Clinical characteristics and the risk of hospitalization of patients with coronavirus disease 2019 quarantined in a designated hotel in Japan

Hiromasa Ito; Tadafumi Sugimoto; Yoshito Ogihara; Tairo Kurita; Masaki Tanabe; Masahiro Hirayama; Shuji Isaji; Kaoru Dohi

doi:10.1371/journal.pone.0280291

. 2023 Jan 17;18(1):e0280291. doi: 10.1371/journal.pone.0280291

Clinical characteristics and the risk of hospitalization of patients with coronavirus disease 2019 quarantined in a designated hotel in Japan

Hiromasa Ito ¹, Tadafumi Sugimoto ^1,^#, Yoshito Ogihara ^1,^#, Tairo Kurita ^1,^#, Masaki Tanabe ^2,^#, Masahiro Hirayama ^3,^#, Shuji Isaji ^4,^#, Kaoru Dohi ^1,^*

Editor: Masaki Mogi⁵

PMCID: PMC9844840 PMID: 36649245

Abstract

The aim was to investigate the clinical characteristics of coronavirus disease 2019 (COVID-19) patients who were admitted to a designated hotel, and to clarify the risk factors for hospitalization of such patients with clinical deterioration. The medical records of COVID-19 patients who were admitted to the designated hotel in Mie Prefecture, Japan, between August 2020 and September 2021 were reviewed retrospectively. Of the 1,087 COVID-19 patients who were admitted to the designated hotel, 936 patients (32.1± 12.8 years, 61.3% male) were recruited after excluding patients under the age of 15 years (n = 33), those admitted from the hospital (n = 111, 10.2%), COVID-19 vaccinated patients (n = 4, 0.4%), and those who were discharged to their own home due to social disorders (n = 3). During the study period, 884 patients (94.4%) were discharged to their own home with improving symptoms, whereas 52 patients (5.6%) were hospitalized for a deteriorating clinical condition. The logistic regression analyses showed that older age (≥ 40 years), higher body mass index (≥ 25 kg/m²), hypertension were the risk factors for hospitalization. As the new risk scale score based on the results of the odds ratios increased, the hospitalization rate increased significantly: 2.0% at 0–1 points, 9.7% at 2–3 points, and 28.8% at 4–5 points (p < 0.001). None of the 52 hospitalized patients died, and none developed serious complications from COVID-19 after hospitalization. In conclusion, the designated accommodation program for COVID-19 patients was safe, especially for those with a low risk for hospitalization.

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global health and medical resources crisis [1]. The first case of COVID-19 was reported in Japan on January 16, 2020. On February 2020, an outbreak of COVID-19 was identified on an international cruise ship that left Yokohama Port [2]. In this outbreak, 712 passengers and crew were positive on SARS-CoV-2 testing, and some positives were quarantined at a tourist hotel in Chiba. This hotel also accepted the temporary returnees from Wuhan, China since January 29, 2020.

On February 1, 2020, the Government of Japan classified COVID-19 as a designated infectious disease under the Infectious Diseases Control Law [3], and individuals with a positive test result for SARS-CoV-2 had to be quarantined in an isolation ward in a hospital based on the law. However, it became difficult to admit all positive patients to the hospital due to the rapid increase in new COVID-19 cases. Accordingly, the Ministry of Health, Labour and Welfare proceeded to prepare an accommodation program in designated hotels for their recovery, and the government’s Novel Coronavirus Response Headquarters notified each Prefecture of the policy and operation manuals for hotel accommodation and home recuperation on April 2, 2020. In response to the notification, the designated accommodation program in a tourist hotel began from August 13, 2020, and use of a second designated hotel was started from June 15, 2021 in Mie Prefecture.

Therefore, the purpose of the present study was to investigate the clinical characteristics of COVID-19 patients who were admitted to a designated hotel, and to clarify the safety of the designated hotel accommodation program and identify the risk factors for hospitalization during outbreaks or pandemic periods using the medical records in Mie Prefecture, Japan.

Materials and methods

Study design and study population

This was a retrospective, single-cohort study that enrolled COVID-19 patients in the designated hotel in Mie Prefecture, Japan, between August 2020 and September 2021.

The accommodation program at a designated hotel

The designated hotel accommodation programs were operated at two tourist hotels for COVID-19 patients in Mie Prefecture. In this study, one hotel, in which the designated hotel accommodation program was first operated, was analyzed. The designated hotel accommodation served as a temporary isolation and monitoring program until the patients had recovered from COVID-19. In principle, the coordinating team of the prefectural office has the authority to suggest whether patients can recuperate at home or should be admitted to the designated hotel based on the patient’s age, general condition, and coexisting disorders reported from public health centers. Doctors and nurses were dispatched from the cooperating medical institutions, including Mie University Hospital, and they were stationed at the hotel to monitor patients’ conditions and to respond to sudden deterioration. If the stationed doctors or nurses determined that a patient’s symptoms or condition had worsened, the coordinating team of the prefectural office quickly arranged for hospital treatment (S1 Fig).

Hotel admission criteria

Patients were allowed to be admitted to the hotel based on the following criteria in the 2nd and 3rd waves: under 40 years of age, asymptomatic or mild/improving symptoms, no pre-existing comorbidities (respiratory disorder, cardiovascular disease, hypertension, diabetes mellitus, and renal disorder), body mass index (BMI) ≤ 30 kg/m², no immunodeficiency, no pregnancy, no moderate/severe pneumonia, and no specific abnormalities (renal failure, hepatic disorder, severe anemia, or coagulation disorder) other than an inflammatory reaction. Hotel admission was permitted if the physician decided that the blood and radiographic examinations were not needed. Indeed, almost all patients (96.5%) who were admitted from their home did not undergo blood or radiographic examinations before the hotel admission.

The admission criteria were revised with every wave of COVID-19 infection (S1 Table); that is, the upper age limit for acceptance was raised to 50 years in the 4th wave and 65 years in the 5th wave. In addition, the admission criteria for the clinical condition including patient symptoms and comorbidities for acceptance were also relaxed in the 4th and 5th waves. The patients gave written, informed consent before hotel admission and were required to follow predetermined rules at the designated hotel [4].

Definitions of patients’ characteristics

Hypertension was diagnosed if peripheral blood pressure was > 140/90 mmHg or if the patient was prescribed medication for hypertension. Diabetes mellitus was diagnosed if hemoglobin A1c was > 6.5% or was assumed if the patient was prescribed medication for the treatment of diabetes mellitus. Heart disease was defined as heart failure, angina pectoris, or a history of myocardial infarction. Heart failure was diagnosed if the patient had a history of a hospitalization for heart failure, symptoms due to heart failure (New York Heart Association functional class ≥ 2), or left ventricular ejection fraction < 40%.

Respiratory disease was defined as persistent lung disorders such as asthma, chronic obstructive pulmonary disease, or restrictive lung diseases. The severity of COVID-19 at admission was classified as mild, moderate, or severe. Patients with mild COVID-19 were defined as those who did not require oxygen, patients with moderate COVID-19 were defined as those who required oxygen, and patients with severe COVID-19 were defined as those who required intubation or extracorporeal membrane oxygenation [5].

Recovery protocol at the hotel

The patients’ vital signs including blood pressure, pulse rate, percutaneous oxygen saturation (SpO₂), and temperature were checked by the nurses when the patients arrived at the hotel. They were then moved to individual rooms after taking the short orientation and agreed to stay [6]. All patients were instructed to measure their temperatures and SpO₂ three times a day (morning, noon, and evening), and to report the results by telephone or the Health Center Real-time Information-sharing System (HER-SYS) [7]. Nurses were stationed around the clock in two shifts to observe the patients’ health condition by telephone twice a day, morning and evening. The designated hotel is a non-medical facility, and, therefore, face-to-face medical care, including oxygen administration and intravenous fluid therapy, could not be provided. However, if a patient described symptoms or requested a medical consultation, the on-call doctor examined the patient by telephone or video. Thus, telemedicine, including drug prescription (without anti-viral drugs), could be provided. Tests for active SARS-CoV-2 infection were not conducted at the hotel. The patients could be discharged from the hotel 10 days after the onset of symptoms and 48 hours after the resolution of fever or respiratory symptoms had improved.

Emergency protocol

If a patient developed cardiopulmonary arrest, shock, severe oxygen desaturation (SpO₂ < 90%), or consciousness disturbance, the doctor or nurse immediately came to the patient’s room and called for transport of the patient to the emergency hospital.

The doctor or nurse also arranged for transportation of a patient to the hospital when the coordinating team of the prefectural office recommended it, if the patient developed sudden onset dyspnea, oxygen desaturation (SpO₂ < 93%), chest pain, orthopnea, wheezing, difficulty breathing, ill complexion, persistent fever (≥ 38.5°C more than 2 consecutive days), inadequate oral intake (less than 30% for more than 2 days), difficulty walking, or wound/fractures that required treatment. All patients who met the above criteria were transferred to the hospital.

Endpoint

The endpoint was hospitalization with clinical deterioration as described above the emergency protocol. In addition, the entire population was divided into two groups, depending on whether they were hospitalized or discharged home.

Ethical considerations

This study conformed to the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Mie University Graduate School of Medicine and each participating hospital ethics committee (Reference number H2021-185). All patients gave their “opt-out” informed consent for the study. Data were acquired from electronic medical records and anonymized after collection. Patients under the age of 15 years were excluded due to ethical considerations.

