Exploring the Digital Health Divide and Determinants among Urban and Rural Elderly in China: Empirical Evidence Based on Capital Theory (Preprint)

Author:

Yang YanbinORCID,Ma ChengyuORCID,Liu HaopengORCID,Lv Siyu,Liao WeizhenORCID

Abstract

BACKGROUND

The aging population in China is becoming increasingly severe, and there is a health inequality phenomenon among urban and rural elderly. With the development of ICT, eHealth has become one of the important factors affecting health. Urban elderly could more conveniently access health information and medical services, while rural elderly may have difficulty enjoying the digital dividends brought by eHealth, highlighting the phenomenon of the “digital health divide”.

OBJECTIVE

This study analyzes the digital health divide and determinants among urban and rural elderly from the perspective of capital theory.

METHODS

The model for analyzing the digital health divide among urban and rural elderly is constructed based on capital theory. Analysis of variance is used to verify the digital health divide among urban and rural elderly. Structural equation modeling is used to analyze the factors, and the Blinder-Oaxaca decomposition method is used to analyze the main causes.

RESULTS

There are three levels of digital health divide among urban and rural elderly, namely digital access divide (F=11.39, P<.01), digital usage divide (F=39.53, P<.001), and digital outcome divide (F=30.20, P<.001). The influence of different levels of divide is transmitted along the digital chain, the impact coefficient of digital access divide on digital usage divide is β=0.060 (P<.05), and digital usage divide on digital outcome divide is β=0.363 (P<.001). The digital usage divide is the most significant level, and cultural capital (β=0.221, P<.001), social support (β=0.361, P<.001), economic capital (β=0.111, P<.01), and habits (β=0.248, P<.001) are most dominant factors contributing to the rural-urban digital usage divide. The Blinder-Oaxaca decomposition results further indicate that cultural capital (33.9%) and social capital (22.5%) are the main factors influencing digital usage divide among urban and rural elderly.

CONCLUSIONS

here exists digital health divide among urban and rural elderly, and the influence of three level of divide is transmitted along the digital chain. The digital usage divide is the main level, cultural capital and social capital are the main reasons for its formation. To against these divides among urban and rural elderly, interventions in policy, society, technology, and economics are recommended.

Publisher

JMIR Publications Inc.

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