Evidence map›Paper›PMID 41615430›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2026

Harmonization of self-reported and performance-based measures of vision using inverse probability weighting: an example using vision and depression in NHATS, CLSA, and LASI.

Melissa Li, Dylan P Thibault, Lindsey B De Lott, Christopher A Gravel, Ellen E Freeman, Ali G Hamedani

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Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 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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5 · Who and what money

Authors and funding

6 authors.

Melissa LiDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Dylan P ThibaultDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Lindsey B De LottDepartments of Ophthalmology and Neurology, Michigan Medicine, Ann Arbor, Michigan, United States.ORCID 0000-0003-2403-0207
Christopher A GravelSchool of Epidemiology and Public Health, Department of Mathematics and Statistics, and Data Literacy Research Institute, University of Ottawa, Ottawa, Canada.
Ellen E FreemanSchool of Epidemiology and Public Health, Department of Mathematics and Statistics, and Data Literacy Research Institute, University of Ottawa, Ottawa, Canada.
Ali G HamedaniDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.ORCID 0000-0001-5949-6395

Funding

Vision and hallucinations in older adultsK23EY033438 · NEI · UNIVERSITY OF PENNSYLVANIA · PI Ali G Hamedani · 2022 to 2026
$1.2M
NEI NIH HHS K23 EY033438
6 · The paper itself

Abstract

backgroundVisual impairment is a potential risk factor for depression and other outcomes in older adults. In population-based studies, vision can be measured using self-report or performance-based visual acuity, but epidemiologic associations often depend on which measure is used.

methodsIn this Research Practice article, we illustrate the use of propensity scores to harmonize analyses of self-reported and performance-based vision in older adults. Using 2021 data from the National Health and Aging Trends Study (NHATS; n = 2447), we measured associations between self-reported visual difficulty, distance visual impairment (logMAR >0.3), and depression. To harmonize self-reported and performance-based measures of vision, we modeled distance visual impairment as a function of self-reported vision and covariates and calculated exposure misclassification overlap weights. External validation was conducted using the Canadian Longitudinal Study on Aging (CLSA) and the Longitudinal Aging Study in India (LASI).

resultsSelf-reported visual difficulty was associated with depression in NHATS (adjusted OR 2.32, 95% CI: 1.46-3.69), but distance visual impairment was not (OR 1.41, 95% CI: 0.99-2.01). After exposure misclassification overlap weighting, self-reported vision was no longer associated with depression, and results mirrored the association between distance visual impairment and depression (OR 1.49, 95% CI: 0.93-2.36). Similar findings were observed in CLSA and LASI.

conclusionsAssociations between vision and depression in older adults differ according to how vision is measured. In studies that measure self-reported vision but not visual acuity, propensity score methods that leverage known relationships between the two can be used to approximate associations between reduced visual acuity and health outcomes.

Indexed as

DepressionSelf ReportVision DisordersAgedAgingCanadaFemaleHumansLongitudinal StudiesMalePropensity ScoreRisk FactorsVisual AcuityCLSALASIMisclassificationNHATSVisual impairment

Identifiers

PMID41615430
PMCPMC13016939

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LicenceCC BY-NC-ND
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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.