Abstract
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.