Unravelling the Role of Socioeconomic Forces in the Early Stage of COVID-19 Pandemic: A Global Analysis

Ρόντος, Κώστας; Syrmali, Maria-Eleni; Salvati, Luca

doi:10.3390/ijerph18126340

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…The reason for this phenomenon is that the majority of participants were more than 20 years old. This age group, which includes university students and workers from all walks of life, is also the most active in social activities, reflecting the impact of the pandemic on the population, especially the main economic forces [17]. In 2021, six cities (Guangzhou, Shenzhen, Dongguan, Zhuhai, Foshan, and Zhanjiang) of Guangdong province were classified into medium-risk or high-risk regions, and most regions of Guangdong province were low-risk regions (Figure 2).…”

Section: Discussionmentioning

confidence: 99%

Cognition and Behavior of COVID-19 Vaccination Based on the Health Belief Model: A Cross-Sectional Study

Cai

Zheng

et al. 2022

Vaccines

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Background: Vaccination is the most effective method for the prevention of COVID-19. However, willingness to be vaccinated is not consistent. This study aimed to explore vaccine cognition, risk perception, and health behavior of COVID-19 in China. Methods: A cross-sectional survey was performed in Guangdong province, China, including demographic characteristics, health status and preventive behaviors, cognition of COVID-19 vaccination, and the health belief model (HBM). Results: A total of 1640 participants were recruited. The main access to information about COVID-19 and vaccination as through official news and broadcasts (67.3%), social network software (58.7%), and professional popularization (46.2%). The precautions taken were wearing a mask (67.0%) and avoiding gathering together (71.3%). COVID-19 vaccination acceptability was different among different age groups and educational levels (p < 0.001). The major reasons for accepting vaccination included that it was an effective way to prevent COVID-19 (61.8%) and that it was required by working units/schools (51.1%). The fitting effect indexes of the (HBM) Model 2 showed better fitting than those of Model 1. In Model 2, perceived benefits (OR = 3.13, 95% CI: 1.79–5.47), cues to action (OR = 2.23, 95% CI: 1.60–3.11), and different occupations (OR = 1.13, 95% CI: 1.04–1.23) were positively correlated with vaccine acceptance; while perceived susceptibility (OR = 0.47, 95% CI: 0.30–0.74) and perceived barriers (OR = 0.44, 95% CI: 0.29–0.69) were negative factors associated with vaccine acceptance. Conclusion: Different sociodemographic characteristics lead to differences in acceptance of vaccination, and the publicity and credibility of government play an indispensable role in epidemic control. The establishment of the HBM further predicted that perceived susceptibility to COVID-19, benefits of vaccination, barriers of cognition, and cue to action were the influencing factors of intention and health behaviors.

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Section: Discussionmentioning

confidence: 99%

Cognition and Behavior of COVID-19 Vaccination Based on the Health Belief Model: A Cross-Sectional Study

Cai

Zheng

et al. 2022

Vaccines

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“…A number of studies have sought to determine the demographic and socioeconomic risk factors of infections and deaths of COVID-19 in a jurisdiction. [ 25 , 26 ] A study on the state of New Mexico during the pre-vaccination months found that areas with greater percentages of American Indians and Alaska Natives had greater numbers of confirmed cases. [ 27 ] Another study of America concluded that counties with higher proportions of Black and Hispanic residents tended to have greater numbers of total deaths.…”

Section: Discussionmentioning

confidence: 99%

The association of demographic and socioeconomic factors with COVID-19 during pre- and post-vaccination periods: A cross-sectional study of Virginia

Tan

2023

Medicine

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Sociodemographic factors have been found to be associated with the transmission of coronavirus disease 2019 , yet most studies focused on the period before the proliferation of vaccination and obtained inconclusive results. In this crosssectional study, the infections, deaths, incidence rates, case fatalities, and mortalities of Virginia's 133 jurisdictions during the prevaccination and post-vaccination periods were compared, and their associations with demographic and socioeconomic factors were studied. The cumulative infections and deaths and medians of incidence rates, case fatalities, and mortalities of COVID-19 in 133 Virginia jurisdictions were significantly higher during the post-vaccination period than during the pre-vaccination period. A variety of demographic and socioeconomic risk factors were significantly associated with COVID-19 prevalence in Virginia. Multiple linear regression analysis suggested that demographic and socioeconomic factors contributed up to 80% of the variation in the infections, deaths, and incidence rates and up to 53% of the variation in the case fatalities and mortalities of COVID-19 in Virginia. The demographic and socioeconomic determinants differed during the pre-and post-vaccination periods. The developed multiple linear regression models could be used to effectively characterize the impact of demographic and socioeconomic factors on the infections, deaths, and incidence rates of COVID-19 in Virginia.

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“…Naively, in the present era of abundant and widely available data, one could expect that most of these questions could be settled down by systematically comparing the COVID-19 numbers with various demographic and environmental parameters. However, while much progress in this direction has been made (e.g., (Adhikari and Yin, 2020; Allel et al, 2020; An et al, 2020; Gupta and Gharehgozli, 2020; Pan et al, 2020; Djordjevic et al, 2021b; Hradsky and Komarek, 2021; Lorenzo et al, 2021; Markovic et al, 2021; Perone, 2021; Rontos et al, 2021; Salom et al, 2021; Singh et al, 2021; Wang et al, 2022)), many methodological obstacles complicate this type of research and often lead to conflicting conclusions of otherwise similar studies.…”

Section: Introductionmentioning

confidence: 99%

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

Tumbas

Marković

Salom

et al. 2023

Preprint

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Understanding sociodemographic factors behind COVID-19 severity relates to significant methodological difficulties, such as differences in testing policies and epidemics phase, as well as a large number of predictors that can potentially contribute to severity. To account for these difficulties, we assemble 115 predictors for more than 3000 US counties and employ a well-defined COVID-19 severity measure derived from epidemiological dynamics modeling. We then use a number of advanced feature selection techniques from machine learning to determine which of these predictors significantly impact the disease severity. We obtain a surprisingly simple result, where only two variables are clearly and robustly selected - population density and proportion of African Americans. Possible causes behind this result are discussed. We argue that the approach may be useful whenever significant determinants of disease progression over diverse geographic regions should be selected from a large number of potentially important factors.

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Unravelling the Role of Socioeconomic Forces in the Early Stage of COVID-19 Pandemic: A Global Analysis

Cited by 5 publications

References 53 publications

Cognition and Behavior of COVID-19 Vaccination Based on the Health Belief Model: A Cross-Sectional Study

Cognition and Behavior of COVID-19 Vaccination Based on the Health Belief Model: A Cross-Sectional Study

The association of demographic and socioeconomic factors with COVID-19 during pre- and post-vaccination periods: A cross-sectional study of Virginia

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

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