travel time, land type, and combination of land type and travel time. Results In the regression analysis, SPR decreased by 5.2% (95% CI: ?7.9–?2.3) per 10-min increase in travel time even after adjusting for potential confounders. The effect of travel time on prevalence was different in the mainland and island groups. There was no travel time-dependent SPR disparity on the islands. The SPR among remote residents (>30 min from facilities) in the mainland was lower (0.77, 95% CI: 0.71–0.85) than that of closer residents (≤30 min; 0.95, 95% CI: 0.92–0.97). Conclusions The prevalence of dialysis patients was lower among remote residents. Geographic difficulties for commuting seem to decrease the prevalence rate.


[1]  OECD (2011) Treatment of renal failure (dialysis and kidney transplants). In: OECD, editor. Health at a Glance 2011: OECD Indicators: OECD Publishing.
[2]  Klag MJ, Whelton PK, Randall BL, Neaton JD, Brancati FL, et al. (1997) End-stage renal disease in African-American and white men. 16-year MRFIT findings. Jama 277: 1293–1298.
[3]  Whittle JC, Whelton PK, Seidler AJ, Klag MJ (1991) Does racial variation in risk factors explain black-white differences in the incidence of hypertensive end-stage renal disease? Arch Intern Med 151: 1359–1364.
[4]  Maheswaran R, Payne N, Meechan D, Burden RP, Fryers PR, et al. (2003) Socioeconomic deprivation, travel distance, and renal replacement therapy in the Trent Region, United Kingdom 2000: an ecological study. J Epidemiol Community Health 57: 523–524.
[5]  Udayaraj UP, Ben-Shlomo Y, Roderick P, Casula A, Ansell D, et al. (2010) Socio-economic status, ethnicity and geographical variations in acceptance rates for renal replacement therapy in England and Wales: an ecological study. J Epidemiol Community Health 64: 535–541.
[6]  Judge A, Caskey FJ, Welton NJ, Ansell D, Tomson CR, et al. (2011) Inequalities in rates of renal replacement therapy in England: does it matter who you are or where you live? Nephrol Dial Transplant.
[7]  White P, James V, Ansell D, Lodhi V, Donovan KL (2006) Equity of access to dialysis facilities in Wales. QJM 99: 445–452.
[8]  Roderick P, Clements S, Stone N, Martin D, Diamond I (1999) What determines geographical variation in rates of acceptance onto renal replacement therapy in England? J Health Serv Res Policy 4: 139–146.
[9]  Nakai S, Iseki K, Itami N, Ogata S, Kazama J, et al. (2012) [An overview of regular dialysis treatment in Japan (As of December 31, 2010)]. Nihon Toseki Igakkai Zasshi 45: 1–47.
[10]  Japanese Society for Dialysis Therapy (2012) [An overview of regular dialysis treatment in Japan as of Dec 31 2011]. Tokyo: Renal Data Registry Committee, Japanese Society for Dialysis Therapy.
[11]  Fukuhara S, Yamazaki C, Hayashino Y, Higashi T, Eichleay MA, et al. (2007) The organization and financing of end-stage renal disease treatment in Japan. Int J Health Care Finance Econ 7: 217–231.
[12]  Ikegami N, Yoo BK, Hashimoto H, Matsumoto M, Ogata H, et al. (2011) Japanese universal health coverage: evolution, achievements, and challenges. The Lancet 378: 1106–1115.
[13]  Matsumoto M, Inoue K, Farmer J, Inada H, Kajii E (2010) Geographic distribution of primary care physicians in Japan and Britain. Health Place 16: 164–166.
[14]  Imasawa T, Nakazato T (2010) [Status of board-certified nephrologists of the Japanese Society of Nephrology among 47 prefectures]. Nihon Jinzo Gakkai Shi 52: 1015–1021.
[15]  Arbor Research Collaborative for Health 2010 Annual Report of the Dialysis Outcomes and Practice Patterns Study: Hemodialysis Data 1999–2008. Available: Accessed 2012 March 1.
[16]  Disability and Welfare Section Hiroshima City (2004) [A physician’s guide for disability certification]. Hiroshima: Disability and Welfare Section, Hiroshima City.
[17]  Matsumoto M, Ogawa T, Kashima S, Takeuchi K (2012) The impact of rural hospital closures on equity of commuting time for haemodialysis patients: simulation analysis using the capacity-distance model. Int J Health Geogr 11: 28.
[18]  Ministry of Internal Affairs and Communications Japan standard Industrial Classiffication (Rev. 12, November 2007). Available: Accessed 2012 June 24.
[19]  Lawson A, Browne WJ, Vidal Rodeiro CL (2003) Disease mapping with WinBUGS and MLwiN. Chichester, West Sussex, England; Hoboken, NJ: J. Wiley. xiii, 277 p.
[20]  Lawson A (2006) Basic models. Statistical Methods in Spatial Epidemiology. 2nd ed. Chichester, England; Hoboken, NJ: Wiley. 41–66.
[21]  Rasbash J, Browne W, Healy M, Cameron B, Charlton C (2010) MLwiN Version 2.22: Centre for Multilevel Modelling, University of Bristol.
[22]  Japanese Society for Dialysis Therapy (2011) [An overview of regular dialysis treatment in Japan as of Dec 31 2010]. Tokyo: Renal Data Registry Committee, Japanese Society for Dialysis Therapy.
[23]  Greenland S, Rothman KJ (2008) Introduction to stratified analysis. In: Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. 3rd ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins. 258–282.
[24]  Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3: Article3.
[25]  R Development Core Team (2011) R: A Language and Environment for Statistical Computing. 1. Available: Accessed 2012 April 30.
[26]  Moist LM, Bragg-Gresham JL, Pisoni RL, Saran R, Akiba T, et al. (2008) Travel time to dialysis as a predictor of health-related quality of life, adherence, and mortality: the Dialysis Outcomes and Practice Patterns Study (DOPPS). Am J Kidney Dis 51: 641–650.
[27]  Rucker D, Hemmelgarn BR, Lin M, Manns BJ, Klarenbach SW, et al. (2011) Quality of care and mortality are worse in chronic kidney disease patients living in remote areas. Kidney Int 79: 210–217.
[28]  Tonelli M, Manns B, Culleton B, Klarenbach S, Hemmelgarn B, et al. (2007) Association between proximity to the attending nephrologist and mortality among patients receiving hemodialysis. CMAJ 177: 1039–1044.
[29]  Sugisawa H, Shimizu Y, Kumagai T, Oohira S, Sugisaki H, et al. (2010) [Differences in attitudes toward care between hemodialysis patients and their family]. The Journal of Japanese Association of Dialysis Physicians 25: 135–147.
[30]  Untas A, Thumma J, Rascle N, Rayner H, Mapes D, et al. (2011) The associations of social support and other psychosocial factors with mortality and quality of life in the dialysis outcomes and practice patterns study. Clin J Am Soc Nephrol 6: 142–152.


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