Statistical analysis

Continuous variables with normal distributions are expressed as means ± standard deviation (SD) according to the distribution of the data. Non-normally distributed data are reported as medians [interquartile range (IQR)]. Categorical variables are expressed as percentages (%) unless otherwise indicated. Baseline characteristics were compared with the chi-squared test or Fisher’s exact test for categorical variables, and with Student’s or Welch’s t-test after testing for a normal distribution of continuous variables. Statistical analyses for multiple groups were performed by one-way ANOVA, followed by Holm’s post hoc test for multiple comparisons. The post hoc tests were performed only if a p value < 0.05 was achieved, and there was no significant variance inhomogeneity. Logistic regression analysis was also performed to investigate the independent predictors of hospitalization, and odds ratio (OR) of each clinical risk factor for hospitalization was calculated.

A simple risk scoring system was created using the significant hospitalization factors. Points were assigned to correlate the score with the ORs determined by the logistic regression model, and the hospitalization rate was stratified by the number of scale points. Internal validation of the original data was performed using the bootstrap method with 200 repetitions of sampling to evaluate whether the scoring system could accurately predict the risk of hospitalization. Significance was defined as a p value < 0.05, and all statistical analyses were performed using EZR (version 1.60), which is a graphical user interface for R (version 4.2.1).

Results

Outcome

The median duration until the admission to the designated hotel was 3 days [IQR 2–4 days] after being diagnosed with COVID-19 by the real-time polymerase chain reaction test. During the study period, 1,087 COVID-19 patients were admitted to the designated hotel, and the following subjects were excluded: patients under the age of 15 years (n = 33, 3.0%), those admitted from the hospital (n = 111, 10.2%), COVID-19-vaccinated patients (n = 4, 0.4%), and patients who were discharged to their own home due to mental or social disorders (n = 3, 0.3%).

Finally, a total of 936 patients were included in the current analysis. Of them, 884 patients (94.4%) were discharged to their own home with improving symptoms, whereas 52 patients (5.6%) were hospitalized due to the deterioration of their conditions (Fig 1). However, no patients died or had serious complications from COVID-19 during the study period. The reasons for the hospitalization were persistent fever (N = 41, 78.8%), followed by poor oral intake (N = 19, 36.5%), oxygen desaturation (N = 17, 32.7%), and worsening respiratory symptoms (N = 11, 21.2%).

Number of COVID-19 patients

Fig 2 shows the changes in the numbers of COVID-19 patients in Mie Prefecture from August 25, 2020 to September 31, 2021. The first patient was admitted to the designated hotel due to the increasing hospital bed occupancy rate in the 2nd wave. As the number of COVID-19 infections increased, the number of patients who were admitted to the designated hotel increased during the 3rd, 4th, and 5th waves. The hospital bed occupancy rate had also reached over 60% in early May 2021 due to increased social activities and spread of the Alpha variant (the 4th wave). Since August 2021, the bed occupancy rate reached about 70% in the 5th wave caused by the spread of the Delta variant, and the number of patients who were admitted to hotels or who recuperated at home increased. The number of infected people in this study according to the public health center is shown in S2 Table.

Patients’ characteristics

Table 1 shows the patients’ characteristics. The average age was 32.1 ± 12.8 years, and 61.3% were male. The mean age was higher (31.3 ± 12.4 vs 46.0 ± 11.9 years, p < 0.01), and BMI was higher (22.2 ± 3.2 vs 24.4 ± 3.3 kg/m², p < 0.01) in the hospitalized patients than in the patients who were discharged to their own homes. Hospitalized patients had a significantly higher prevalence of hypertension than those who were discharged to their own home. On hotel admission, the temperature was higher (36.7 ± 0.5 vs 37.4 ± 0.8°C, p < 0.01) and the SpO₂ level was lower (98.3 ± 1.0 vs 97.4 ± 1.8%, p < 0.01) in the hospitalized patients compared with those who were discharged to their own home. Table 1 shows patients’ symptoms during hotel accommodation. Of the total cases, 7.2% reported no symptoms during the clinical course. Compared with the patients who were discharged to their own home, the hospitalized patients had significant higher rates of fever (≥ 37.5°C), cough, shortness of breath, fatigue, loss of appetite, nausea/vomiting, and diarrhea.

Table 1. Patients’ characteristics and symptoms.

	All	Home discharge	Hospitalized	p value
No. of patients	936	884	52
Age, years	27 [21–42]	26 [21–41]	49 [40–55]	<0.01
≤29 years, %	512 (54.7)	504 (57.0)	8 (15.4)	<0.01
30–39 years, %	139 (14.9)	134 (15.2)	5 (9.6)	0.37
40–49 years, %	160 (17.1)	144 (16.3)	16 (30.8)	0.01
≥50 years, %	125 (13.4)	102 (11.5)	23 (44.2)	<0.01
Male, %	574 (61.3)	538 (60.9)	36 (69.2)	0.29
BW, kg	62.8 ± 11.8^a	62.3 ± 11.8^b	68.9 ± 11.1^c	<0.01
BMI, kg/m²	22.4 ± 3.3^d	22.2 ± 3.2^e	24.4 ± 3.3^c	<0.01
<18.5 kg/m², %	73 (10.9)^d	71 (11.3)^e	2 (4.5)^c	0.25
18.5–25.0 kg/m², %	457 (68.0)^d	434 (69.1)^e	23 (52.3)^c	0.03
≥25.0 kg/m²	142 (21.1)^d	123 (19.6)^e	19 (43.2)^c	<0.01
Comorbidities
Hypertension	43 (4.6)	36 (4.1)	7 (13.5)	<0.01
Diabetes mellitus	1 (0.1)	1 (0.1)	0 (0.0)	1.00
Dyslipidemia	18 (1.9)	16 (1.8)	2 (3.8)	0.26
Hyperuricemia	12 (1.3)	10 (1.1)	2 (3.8)	0.14
Malignancy (including history), %	5 (0.5)	5 (0.6)	0 (0.0)	1.00
Asthma (including history), %	17 (1.8)	17 (1.9)	0 (0.0)	0.62
Smoking, %	207 (22.1)	195 (22.1)	12 (23.1)	0.86
Temperature on hotel admission, °C	36.7 ± 0.5	36.7 ± 0.5	37.4 ± 0.8	<0.01
Temperature ≥37.0°C at hotel admission, %	290 (31.0)	254 (28.7)	36 (69.2)	<0.01
SpO₂ on room air at hotel admission, %	98.3 ± 1.1	98.3 ± 1.0	97.4 ± 1.8	<0.01
SpO₂ on room air at hotel admission ≥ 97%, %	204 (21.8)	181 (20.5)	23 (44.2)	<0.01
Symptoms during clinical course
Asymptomatic, %	67 (7.2)	67 (7.6)	0 (0.0)	0.05
Fever (≥ 37.5°C), %	670 (71.6)	619 (70.0)	51 (98.1)	<0.01
Fever (≥ 38.5°C), %	91 (9.8)	57 (6.5)	34 (66.7)	<0.01
Max temperature, °C	37.3 ± 0.7	37.2 ± 0.6	38.7 ± 0.8	<0.01
Cough, %	490 (52.4)	447 (50.6)	43 (82.7)	<0.01
Shortness of breath, %	37 (4.0)	18 (2.0)	19 (36.5)	<0.01
Sore throat, %	438 (46.8)	410 (46.4)	28 (53.8)	0.37
Runny nose / nasal obstruction, %	339 (36.2)	322 (36.4)	17 (32.7)	0.69
Olfactory dysfunction, %	154 (16.5)	151 (17.1)	3 (5.8)	0.05
Dysgeusia, %	151 (16.1)	147 (16.6)	4 (7.7)	0.12
Fatigue, %	419 (44.8)	371 (42.0)	48 (92.3)	<0.01
Loss of appetite, %	35 (3.7)	12 (1.4)	23 (44.2)	<0.01
Headache, %	324 (34.6)	308 (34.8)	16 (30.8)	0.65
Nausea / Vomiting, %	28 (3.0)	19 (2.1)	9 (17.3)	<0.01
Diarrhea, %	105 (11.2)	92 (10.4)	13 (25.0)	<0.01
Arthralgia / Myalgia, %	178 (19.0)	163 (18.4)	15 (28.8)	0.09

Open in a new tab

BW: body weight; BMI: body mass index; SpO₂: percutaneous oxygen saturation.

^aN = 645,

^bN = 601,

^cN = 44,

^dN = 672,

^eN = 628.

S3 Table presents the patients’ characteristics divided into three groups according to the waves of COVID-19 infections. Due to the revision of the admission criteria, the age of patients of the 5th wave was higher, and they had a significantly higher prevalence of comorbidities (especially hypertension) compared to the those of the 2nd/3rd wave or 4th wave. Some symptoms, such as fever, cough, sore throat, and arthralgia/myalgia were more frequent in the patients of the 5th wave than of the 2nd/3rd wave or 4th wave, but there was no significant difference in the hospitalization rates.

Risk of hospitalization

Table 2 reports the crude and adjusted ORs and 95% confidence intervals from the logistic regression analysis for hospitalization. The rates of hospitalization were not significantly changed among the waves of COVID-19 patient surge. The odds ratio increased with age and was significantly higher among patients aged > 50 years (OR = 14.2, 95% confidence interval: 6.18–32.6) than that in the other age groups. Presence of hypertension and obesity were also associated with hospitalization.

Table 2. Predictors of hospitalization on logistic regression analysis.

