Background: The empirical basis for a quantitative assessment of the disease burden imposed by long-COVID is currently scant. We aimed to assess the disease burden caused by long-COVID in Japan. Methods: We conducted a cross sectional self-report questionnaire survey. The questionnaire was mailed to 530 eligible patients, who were recovered from acute COVID-19 in April 2021. Answers were classified into two groups; participants who have no symptom and those who have any ongoing symptoms that lasted longer than four weeks at the time of the survey. We compared health-related quality of life scores estimated by the EQ-5D-3L questionnaire between these two groups after adjusting basic characteristics of the participants by propensity score matching. Results: 349 participants reported no symptoms and 108 reported any symptoms at the time of the survey. The participants who reported any symptoms showed a lower value on a Visual Analogue Scale (median 70 [IQR 60-80]) and on the EQ-5D-3L (median 0.81 [IQR 0.77-1.0]) than those reporting no symptoms (median 85 [IQR 75-90] and 1.0 [IQR 1.0-1.0], respectively). After adjusting for background characteristics, these trends did not change substantially (Visual Analog Scale: median 70 [IQR 60-80] vs 80 [IQR 77-90], EQ-5D-3L: median 0.81 [IQR 0.76-1.0] vs 1.0 [IQR 1.0-1.0]). Conclusions: Due to their long duration, long-COVID symptoms represent a substantial disease burden expressed in impact on health-related quality of life.

Background: We aimed to assess the impact of regional heterogeneity on the severity of COVID-19 in Japan. Methods: We included 27,865 cases registered between January 2020 and February 2021 in the COVID-19 Registry of Japan to examine the relationship between the National Early Warning Score (NEWS) of COVID-19 patients on the day of admission and the prefecture where the patients live. A hierarchical Bayesian model was used to examine the random effect of each prefecture in addition to the patients’ backgrounds. In addition, we compared the results of two models; one model included the number of beds secured for COVID-19 patients in each prefecture as one of the fixed effects, and the other model did not. Results: The results indicated that the prefecture had a substantial impact on the severity of COVID-19 on admission. Even when considering the effect of the number of beds separately, the heterogeneity caused by the random effect of each prefecture affected the severity of the case on admission. Conclusions: Our analysis revealed a possible association between regional heterogeneity and increased/decreased risk of severe COVID-19 infection on admission. This heterogeneity was derived not only from the number of beds secured in each prefecture but also from other factors.