Influencing factors of Barthel index scores among the community-dwelling elderly in Hong Kong : a random intercept model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Yang Zhao
  • Hailiang Wang
  • Eman Leung
  • Javier Fernandez CABRERA
  • Kwok Leung Tsui

Detail(s)

Original languageEnglish
Article number484
Journal / PublicationBMC Geriatrics
Volume21
Online published6 Sep 2021
Publication statusPublished - 2021

Link(s)

Abstract

Background: Barthel Index (BI) is one of the most widely utilized tools for assessing functional independence in activities of daily living. Most existing BI studies used populations with specific diseases (e.g., Alzheimer’s and stroke) to test prognostic factors of BI scores; however, the generalization of these findings was limited when the target populations varied.
Objectives: The aim of the present study was to utilize electronic health records (EHRs) and data mining techniques to develop a generic procedure for identifying prognostic factors that influence BI score changes among community-dwelling elderly.
Methods: Longitudinal data were collected from 113 older adults (81 females; mean age = 84 years, SD = 6.9 years) in Hong Kong elderly care centers. Visualization technologies were used to align annual BI scores with individual EHRs chronologically. Linear mixed-effects (LME) regression was conducted to model longitudinal BI scores based on socio-demographics, disease conditions, and features extracted from EHRs.
Results: The visualization presented a decline in BI scores changed by time and health history events. The LME model yielded a conditional R2 of 84%, a marginal R2 of 75%, and a Cohen’s f2 of 0.68 in the design of random intercepts for individual heterogeneity. Changes in BI scores were significantly influenced by a set of socio-demographics (i.e., sex, education, living arrangement, and hobbies), disease conditions (i.e., dementia and diabetes mellitus), and EHRs features (i.e., event counts in allergies, diagnoses, accidents, wounds, hospital admissions, injections, etc.).
Conclusions: The proposed visualization approach and the LME model estimation can help to trace older adults’ BI score changes and identify the influencing factors. The constructed long-term surveillance system provides reference data in clinical practice and help healthcare providers manage the time, cost, data and human resources in community-dwelling settings.

Research Area(s)

  • Barthel index, Influencing factors, Electronic health records, Linear mixed effects model

Citation Format(s)

Influencing factors of Barthel index scores among the community-dwelling elderly in Hong Kong : a random intercept model. / Pan, Hao; Zhao, Yang; Wang, Hailiang; Li, Xinyue; Leung, Eman; Chen, Frank; CABRERA, Javier Fernandez; Tsui, Kwok Leung.

In: BMC Geriatrics, Vol. 21, 484, 2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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