	OR	95% CI	p value
The waves of infection
2nd and 3rd (August 2020 to February 2021)	Ref
4th (March 2021 to June 2021)	0.78	0.30–2.02	0.614
5th (July 2021 to September 2021)	1.36	0.55–3.38	0.504
Age
≤29	Ref
30–39	2.35	0.76–7.30	0.139
40–49	7.00	2.94–16.7	<0.001
≥50	14.2	6.18–32.6	<0.001
Male sex	1.45	0.79–2.65	0.231
BMI
<18.5	0.53	0.12–2.30	0.398
18.5–25.0	Ref
≥25.0	5.48	1.24–24.2	0.025
Hypertension	3.66	1.55–8.69	0.003
Dyslipidemia	2.17	0.49–9.70	0.311
Hyperuricemia	3.50	0.75–16.4	0.112
Smoking	1.06	0.55–2.06	0.864

Open in a new tab

OR: Odds ratio; CI: confidence interval; BMI: body mass index.

We created a simple scale based on the results of the logistic regression model, and classified patients based on their score on this scale as follows: Group 1 (score 0–1, 69.4%), Group 2 (score 2–3, 24.3%), and Group 3 (score 4–5, 6.3%) (Table 3). Fig 3 shows that the hospitalization rate according to the risk scale of COVID-19 severity identified in this study. As the new risk scale score increased, the hospitalization rate increased significantly: 2.0% at 0–1 points, 9.7% at 2–3 points, and 28.8% at 4–5 points (p < 0.001). The bootstrap method was used to internally validate the risk scoring, and the calibration curve was plotted after the original data were repeatedly sampled 200 times (Fig 4). The corrected C-index, calibration intercept, and calibration slope were 0.7612, 0.0412, and 1.0084, respectively. After bootstrap calibration, the mean absolute error was 0.009, and the Hosmer-Lemeshow goodness-of-fit test χ² = 44.739, P = 0.5806, indicating that the model had good prediction accuracy.

Table 3. Risk factors of hospitalization and adjustment factor points.

Risk factor	OR	Adjustment factor points
Age
40–49	7.00	2
≥50	14.2	3
BMI
≥25.0	5.48	1
Hypertension	3.66	1
Risk group	Patients, n (%)	Risk score, points
Group 1	650 (69.4)	0–1
Group 2	227 (24.3)	2–3
Group 3	59 (6.3)	4–5

Open in a new tab

OR: Odds ratio; BMI: body mass index.

Fig 3 — The risk scale is shown in Table 3. Statistical analyses were performed with Fisher’s exact test, followed by Holm’s post hoc test for multiple comparisons. *p < 0.001 vs 0–1 points; † p < 0.001 vs 0–1 points and 2–3 points.

Discussion

The present study investigated the safety of the designated hotel accommodation program and identified risk factors for hospitalization in patients with COVID-19 in Japan. The major findings were: 1) around 6% of patients were hospitalized, but none of them died or developed serious complications from COVID-19; 2) older age, higher BMI, and presence of hypertension were the risk factors for hospitalization due to clinical deterioration; and 3) the new validated risk scale successfully identified high-risk patients for hospitalization. Furthermore, all patients in the present cohort had not yet received the first dose of COVID-19 vaccination. It is important to note that this study was based on data from the unvaccinated population. In Japan, the vaccination rate was low until June 2021, and it dramatically increased to about 50% by September 2021. The number of vaccinated patients who were admitted to a designated hotel between August 2020 and September 2021 was very small in the present cohort, presumably because of the low vaccination rate and the high effectiveness of vaccination in the younger population.

Role and purpose of admission to the hotel

Many countries may not have enough hospital beds for isolating and treating COVID-19 patients [8, 9]. For example, Wuhan in China faced a lack of critical care resources [10]. In Singapore, Hong Kong, and the state of Wisconsin in the USA, community isolation facilities for COVID-19 patients were operated, and the admission of COVID-19 patients to the isolation facility in Singapore was effective in keeping bed occupancy rates low [11].

In Japan, all cases infected with COVID-19 were required to be quarantined with hospitalization based on the initial law. However, a previous study indicated that the Japanese healthcare system and disaster response capabilities had not functioned well in the face of the COVID-19 pandemic due to its dispersed medical resources [12]. The Mie Prefecture Government requested each medical institution to secure the beds for all patients with COVID-19. In addition, 240 rooms for recuperating patients were prepared in the designated hotel in August 2020, and another facility was assigned a designated hotel in preparing for the spread of infection in advance in June 2021. Since the 4th wave, the coordinating team of the prefectural office and public health center aimed to decrease the hospital bed occupancy rate in the face of a further increase in COVID-19 patients, and for that purpose, they endorsed home-isolation for low-risk individuals with no or mild symptoms. In retrospect, the system of admission to the designated hotel combined with home-isolation contributed to saving the limited medical resources and hospital beds.

Safety of recovering at the hotel

Recently, Sakamoto et al. has reported the clinical characteristics of COVID-19 patients who were admitted to public accommodation facilities and risk factors for hospitalization in metropolitan Tokyo [13]. In their research, 6.4% of patients were hospitalized, comparable to the present result (5.9%). Although the accommodation program was managed by each local government, the hospitalization rates were similar between a relatively sparsely populated rural prefectures and a densely populated metropolitan Tokyo areas. Furthermore, no fatal case was observed in the designated accommodation program during hotel stays in Mie Prefecture. However, there is some concern that sufficient support may not be provided depending on the number of infected patients or the scale of the designated accommodation. For example, a man in his 50s with COVID-19 and a history of heart disease was admitted to the designated hotel in Kanagawa Prefecture and died in August 2021 [14].

In each facility in Japan, revision of hotel admission criteria was required according to the COVID-19 infection status. As the hospital bed occupancy rate increases, higher risk patients should be accepted for hotel admission. Indeed, the upper limit age for acceptance was raised to 50 years in the 4th wave and 65 years in the 5th wave, and the admission criteria related to the clinical condition including patient symptoms and comorbidities for acceptance were also relaxed in the 4th and 5th waves in Mie Prefecture. Medical staff stationed at the hotel usually needed to assess the condition or severity of admitted patients from the vital data and symptoms recorded by the patients themselves. Also, in principle, there is no monitoring system and no routine visit from the perspective of infection risk. Thus, it is difficult to manage a sudden change in the patient’s condition immediately. Considering this fact, appropriate risk management for COVID-19 severity on hotel admission is very important for safe recovery at the hotel.

Risk management of COVID-19 severity and hotel admission

There are many reports of the risk of worsening severity of COVID-19. A previous meta-analysis reported that the proportion of diabetes mellitus, cardiovascular disease, and respiratory disease was significantly higher in the critically or mortally ill group compared with the non-critically ill group. Clinical manifestations such as fever, shortness of breath, or dyspnea could also imply the progression of COVID-19 [15]. A cohort study in New York demonstrated that severe obesity (BMI ≥ 35 kg/m²), older age (> 50 years), and male sex were independently associated with higher in-hospital mortality [16]. Public authorities including the Robert Koch Institute and the U.S. CDC reported that older age is a risk factor for severe and fatal disease courses of COVID-19, and some publications identified the age for increased risk to be from 50–60 years [17] and 65 years [18, 19]. Another previous study reported that the maximum temperature during COVID-19 infection was significantly correlated with the mortality rate, and there was a significant increase in mortality for every 0.5°C increase from 37.0°C [20]. An elevated temperature may indicate COVID-19 activity and may increase metabolic demand and oxygen consumption of multiple organs.

Obesity is also one of the most important risk factors for severe COVID-19. The U.S. CDC reported that the adjusted risk ratio for hospitalization was increased in patients with BMI ≥ 30 kg/m² compared with those with a BMI of 18.5–24.9 kg/m² among patients in whom approximately 79% were overweight or obese [21]. SpO₂ is also useful in COVID-19 patients for monitoring acute respiratory failure [22]. A previous study reported that patients with an SpO₂ of less than 92% at rest were more likely to need hospitalization than were patients with higher SpO₂ levels [23].

Furthermore, Ninomiya et al. in Japan showed that overweight (BMI ≥ 25 kg/m²) was one of the independent risk factors for oxygen administration because of pneumonia or a low SpO₂ level (≤ 93%) at rest among hospitalized COVID-19 patients with mild symptoms on admission [24]. Many previous reports suggested that patients with obesity had a background of hypoxemia or respiratory failure associated with pulmonary restriction, ventilation-perfusion mismatch, respiratory muscle fatigue, and sleep apnea syndrome [25, 26]. Therefore, one reason for the low COVID-19 mortality rate in Japan may be that the rate of obese people is lower than in Western countries. Thus, the optimal cut-off for BMI may be associated with race or ethnicity. In fact, only 21% of patients had BMI ≥ 25 kg/m² in the present study. Sakamoto et al. reported that age over 40 years and BMI ≥ 25 kg/m² were the risks for hospitalization in metropolitan Tokyo [13]. Importantly, the present study presented a novel risk score to identify the high-risk patients based on simple parameters including age, BMI, body temperature and SpO₂. Since body temperature and SpO₂ are essential elements in the management of respiratory infections, this novel risk scoring system can be a reliable method to identify COVID-19 patients at high risk of hospitalization.

