{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T21:25:59Z","timestamp":1793827559076,"version":"4.6.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R56AG084578"],"award-info":[{"award-number":["R56AG084578"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The Centers for Medicare &amp; Medicaid Services (CMS) Chronic Conditions Data Warehouse (CCW) algorithms are widely used to identify chronic conditions in Medicare fee-for-service (FFS) claims. However, their validity in Medicare Advantage (MA) encounters remains uncertain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a cross-sectional study using the National Health and Aging Trends Study (NHATS) linked to Medicare data. We analyzed NHATS rounds 9 and 11, aged \u2265 65\u2009years, with continuous 24-month enrollment in MA or FFS. Chronic Conditions Data Warehouse algorithms were applied to identify 6 conditions: arthritis, chronic lung disease, diabetes, heart disease, hypertension, and osteoporosis. Self-reported, physician-diagnosed conditions from NHATS interviews served as the reference standard. Survey-weighted sensitivity, specificity, positive predictive value (PPV), negative predictive value, and balanced accuracy were compared between MA and FFS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We included 1708 MA and 2239 FFS beneficiaries in round 9 and 1472 MA and 1566 FFS beneficiaries in round 11. Across both rounds, balanced accuracy was similar between MA and FFS for all 6 conditions. Diabetes and hypertension demonstrated high sensitivity (0.88-0.94), while arthritis and osteoporosis showed lower sensitivity but high specificity. Although statistically significant MA\u2013FFS differences were observed for select metrics (diabetes PPV in round 9; chronic lung disease specificity in round 11), absolute differences were minor and not clinically meaningful.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Chronic Conditions Data Warehouse algorithms perform comparably in MA and FFS Medicare data. These findings support the validity of using CCW algorithms in MA encounter data for epidemiologic research for risk adjustment, facilitating more generalizable studies of the growing MA population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/gerona\/glag230","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T12:04:39Z","timestamp":1789473879000},"source":"Crossref","is-referenced-by-count":0,"title":["Performance of Chronic Conditions Data Warehouse algorithms in Medicare Advantage and fee-for-service populations"],"prefix":"10.1093","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1880-048X","authenticated-orcid":false,"given":"Chan Mi","family":"Park","sequence":"first","affiliation":[{"name":"Frailty Research Center, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife , Boston, Massachusetts,","place":["United States"]},{"name":"Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiecheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Frailty Research Center, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife , Boston, Massachusetts,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6801-5602","authenticated-orcid":false,"given":"Sandra","family":"Shi","sequence":"additional","affiliation":[{"name":"Frailty Research Center, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife , Boston, Massachusetts,","place":["United States"]},{"name":"Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts,","place":["United 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