{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T10:32:22Z","timestamp":1774780342726,"version":"3.50.1"},"reference-count":31,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T00:00:00Z","timestamp":1532649600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dialysis Clinic, Inc"},{"name":"National Institutes of Health through grant","award":["UL1 TR001860"],"award-info":[{"award-number":["UL1 TR001860"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Medicare is one of the world\u2019s largest health insurance programs. It provides health insurance to nearly 44 million beneficiaries whose entitlements are based on age, disability, or end-stage renal disease (ESRD). Data of these ESRD beneficiaries are collected in the US Renal Data System (USRDS), which includes comorbidity information entered at the time of dialysis initiation (medical evidence data), and are used to shape health care policy. One limitation of USRDS data is the lack of validation of these medical evidence comorbidities against other comorbidity data sources, such as medical claims data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We examined the potential for discordance between USRDS Medical Evidence and medical claims data for 11 comorbid conditions amongst Medicare beneficiaries in 2011\u20132013 via sensitivity, specificity, kappa and hierarchical logistic regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 61,280 patients, most comorbid conditions recorded on the Medical Evidence forms showed high specificity (&gt;0.9), compared to prior medical claims as reference standard. However, both sensitivity and kappa statistics varied greatly and tended to be low (most &lt;0.5). Only diabetes appeared accurate, whereas tobacco use and drug dependence showed the poorest quality (sensitivity and kappa &lt;0.1). Institutionalization and patient region of residency were associated with data discordance for six and five comorbidities out of 11, respectively, after conservative adjustment of multiple testing. Discordance appeared to be non-informative for congestive heart failure but was most varied for drug dependence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We conclude that there is no improvement in comorbidity data quality in incident ESRD patients over the last two decades. Since these data are used in case-mix adjustment for outcome and quality of care metrics, the findings in this study should press regulators to implement measures to improve the accuracy of comorbidity data collection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj.5284","type":"journal-article","created":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T03:53:34Z","timestamp":1532663614000},"page":"e5284","source":"Crossref","is-referenced-by-count":7,"title":["Data concordance between ESRD Medical Evidence Report and Medicare claims: is there any improvement?"],"prefix":"10.7717","volume":"6","author":[{"given":"Yi","family":"Mu","sequence":"first","affiliation":[{"name":"Office of Population Health and Accountable Care, UCSF Medical Center, University of California, San Francisco, San Francisco, CA, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew I.","family":"Chin","sequence":"additional","affiliation":[{"name":"Division of Nephrology, University of California, Davis School of Medicine, University of California, Davis, Sacramento, CA, United States of America"},{"name":"Division of Nephrology, Sacramento VA Medical Center, VA Northern California Health Care Systems, Mather Field, CA, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhijit V.","family":"Kshirsagar","sequence":"additional","affiliation":[{"name":"UNC Kidney Center and Division of Nephrology and Hypertension, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heejung","family":"Bang","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis, CA, United States of America"},{"name":"Center for Healthcare Policy and Research, Davis School of Medicine, University of California, Davis, Sacramento, CA, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"4443","published-online":{"date-parts":[[2018,7,27]]},"reference":[{"key":"10.7717\/peerj.5284\/ref-1","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1089\/pop.2014.0158","article-title":"Socioeconomic status of countries where dialysis clinics are located is an important factor in comparing dialysis providers","volume":"19","author":"Almachraki","year":"2016","journal-title":"Population Health Management"},{"key":"10.7717\/peerj.5284\/ref-2","article-title":"Statistical issues in assessing hospital performance","author":"Ash","year":"2012"},{"key":"10.7717\/peerj.5284\/ref-3","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1097\/01.mlr.0000223475.70440.07","article-title":"In search of the perfect comorbidity measure for use with administrative claims data: does it exist?","volume":"44","author":"Baldwin","year":"2006","journal-title":"Medical Care"},{"key":"10.7717\/peerj.5284\/ref-4","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1377\/hlthaff.2015.0394","article-title":"Accounting for patients\u2019 socioeconomic status does not change hospital readmission rates","volume":"35","author":"Bernheim","year":"2016","journal-title":"Health Affairs"},{"key":"10.7717\/peerj.5284\/ref-5","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/S0272-6386(12)80098-9","article-title":"Validation of the cause of renal failure of patients in the Medicare end-stage renal disease program","volume":"18","author":"Byrne","year":"1991","journal-title":"American Journal of Kidney Diseases"},{"key":"10.7717\/peerj.5284\/ref-6","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/0895-4356(93)90018-V","article-title":"Bias, prevalence and kappa","volume":"46","author":"Byrt","year":"1993","journal-title":"Journal of Clinical Epidemiology"},{"key":"10.7717\/peerj.5284\/ref-7","article-title":"Centers for Medicare & Medicaid Services. 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