The disparity of utilization rate among specific groups for a rapid spreading telehealth application called LEBER during the COVID-19 state of emergency in Japan.

Objectives: The purpose of this research is to describe the social demographics and chief complaints of users of a free medical consultation application in Ibaraki Prefecture, where a free medical consultation application was released. Methods: The present study included users of a telehealth application in Ibaraki Prefecture between April 9 and May 17, 2020, during the state of emergency. User background characteristics were descriptively analyzed to clarify individual factors with the potential to act as barriers to equally using innovative solutions. Additionally, the age and consultation time distribution by sex were examined for those who used the application for COVID-19 and non-COVID-19 issues. Results: Most of the participants were in their thirties. Moreover, 72% were female, with most being in their thirties (86%) and the least being in their sixties (45%). The number of consultations was concentrated between 6 p.m. and 10 p.m., with the least between 1 a.m. and 5 a.m. The telehealth application users were mainly females in their thirties and forties. Conclusions: To prevent the widening of health disparities due to the rapid introduction of telehealth, further research is required to identify why the use of the application did not spread beyond the aforementioned user groups.

SARS-CoV-2 suppression and early closure of bars and restaurants: a longitudinal natural experiment.

Despite severe economic damage, full-service restaurants and bars have been closed in hopes of suppressing the spread of SARS-CoV-2 worldwide. This paper explores whether the early closure of restaurants and bars in February 2021 reduced symptoms of SARS-CoV-2 in Japan. Using a large-scale nationally representative longitudinal survey, we found that the early closure of restaurants and bars decreased the utilization rate among young persons (OR 0.688; CI95 0.515-0.918) and those who visited these places before the pandemic (OR 0.754; CI95 0.594-0.957). However, symptoms of SARS-CoV-2 did not decrease in these active and high-risk subpopulations. Among the more inactive and low-risk subpopulations, such as elderly persons, no discernible impacts are observed in both the utilization of restaurants and bars and the symptoms of SARS-CoV-2. These results suggest that the early closure of restaurants and bars without any other concurrent measures does not contribute to the suppression of SARS-CoV-2.

CNN-based framework for classifying temporal relations with question encoder.

Temporal-relation classification plays an important role in the field of natural language processing. Various deep learning-based classifiers, which can generate better models using sentence embedding, have been proposed to address this challenging task. These approaches, however, do not work well due to the lack of task-related information. To overcome this problem, we propose a novel framework that incorporates prior information by employing awareness of events and time expressions (time-event entities) with various window sizes to focus on context words around the entities as a filter. We refer to this module as "question encoder." In our approach, this kind of prior information can extract task-related information from simple sentence embedding. Our experimental results on a publicly available Timebank-Dense corpus demonstrate that our approach outperforms some state-of-the-art techniques, including CNN-, LSTM-, and BERT-based temporal relation classifiers.