{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T02:13:52Z","timestamp":1783390432487,"version":"3.54.6"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ther Apher Dial"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post\u2010peak phase or whether recent declines partly reflect pandemic\u2010related disruptions remains uncertain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed nationwide data from the Japanese Society for Dialysis Therapy Renal Data Registry (1983\u20132022) to project dialysis prevalence and incidence through 2050. Seven forecasting approaches were implemented, including classical time\u2010series models, demographic regression models, machine\u2010learning algorithms, and a performance\u2010weighted ensemble. Models were trained on 1983\u20132017 data and validated against a holdout period (2018\u20132022) encompassing the historical peak in dialysis prevalence in order to forecast accuracy using RMSE, MAE, and MAPE.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Dialysis prevalence increased from 52\u2009505 patients in 1983 to a peak of 349\u2009700 in 2021, followed by a decline in 2022, though the contribution of COVID\u201019\u2010related excess mortality to this inflection cannot be excluded. During validation, the ensemble model demonstrated the highest predictive accuracy for prevalence and incidence (MAPE 0.60% and 0.48% respectively). Individual models produced divergent long\u2010term trajectories, ranging from substantial decline to continued growth. The ensemble model projected a gradual decrease in dialysis prevalence to approximately 322\u2009000 patients by 2050 (\u22127.2% vs. 2022) and a 24.4% reduction in annual dialysis incidence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Dialysis prevalence in Japan shows signs of a potential shift toward a downward trend with future burden increasingly driven by demographic change rather than rising incidence. Ensemble forecasting grounded in empirical validation provides balanced projections and highlights substantial uncertainty across modeling paradigms. Japan's experience offers important insights for other aging societies planning future kidney care services.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/1744-9987.70183","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T01:18:02Z","timestamp":1783387082000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Forecasting the Dialysis Burden in Japan: Validation\u2010Based Projections of Prevalence and Incidence Through 2050"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7146-4828","authenticated-orcid":false,"given":"Hatice","family":"\u015eahin","sequence":"first","affiliation":[{"name":"Department of Nephrology Etlik City Hospital  Ankara T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0834-900X","authenticated-orcid":false,"given":"Berrak It\u0131r","family":"Ayl\u0131","sequence":"additional","affiliation":[{"name":"University of Westminster  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8004-4342","authenticated-orcid":false,"given":"N\u00fcket","family":"Paksoy","sequence":"additional","affiliation":[{"name":"Department of Public Health Hacettepe University Faculty of Medicine  Ankara T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(14)61601\u20109"},{"key":"e_1_2_11_3_1","volume-title":"ERA Registry Annual Report 2023","author":"ERA 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