On the other hand, a recent randomized, controlled trial reported that monitoring of SpO₂ in COVID-19 patients at home did not predict clinical deterioration. There was no significant difference in the number of days alive without hospitalization at 30 days between the pulse oximetry group and the standard program group. In that study, care escalation was triggered if the SpO₂ was ≥ 3% lower than the baseline first SpO₂ measurement (95–100%) [27]. Therefore, the present cut-off value of SpO₂, which is a higher threshold than previously reported, may be useful for the safe management of patients admitted to the designated hotel according to the criteria.

The present findings may contribute to constructing an optimal risk stratification system for safe management in the outpatient accommodation program for patients with COVID-19. Since the majority of patients in the present study were previously healthy and young members of the Japanese population, the optimal threshold of each risk was relatively milder than in previous reports. The independent risk factors for clinical deterioration and their optimal cut-off values may vary depending on patient background characteristics, including race, ethnicity, age range, and comorbidities.

Limitations

This study has several potential limitations. First, because this was an observational, single-center study, unexpected and unmeasured confounders may have affected the outcomes. Second, the decisions to transfer patients to the hospital were at the discretion of the attending physicians or nurses. Therefore, the potential for selection bias cannot be excluded. A well-designed, multi-center, large-scale, prospective trial is needed to confirm the present findings. Third, the present findings may not be generally applied to populations outside of the hotel accommodation program, such as patients advised to stay at home. Fourth, surveillance tests to identify SARS-CoV-2 variants were not performed in all cases, though the characteristics and the variants of SARS-CoV-2 differed. Fifth, the data of home isolation were not available, and it was not possible to evaluate the differences in safety between hotel accommodation and home isolation. If families or roommates monitored the patients isolated at home, home quarantine may be safer than hotel isolation. Finally, although the operation and risk management of the hotel accommodation program were found to be safe in this study, whether this program was effective for reducing the mortality or other serious complication of COVID-19 remains unclear.

Conclusions

The designated hotel program for COVID-19 patients was safe, especially for those with a low risk for hospitalization in Japan. Age ≥ 40 years, BMI ≥ 25 kg/m², and presence of hypertension were the risk factors for hospitalization, and the new risk score successfully identified the higher risk patients.

Supporting information

S1 Fig. Flow chart of COVID-19 patients.

(TIF)

Click here for additional data file.^{(580.5KB, tif)}

S1 Table. Admission criteria and the waves of infection.

(DOCX)

Click here for additional data file.^{(19.3KB, docx)}

S2 Table. Number of infected people reported by each public health center.

(DOCX)

Click here for additional data file.^{(20.1KB, docx)}

S3 Table. Patients’ characteristics stratified by the period of admission to the hotel.

(DOCX)

Click here for additional data file.^{(28.4KB, docx)}

Acknowledgments

The authors are grateful to the coordinating team of the Department of Medical Health, Mie Prefectural Government and all staff working at the hotel.

Data Availability

Data cannot be shared publicly because of the Mie Prefectural Government policy. Qualified researchers may request access to this data from the Clinical Research Ethics Review Committee of Mie University Hospital (kk-sien@med.mie-u.ac.jp).

Funding Statement

The authors received no specific funding for this work.

References

1.Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020; 395(10229): 1054–1062. doi: 10.1016/S0140-6736(20)30566-3 . [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Yamagishi T, Kamiya H, Kakimoto K, Suzuki M, Wakita T. Descriptive study of COVID-19 outbreak among passengers and crew on Diamond Princess cruise ship, Yokohama Port, Japan, 20 January to 9 February 2020. Euro Surveill. 2020; 25(23). doi: 10.2807/1560-7917.ES.2020.25.23.2000272 . [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Japanese Law Translation Database System. Act on the Prevention of Infectious Diseases and Medical Care for Patients with Infectious Diseases. 1998 October 2 [Cited 2022 October 19]. https://www.japaneselawtranslation.go.jp/en/laws/view/2830.
4.Mie Prefectural Government. To COVID-19 patients who will recover at a hotel (Designated Non-Medical Facility). 2021 November 26 [Cited 2022 October 19]. https://www.pref.mie.lg.jp/common/content/001031481.pdf.
5.Yamashita Y, Yachi S, Takeyama M, Nishimoto Y, Tsujino I, Nakamura J, et al. Influence of sex on development of thrombosis in patients with COVID-19: From the CLOT-COVID study. Thromb Res. 2022; 213: 173–178. doi: 10.1016/j.thromres.2022.03.023 . [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Mie Prefectural Government. Recovering at a Designated Hotel 2021. 2021 November 26 [Cited 2022 October 19]. https://www.pref.mie.lg.jp/common/content/000988269.pdf.
7.Ministry of Health, Labor and Welfare in Japan. Health Center Real-time Information-sharing System on COVID-19 (HER-SYS). [Cited 2022 October 19]. https://www.mhlw.go.jp/content/10900000/000678061.pdf.
8.Grasselli G, Pesenti A, Cecconi M. Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response. Jama. 2020; 323(16): 1545–1546. doi: 10.1001/jama.2020.4031 . [DOI] [PubMed] [Google Scholar]
9.Sen-Crowe B, Sutherland M, McKenney M, Elkbuli A. A Closer Look Into Global Hospital Beds Capacity and Resource Shortages During the COVID-19 Pandemic. J Surg Res. 2021; 260: 56–63. doi: 10.1016/j.jss.2020.11.062 . [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Xie J, Tong Z, Guan X, Du B, Qiu H, Slutsky AS. Critical care crisis and some recommendations during the COVID-19 epidemic in China. Intensive Care Med. 2020; 46(5): 837–840. doi: 10.1007/s00134-020-05979-7 . [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Fan EMP, Nguyen NHL, Ang SY, Aloweni F, Goh HQI, Quek LT, et al. Impact of COVID-19 on acute isolation bed capacity and nursing workforce requirements: A retrospective review. J Nurs Manag. 2021; 29(5): 1220–1227. doi: 10.1111/jonm.13260 . [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Yamamoto T, Ozaki M, Kasugai D, Burnham G. Assessment of Critical Care Surge Capacity During the COVID-19 Pandemic in Japan. Health Secur. 2021; 19(5): 479–487. doi: 10.1089/hs.2020.0227 . [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Sakamoto Y, Satoh K, Tanaka T, Yamaguchi T, Miyamoto T, Sawaki K, et al. Risk factors associated with hospitalization in patients with asymptomatic or mild COVID-19 in public accommodation facilities in Tokyo. J Infect Chemother. 2022; 28(10): 1439–1444. doi: 10.1016/j.jiac.2022.06.002 . [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Iwamoto S. Man with mild COVID-19 found in a state of cardiac arrest. The Asahi Shimbun. 2020 December 12 [Cited 2022 October 19]. https://www.asahi.com/ajw/articles/14013991.
15.Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, et al. Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect. 2020; 81(2): e16–e25. doi: 10.1016/j.jinf.2020.04.021 . [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Palaiodimos L, Kokkinidis DG, Li W, Karamanis D, Ognibene J, Arora S, et al. Severe obesity, increasing age and male sex are independently associated with worse in-hospital outcomes, and higher in-hospital mortality, in a cohort of patients with COVID-19 in the Bronx, New York. Metabolism. 2020; 108: 154262. doi: 10.1016/j.metabol.2020.154262 . [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Robert Koch Institute. Informationen und Hilfestellungen für Personen mit einem höheren Risiko für einen schweren COVID-19-Krankheitsverlauf. 2020 October 29 [Cited 2022 October 19]. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Steckbrief.html;jsessionid=451FF151589CCAE044C8142E4E45EF5C.internet052?nn=13490888#doc13776792bodyText15.
18.U.S.CDC. People with Certain Medical Conditions. 2022 May 2 [Cited 2022 October 19]. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html.
19.Johns Hopkins Medicine. Coronavirus COVID-19 (SARS-CoV-2). 2022 July 12 [Cited 2022 October 19]. https://www.hopkinsguides.com/hopkins/view/Johns_Hopkins_ABX_Guide/540747/all/Coronavirus_COVID_19__SARS_CoV_2.
20.Tharakan S, Nomoto K, Miyashita S, Ishikawa K. Body temperature correlates with mortality in COVID-19 patients. Crit Care. 2020; 24(1): 298. doi: 10.1186/s13054-020-03045-8 . [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Kompaniyets L, Goodman AB, Belay B, Freedman DS, Sucosky MS, Lange SJ, et al. Body Mass Index and Risk for COVID-19-Related Hospitalization, Intensive Care Unit Admission, Invasive Mechanical Ventilation, and Death—United States, March-December 2020. MMWR Morb Mortal Wkly Rep. 2021; 70(10): 355–361. doi: 10.15585/mmwr.mm7010e4 . [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Torjesen I. Covid-19: Patients to use pulse oximetry at home to spot deterioration. Bmj. 2020; 371. doi: 10.1136/bmj.m4151 [DOI] [Google Scholar]
23.Shah S, Majmudar K, Stein A, Gupta N, Suppes S, Karamanis M, et al. Novel Use of Home Pulse Oximetry Monitoring in COVID-19 Patients Discharged From the Emergency Department Identifies Need for Hospitalization. Acad Emerg Med. 2020; 27(8): 681–692. doi: 10.1111/acem.14053 . [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Ninomiya T, Otsubo K, Hoshino T, Shimokawa M, Nakazawa M, Sato Y, et al. Risk factors for disease progression in Japanese patients with COVID-19 with no or mild symptoms on admission. BMC Infect Dis. 2021; 21(1): 850. doi: 10.1186/s12879-021-06574-x . [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Sattar N, McInnes IB, McMurray JJV. Obesity Is a Risk Factor for Severe COVID-19 Infection: Multiple Potential Mechanisms. Circulation. 2020; 142(1): 4–6. doi: 10.1161/CIRCULATIONAHA.120.047659 . [DOI] [PubMed] [Google Scholar]
26.Huang Y, Lu Y, Huang YM, Wang M, Ling W, Sui Y, et al. Obesity in patients with COVID-19: a systematic review and meta-analysis. Metabolism. 2020; 113: 154378. doi: 10.1016/j.metabol.2020.154378 . [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Lee KC, Morgan AU, Chaiyachati KH, Asch DA, Xiong RA, Do D, et al. Pulse Oximetry for Monitoring Patients with Covid-19 at Home—A Pragmatic, Randomized Trial. N Engl J Med. 2022. doi: 10.1056/NEJMc2201541 . [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0280291.r001

Decision Letter 0

Masaki Mogi

21 Sep 2022

PONE-D-22-22615Clinical Characteristics and the Risk of Hospitalization of Patients with Coronavirus Disease 2019 Quarantined in a Designated Hotel in JapanPLOS ONE

Dear Dr. Dohi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

The manuscript should be revised according to the Reviewer's suggestions. AE did not see the second reviewer for this manuscript; however, AE decided to avoid the delay in the fate of this manuscript. See the Reviewer's comments carefully and respond to them appropriately.

