Association Between Socioeconomic Factors and the Covid-19 Outbreak in the 39 Well-Developed Cities of China

Bibliographic Data

ID22089442
AuthorsYi‐Ting Lin (0000-0002-5622-2935, Xiamen Chang Gung Hospital), Yiting Lin (0000-0001-8782-4368), Ping Zhong (0000-0002-4415-2992, First Affiliated Hospital of Xiamen University, corresponding author), Ting Chen (0009-0007-4319-1117, Xiamen Blood Center)
Year2020
Volume8
Pages546637-546637
Publication date2020-10-30
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2020.546637
PMID33194948
OpenAlexW3095237950
LanguageEN
Citations received5
References cited25

Background: Socioeconomic factors play an indispensable role in the spread of emerging infections diseases. To date, no study investigates the role of socioeconomic factors in the spread of COVID-19. Methods: The number of COVID-19 cases in the 39 well-developed cities of China was aggregated by searching the publicly available sources. Socioeconomic indicators (e.g. population, population density, gross domestic product, rural-to-urban migrants, urbanization rate, per person-disposable income, and level of health care) in these cities were also aggregated from the Bureau of Statistics. The data referring to travelers from Wuhan were collected from the Baidu Migration database. A multiple stepwise linear regression model was performed to identify the independent risk factors of the number of cases. Results: As of Mar 19, 2020, a total of 5, 939 cases were reported in the 39 well-developed cities with almost half of total cases in China outside of Hubei. The number of cases ranged 20 to 576, and the median number of cases was 93 (IQR 54-180) in these cities. Nine socioeconomic variables including the number of travelers from Wuhan, population, native population, gross domestic product, Per person-GDP, the number of hospitals, the number of rural-to-urban migrants, traffic capacity, and person-disposable income were recognized as potential confounders of the number of cases. Results of multiple linear regression showed a statistically significant association between the number of cases and the number of travelers from Wuhan (t = 6.746, P = 0.000) or the number of rural-to-urban migrants (t = 3.776, P = 0.001) in these cities. However, other seven potential confounders were not associated with the number of cases. Moreover, a well fittedmultiple regression model was built in this study, and a regression equation was as follows: Y = 0.007Xt + 0.200Xm (adjusted R2 = 0.833). Conclusions: Travelers from Wuhan and rural-to-urban migrants were independently associated with the COVID-19 outbreak in the 39 well-developed cities of China. These findings suggested that travelers form the epicenter and rural-to-urban migrants should be paid more attention in the early stage of the COVID-19 outbreak in the well-developed city

China · Confounding · Economic growth · Economics · Environmental health · Geography · Gross domestic product · Household income · Outbreak · Population · Socioeconomic status · Socioeconomics · Urbanization · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Demography · Medicine · Vaccine Coverage and Hesitancy

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