Classification Matters: Divergent Estimates of Dementia Risk Factors in the Health and Retirement Study

Clicks: 6
ID: 332476
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #350 of 355 articles by views in american journal of epidemiology

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 355 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Clinical dementia diagnosis requires substantial expertise and is complex and costly. Therefore, large population-based studies often lack gold-standard longitudinal diagnoses and instead use classification algorithms employing cognitive, functional, and health-related measures to identify dementia. We evaluated whether algorithm choice affects conclusions regarding dementia risk in the nationally representative Health and Retirement Study (HRS; 2010-2020). We constructed four algorithm-specific samples of participants age 70+ without dementia in 2010: Langa-Weir (n=6,498), Hudomiet (n=6,556), Expert (n=5,751), and Wu (n=5,935). Participants were followed until first dementia classification, death, or censoring. We estimated dementia incidence rates (IRs) and used adjusted Cox models to estimate hazard ratios (HRs) for known risk factors. Dementia IRs per 1000 person-years meaningfully varied across algorithms, from 26.5 (95% CI 24.5–28.5, Hudomiet) to 62.8 (59.9–65.8, Expert). Sociodemographic risk factor associations also varied substantially. HRs for education (< vs. ≥high school) ranged from 1.36 (1.19–1.54, Hudomiet) to 2.22 (1.98–2.50, Langa-Weir), and HRs non-Hispanic Black vs. non-Hispanic White ranged from 1.86 (1.53–2.27, Expert) to 2.74 (2.40–3.12, Langa-Weir). Differences were less pronounced for health-related risk factors. In the absence of gold standard longitudinal diagnosis in HRS, researchers should examine the robustness of findings across multiple algorithms.
Reference Key
openalex_W7222057101 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gina E. Nam, Junxian Liu, Suhani Amin, Karla Renata Flores Romero, Zachary J. Kunicki, M. Maria Glymour, Kaitlin Blackstone Casaletto, Jacqueline M. Torres, Eleanor Hayes‐Larson
Journal american journal of epidemiology
Year 2026
DOI
10.1093/aje/kwag260
URL
https://doi.org/10.1093/aje/kwag260
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.