==============================

Please submit your revised manuscript by Nov 05 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Masaki Mogi

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records/samples used in your retrospective study. Specifically, please ensure that you have discussed whether all data/samples were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data/samples from their medical records used in research, please include this information.

3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

"Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

4. We note that Figure S3 in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

a. You may seek permission from the original copyright holder of Figure S3 to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an ""Other"" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

b. If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study was a single-center, retrospective, observational study conducted at a COVID-19-specific quarantine facility in Japan.

The study was mainly descriptive; however, the authors have also developed a predictive model to predict hospitalization from the quarantine facility.

The following points need to be revised appropriately for publication.

(1) I'm afraid the study concerning an isolation facility in Tokyo, Japan, has already been published. (Sakamoto, et al. PMID: 35717438) Since the sample size of this study was smaller than that of the previous research, it is necessary to show the strengths of this study compared to the previous research. The authors need to focus on the novelty of this study.

(2) The prediction model's method is not clearly stated. In other words, the method of making the predictive model seemed somewhat arbitrary: one of my concerns is the cutoff value (e.g., age of 37 and BMI of 24), and the other concern is the fitting of the model.

How did you decide on the fitting of the model? Did you collect only those that were clinically significant or only those that were statistically significant? Further mention would be needed on this point. If this point cannot be addressed, the study should be revised, focusing on a descriptive study.

(3) Since the model did not do the external validation, the model had severe limitations. Could the authors perform external validations using another dataset or boot-strap method?

(1) Regarding Table S1, did the all patients take blood or radiographic tests before the hotel admission?

(2)Were the patients with mental disorders included in the hotel-quarantine patients? If so, could that information (having mental illnesses or not) be added to the covariates?

(3) As for the criteria of discharge from the quarantine hotel, did it need "COMPLETE" improvement of the patient's symptoms? This seems to differ from the criteria generally used in Japan. In Japan, the end of isolation requires that “respiratory symptoms are improving.” (https://www.mhlw.go.jp/stf/covid-19/qa.html)

(4) Although the authors said "does not provide medical treatment (line 75)", the authors also described "the doctor could also prescribe oral medications for the patients if necessary. (line 117)". This seems to be confusing for readers outside Japan. The authors should state that "face-to-face medical care, including oxygen administration and intravenous fluid therapy, could not be provided" and "however, telemedicine, including drug prescription (without anti-viral drugs), could be provided."

(5) Were all patients with fever lasting more than 2 (consecutive?) days transported and admitted to the hospital? Or did medical providers "consider" transportation? This difference may be significant because of selection bias. The authors should describe it in detail.

(6) was the age of patients normally distributed? If not, the authors should change the notation using median and IQR.

(7) Although about half of the Japanese people had been vaccinated as of September 2021, the number of vaccinated patients in this cohort was too small. Could the authors explain the reason?

(8) The authors should include the waves of COVID-19 surge in the covariate. This is because the characteristics and pathogen of the variants of SARS-CoV-2 (e.g., alpha variant, delta variant…) differed. For the authors, it may be challenging to identify the SARS-CoV-2 variant of the eligible patients. The waves of COVID-19 should be a surrogate covariate for the SARS-CoV-2 variants.

(9) Could the authors show the reference of the sentence in Lines 279-281 in terms of privacy protection? The readers outside Japan may wonder how the authors knew this information.

(10) The authors should discuss the difference between isolation in hotels and isolation at home. For example, their families monitored the patients staying in their homes. While the patients isolated in the hotels might die without anyone noticing because the room in the hotel does not have a security camera and because the patients communicate with someone only by the telephone or some SNS tool. This may imply home quarantine may be safer than hotel isolation. Could the authors show the merit of hotel isolation using the data in this study?

(11) In Line 335, the sentence, "The findings might suggest that the timing of care escalation was too late," would be an intuitive leap. If the authors' opinion was correct, an overwhelming number of patients must have been transported to the hospitals, and as a result, the healthcare system would collapse.</minor></major>

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2023 Jan 17;18(1):e0280291. doi: 10.1371/journal.pone.0280291.r002

Author response to Decision Letter 0

11 Nov 2022

Response to reviewer:

Thank you very much for your suggestions. Your comments were very useful for improving our manuscript. According to your and the other reviewers’ comments, we have updated our manuscript, Table 1, Table 2, Figure 4, and S3 Table.

Major Comments

1. I'm afraid the study concerning an isolation facility in Tokyo, Japan, has already been published. (Sakamoto, et al. PMID: 35717438) Since the sample size of this study was smaller than that of the previous research, it is necessary to show the strengths of this study compared to the previous research. The authors need to focus on the novelty of this study.

RESPONSE:

Thank you for your important suggestion. As you pointed out, the results from Sakamoto et al. were similar to those of our research. However, 1) this is the first report to investigate the clinical characteristics of COVID-19 patients who were admitted to a designated hotel in rural a prefecture, and 2) to present the novel risk score to identify the high-risk patients based on simple parameters including age, BMI, body temperature and SpO2.

We added the reference you provided, and revised the sentence in the introduction and discussion section as follows:

[Introduction]

“Therefore, the purpose of the present study was to investigate the clinical characteristics of COVID-19 patients who were admitted to a designated hotel, and to clarify the safety of the designated hotel accommodation program and identify the risk factors for hospitalization during outbreaks or pandemic periods using the medical records in Mie Prefecture, Japan.” (Page 3, Line 57 – Page 4, Line 60)

[Discussion]

“The present study is the first to have investigated the safety of the designated hotel accommodation program and identified risk factors for hospitalization in patients with COVID-19 in Japan.” (Page 18, Line 268–270)

“… and 3) the new validated 7-point risk scale successfully identified high-risk patients for hospitalization.” (Page 18, Line 273)

“Recently, Sakamoto et al. has reported the clinical characteristics of COVID-19 patients who were admitted to public accommodation facilities and risk factors for hospitalization in metropolitan Tokyo [Y Sakamoto, et al. J Infect Chemother. 2022;28:1439-1444.]. In their research, 6.4% of patients were hospitalized, comparable to the present result (5.9%). Although the accommodation program was managed by each local government, the hospitalization rates were similar between rural prefectures and Tokyo metropolitan areas. Furthermore, no fatal case was observed in the designated accommodation program during hotel stays in Mie Prefecture.” (Page 20, Line 301-307)

“Sakamoto et al. reported that age over 40 years and BMI ≥ 25 kg/m2 were the risks for hospitalization in metropolitan Tokyo [Y Sakamoto, et al. J Infect Chemother. 2022;28:1439-1444.]. Importantly, the present study presented a novel risk score to identify the high-risk patients based on simple parameters including age, BMI, body temperature and SpO2. Since body temperature and SpO2 are essential elements in the management of respiratory infections, this novel risk scoring system can be a reliable method to identify COVID-19 patients at high risk of hospitalization.” (Page 23, Line 359-364)

2. The prediction model's method is not clearly stated. In other words, the method of making the predictive model seemed somewhat arbitrary: one of my concerns is the cutoff value (e.g., age of 37 and BMI of 24), and the other concern is the fitting of the model.

RESPONSE

Thank you for your important suggestion. We agree that the method in the original manuscript was poorly documented. At first, we identified the independent predictors of hospitalization using a logistic regression analysis by adjusting for clinically relevant factors, such as general background characteristics, comorbidities, and vital sign data with p values < 0.01 on univariate analysis (Please see New Table 2). Second, the optimal cut-off values of age, BMI, temperature, and SpO2 were investigated using ROC curve analyses (Please see S2 Figure). Third, we created new risk scale based on the results of adjusted odds ratios of binary logistic regression analysis.

We added New Table 2, revised Table 3 and revised the sentence in the method section as follows.

