DESIGNING FOR RESILIENCE Infrastructural adaptation Sub- No. of No. of hospitals P value
categories hospitals with a large increase in admissions
Management of healthcare staff Employment of infectious
disease specialist
Employment of infection control nurse
Employment of infection control doctor
Facility management Installation of
ICU department Installation of
isolation room
Installation of negative pressure room
Ward remodeling
Management of hospital beds Operational change in
bed management Mandatory securing
of COVID beds Ward closure
due to cluster
Management of medical equipment Additional purchase
of ventilators
Additional purchase of ECMOs
Additional purchase of haemodialysis machines
Additional purchase of portable negative pressure systems
Total no.
Table 2: Univariate analysis of increased accommodation capacity for COVID-19 patients in 257 hospitals.
No Yes
No Yes
No Yes
No Yes
No Yes
No Yes
No Yes
No Yes
No Yes
No Yes
No Yes
No Yes
No Yes
No Yes
196 61
103 154
83 174
179 78
201 56
204 53
148 109
212 45
107 150
231 26
139 118
221 36
207 50
157 100
257
72 38
23 87
17 93
60 50
66 44
68 42
36 74
74 36
11
99 91
19
32 78
84 26
75 35
53 57
110
often needed to achieve this are generally not feasible because they take considerable time and money. Other anti-pandemic measures should be considered as practical infrastructural adaptations, particularly during the early phase of the pandemic, when it is crucial to promptly secure sufficient accommodation capacity for emergent infected patients. We surveyed the infrastructural adaptations against
the COVID-19 pandemic during its acute phase. The responses to the questionnaire on anti-pandemic measures were analysed statistically using relevant clinical data to identify the key factors related to accommodating the explosion in infected patients
Materials and methods
Survey of infrastructural adaptations We conducted a nationwide survey on the measures taken by hospitals in Japan to address the COVID-19 pandemic, sending a questionnaire to the directors of 4,825 hospitals with 100 or more beds. In the questionnaire, we asked what anti-pandemic measures the hospitals had implemented to mitigate the spread of
26 Health Estate Journal May 2025 <0.0000 0.0001 <0.0000 0.0002 0.0004 <0.0000 <0.0000 <0.0000 <0.0000 <0.0000 <0.0000 <0.0000 <0.0000 0.001
= (Adjusted number of COVID-19 patient admissions in 2020) − (Adjusted number of COVID-19 patient admissions in 2019)
= Increased accommodation capacity for COVID-19 patients
Statistical analysis We statistically analysed the questionnaire responses to identify significant explanatory variables for increasing the accommodation capacity for COVID-19 patients. Statistical analysis was performed using both univariate and multivariate analyses. To assess candidate explanatory variables for an increase in COVID-19 patient admissions, Pearson’s chi-squared test for 2 x 2 tables was used initially to identify variables possibly correlated with increased accommodation capacity for COVID-19 patients. Fisher’s exact test was used for small samples. Relevant variables with P<0.2 were selected for inclusion in the next step of multivariate analysis. The logistic model was used for multivariate analysis with odds ratio as a measure of association. The increased accommodation capacity was classified into categorical variables. When P>0.1, the increase was considered large. We used a stepwise procedure to select important variables relating to increased accommodation capacity for COVID-19 patients using the minimal Bayesian information criterion. We used R software (version 4.4.2, the R Foundation for Statistical Computing Platform) for statistical analysis.
Candidate explanatory variables In this particular study, we only investigated the infrastructural adaptations. Clinical preventative practices such as standard precautions were excluded from candidate variables possibly related to increasing the accommodation capacity. The following variables were included in the univariate analysis: employment of infectious disease specialists, infection control nurses, and infection control doctors, installation of an ICU department, isolation rooms, and negative pressure patient rooms, remodelling general wards associated with improved air ventilation, operational changes in patient beds, mandatory securing of COVID beds, ward closures due to infection ‘clusters’, and additional purchase of ventilators, ECMOs, haemodialysis machines, and portable negative pressure systems. We carried out the same statistical procedures to identify significant variables for the incidence of clusters. We used the aforementioned candidate explanatory variables, except for ward closure due to a cluster, because this variable is closely correlated with the incidence of clusters.
Coronavirus infection between 2019 and 2020. We also asked about the hospital features and clinical activities, such as the number of beds, healthcare staff, non-COVID patient admissions, and COVID-19 patient admissions. The answers were collected online using the Google Forms application. For analysis, we calculated the increase in COVID-19 patient admissions between 2019 and 2020. As the total number of admissions depends on the hospital size, the calculated results were adjusted by the number of nurses. We used the equation below to obtain an objective value of increased accommodation capacity for COVID-19 patients.
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