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Influence of Socio-Ecological Factors on COVID-19 Risk: A Cross-Sectional Study Based on 178 Countries/Regions Worldwide

46 Pages Posted: 22 May 2020

See all articles by Dai Su

Dai Su

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Health Management

Yingchun Chen

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Health Management

Kevin He

University of Michigan at Ann Arbor - Department of Biostatistics

Tao Zhang

Sichuan University - Department of Epidemiology and Health Statistics

Min Tan

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Health Management

Yunfan Zhang

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Health Management

Xingyu Zhang

University of Michigan at Ann Arbor - Department of Systems, Populations, and Leadership

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Abstract

Background: The initial outbreak of COVID-19 caused by SARS-CoV-2 in China in 2019 has been severely tested in other countries worldwide. To address the challenges posed by COVID-19, the links among the transmission of COVID-19, socio-economic factors and climatic factors must be understood to suggest better strategies for predicting, preventing, coping with and mitigating the associated challenges. We aimed to describe the spatial distribution of the COVID-19 pandemic worldwide and assess the effects of various socio-ecological factors, including climate and socio-economic factors, on COVID-19 risk.

Methods: We collected COVID-19 pandemic infection data of 178 countries/regions worldwide from the 2019 Novel Coronavirus Visual Dashboard data repository, operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU-CSSE), and collected socio-ecological data from the Global Surface Summary of the Day (GSOD) and World Development Indicators dataset. We established the Potential Risk Assessment Framework for COVID-19. We used spatial econometrics method to assess the global and local correlation of COVID-19 risk indicators (the incidence rate, cumulative mortality rate and daily cumulative index) for COVID-19. To estimate the adjusted IRR, we modelled negative binomial regression analysis with spatial information and socio-ecological factors.

Findings: By April 6, 2020, 178 cases were reported globally. The test results for local spatial correlations indicated that 37, 29 and 39 countries/regions were strongly opposite from the IR, CMR and DCI index "spatial autocorrelation hypothesis", respectively. The IRs were significantly positively associated with GDP per capita, the use of at least basic sanitation services and social insurance program coverage, and were significantly negatively associated with the proportion of the population spending more than 25% of household consumption or income on out-of-pocket health care expenses and the poverty headcount ratio at the national poverty lines. The CMR was significantly positively associated with urban populations, GDP per capita and current health expenditure, and was significantly negatively associated with the number of hospital beds, number of nurses and midwives, and poverty headcount ratio at the national poverty lines. The DCI was significantly positively associated with urban populations, population density and researchers in R&D, and was significantly negatively associated with the number of hospital beds, number of nurses and midwives and poverty headcount ratio at the national poverty lines. We also found that climatic factors (temperature, relative humidity, precipitation and wind speed) did not significantly reduce COVID-19 risk.I

nterpretation: Attention must be paid to the similarities in COVID-19 transmission characteristics (such as transmission speed and mortality rate) across countries/regions, and national/regional early warning and protection measures must be successfully implemented. Governmental regulation of social and economic factors is particularly important to reduce COVID-19 risk. To fight against COVID-19 more effectively, countries/regions should pay more attention to controlling population flow, improving diagnosis and treatment capacity, and improving public welfare policies.

Funding Statement: National Natural Science Foundation of China, Michigan Institute for Clinical and Health Research.

Declaration of Interests: DS, YC, MT and YZ are funded by National Natural Science Foundation of China (No. 71473096; No. 71673101; No. 71974066). YZ is funded by Michigan Institute for Clinical and Health Research (MICHR No. UL1TR002240). All other authors declare no competing interests.

Keywords: COVID-19 risk; socio-ecological factors; cross-sectional study; 178 countries/regions worldwide

Suggested Citation

Su, Dai and Chen, Yingchun and He, Kevin and Zhang, Tao and Tan, Min and Zhang, Yunfan and Zhang, Xingyu, Influence of Socio-Ecological Factors on COVID-19 Risk: A Cross-Sectional Study Based on 178 Countries/Regions Worldwide (4/17/2020). Available at SSRN: https://ssrn.com/abstract=3582852 or http://dx.doi.org/10.2139/ssrn.3582852

Dai Su

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Health Management

Wuhan
China

Yingchun Chen

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Health Management

Wuhan
China

Kevin He

University of Michigan at Ann Arbor - Department of Biostatistics

Ann Arbor, MI
United States

Tao Zhang

Sichuan University - Department of Epidemiology and Health Statistics

Sichuan
China

Min Tan

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Health Management

Wuhan
China

Yunfan Zhang

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Health Management

Wuhan
China

Xingyu Zhang (Contact Author)

University of Michigan at Ann Arbor - Department of Systems, Populations, and Leadership ( email )

Ann Arbor, MI
United States

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