“Logistic regression analysis was also performed to investigate the independent predictors of hospitalization, which was constructed by adjusting for clinically relevant factors, such as general background characteristics, comorbidities, and vital sign data with p values < 0.01 on univariate analysis. Then, the optimal cut-off values of the identified independent predictors of hospitalization such as age, BMI, temperature, and SpO2 were investigated using ROC curve analyses (S2 Figure). The odds ratio (OR) of each clinical risk factor for hospitalization was calculated by binary logistic regression analysis adjusted by age, male sex, BMI, hypertension, temperature at hotel admission, and SpO2 at hotel admission. A new risk scale based on the results of adjusted odds ratios was created.” (Page 9, Line 160-168)

“Table 3 shows that older age (≥ 37 years), higher BMI (≥ 24 kg/m2), higher temperature (≥ 37.0 °C), and lower SpO2 (≤ 97%) on hotel admission were the independent prognostic factors for hospitalization.” (Page 12, Line 223-225)

[Table legend]

Table 2. Predictors of hospitalization on logistic regression analysis

3. Since the model did not do the external validation, the model had severe limitations. Could the authors perform external validations using another dataset or boot-strap method?

RESPONSE

We appreciate the reviewer’s comment of this point. According to your advice, we performed internal validation using Boot-strap method. Our scoring model also had good discrimination (C-index, 0.8561) and calibration (Hosmer–Lemeshow test χ2 = 40.500, P value = 0.1958). These statistical analyses were performed using R (version 4.2.1).

We added Figure 4 and revised the sentence in the method and results section as follows.

[Methods]

“Internal validation of the original data was performed using the bootstrap method with 200 repetitions of sampling to evaluate whether the scoring system could accurately predict the risk of hospitalization. Significance was defined as a p value < 0.05, and all statistical analyses were performed using EZR (version 1.60), which is a graphical user interface for R (version 4.2.1).” (Page 9, Line 171 - Page 10, Line 174)

[Results]

“The bootstrap method was used to internally validate the risk scoring, and the calibration curve was plotted after the original data were repeatedly sampled 200 times (Fig 4). The corrected C-index, calibration intercept, and calibration slope were 0.8561, 0.0294, and 1.0042, respectively. After bootstrap calibration, the mean absolute error was 0.009, and the Hosmer-Lemeshow goodness-of-fit test χ2 = 40.500, P = 0.1958, indicating that the model had good prediction accuracy.” (Page 17, Line 245-249)

[Figure legend]

Fig 4. Calibration curve with bootstrap resampling validation for predicting the risk of hospitalization.

Mainor Comments

1. Regarding Table S1, did the all patients take blood or radiographic tests before the hotel admission?

RESPONSE

Thank you for your important suggestion.

“Hotel admission was permitted if the physician decided that the blood and radiographic examinations were not needed. Indeed, almost all patients (96.5%) who were admitted from their home did not undergo blood or radiographic examinations before the hotel admission.”

We added the above sentences in the section of the method. (Page 5, Line 87-90)

2. Were the patients with mental disorders included in the hotel-quarantine patients? If so, could that information (having mental illnesses or not) be added to the covariates?

RESPONSE

Thank you for your important suggestion. Unfortunately, there were no detailed data on mental disorders for each patient before hotel admission in the present study.

3. As for the criteria of discharge from the quarantine hotel, did it need "COMPLETE" improvement of the patient's symptoms? This seems to differ from the criteria generally used in Japan. In Japan, the end of isolation requires that “respiratory symptoms are improving.” (https://www.mhlw.go.jp/stf/covid-19/qa.html)

RESPONSE

We apologize for confused you. We revised the incorrect description as follows.

“The patients could be discharged from the hotel 10 days after the onset of symptoms and 48 hours after the resolution of fever or respiratory symptoms had improved.” (Page 7, Line 126-127)

4. Although the authors said "does not provide medical treatment (line 75)", the authors also described "the doctor could also prescribe oral medications for the patients if necessary. (line 117)". This seems to be confusing for readers outside Japan. The authors should state that "face-to-face medical care, including oxygen administration and intravenous fluid therapy, could not be provided" and "however, telemedicine, including drug prescription (without anti-viral drugs), could be provided."

REPONSE

Thank you for your valuable suggestions on our manuscript. We revised the manuscript according to your suggestion.

“Since the designated hotel is a non-medical facility and does not provide medical treatment, Doctors and nurses were dispatched from the cooperating medical institutions, including Mie University Hospital, and they were stationed at the hotel to monitor patients’ conditions and to respond to sudden deterioration. If the stationed doctors or nurses determined that a patient’s symptoms or condition had worsened, the coordinating team of the prefectural office quickly arranged for hospital treatment (S1 Figure). ” (Page 4, Line 74 – Page 5, Line 79)

“The designated hotel is a non-medical facility, and, therefore, face-to-face medical care including oxygen administration and intravenous fluid therapy could not be provided. However, if a patient described symptoms or requested a medical consultation, the on-call doctor examined the patient by telephone or video. Thus, telemedicine, including drug prescription (without anti-viral drugs), could be provided.” (Page 7, Line 120-124)

5. Were all patients with fever lasting more than 2 (consecutive?) days transported and admitted to the hospital? Or did medical providers "consider" transportation? This difference may be significant because of selection bias. The authors should describe it in detail.

RESPONSE

We completely agree with your suggestion. In our research, all patients with persistent fever (≥ 38.5 ℃ more than consecutive 2 days) were transported and admitted to the hospital. Then, we revised the manuscript as follows.

“…, if the patient developed sudden onset dyspnea, oxygen desaturation (SpO2 < 93%), chest pain, orthopnea, wheezing, difficulty breathing, ill complexion, persistent fever (≥ 38.5 ℃ more than 2 consecutive days), inadequate oral intake (less than 30% for more than 2 days), difficulty walking, or wound/fractures that required treatment. All patients who met the above criteria were transferred to the hospital.” (Page 7, Line 136-138)

6. Was the age of patients normally distributed? If not, the authors should change the notation using median and IQR.

RESPONSE

Thank you for your suggestion. As you pointed out, the age is non-normally distributed data. We revised Table 1, New S4 Table, and the manuscript as follows.

“Non-normally distributed data are reported as medians [interquartile range (IQR)].” (Page 9, Line 154-155)

7. Although about half of the Japanese people had been vaccinated as of September 2021, the number of vaccinated patients in this cohort was too small. Could the authors explain the reason?

RESPONSE

Thank you for your important suggestion. Until June 2021, the number of vaccinated people was low. Indeed, Sakamoto, et al. reported that the vaccinated patients who were admitted to the recovery accommodation facilities was also very low [Y Sakamoto, et al. J Infect Chemother. 2022;28:1439-1444.] (no patients were vaccinated between November 2020 and March 2021, 0.1% were vaccinated between April 2021 and May 2021, and 11.8% were vaccinated between June 2021 and July 2021. However, as you pointed out, the vaccinated rate in our study cohort was lower than expected. We revised the manuscript on this point as follows.

“In Japan, the vaccination rate was low until June 2021, and it dramatically increased to about 50% by September 2021. The number of vaccinated patients who were admitted to a designated hotel between August 2020 and September 2021 was very small in the present cohort, presumably because of the low vaccination rate and the high effectiveness of vaccination in the younger population.” (Page 18, Line 276-279)

8. The authors should include the waves of COVID-19 surge in the covariate. This is because the characteristics and pathogen of the variants of SARS-CoV-2 (e.g., alpha variant, delta variant…) differed. For the authors, it may be challenging to identify the SARS-CoV-2 variant of the eligible patients. The waves of COVID-19 should be a surrogate covariate for the SARS-CoV-2 variants.

RESPONSE

Thank you for your important suggestion. Unfortunately, surveillance tests to identify SARS-CoV-2 variants were not performed in all cases. Furthermore, as shown in S1 Table, the admission criteria were revised at every wave of COVID-19 infection in Mie prefecture. Based on the results, we did not use the waves of COVID-19 as a surrogate covariate for the SARS-CoV-2 variants. We added the below sentences in the limitations section.

“Fourth, surveillance tests to identify SARS-CoV-2 variants were not performed in all cases, though the characteristics and the variants of SARS-CoV-2 differed. Furthermore, the admission criteria were revised with every wave of COVID-19 infection in Mie prefecture. Thus, the waves of COVID-19 were not used as a surrogate covariate for the SARS-CoV-2 variants.” (Page 24, Line 387-391)

9. Could the authors show the reference of the sentence in Lines 279-281 in terms of privacy protection? The readers outside Japan may wonder how the authors knew this information.

RESPONSE

Thank you for your suggestion. According to your suggestion, we added the reference, Iwamoto S. Man with mild COVID-19 found in a state of cardiac arrest. The Asahi Shimbun. 2020 December 12 [Cited 2022 October 19]. Available from: https://www.asahi.com/ajw/articles/14013991.

Removed:

“Sudden deaths were also reported in Tokyo, Saitama, Osaka, and Hyogo Prefecture.” (Page 310, Line 310-311)

10. The authors should discuss the difference between isolation in hotels and isolation at home. For example, their families monitored the patients staying in their homes. While the patients isolated in the hotels might die without anyone noticing because the room in the hotel does not have a security camera and because the patients communicate with someone only by the telephone or some SNS tool. This may imply home quarantine may be safer than hotel isolation. Could the authors show the merit of hotel isolation using the data in this study?

RESPONSE

Thank you for the thoughtful comment. This point is very important, however, we don’t have the data of home isolation, and could not evaluate the differences between isolation in the hotel and isolation at home. So, we added the sentence to the limitation section as follows.

“Fifth, the data of home isolation were not available, and it was not possible to evaluate the differences in safety between hotel accommodation and home isolation. If families or roommates monitored the patients isolated at home, home quarantine may be safer than hotel isolation.” (Page 24, Line 391 – Page 25, Line 393)

11. In Line 335, the sentence, "The findings might suggest that the timing of care escalation was too late," would be an intuitive leap. If the authors' opinion was correct, an overwhelming number of patients must have been transported to the hospitals, and as a result, the healthcare system would collapse.

