Author:
Wang Liuying,Cheng Zirong,Ye Li,Rong Lijuan,Chien Ching-Wen,Tung Tao-Hsin
Abstract
Abstract
Background
As older people have complex medical needs and still encounter challenges in accessing online health information, the relationship between Internet use and the choice of medical institution made by them is unclear, and we aimed to examine this relationship.
Methods
Data from the newly released 2020 China Family Panel Survey database were used. Furthermore, we used descriptive statistics to analyze the background characteristics of the sample and a logistic regression model to estimate the impact of Internet use on the choice of medical institution made by older adults. We conducted a stratified analysis to explore the influence of different characteristics on the relationship between Internet use and the choice of medical institution.
Results
Totally 4,948 older adults were included. Multivariate logistic regression showed that, compared to non-Internet users, Internet users were less likely to choose community health service centers over general hospitals (P < 0.001, OR = 0.667, 95CI%: 0.558–0.797). The subgroup analyses found that Internet use only had an impact on the choice of medical institution in older adults aged 65–69 years, those with partners, those with primary or secondary education, those residing in urban areas, those without medical insurance, those with a self-rated health status as average or healthy, those with unchanged or better health trend, and those without chronic disease. The effect of Internet use on the choice of medical institution did not differ by sex, satisfaction, or trust in doctors.
Conclusion
Internet use may significantly affect older adults’ tendency to choose general hospitals to meet their daily medical needs. The subgroup analyses indicated that different characteristics of older people affected this association.
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Liu E, Zhang Q, Feng Y. Elderly Poverty Risk due to chronic diseases: theoretical mechanism and empirical analysis. Insurance Stud. 2020;(11):63–78.
2. Chen J, Zhang F, Zhang Y, et al. Trajectories network analysis of chronic diseases among middle-aged and older adults: evidence from the China Health and Retirement Longitudinal Study (CHARLS). BMC Public Health. 2024;24(1):559.
3. Zheng J, Tao Q. The Enlightenment of Japan’s Graded diagnosis and treatment system on China. J Harbin Univ. 2023;44(8).
4. Huang X, Chen Y, Yuan M. The realization path of Hierarchical Medical System from the perspective of Supply Side ——Taking the NHS reform in the UK and the practice of family doctors in Shanghai as examples. Health Econ Res. 2022;39(3).
5. Wang S, Tao Q. Hierarchical diagnosis and treatment system in Germany and its enlightenment to China. Mod Hosp Manage. 2021;19(3).