{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T05:26:57Z","timestamp":1786685217741,"version":"build-2736575974"},"reference-count":50,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AG060967"],"award-info":[{"award-number":["R01AG060967"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["agsjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J American Geriatrics Society"],"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n Accurate identification of Alzheimer's disease and related dementias (ADRD) in administrative data is increasingly important for research, care delivery innovation, and policy applications such as risk adjustment. Despite the widespread use of existing claims\u2010based algorithms to identify ADRD, few studies have compared their use to ongoing longitudinal aging studies.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this cross\u2010sectional study, we included 2204 NHATS respondents aged 65\u2009years or older classified as having dementia and enrolled in fee\u2010for\u2010service Medicare in 2019. We determined the performance characteristics of 3\u2010claims\u2010based dementia algorithms using NHATS as a reference standard.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Among NHATS respondents classified as having \u201cprobable\u201d dementia, sensitivity was generally low (48.8%\u201370.5%), while specificity was high (91.5%\u201397.8%) across all 3 algorithms. The Chronic Conditions Warehouse (CCW) algorithm had the highest sensitivity (70.5, 95% CI: 64.3\u201376.6), followed by Bynum\u2010Standard 3\u2010year (65.8, 95% CI: 59.6\u201371.9). Bynum\u2010Standard 1\u2010year had the highest PPV (65.6, 95% CI: 56.9\u201374.4). The Youden Index and F1 score were generally low and clustered closely across the three algorithms. Odds of ADRD misclassification differed across patient characteristics.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Commonly used claims\u2010based algorithms to identify ADRD had reasonable validity, though each presented important context\u2010dependent tradeoffs and none was uniformly superior. These findings highlight the importance of accurately diagnosing ADRD in clinical settings and of understanding each algorithm's strengths and limitations based on the use case. Limitations include that NHATS is not a \u201cgold standard\u201d for comparison of claims\u2010based algorithms and we only analyzed NHATS respondents in a single year.<\/jats:p>\n <\/jats:sec>","DOI":"10.1111\/jgs.70650","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:31:29Z","timestamp":1786681889000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of Claims\u2010Based Algorithms for Dementia Identification: A Cross\u2010Sectional Analysis of the National Health and Aging Trends Study (\n NHATS<\/scp>\n )"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5902-6414","authenticated-orcid":false,"given":"Ravi","family":"Gupta","sequence":"first","affiliation":[{"name":"Division of General Internal Medicine Johns Hopkins University School of Medicine Baltimore Maryland USA"},{"name":"Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"},{"name":"Hopkins Business of Health Initiative Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Charankevich","sequence":"additional","affiliation":[{"name":"Center for Equity in Aging Johns Hopkins School of Nursing Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7470-4391","authenticated-orcid":false,"given":"Emmanuel F.","family":"Drabo","sequence":"additional","affiliation":[{"name":"Department of Health Technology Assessment Erasmus School of Health Policy & Management Rotterdam the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teneil","family":"Brown","sequence":"additional","affiliation":[{"name":"Hopkins Business of Health Initiative Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alden L.","family":"Gross","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"},{"name":"Center on Aging and Health Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Polsky","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"},{"name":"Hopkins Business of Health Initiative Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"},{"name":"Carey Business School Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jodi B.","family":"Segal","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine Johns Hopkins University School of Medicine Baltimore Maryland USA"},{"name":"Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"},{"name":"Hopkins Business of Health Initiative Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine A.","family":"Ornstein","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"},{"name":"Hopkins Business of Health Initiative Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA"},{"name":"Center for Equity in Aging Johns Hopkins School of Nursing Baltimore Maryland USA"},{"name":"Division of Geriatric Medicine and Gerontology, Department of Medicine Johns Hopkins University School of Medicine Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Centers for Medicare & Medicaid Services \u201cGuiding an Improved Dementia Experience (GUIDE) Model \u201d 2023 https:\/\/www.cms.gov\/priorities\/innovation\/innovation\u2010models\/guide."},{"key":"e_1_2_11_3_1","unstructured":"U.S. Food and Drug Administration \u201cFDA Clears First Blood Test Used in Diagnosing Alzheimer's Disease \u201d 2025 https:\/\/www.fda.gov\/news\u2010events\/press\u2010announcements\/fda\u2010clears\u2010first\u2010blood\u2010test\u2010used\u2010diagnosing\u2010alzheimers\u2010disease."},{"issue":"1","key":"e_1_2_11_4_1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.3233\/JAD-160105","article-title":"Identification of Physician\u2010Diagnosed Alzheimer's Disease and Related Dementias in Population\u2010Based Administrative Data: A Validation Study Using Family Physicians' Electronic Medical Records","volume":"54","author":"Jaakkimainen R. 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