{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T20:40:40Z","timestamp":1777927240991,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Perit Dial Int"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chronic kidney disease and end-stage renal disease (ESRD) significantly burden the U.S. healthcare system. Despite its benefits, such as cost savings and increased autonomy, peritoneal dialysis (PD) is underutilized. This study examines how state and ESRD network-level variations impact PD utilization across the United States, addressing a gap in previous investigations that have not fully disentangled the effects of measurable patient-level factors from harder-to-capture influences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed publicly available data from the United States Renal Data System, Centers for Medicare and Medicaid Services, and the U.S. Census Bureau covering 50 states and two territories (2015\u20132020). Multi-level Beta regression models assessed how state-level predictors are associated with PD utilization, accounting for state and network-level variations. Ranked random effects were compared to 2020 PD rates to identify overperforming or underperforming regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Between 2015 and 2020, PD utilization rose from 9.6% to 12.8%. The proportions of incident dialysis patients who were male,\u2009\u2265\u200965 years, and White, respectively, and dialysis facilities per 100,000 people did not significantly affect PD utilization, whereas population density was significantly negatively associated. The final model revealed that unmeasured variations in PD utilization were significantly explained by both state (intraclass correlation coefficient (ICC)\u2009=\u20090.47) and network (ICC\u2009=\u20090.53) factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The chosen demographic, provider, and geographical factors explain only 18.5% of PD utilization. About half of the remaining variation resides at the state level and half at the ESRD network level, verifying the importance of unmeasured factors at both levels. We provide adjusted PD utilization rankings\u2014identifying overperforming and underperforming states and networks\u2014where future research can identify disparate effective and ineffective regional policies with the aim of optimizing PD uptake.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/08968608251370857","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T13:32:28Z","timestamp":1756992748000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["State- and end-stage renal disease-network determinants of peritoneal dialysis utilization in the United States: A multi-level Beta-regression analysis to State- and end-stage renal disease-network determinants of peritoneal dialysis utilization in the United States"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4655-6707","authenticated-orcid":false,"given":"George","family":"Miller","sequence":"first","affiliation":[{"name":"Imperial College London"},{"name":"Clinical Affairs, Medical Components, Inc., Harleysville, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1834-5878","authenticated-orcid":false,"given":"Ahmad","family":"Alkhatib","sequence":"additional","affiliation":[{"name":"Imperial College London"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2018.12.033"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1681\/ASN.2006101130"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/nep.14024"},{"key":"e_1_3_3_5_2","unstructured":"Annual Data Report. USRDS https:\/\/usrds-adr.niddk.nih.gov\/2024\/end-stage-renal-disease\/11-international-comparisons."},{"key":"e_1_3_3_6_2","unstructured":"United States Renal Data System. 2024 USRDS Annual Data Report: Epidemiology of kidney disease in the United States. National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases Bethesda MD 2024."},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.23876\/j.krcp.20.144"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2019.12.001"},{"key":"e_1_3_3_9_2","unstructured":"End-Stage Renal Disease Treatment Choices (ETC) Model Fact Sheet. Centers for Medicare & Medicaid Services www.cms.gov\/newsroom\/fact-sheets\/end-stage-renal-disease-treatment-choices-etc-model-fact-sheet."},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.37526\/1526-744X.2021.48.1.11"},{"key":"e_1_3_3_11_2","unstructured":"Advancing American Kidney Health. U.S. Department of Health and Human Services aspe.hhs.gov\/sites\/default\/files\/private\/pdf\/262046\/AdvancingAmericanKidneyHealth.pdf."},{"key":"e_1_3_3_12_2","unstructured":"Kidney Care Choices (KCC) Model. Centers for Medicare & Medicaid Services www.cms.gov\/priorities\/innovation\/innovation-models\/kidney-care-choices-kcc-model."