THE CARE DEPENDENCY OF COMMUNITY-DWELLING OLDER ADULTS - A LATENT VARIABLE APPROACH USING ITEM RESPONSE THEORY

Q: What is the study’s primary goal regarding care needs?

To assess the individual degree of care dependency among community-dwelling older adults and evaluate how it relates to socioeconomic and health characteristics.

Q: How were care needs measured in this study?

The study used 11 Activities of Daily Living and self/proxy-reported information on the help needed to perform these activities.

Q: What methods were used to estimate and analyze dependency?

An Item Response Theory model estimated a dependency score (DS), and linear regression models analyzed associations between DS and age, sex, education, income, and health characteristics.

Sep 29, 2026

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Community-dwelling older adults may face functional limitations and require different levels of assistance to meet care needs. This study evaluates each individual’s care needs using a latent variable approach, estimating care dependency and examining its links with socioeconomic and health characteristics. Data come from Uruguayan ELPS participants aged 60+, with 11 Activities of Daily Living and help requirements reported by self or proxies. Item Response Theory produces a dependency score, followed by linear regression to test associations across demographic and health factors.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages1

Reading Time3 min

AccessFree to download

Background

Methods

Results

Conclusions

Innovation in Aging, 2025, Vol. 9, No. S2 725 Abstract citation ID: igaf122.2468 THE CARE DEPENDENCY OF COMMUNITY-DWELLING OLDER ADULTS: A LATENT VARIABLE APPROACH USING ITEM RESPONSE THEORY Alejandra Marroig1, Fernando Massa1, Janet Trujillo1, Graciela Muniz-Terrera2, and Sofia Domingorena1, 1. Universidad de la Republica Uruguay, Montevideo, Montevideo, Uruguay, 2. Ohio University, Athens, Ohio, United States Background: Older adults living in the community may have functional limitations and require varying levels of assistance to satisfy their care needs. This study aimed to assess the individual degree of care needs in a population-based study of community-dwelling older adults. Using a latent variable approach, we assessed the degree of care dependency and studied its association with socioeconomic and health characteristics. Methods: We used data on adults aged 60 years and older from the Uruguayan population-based study Encuesta Longitudinal de Protección Social (ELPS) (N = 5102) . To assess care needs, we considered 11 Activities of Daily Living and the self/ proxy-reported information about the help needed to perform these activities. Using an Item Response Theory model, we estimated the dependency score (DS) used to measure the degree of care dependency. Then, we applied linear regression models to analyze the association between DS and age, sex, education, income, and health characteristics. Results: The results showed differences in the ADLs in which older adults with a low level of dependency and those with a high level of dependency needed assistance. Additionally, older women (p= 0.002) with multimorbidity (p < 0.001) and individuals with lower levels of education (p = 0.006) had the highest degree of care dependency. Conclusions: Social and healthcare public policies will benefit from considering the socioeconomic and health differences of older adults. The degree of care dependency should be considered for the design of services providing support to older adults with care dependency living in the community.

FAQs

What is the study’s primary goal regarding care needs?

To assess the individual degree of care dependency among community-dwelling older adults and evaluate how it relates to socioeconomic and health characteristics.

How were care needs measured in this study?

The study used 11 Activities of Daily Living and self/proxy-reported information on the help needed to perform these activities.

What methods were used to estimate and analyze dependency?

An Item Response Theory model estimated a dependency score (DS), and linear regression models analyzed associations between DS and age, sex, education, income, and health characteristics.

Sep 29, 2026

Upload by Rhys