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Trust, risk perception, and COVID-19 infections: Evidence from multilevel analyses of combined original dataset in China

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  • Ye, Maoxin
  • Lyu, Zeyu

Abstract

Previous studies have revealed medical, democratic, and political factors altering responses to unexpected infectious diseases. However, few studies have attempted to explore the factors affecting disease infection from a social perspective. Here, we argue that trust, which plays an important role in shaping people’ s risk perception toward hazards, can also affect risk perception toward infections from a social perspective. Drawing on the indication that risk perception of diseases helps prevent people from being infected by promoting responsible behaviors, it can be further asserted that trust may alter the infection rate of diseases as a result of risk perception toward infectious diseases. This is an essential point for preventing the spread of infectious diseases and should be demonstrated. To empirically test this prediction, this study uses the COVID-19 outbreak in China as an example and applies an original dataset combining real-time big data, official data, and social survey data from 317 cities in 31 Chinese provinces to demonstrate whether trust influences the infection rate of diseases. Multilevel regression analyses reveal three main results: (1) trust in local government and media helps to reduce the infection rate of diseases; (2) generalized trust promotes a higher rather than lower infection rate; and (3) the effects of different types of trust are either completely or partly mediated by risk perception toward diseases. The theoretical and practical implications of this study provide suggestions for improving the public health system in response to possible infectious diseases.

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  • Ye, Maoxin & Lyu, Zeyu, 2020. "Trust, risk perception, and COVID-19 infections: Evidence from multilevel analyses of combined original dataset in China," Social Science & Medicine, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:socmed:v:265:y:2020:i:c:s027795362030736x
    DOI: 10.1016/j.socscimed.2020.113517
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    3. Sabrina Cipolletta & Gabriela Rios Andreghetti & Giovanna Mioni, 2022. "Risk Perception towards COVID-19: A Systematic Review and Qualitative Synthesis," IJERPH, MDPI, vol. 19(8), pages 1-25, April.
    4. Shin, Su Hyun & Ji, Hyunjung & Lim, HanNa, 2021. "Heterogeneity in preventive behaviors during COVID-19: Health risk, economic insecurity, and slanted information," Social Science & Medicine, Elsevier, vol. 278(C).
    5. Lin, Thung-Hong & Chang, Min-Chiao & Chang, Chun-Chih & Chou, Ya-Hsuan, 2022. "Government-sponsored disinformation and the severity of respiratory infection epidemics including COVID-19: A global analysis, 2001–2020," Social Science & Medicine, Elsevier, vol. 296(C).
    6. Jin, Ming & Liu, Jinshan & Chen, Zhongfei, 2022. "Impacts of social trust on corporate leverage: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 505-521.
    7. Alraja, Mansour, 2022. "Frontline healthcare providers’ behavioural intention to Internet of Things (IoT)-enabled healthcare applications: A gender-based, cross-generational study," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

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