TY - JOUR
T1 - Influencing factors of Barthel index scores among the community-dwelling elderly in Hong Kong
T2 - a random intercept model
AU - Pan, Hao
AU - Zhao, Yang
AU - Wang, Hailiang
AU - Li, Xinyue
AU - Leung, Eman
AU - Chen, Frank
AU - Cabrera, Javier
AU - Tsui, Kwok Leung
N1 - Funding Information:
We thank Yan Oi Tong Ltd. for providing the data that was used. We thank Dr. Wenbin Wu, Geriatrics Department, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China. for his professional comments on the utility of our methods.
Funding Information:
This work was partly supported by the Hong Kong RGC Theme-Based Research Scheme [No. T32–102-14 N], CityU Grant [No. 9610406; No. 9610404] and PolyU Grant [No. P0036146]. The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Barthel index
KW - Electronic health records
KW - Influencing factors
KW - Linear mixed effects model
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U2 - 10.1186/s12877-021-02422-4
DO - 10.1186/s12877-021-02422-4
M3 - Article
C2 - 34488653
AN - SCOPUS:85114289364
SN - 1471-2318
VL - 21
JO - BMC Geriatrics
JF - BMC Geriatrics
IS - 1
M1 - 484
ER -