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

Hao Pan; Yang Zhao; Hailiang Wang; Xinyue Li; Eman Leung; Frank Chen; Javier Cabrera; Kwok Leung Tsui

doi:10.1186/s12877-021-02422-4

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

BMC Geriatr. 2021 Sep 6;21(1):484. doi: 10.1186/s12877-021-02422-4.

Authors

Hao Pan¹, Yang Zhao², Hailiang Wang³, Xinyue Li¹, Eman Leung⁴, Frank Chen⁵, Javier Cabrera⁶, Kwok Leung Tsui^{1

7}

Affiliations

¹ School of Data Science, City University of Hong Kong, Hong Kong, China.
² School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.
³ School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China. hailiang.wang@polyu.edu.hk.
⁴ School of Public Health & Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
⁵ Department of Management Sciences, City University of Hong Kong, Hong Kong, China.
⁶ Department of Statistics, Rutgers University, New Brunswick, NJ, USA.
⁷ Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA.

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.

Keywords: Barthel index; Electronic health records; Influencing factors; Linear mixed effects model.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Activities of Daily Living
Aged
Aged, 80 and over
Female
Hong Kong / epidemiology
Hospitalization
Humans
Independent Living*
Stroke*