{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T21:03:28Z","timestamp":1781211808290,"version":"3.54.1"},"reference-count":38,"publisher":"The Journal of Health Economics and Outcomes Research","issue":"1","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["jheor.org"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec id=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Real-world evidence studies increasingly rely on integrated data resources that combine Medicare fee-for-service claims with electronic medical records (EMRs), laboratory results, and patient-reported outcomes. The Medicare-Enhanced Laboratory and Demographics (MELD\u2122) dataset, developed by Columbia Data Analytics, spans 2020 through 2024 and contains 60 million unique patients, roughly 1 billion visits, and more than 241\u2009000 clinicians. Its data-quality properties must be characterized before regulatory-grade use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To provide a preregistered, multidomain validation of MELD, quantifying internal consistency, concurrent validity against Centers for Medicare &amp; Medicaid Services (CMS) benchmarks, construct validity, record-linkage quality, missing-data mechanisms, temporal stability, and predictive validity across 20 indication cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An analytic cohort of 32\u2009118\u2009604 patients with at least 12 months of continuous Medicare enrollment was derived. Internal consistency used Cronbach\u2019s \u03b1 and Kuder-Richardson KR-20. Concurrent validity compared MELD with CMS benchmarks using bias, mean absolute percentage error, Lin\u2019s concordance correlation coefficient, and Bland-Altman limits of agreement. Construct validity used exploratory and confirmatory factor analysis. Record linkage used Fellegi-Sunter probabilistic matching. Predictive validity was assessed for 12-month mortality and 30-day readmission using the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration slope\/intercept.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Internal consistency was high (Cronbach\u2019s \u03b1, 0.87; KR-20, 0.84). MELD replicated CMS prevalence with mean bias, \u22121.32 per 1000; Lin\u2019s concordance correlation coefficient, 0.993 (95% confidence interval, 0.986-0.997); and mean absolute percentage error, 3.1%. The 3-factor confirmatory model achieved excellent fit (comparative fit index, 0.962; root mean square error of approximation, 0.041; standardized root mean square residual, 0.036). Fellegi-Sunter linkage produced a 94.7% match rate with false-match probability &lt;0.3%. Weighted \u03ba between International Classification of Diseases, Tenth Revision claims and EMR problem lists ranged 0.66 to 0.91. AUROC was 0.821 for mortality and 0.783 for readmission, with calibration slopes 0.97 and 0.94.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>MELD demonstrated strong psychometric, epidemiologic, and predictive properties, supporting use in health economics and outcomes research for Medicare-aged and adjacent populations. Limitations include residual missingness of race\/ethnicity (26%) and EMR fragmentation outside networked provider groups.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.36469\/001c.162896","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T20:11:23Z","timestamp":1781208683000},"update-policy":"https:\/\/doi.org\/10.36469\/00001","source":"Crossref","is-referenced-by-count":0,"title":["Validation of the Medicare-Enhanced Laboratory and Demographics (MELD\u2122) Dataset: A Comprehensive Psychometric, Epidemiologic, and Predictive-Utility Assessment of a 60-Million-Patient Real-World Evidence Resource"],"prefix":"10.36469","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7447-5672","authenticated-orcid":true,"given":"Onur","family":"Baser","sequence":"first","affiliation":[{"name":"City University of New York, New York, NY","department":["Graduate School of Public Health"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"22273","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"ref1","article-title":"FollowMyHealth Patient Portal","author":"National Institute of Health (NIH)"},{"issue":"8","key":"ref2","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1016\/j.jval.2017.08.3019","article-title":"Good practices for real-world data studies of treatment and\/or comparative effectiveness: Recommendations from the joint ISPOR-ISPE Special Task Force on Real-World Evidence in Health Care Decision Making","volume":"20","author":"M. L. Berger","year":"2017","journal-title":"Value Health"},{"issue":"1","key":"ref3","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1002\/pds.3891","article-title":"Guidelines for good pharmacoepidemiology practice","volume":"25","author":"Public Policy Committee, International Society of Pharmacoepidemiology","year":"2016","journal-title":"Pharmacoepidemiol Drug Saf"},{"issue":"10","key":"ref4","doi-asserted-by":"publisher","first-page":"e1001885","DOI":"10.1371\/journal.pmed.1001885","article-title":"The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement","volume":"12","author":"E. I. Benchimol","year":"2015","journal-title":"PLoS Med"},{"key":"ref5","article-title":"American Community Survey 5-Year Estimates, 2019-2023","author":"United States Census Bureau"},{"issue":"3","key":"ref6","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/BF02310555","article-title":"Coefficient alpha and the internal structure of tests","volume":"16","author":"L. J. Cronbach","year":"1951","journal-title":"Psychometrika"},{"issue":"3","key":"ref7","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/BF02288391","article-title":"The theory of the estimation of test reliability","volume":"2","author":"G. F. Kuder","year":"1937","journal-title":"Psychometrika"},{"issue":"1","key":"ref8","doi-asserted-by":"publisher","first-page":"255","DOI":"10.2307\/2532051","article-title":"A concordance correlation coefficient to evaluate reproducibility","volume":"45","author":"L. I. Lin","year":"1989","journal-title":"Biometrics"},{"issue":"8476","key":"ref9","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","article-title":"Statistical methods for assessing agreement between two methods of clinical