TY - JOUR
T1 - Influencing factors of disability among the elderly in China, 2003–2016
T2 - application of Bayesian quantile regression
AU - Xu, Xiaocang
AU - Chen, Linhong
PY - 2019/6/3
Y1 - 2019/6/3
N2 - Background: More and more disabled elderly need long-term care as China becomes an aging society. In 2016, there were 220 million people over the age of 60, and nearly 10 million completely disabled elderly people who cannot complete Activities of Daily Living (ADLs). Therefore, the topic of influencing factors for disability among the elderly in China has attracted close attention from researchers, most of which use the traditional empirical methods, such as Ordinary Least Squares (OLS) and logistic.Objective: The purpose of this paper was to introduce the Bayesian Quantile Regression (BQR) method to the topic of the disabled elderly, which was achieved by using BQR to study the influencing factors of disability among the elderly in China during 2003–2016.Methods: This paper was the first attempt to use the BQR for the influencing factors of disability among the elderly in China. Furthermore, a comparison was made between the regression results of BQR, OLS, Quantile Regression (QR), and Bayesian Linear Regression (BLR).Results: It was found that there was a relatively stable relationship between chronic diseases and disability, although there was a little difference in different quantiles. In addition, the BQR can obtain results similar to the traditional method. For instance, the coefficient of chronic diseases (to total disability) obtained by OLS, QR, and BLR were basically consistent (around 0.778), which was similar to BQR. The BQR not only provided estimates for all the quantiles, but also provided upper and lower values of a certain confidence interval.Conclusions: By applying the BQR to the influencing factors of disability among the elderly in China, we reached the conclusion that BQR methods are adaptable for this research topic because of their characteristics and advantages over the traditional methods, such as less strict constraints, the estimates for all quantiles, and the combination of historical information with prior information. Moreover, the BQR method appropriately obtained the lower and upper values in a confidence interval, which can provide prediction space for the future.
AB - Background: More and more disabled elderly need long-term care as China becomes an aging society. In 2016, there were 220 million people over the age of 60, and nearly 10 million completely disabled elderly people who cannot complete Activities of Daily Living (ADLs). Therefore, the topic of influencing factors for disability among the elderly in China has attracted close attention from researchers, most of which use the traditional empirical methods, such as Ordinary Least Squares (OLS) and logistic.Objective: The purpose of this paper was to introduce the Bayesian Quantile Regression (BQR) method to the topic of the disabled elderly, which was achieved by using BQR to study the influencing factors of disability among the elderly in China during 2003–2016.Methods: This paper was the first attempt to use the BQR for the influencing factors of disability among the elderly in China. Furthermore, a comparison was made between the regression results of BQR, OLS, Quantile Regression (QR), and Bayesian Linear Regression (BLR).Results: It was found that there was a relatively stable relationship between chronic diseases and disability, although there was a little difference in different quantiles. In addition, the BQR can obtain results similar to the traditional method. For instance, the coefficient of chronic diseases (to total disability) obtained by OLS, QR, and BLR were basically consistent (around 0.778), which was similar to BQR. The BQR not only provided estimates for all the quantiles, but also provided upper and lower values of a certain confidence interval.Conclusions: By applying the BQR to the influencing factors of disability among the elderly in China, we reached the conclusion that BQR methods are adaptable for this research topic because of their characteristics and advantages over the traditional methods, such as less strict constraints, the estimates for all quantiles, and the combination of historical information with prior information. Moreover, the BQR method appropriately obtained the lower and upper values in a confidence interval, which can provide prediction space for the future.
KW - Bayesian quantile regression
KW - chronic diseases
KW - Disability
KW - elderly
KW - long-term care
UR - http://www.scopus.com/inward/record.url?scp=85064013029&partnerID=8YFLogxK
U2 - 10.1080/13696998.2019.1600525
DO - 10.1080/13696998.2019.1600525
M3 - Article
C2 - 30913934
AN - SCOPUS:85064013029
SN - 1369-6998
VL - 22
SP - 605
EP - 611
JO - Journal of Medical Economics
JF - Journal of Medical Economics
IS - 6
ER -