RESPONSE

We agree with your suggestion. According to your suggestion, we removed the sentence.

Response to Journal Requirements:

・We removed the map in S3 Figure, and changed the citation name from “S3 Figure” to “S2 Table”.

・We added the information about the patient records used in our study

“All patients gave their “opt-out” informed consent for the study. Data were acquired from electronic medical records and anonymized after collection.” (Page 8, Line 148-150)

・We added the Data Availability section and the sentence as follows.

“Data cannot be shared publicly because of the Mie Prefectural Government policy. A request for deidentified data can be available from the corresponding author (dohik@med.mie-u.ac.jp).”

・We updated the URL of references due to the broken link (No. 6 and 17).

・We revised the manuscript according to the editation by a native English speaker. (Page 2, Line 27, 28; Page 3, Line 48; Page 5, Line 91; Page 6, Line 98, 101; Page 10, Line 178-182; Page 11, Line 194; Page 24, Line 385; Page 25, Line 394-396, and the legends of Supplemental materials)

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(37.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0280291.r003

Decision Letter 1

Masaki Mogi

5 Dec 2022

PONE-D-22-22615R1Clinical Characteristics and the Risk of Hospitalization of Patients with Coronavirus Disease 2019 Quarantined in a Designated Hotel in JapanPLOS ONE

Dear Dr. Dohi,

The manuscript still needs major revisions.

Please submit your revised manuscript by Jan 19 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Masaki Mogi

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: I thank the authors for the comments on my concerns.

While some points have been appropriately addressed, several problems remain unresolved, and I will provide some additional comments below.

Initially, I thought that the novelty of this study was in the description of the patient characteristics who received treatments at public quarantine accommodations in Mie Prefecture. However, if the authors think that the main subject of this study is to assess the risk factors for hospitalization from the facilities, the authors and I need to discuss this further.

(1) Since patients are admitted to the hospital if their body temperature (BT) is over 38.5°C for more than two days, BT ≥37.0, one of the risk factors, seems to be an intermediate variable (not a confounding factor) of admission. In other words, it is obvious that patients with high body temperature will be transferred and admitted.

SpO2 may also be an intermediate factor; however, we can also interpret that SpO2 may be a confounding factor because hypoxia of hotel-admitted patients was derived from COPD or other lung diseases. The authors should reconsider whether these are the appropriate risk factors as well.

The authors should provide references that indicate that such intermediate variables are allowed to be included in the prediction model, or the authors should remove these vital signs from the prediction model.

(2) If the above risk factors are not included in the prediction model, the authors cannot show novelty compared with the previous studies.

This is a personal opinion, though; the fact that the population density is different from that of previous studies may lead to the novelty of this study.

Specifically, the novelty could be expressed by focusing on the difference between Tokyo, a densely populated area, and Mie, a relatively sparsely populated area.

However, this comment can be substantially different from the authors' thoughts, so if the authors have a good idea, please discard this opinion of mine.

(3) Can the age be categorized? If the authors want to create a scoring system, they should divide the population into 3-4 categories of age and score them according to odds ratios. This may show the difference from the previous studies.

I could not understand why you did not include N waves of COVID-19 patient surge as a covariate.

If the admission criteria were changed depending on the waves, it is even more likely to be a confounding factor of the outcomes and should be included in the covariate.

I believe that regarding the waves of surge as covariates are not sufficient for addressing the confounding. If the authors consider that the heterogeneity of the patients in each wave is stronger, it is necessary to stratify the calculation of odds ratios by each wave.

At the very least, the fact that "different waves have different criteria for hospitalization" is not a reason to "not add waves to the covariate."

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

**********

PLoS One. 2023 Jan 17;18(1):e0280291. doi: 10.1371/journal.pone.0280291.r004

Author response to Decision Letter 1

20 Dec 2022

Response to reviewer:

Thank you very much for your suggestions. Your comments were very useful for improving our manuscript. According to your comments, we have updated our manuscript, Table 1, Table 2, Table 3, Figure 3, and Figure 4.

1. Initially, I thought that the novelty of this study was in the description of the patient characteristics who received treatments at public quarantine accommodations in Mie Prefecture. However, if the authors think that the main subject of this study is to assess the risk factors for hospitalization from the facilities, the authors and I need to discuss this further.

RESPONSE:

As you pointed out, body temperature and SpO2 seem to be an intermediate variable of hospitalization. We removed these variables from the regression analysis.

We revised Table 2, Table 3, Figure 3, Figure 4, and sentence as follows.

[Abstract]

“The logistic regression analyses showed that older age (≥ 40 years), higher body mass index (≥ 25 kg/m2), and presence of hypertension were the independent risk factors for hospitalization. As the new risk scale score based on the results of the odds ratios increased, the hospitalization rate increased significantly: 2.0% at 0-1 points, 9.7% at 2-3 points, and 28.8% at 4-5 points (p < 0.001).” (Page 2, Line 30 – 35)

[Methods]

“Logistic regression analysis was also performed to investigate the independent predictors of hospitalization, and odds ratio (OR) of each clinical risk factor for hospitalization was calculated. ” (Page 9, Line 160 – 168)

“A simple risk scoring system was created using the significant hospitalization factors. Points were assigned to correlate the score with the ORs determined by the logistic regression model, and the hospitalization rate was stratified by the number of scale points.” (Page 9, Line 169 – Page 10, Line 173)

[Results]

“We created a simple scale based on the results of the logistic regression model, and classified patients based on their score on this scale as follows: Group 1 (score 0-1, 69.4%), Group 2 (score 2-3, 24.3%), and Group 3 (score 4-5, 6.3%) (Table 3). Fig 3 shows that the hospitalization rate according to the risk scale of COVID-19 severity identified in this study. As the new risk scale score increased, the hospitalization rate increased significantly: 2.0% at 0-1 points, 9.7% at 2-3 points, and 28.8% at 4-5 points (p < 0.001). The bootstrap method was used to internally validate the risk scoring, and the calibration curve was plotted after the original data were repeatedly sampled 200 times (Fig 4). The corrected C-index, calibration intercept, and calibration slope were 0.7612, 0.0412, and 1.0084, respectively. After bootstrap calibration, the mean absolute error was 0.009, and the Hosmer-Lemeshow goodness-of-fit test χ2 = 44.739, P = 0.5806, indicating that the model had good prediction accuracy.” (Page 13, Line 240 – Page 14, Line 250)

[Discussion]

“The major findings were: 1) around 6% of patients were hospitalized, but none of them died or developed serious complications from COVID-19; 2) older age, higher BMI, and presence of hypertension were the risk factors for hospitalization due to clinical deterioration; and 3) the new validated risk scale successfully identified high-risk patients for hospitalization.” (Page 19, Line 299 – 304)

Removed:

“The present study showed that older age, higher BMI, higher temperature, and lower SpO2 were the independent risk factors associated with deterioration and hospitalization. However, the cut-off values of any risks were milder in the current cohort population than in previous studies. These results may be mainly due to differences in race or ethnicity and the hotel admission criteria. At the start of hotel recuperation, the hotel admission criteria were set based on the following criteria: under 40 years of age, asymptomatic or mild/improving symptoms, no preexisting comorbidities, and BMI ≤ 30 kg/m2.” (Page 23, Line 375 – 381)

[Conclusions]

“Age ≥ 40 years, BMI ≥ 25 kg/m2, and presence of hypertension were the risk factors for hospitalization, and the new risk score successfully identified the higher risk patients.” (Page 26, Line 429 - 432)

(2) If the above risk factors are not included in the prediction model, the authors cannot show novelty compared with the previous studies.

This is a personal opinion, though; the fact that the population density is different from that of previous studies may lead to the novelty of this study.

Specifically, the novelty could be expressed by focusing on the difference between Tokyo, a densely populated area, and Mie, a relatively sparsely populated area.

However, this comment can be substantially different from the authors' thoughts, so if the authors have a good idea, please discard this opinion of mine.

RESPONSE:

Thank you for your valuable suggestions on our manuscript. We revised the manuscript in the discussion section according to your suggestion.

“Although the accommodation program was managed by each local government, the hospitalization rates were similar between a relatively sparsely populated rural prefectures and a densely populated metropolitan Tokyo areas.” (Page 21, Line 335 – 337)

RESPONSE:

Thank you for your important suggestion. We evaluated the risk of hospitalization by stratified age and BMI. Furthermore, we scored them according to the odds ratios.

We revised Table 1, Table 3 and the sentence as follows.

[Results]

“The odds ratio increased with age and was significantly higher among patients aged > 50 years (OR = 14.2, 95% confidence interval: 6.18-32.6) than that in the other age groups. Presence of hypertension and obesity were also associated with hospitalization.” (Page 13, Line 236 - 239)

[Figure legend]

“Fig 3. The hospitalization rate according to the risk scale score of COVID-19 severity identified in this study. The risk scale is shown in Table 3. Statistical analyses were performed with Fisher’s exact test, followed by Holm’s post hoc test for multiple comparisons. *p < 0.001 vs 0-1 points; † p < 0.001 vs 0-1 points and 2-3 points.”

2. I could not understand why you did not include N waves of COVID-19 patient surge as a covariate.

If the admission criteria were changed depending on the waves, it is even more likely to be a confounding factor of the outcomes and should be included in the covariate.

