• Assessing the 10/66 dementia c...

Treffer: Assessing the 10/66 dementia classification algorithm for international comparative analyses with the United States.

Title:
Assessing the 10/66 dementia classification algorithm for international comparative analyses with the United States.
Authors:
Llibre Guerra JJ; Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States., Weiss J; Stanford Center on Longevity, Stanford University, Stanford, CA, United States., Li J; Department of Pharmacy, University of Washington, Seattle, WA, United States., Soria C; Department of Demography, University of California at Berkeley, Berkeley, CA, United States., Rodriguez-Salgado A; Medical University of Havana, Havana, Cuba., Jesús Llibre Rodriguez J; Medical University of Havana, Havana, Cuba., Jiménez Velázquez IZ; Department of Medicine, University of Puerto Rico, San Juan, Puerto Rico., Acosta D; Universidad Nacional Pedro Henriquez Ureña, Santo Domingo, Dominican Republic., Liu MM; Department of Demography, University of California at Berkeley, Berkeley, CA, United States., Dow WH; Department of Demography, University of California at Berkeley, Berkeley, CA, United States.; Department of Demography, School of Public Health, University of California at Berkeley, Berkeley, CA, United States.
Source:
American journal of epidemiology [Am J Epidemiol] 2025 Nov 04; Vol. 194 (11), pp. 3117-3125.
Publication Type:
Journal Article; Comparative Study
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: United States NLM ID: 7910653 Publication Model: Print Cited Medium: Internet ISSN: 1476-6256 (Electronic) Linking ISSN: 00029262 NLM ISO Abbreviation: Am J Epidemiol Subsets: MEDLINE
Imprint Name(s):
Publication: Cary, NC : Oxford University Press
Original Publication: Baltimore, School of Hygiene and Public Health of Johns Hopkins Univ.
MeSH Terms:
Algorithms* , Dementia*/classification , Dementia*/epidemiology , Dementia*/diagnosis, Humans ; United States/epidemiology ; Aged ; Male ; Female ; Aged, 80 and over ; Middle Aged ; Risk Factors ; Sensitivity and Specificity ; Prevalence ; Developing Countries ; Classification Algorithms
References:
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Grant Information:
K01AG073526 United States NH NIH HHS; R01 AG064778 United States AG NIA NIH HHS; R01AG064778 United States NH NIH HHS; United Kingdom WT_ Wellcome Trust; 24HPE-1287320 United States ALZ Alzheimer's Association; K01 AG073526 United States AG NIA NIH HHS; T32 HD007275 United States HD NICHD NIH HHS
Contributed Indexing:
Keywords: Alzheimer’s disease; algorithms; dementia; international comparison
Entry Date(s):
Date Created: 20250102 Date Completed: 20251120 Latest Revision: 20251228
Update Code:
20251228
PubMed Central ID:
PMC12634119
DOI:
10.1093/aje/kwae470
PMID:
39745806
Database:
MEDLINE

Weitere Informationen

Cross-national comparisons of dementia prevalence are essential for identifying unique determinants and cultural-specific risk factors, but methodological differences in dementia classification across countries hinder global comparisons. This study maps the 10/66 algorithm for dementia classification, widely used and validated in low- and middle-income countries (LMICs), to the US Aging, Demographics, and Memory Study (ADAMS), the dementia sub-study of the Health and Retirement Study, and assesses its performance in ADAMS. We identified the subset of 10/66 algorithm items comparably measured in ADAMS, then used these items to retrain the 10/66 algorithm against the ADAMS clinical dementia diagnosis, using k-fold cross-validation to assess performance. We compared the modified 10/66 algorithm to 4 other dementia classification algorithms previously validated in ADAMS, both for overall dementia estimation as well as for estimating education gradients. The modified 10/66 algorithm had higher sensitivity (87%) and specificity (93%) than the comparison algorithms. All the algorithms overestimated the education gradient in dementia, although the modest ADAMS sample size precludes precise comparisons of education gradient accuracy. Overall, we found that the modified 10/66 algorithm performs well in classifying dementia status in the United States. Our results support the validity of risk factor comparisons between US and 10/66 LMIC dementia data sets. This article is part of a Special Collection on Cross-National Gerontology.
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