ABSTRACT
Background: Chest X ray (CXR) has been the most common screen procedure for detection of lung cancer. We have shown that there is a profit to repeated visitors to the same facility for the lung cancer detection screening (Kimura T. Health Prim Car, 2021). Declaration of Emergency by Japan government in response to the COVID-19 pandemic and subsequent changes made to healthcare provision impacted people's abilities to effectively manage their health condition. The hypothesis was that many people would be reluctant to visit health checkup centers, and that opportunities for detection of cancers would decrease. Methods: Our clinic “MedCity21” is a university outpatient clinic to undergo a complete medical checkup in private health screening program. The visitors with abnormalities detected in CXR were announced by call request and invited to our specialty clinic for chest CT scan as further examination. Per year from 2018 to 2021, we examined the varieties of abnormal shadows by CXR and CT scans and compared the differences between the repeated and the first-time visitors using the chisquare tests and one-way ANOVA. We determined 2018 and 2019 to be before COVID-19 and 2020 and 2021 to be during COVID-19. We have been checking for previous COVID-19, and those with previous COVID-19 can be seen after 4 weeks of recovery. Results: From 2018 to 2021, in order, there were 12540,13690, 12070, and 13409 visitors of which 45.0%, 42.5%, 32.1%, and 29.2% were first-time visitors, respectively. There was a significant decrease of first-time visitors during COVID-19 compared to before COVID-19 (p = 0.0454). From 2018 to 2021, the CXR abnormalities requiring further examinations were 2.7%, 2.4%, 2.4%, and 2.3%, of which 2.1% and 3.4% were repeated and first-time visitors, 1.8% and 3.2%, 1.8% and 3.8%, and 1.5% and 4.0%, respectively. Each year, the detection rate was significantly lower for repeated comparing to first-time visitors (p < 0.01). The CT confirmation revealed that CXR abnormalities in repeated visitors were diagnosed with different variations compared to those of first-time visitors. Repeated visitors had a significantly lower proportion of old inflammatory changes than first-time visitors. This distribution is consistent with our previous report. It should be noted that there were no lung cancer patients in first-time visitors, on the contrary, there were 3 confirmed lung cancer in repeated visitors in 2021. Conclusions: There was a significant decrease of first-time visitors during COVID-19 compared to before COVID-19, but the rate of decrease was not as high as expected. The repeated visitors had significantly lower rate of CXR abnormalities detection, but higher detection of lung cancer. The number of people with previous COVID-19 will continue to increase. If the facility has adequate infection control measures in place, it is recommended that medical checkups be conducted annually.
ABSTRACT
As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections. Such restrictions have a direct effect on the volume of international flights among these countries, resulting in extensive social and economic costs. To better understand the situation in a quantitative manner, we analyzed the OpenSky Network data to clarify flight patterns and flight densities around the world. Then we observed relationships between flight numbers with new infection cases and the economy (the unemployment rate) in Barcelona. We found that the number of daily flights gradually decreased and then suddenly dropped 64% during the second half of March in 2020 after the United States and Europe enacted travel restrictions. We also observed a 51% decrease in the global flight network density decreased during this period. Regarding new COVID-19 cases, the United States had an unexpected surge regardless of travel restrictions. Finally, the layoffs for temporary workers in the tourism and airplane business increased by 4.3 fold in the weeks following Spain's decision to close its borders. © 2020 IEEE.
ABSTRACT
The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent. Besides direct physical and economic threats, the pandemic also indirectly impact people’s mental health conditions, which can be overwhelming but difficult to measure. The problem may come from various reasons such as unemployment status, stay-at-home policy, fear for the virus, and so forth. In this work, we focus on applying natural language processing (NLP) techniques to analyze tweets in terms of mental health. We trained deep models that classify each tweet into the following emotions: anger, anticipation, disgust, fear, joy, sadness, surprise and trust. We build the EmoCT (Emotion-Covid19-Tweet) dataset for the training purpose by manually labeling 1,000 English tweets. Furthermore, we propose an approach to find out the reasons that are causing sadness and fear, and study the emotion trend in both keyword and topic level. © Springer Nature Switzerland AG 2020.