Demographics, Socioeconomic Context, and the Spread of Infectious Disease: The Case of COVID-19

Yung-Hsiang Ying, Wen-Li Lee, Ying-Chen Chi, Mei-Jung Chen, Koyin Chang

研究成果: 雜誌貢獻期刊論文同行評審

4 引文 斯高帕斯(Scopus)


Importance: Due to the evolving variants of coronavirus disease 2019 (COVID-19), it is important to understand the relationship between the disease condition and socioeconomic, demographic, and health indicators across regions. Background: Studies examining the relationships between infectious disease and socioeconomic variables are not yet well established. Design: A total of 3042 counties in the United States are included as the observation unit in the study. Two outcome variables employed in the study are the control of disease spread and infection prevalence rates in each county. Method: Data are submitted to quantile regression, hierarchical regression, and random forest analyses to understand the extent to which health outcomes are affected by demographics, socioeconomics, and health indicators. Results: Counties with better control of the disease spread tend to have lower infection rates, and vice versa. When measuring different outcome variables, the common risk factors for COVID-19 with a 5% level of statistical significance include employment ratio, female labor ratio, young population ratio, and residents’ average health risk factors, while protective factors include land size, housing value, travel time to work, female population ratio, and ratio of residents who identify themselves as mixed race. Conclusions: The implications of the findings are that the ability to maintain social distancing and personal hygiene habits are crucial in deterring disease transmission and lowering incidence rates, especially in the early stage of disease formation. Relevant authorities should identify preventive factors and take early actions to fight infectious diseases in the future. View Full-Text
頁(從 - 到)1-24
期刊International journal of environmental research and public health
出版狀態已發佈 - 2022


  • infectious diseases
  • socioeconomics
  • quantile regression
  • mixed effect model


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