measurement","volume":"1","author":"J. M. Bland","year":"1986","journal-title":"Lancet"},{"issue":"1","key":"ref10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10705519909540118","article-title":"Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives","volume":"6","author":"L. Hu","year":"1999","journal-title":"Struct Equ Modeling"},{"key":"ref11","article-title":"Framework for FDA's Real-World Evidence Program","author":"U.S. Food and Drug Administration","year":"2018"},{"issue":"25","key":"ref12","doi-asserted-by":"publisher","first-page":"3083","DOI":"10.1002\/sim.3697","article-title":"Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples","volume":"28","author":"P. C. Austin","year":"2009","journal-title":"Stat Med"},{"issue":"260","key":"ref13","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1080\/01621459.1952.10483446","article-title":"A generalization of sampling without replacement from a finite universe","volume":"47","author":"D. G. Horvitz","year":"1952","journal-title":"J Am Stat Assoc"},{"key":"ref14","article-title":"21st Century Cures Act, Pub. L. No. 114-255, 130 Stat. 1033 (December 13, 2016)"},{"issue":"404","key":"ref15","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1080\/01621459.1988.10478722","article-title":"A test of missing completely at random for multivariate data with missing values","volume":"83","author":"R. J. A. Little","year":"1988","journal-title":"J Am Stat Assoc"},{"issue":"328","key":"ref16","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1080\/01621459.1969.10501049","article-title":"A theory for record linkage","volume":"64","author":"I. P. Fellegi","year":"1969","journal-title":"J Am Stat Assoc"},{"issue":"4","key":"ref17","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1002\/sim.4067","article-title":"Multiple imputation using chained equations: Issues and guidance for practice","volume":"30","author":"I. R. White","year":"2011","journal-title":"Stat Med"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316696","article-title":"Multiple Imputation for Nonresponse in Surveys","author":"D. B. Rubin","year":"1987"},{"key":"ref19","article-title":"Overview of Record Linkage and Current Research Directions","author":"W. E. Winkler","year":"2006"},{"issue":"5-7","key":"ref20","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1002\/sim.4780140510","article-title":"Probabilistic linkage of large public health data files","volume":"14","author":"M. A. Jaro","year":"1995","journal-title":"Stat Med"},{"issue":"7","key":"ref21","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1001\/jama.2015.19417","article-title":"Integrating predictive analytics into high-value care","volume":"315","author":"R. B. Parikh","year":"2016","journal-title":"JAMA"},{"issue":"1","key":"ref22","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"J. R. Landis","year":"1977","journal-title":"Biometrics"},{"issue":"3","key":"ref23","doi-asserted-by":"publisher","first-page":"245","DOI":"10.2307\/1907187","article-title":"Nonparametric tests against trend","volume":"13","author":"H. B. Mann","year":"1945","journal-title":"Econometrica"},{"issue":"1","key":"ref24","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"R. Tibshirani","year":"1996","journal-title":"J R Stat Soc B"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: A scalable tree boosting system","author":"T. Chen","year":"2016","journal-title":"Proceedings of the 22nd ACM SIGKDD International Conference"},{"issue":"1","key":"ref26","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1097\/00005650-199801000-00004","article-title":"Comorbidity measures for use with administrative data","volume":"36","author":"A. Elixhauser","year":"1998","journal-title":"Med Care"},{"issue":"13","key":"ref27","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1002\/sim.1802","article-title":"Overall C as a measure of discrimination in survival analysis","volume":"23","author":"M. J. Pencina","year":"2004","journal-title":"Stat Med"},{"issue":"1","key":"ref28","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the performance of prediction models: a framework for traditional and novel measures","volume":"21","author":"E. W. Steyerberg","year":"2010","journal-title":"Epidemiology"},{"key":"ref29","article-title":"Modern Epidemiology","author":"K. J. Rothman","year":"2008","edition":"3"},{"issue":"2","key":"ref30","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10742-009-0047-1","article-title":"Using the Census Bureau's surname list to improve estimates of race\/ethnicity","volume":"9","author":"M. N. Elliott","year":"2009","journal-title":"Health Serv Outcomes Res Methodol"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"574","DOI":"10.3233\/978-1-61499-564-7-574","article-title":"Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers","volume":"216","author":"G. Hripcsak","year":"2015","journal-title":"Stud Health Technol Inform"},{"issue":"23","key":"ref32","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1056\/NEJMsb1609216","article-title":"Real-world evidence \u2014 what is it and what can it tell us?","volume":"375","author":"R. E. Sherman","year":"2016","journal-title":"N Engl J Med"},{"key":"ref33","article-title":"Master Beneficiary Summary File (MBSF), Research Identifiable File","author":"Centers for Medicare & Medicaid Services","year":"2024"},{"key":"ref34","article-title":"Inflation Reduction Act of 2022, Pub. L. No. 117-169, 136 Stat. 1818 (August 16, 2022)."},{"key":"ref35","article-title":"Medicare Drug Price Negotiation Program: Negotiated Prices for Initial Price Applicability Year 2026","author":"Centers for Medicare & Medicaid Services","year":"2024"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1038\/s41746-021-00542-0","article-title":"Broadening the reach of the FDA Sentinel system","volume":"4","author":"R. J. Desai","year":"2021","journal-title":"Digit Med"},{"issue":"3","key":"ref37","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1002\/cpt.320","article-title":"The FDA's Sentinel Initiative","volume":"99","author":"R. Ball","year":"2016","journal-title":"Clin Pharmacol Ther"},{"issue":"1","key":"ref38","first-page":"1203","article-title":"A general framework for considering selection bias in EHR-based studies","volume":"4","author":"S. 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