},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1046\/j.1525-139X.1999.99062.x"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.00080110"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1111\/sdi.12868"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.23958\/ijirms\/vol06-i07\/1156"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.898650"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.3747\/pdi.2016.00025"},{"key":"e_1_3_3_19_2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1681\/ASN.20203110S1415b","article-title":"Geographic variation of home dialysis utilization in the United States","volume":"31","author":"Weinhandl ED","year":"2020","unstructured":"Weinhandl ED, Gilbertson DT, Wetmore JB, et al. Geographic variation of home dialysis utilization in the United States. J Am Soc Nephrol 2020; 31: 415\u2013415.","journal-title":"J Am Soc Nephrol"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1681\/ASN.20203110S1416b"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1466-7657.2011.00913.x"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1681\/ASN.20223311S1924a"},{"key":"e_1_3_3_23_2","first-page":"756","article-title":"Where we come from and where we are going in terms of peritoneal dialysis: identifying barriers and strategies for the future","volume":"34","author":"Moreiras-Plaza M","year":"2014","unstructured":"Moreiras-Plaza M. Where we come from and where we are going in terms of peritoneal dialysis: identifying barriers and strategies for the future. Nefrologia 2014; 34: 756\u2013767.","journal-title":"Nefrologia"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.34067\/KID.0004762021"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.0000000000000274"},{"key":"e_1_3_3_26_2","unstructured":"ESRD Network Organizations. CMS www.cms.gov. https:\/\/www.cms.gov\/training-education\/open-door-forums\/end-stage-renal-disease-clinical-laboratories-esrd\/network."},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.01450212"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2021.02.117"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1111\/hdi.12579"},{"key":"e_1_3_3_30_2","unstructured":"ESRD Incident Count | USRDS. NIDDK. National Institute of Diabetes and Digestive and Kidney Diseases https:\/\/www.niddk.nih.gov\/about-niddk\/strategic-plans-reports\/usrds\/data-query-tools\/esrd-incident-count."},{"key":"e_1_3_3_31_2","unstructured":"PQDC data.cms.gov. https:\/\/data.cms.gov\/provider-data\/topics\/dialysis-facilities."},{"key":"e_1_3_3_32_2","unstructured":"National Population Totals and Components of Change: 2020\u20132022. Census.gov https:\/\/www.census.gov\/data\/datasets\/time-series\/demo\/popest\/2020s-national-total.html."},{"key":"e_1_3_3_33_2","unstructured":"State Area Measurements and Internal Point Coordinates. The United States Census Bureau https:\/\/www.census.gov\/geographies\/reference-files\/2010\/geo\/state-area.html (2010)."},{"key":"e_1_3_3_34_2","unstructured":"State Population Totals: 2010\u20132019. The United States Census Bureau https:\/\/www.census.gov\/data\/tables\/time-series\/demo\/popest\/2010s-state-total.html (30 December 2019)."},{"key":"e_1_3_3_35_2","unstructured":"ESRD NCC esrdncc.org. https:\/\/esrdncc.org\/en\/."},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.32614\/RJ-2017-066"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1080\/0266476042000214501"},{"key":"e_1_3_3_38_2","volume-title":"Data analysis using regression and multilevel\/hierarchical models","author":"Gelman A","year":"2007","unstructured":"Gelman A, Hill J. Data analysis using regression and multilevel\/hierarchical models. Cambridge: Cambridge University Press, 2007."}],"container-title":["Peritoneal Dialysis International: Journal of the International Society for Peritoneal Dialysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/08968608251370857","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/08968608251370857","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/08968608251370857","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:38:37Z","timestamp":1777675117000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/08968608251370857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,4]]},"references-count":37,"alternative-id":["10.1177\/08968608251370857"],"URL":"https:\/\/doi.org\/10.1177\/08968608251370857","relation":{},"ISSN":["0896-8608","1718-4304"],"issn-type":[{"value":"0896-8608","type":"print"},{"value":"1718-4304","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,4]]},"article-number":"08968608251370857"}}