Burnout among physiotherapists has been reported worldwide during the coronavirus disease 2019 (COVID-19) pandemic. However, no information was found on the prevalence of burnout among physiotherapists in Japan during the COVID-19 pandemic. Physiotherapists directly providing physiotherapy to patients with COVID-19 in the red zone of 487 medical facilities were evaluated for the prevalence of burnout using the Japanese version of the Maslach Burnout Inventory-General Survey (MBI-GS). The association between the presence or absence of burnout and the working environment was analyzed using logistic regression analysis. Among the 566 physiotherapists analyzed, 99 (17.5%) satisfied the MBI-GS criteria for burnout. Multivariate analysis showed that burnout was associated with the year of physiotherapy experiences [odds ratio (OR) 0.96, 95% confidence interval (CI) 0.93–0.99], feeling slight burden with infection control (OR 0.53, 95% CI 0.32–0.87), not feeling too burdened with infection control (OR 0.27, 95% CI 0.06–0.83), establishment of staffing standards for physiotherapy according to the number of beds (OR 1.80, 95% CI 1.09–2.96), and relaxation time (OR 0.49, 95% CI 0.30–0.82). Moreover, the OR increased as the self-improvement time decreased (OR 38.3, 95% CI 6.64–731). In Japan, the prevalence of burnout among physiotherapists during the COVID-19 pandemic was an intermediate value between the prevalence of burnout among physicians and nurses reported in previous studies. This study found the need to establish appropriate staffing standards for physiotherapy and support systems including secure self-improvement time and appropriate training according to physiotherapy experiences and each medical facility.

Objectives Numerous people have died from coronavirus disease 2019 (COVID-19) infection. Identifying crucial predictive biomarkers of disease mortality is critical to support decision-making and logistic planning in healthcare systems. This study investigated the association between mortality and medical factors and prescription records in 2020 in Japan, where COVID-19 prevalence and mortality remain relatively low. Methods This retrospective cohort study analyzed anonymous administrative data from the Diagnosis Procedure Combination (DPC) database in Japan. Results A total of 22,795 patients were treated in DPC hospitals in 2020 in Japan, and of these, 5,980 patients over 50 years old were hospitalized, with 299 (5.0%) dying. There were 2,399 severe patients among 11,440 total hospitalized patients (all ages). The results of a logistic model analysis revealed that an older age, male sex, Parkinson's disease, cerebrovascular diseases, and chronic kidney diseases were risk factors for mortality. A machine learning analysis identified an older age, male sex (mortality), pneumonia, drugs for acid-related disorders, analgesics, anesthesia, upper respiratory tract disease, drugs for functional gastrointestinal disorders, drugs for obstructive airway diseases, topical products for joint and muscular pain, diabetes, lipid-modifying agents, calcium channel blockers, drugs for diabetes, and agents acting on the reninangiotensin system as risk factors for a severe status. Conclusions This COVID-19 mortality risk tool is a well-calibrated and accurate model for predicting mortality risk among hospitalized patients with COVID-19 in Japan, which is characterized by a relatively low COVID-19 prevalence, aging society, and high population density. This COVID-19 mortality prediction model can assist in resource utilization and patient and caregiver education and be useful as a risk stratification instrument for future research trials.

The Japanese government adopted policies to control human mobility in 2020 to prevent the spread of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). The present study examined the impact of human mobility on COVID-19 cases at the prefectural level in Japan by devising an indicator to have a relationship between the number of infected people and on human mobility. We calculated origin–destination travel mobility within prefectures in Japan from March 1st to December 31st, 2020, using mobile phone data. A cross-correlation function (CCF) was used to examine the relationship between human mobility and a COVID-19 infection acceleration indicator (IAI), which represents the rate of change in the speed of COVID-19 infection. The CCF of intraprefectural human mobility and the IAI in Tokyo showed a maximum value of 0.440 at lag day 12, and the IAI could be used as an indicator to predict COVID-19 cases. Therefore, the IAI and human mobility during the COVID-19 pandemic were useful for predicting infection status. The number of COVID-19 cases was associated with human mobility at the prefectural level in Japan in 2020. Controlling human mobility could help control infectious diseases in a pandemic, especially prior to starting vaccination.