Evidence map›Paper›PMID 39890090›Full record

ArticleJournal of the American Medical Directors Association2025

A Comparison of Measures for Identifying Possible Dementia in Veterans Affairs Nursing Home Residents.

Joshua D Niznik, Florentia E Sileanu, Xinhua Zhao, Kelvin Tran, Laura C Hanson, Alan Kinlaw, Thomas R Radomski, Alexa Ehlert, Sydney Springer, Binxin Cao and 2 more

Abstract readComparative Study
In one paragraph

Article in Journal of the American Medical Directors Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Joshua D NiznikDivision of Geriatric Medicine, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC, USA; Center for Aging and Health, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC, USA; Division of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill, Eshelman School of Pharmacy, Chapel Hill, NC, USA; Center for Health Equity Research and Promotion, Veterans Affairs (VA) Pittsburgh Healthcare System, Pittsburgh, PA, USA. Electronic address: jdniznik@email.unc.edu.
Florentia E SileanuCenter for Health Equity Research and Promotion, Veterans Affairs (VA) Pittsburgh Healthcare System, Pittsburgh, PA, USA.
Xinhua ZhaoCenter for Health Equity Research and Promotion, Veterans Affairs (VA) Pittsburgh Healthcare System, Pittsburgh, PA, USA.
Kelvin TranCenter for Health Equity Research and Promotion, Veterans Affairs (VA) Pittsburgh Healthcare System, Pittsburgh, PA, USA.
Laura C HansonDivision of Geriatric Medicine, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC, USA.
Alan KinlawDivision of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill, Eshelman School of Pharmacy, Chapel Hill, NC, USA.
Thomas R RadomskiCenter for Health Equity Research and Promotion, Veterans Affairs (VA) Pittsburgh Healthcare System, Pittsburgh, PA, USA; Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Alexa EhlertDivision of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill, Eshelman School of Pharmacy, Chapel Hill, NC, USA.
Sydney SpringerVA Maine Healthcare System, Augusta, ME, USA.
Binxin CaoDivision of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill, Eshelman School of Pharmacy, Chapel Hill, NC, USA.
Loren J SchleidenCenter for Health Equity Research and Promotion, Veterans Affairs (VA) Pittsburgh Healthcare System, Pittsburgh, PA, USA.
Carolyn T ThorpeDivision of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill, Eshelman School of Pharmacy, Chapel Hill, NC, USA; Center for Health Equity Research and Promotion, Veterans Affairs (VA) Pittsburgh Healthcare System, Pittsburgh, PA, USA.

Funding

Training Core (I)U54AG063546 · NIA · BROWN UNIVERSITY · PI Heather Gwynn Allore · 2019 to 2026
$125.9M
Health Outcomes of Discontinuing Aspirin in Older Adults with Alzheimer's Disease and Related DementiasR01AG079219 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Carolyn Timberlake Thorpe · 2023 to 2026
$2.5M
Deprescribing Bisphosphonates in Older Nursing Home Residents with DementiaK08AG071794 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NIZNIK, JOSHUA DAVID · 2021 to 2025
$724k
HSRD VA SDR 02-237NIA NIH HHS K08 AG071794NIA NIH HHS R01 AG079219NIA NIH HHS U54 AG063546
6 · The paper itself

Abstract

objectivesIdentifying people with possible dementia in health care systems is important to study outcomes and target improvements in care. This study sought to compare the performance of diagnostic codes and Minimum Data Set (MDS)-based measures for identifying dementia and cognitive impairment in older veteran nursing home residents.

designRetrospective, cross-sectional analysis. SETTING AND

participantsWe used real-world health care data from the Veterans Affairs (VA) Residential History File, VA Corporate Data Warehouse (CDW), Medicare claims, and the MDS to assemble a cohort of VA Community Living Center (CLC) admissions over 2015 to 2021 for veterans aged ≥ 65 with dual VA and Medicare enrollment (n = 54,234).

methodsWe defined 3 measures of possible dementia: (1) claims/CDW diagnoses using Chronic Conditions Warehouse (CCW) algorithms for Alzheimer's disease or non-Alzheimer's dementia; (2) MDS active diagnosis items for Alzheimer's disease and non-Alzheimer's dementia; and (3) MDS Cognitive Function Scale (CFS) assessment indicating at least mild cognitive impairment. We calculated proportions identified with each definition, and sensitivity, specificity, and positive predictive value of claims/CDW diagnoses and MDS indicators for dementia for identifying CFS impairment.

resultsAmong VA CLC residents, 61.4% met at least 1 criterion for possible dementia (38.6% claims/CDW, 23.3% MDS active diagnosis, 50.8% CFS). Diagnoses from claims/CDW had 56.5% sensitivity and 80.0% specificity for identifying veterans with CFS cognitive impairment. Active diagnoses from the MDS exhibited poorer sensitivity (38.1%), but higher specificity (92.0%) identifying veterans with cognitive impairment on the CFS. CONCLUSIONS AND IMPLICATIONS: Consistent with what has been reported in Medicare nursing home residents, we observed only partial overlap between indicators of possible dementia across diagnosis codes and other indicators vs cognitive assessments in MDS. Our findings support the utility of these measures for identifying individuals with possible dementia across different systems, but further work is needed to understand implications when using diagnosis codes or cognitive assessments.

Indexed as

DementiaNursing HomesVeteransAgedAged, 80 and overCross-Sectional StudiesFemaleHumansMaleRetrospective StudiesUnited StatesUnited States Department of Veterans AffairsDementiadiagnosis codesMinimum Data Setnursing homeveterans

Identifiers

PMID39890090
PMCPMC11972881

What OpenQuestion holds

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Registered trials

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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.