Evidence map›Paper›PMID 42596083›Full record

ArticleJournal of the American Geriatrics Society2026

Comparison of Claims-Based Algorithms for Dementia Identification: A Cross-Sectional Analysis of the National Health and Aging Trends Study (NHATS).

Ravi Gupta, Hanna Charankevich, Emmanuel F Drabo, Teneil Brown, Alden L Gross, Daniel Polsky, Jodi B Segal, Katherine A Ornstein

Abstract read
In one paragraph

Article in Journal of the American Geriatrics Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ravi GuptaDivision of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0001-5902-6414
Hanna CharankevichCenter for Equity in Aging, Johns Hopkins School of Nursing, Baltimore, Maryland, USA.
Emmanuel F DraboDepartment of Health Technology Assessment, Erasmus School of Health Policy & Management, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0001-7470-4391
Teneil BrownHopkins Business of Health Initiative, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Alden L GrossDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Daniel PolskyDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Jodi B SegalDivision of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Katherine A OrnsteinDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Funding

Remote Data Enclave Core (RDEC)P30AG066587 · NIA · JOHNS HOPKINS UNIVERSITY · PI KATHERINE A ORNSTEIN · 2020 to 2026
$6.7M
Home, but homebound: The impact of caregiving, financial, and community resources on the lives of individuals with dementiaR01AG060967 · NIA · JOHNS HOPKINS UNIVERSITY · PI ORNSTEIN, KATHERINE A · 2019 to 2023
$2.5M
Hopkins' Economics of Alzheimer's Disease and Services (HEADS) Center of the National Institute on Aging P30AG066587NIA NIH HHS P30 AG066587NIA NIH HHS R01 AG060967NIA NIH HHS R01AG060967
6 · The paper itself

Abstract

backgroundAccurate 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-based algorithms to identify ADRD, few studies have compared their use to ongoing longitudinal aging studies.

methodsIn this cross-sectional study, we included 2204 NHATS respondents aged 65 years or older classified as having dementia and enrolled in fee-for-service Medicare in 2019. We determined the performance characteristics of 3-claims-based dementia algorithms using NHATS as a reference standard.

resultsAmong NHATS respondents classified as having "probable" dementia, sensitivity was generally low (48.8%-70.5%), while specificity was high (91.5%-97.8%) across all 3 algorithms. The Chronic Conditions Warehouse (CCW) algorithm had the highest sensitivity (70.5, 95% CI: 64.3-76.6), followed by Bynum-Standard 3-year (65.8, 95% CI: 59.6-71.9). Bynum-Standard 1-year had the highest PPV (65.6, 95% CI: 56.9-74.4). The Youden Index and F1 score were generally low and clustered closely across the three algorithms. Odds of ADRD misclassification differed across patient characteristics.

conclusionCommonly used claims-based algorithms to identify ADRD had reasonable validity, though each presented important context-dependent 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 "gold standard" for comparison of claims-based algorithms and we only analyzed NHATS respondents in a single year.

Indexed as

administrative dataalgorithmAlzheimer's diseasedementiadiagnosisMedicareNHATSvalidity

Identifiers

PMID42596083
PMCPMC13548100

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.