Methods
We included 27,865 cases registered between January 2020 and February
2021 in the COVID-19 Registry of Japan
(COVIREGI-JP)11. We used a Bayesian hierarchical model
to construct a prefecture-specific random intercept to evaluate the
effect of regional heterogeneity.
The capacity of medical facilities can be evaluated by the number of
hospital beds; however, the differences in health policies against
COVID-19 among local municipalities should also be taken into
consideration as the planning of hospital bed distribution and the
priority of hospitalization indication are influenced by the policy of
each local municipality. Considering this, we constructed two models:
one which included the number of beds secured for COVID-19 patients in
each prefecture as a fixed effect (model B) and one which did not (model
A).
The independent variable was the National Early Warning Score
(NEWS)12 of COVID-19 patients on the day of admission,
and the explanatory variables for the fixed effect were sex, age of 65
years and older, and any risk factor of severe infection (cardiovascular
disease, cerebrovascular disease, chronic respiratory disease, liver
disease, diabetes, hypertension, obesity, dialysis or sever renal
failure, and malignancy) for model A and the number of beds secured for
COVID-19 patients in each prefecture for model B.
The independent variable, the NEWS score at the time of admission, was
assumed to follow a Poisson distribution. We set four separate sampling
chains, each consisting of 12,000 random samples, including 1,000
burnin. The sampling convergence was evaluated using the Gelman–Rubin
statistics (R-hat below 1.1) and by visually inspecting a trace plot.
All analyses were conducted using the R software version
4.0.5.13