At the very least, the fact that "different waves have different criteria for hospitalization" is not a reason to "not add waves to the covariate."

RESPONSE

Thank you for your important suggestion. We agree with your opinion. We calculated the odds ratios using the logistic regression model.

We revised Table 2 and revised the sentence as follows.

[Results]

“The rates of hospitalization were not significantly changed among the waves of COVID-19 patient surge.” (Page 13, Line 235 - 236)

[Limitations]

Removed:

“Furthermore, the admission criteria were revised with every wave of COVID-19 infection in Mie prefecture. Thus, the waves of COVID-19 were not used as a surrogate covariate for the SARS-CoV-2 variants.” (Page 25, Line 419 – Line 421)

We moved the following sentence to Page 12, Line 222.

“S3 Table presents the patients’ characteristics divided into three groups according to the waves of COVID-19 infections. Due to the revision of the admission criteria, the age of patients of the 5th wave was higher, and they had a significantly higher prevalence of comorbidities (especially hypertension) compared to the those of the 2nd/3rd wave or 4th wave. Some symptoms, such as fever, cough, sore throat, and arthralgia/myalgia were more frequent in the patients of the 5th wave than of the 2nd/3rd wave or 4th wave, but there was no significant difference in the hospitalization rates.”

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(29.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0280291.r005

Decision Letter 2

Masaki Mogi

26 Dec 2022

Clinical Characteristics and the Risk of Hospitalization of Patients with Coronavirus Disease 2019 Quarantined in a Designated Hotel in Japan

PONE-D-22-22615R2

Dear Dr. Dohi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Masaki Mogi

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: I thank the authors for their great work.

I believe the authors have adequately addressed all issues.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

**********

PLoS One. doi: 10.1371/journal.pone.0280291.r006

Acceptance letter

Masaki Mogi

5 Jan 2023

PONE-D-22-22615R2

Clinical Characteristics and the Risk of Hospitalization of Patients with Coronavirus Disease 2019 Quarantined in a Designated Hotel in Japan

Dear Dr. Dohi:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Masaki Mogi

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Flow chart of COVID-19 patients.

(TIF)

Click here for additional data file.^{(580.5KB, tif)}

S1 Table. Admission criteria and the waves of infection.

(DOCX)

Click here for additional data file.^{(19.3KB, docx)}

S2 Table. Number of infected people reported by each public health center.

(DOCX)

Click here for additional data file.^{(20.1KB, docx)}

S3 Table. Patients’ characteristics stratified by the period of admission to the hotel.

(DOCX)

Click here for additional data file.^{(28.4KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(37.5KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(29.7KB, docx)}

Data Availability Statement

[pone.0280291.ref001] 1.Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020; 395(10229): 1054–1062. doi: 10.1016/S0140-6736(20)30566-3 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref002] 2.Yamagishi T, Kamiya H, Kakimoto K, Suzuki M, Wakita T. Descriptive study of COVID-19 outbreak among passengers and crew on Diamond Princess cruise ship, Yokohama Port, Japan, 20 January to 9 February 2020. Euro Surveill. 2020; 25(23). doi: 10.2807/1560-7917.ES.2020.25.23.2000272 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref003] 3.Japanese Law Translation Database System. Act on the Prevention of Infectious Diseases and Medical Care for Patients with Infectious Diseases. 1998 October 2 [Cited 2022 October 19]. https://www.japaneselawtranslation.go.jp/en/laws/view/2830.

[pone.0280291.ref004] 4.Mie Prefectural Government. To COVID-19 patients who will recover at a hotel (Designated Non-Medical Facility). 2021 November 26 [Cited 2022 October 19]. https://www.pref.mie.lg.jp/common/content/001031481.pdf.

[pone.0280291.ref005] 5.Yamashita Y, Yachi S, Takeyama M, Nishimoto Y, Tsujino I, Nakamura J, et al. Influence of sex on development of thrombosis in patients with COVID-19: From the CLOT-COVID study. Thromb Res. 2022; 213: 173–178. doi: 10.1016/j.thromres.2022.03.023 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref006] 6.Mie Prefectural Government. Recovering at a Designated Hotel 2021. 2021 November 26 [Cited 2022 October 19]. https://www.pref.mie.lg.jp/common/content/000988269.pdf.

[pone.0280291.ref007] 7.Ministry of Health, Labor and Welfare in Japan. Health Center Real-time Information-sharing System on COVID-19 (HER-SYS). [Cited 2022 October 19]. https://www.mhlw.go.jp/content/10900000/000678061.pdf.

[pone.0280291.ref008] 8.Grasselli G, Pesenti A, Cecconi M. Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response. Jama. 2020; 323(16): 1545–1546. doi: 10.1001/jama.2020.4031 . [DOI] [PubMed] [Google Scholar]

[pone.0280291.ref009] 9.Sen-Crowe B, Sutherland M, McKenney M, Elkbuli A. A Closer Look Into Global Hospital Beds Capacity and Resource Shortages During the COVID-19 Pandemic. J Surg Res. 2021; 260: 56–63. doi: 10.1016/j.jss.2020.11.062 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref010] 10.Xie J, Tong Z, Guan X, Du B, Qiu H, Slutsky AS. Critical care crisis and some recommendations during the COVID-19 epidemic in China. Intensive Care Med. 2020; 46(5): 837–840. doi: 10.1007/s00134-020-05979-7 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref011] 11.Fan EMP, Nguyen NHL, Ang SY, Aloweni F, Goh HQI, Quek LT, et al. Impact of COVID-19 on acute isolation bed capacity and nursing workforce requirements: A retrospective review. J Nurs Manag. 2021; 29(5): 1220–1227. doi: 10.1111/jonm.13260 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref012] 12.Yamamoto T, Ozaki M, Kasugai D, Burnham G. Assessment of Critical Care Surge Capacity During the COVID-19 Pandemic in Japan. Health Secur. 2021; 19(5): 479–487. doi: 10.1089/hs.2020.0227 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref013] 13.Sakamoto Y, Satoh K, Tanaka T, Yamaguchi T, Miyamoto T, Sawaki K, et al. Risk factors associated with hospitalization in patients with asymptomatic or mild COVID-19 in public accommodation facilities in Tokyo. J Infect Chemother. 2022; 28(10): 1439–1444. doi: 10.1016/j.jiac.2022.06.002 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref014] 14.Iwamoto S. Man with mild COVID-19 found in a state of cardiac arrest. The Asahi Shimbun. 2020 December 12 [Cited 2022 October 19]. https://www.asahi.com/ajw/articles/14013991.

[pone.0280291.ref015] 15.Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, et al. Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect. 2020; 81(2): e16–e25. doi: 10.1016/j.jinf.2020.04.021 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref016] 16.Palaiodimos L, Kokkinidis DG, Li W, Karamanis D, Ognibene J, Arora S, et al. Severe obesity, increasing age and male sex are independently associated with worse in-hospital outcomes, and higher in-hospital mortality, in a cohort of patients with COVID-19 in the Bronx, New York. Metabolism. 2020; 108: 154262. doi: 10.1016/j.metabol.2020.154262 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref017] 17.Robert Koch Institute. Informationen und Hilfestellungen für Personen mit einem höheren Risiko für einen schweren COVID-19-Krankheitsverlauf. 2020 October 29 [Cited 2022 October 19]. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Steckbrief.html;jsessionid=451FF151589CCAE044C8142E4E45EF5C.internet052?nn=13490888#doc13776792bodyText15.

[pone.0280291.ref018] 18.U.S.CDC. People with Certain Medical Conditions. 2022 May 2 [Cited 2022 October 19]. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html.

[pone.0280291.ref019] 19.Johns Hopkins Medicine. Coronavirus COVID-19 (SARS-CoV-2). 2022 July 12 [Cited 2022 October 19]. https://www.hopkinsguides.com/hopkins/view/Johns_Hopkins_ABX_Guide/540747/all/Coronavirus_COVID_19__SARS_CoV_2.

[pone.0280291.ref020] 20.Tharakan S, Nomoto K, Miyashita S, Ishikawa K. Body temperature correlates with mortality in COVID-19 patients. Crit Care. 2020; 24(1): 298. doi: 10.1186/s13054-020-03045-8 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref021] 21.Kompaniyets L, Goodman AB, Belay B, Freedman DS, Sucosky MS, Lange SJ, et al. Body Mass Index and Risk for COVID-19-Related Hospitalization, Intensive Care Unit Admission, Invasive Mechanical Ventilation, and Death—United States, March-December 2020. MMWR Morb Mortal Wkly Rep. 2021; 70(10): 355–361. doi: 10.15585/mmwr.mm7010e4 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref022] 22.Torjesen I. Covid-19: Patients to use pulse oximetry at home to spot deterioration. Bmj. 2020; 371. doi: 10.1136/bmj.m4151 [DOI] [Google Scholar]

[pone.0280291.ref023] 23.Shah S, Majmudar K, Stein A, Gupta N, Suppes S, Karamanis M, et al. Novel Use of Home Pulse Oximetry Monitoring in COVID-19 Patients Discharged From the Emergency Department Identifies Need for Hospitalization. Acad Emerg Med. 2020; 27(8): 681–692. doi: 10.1111/acem.14053 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref024] 24.Ninomiya T, Otsubo K, Hoshino T, Shimokawa M, Nakazawa M, Sato Y, et al. Risk factors for disease progression in Japanese patients with COVID-19 with no or mild symptoms on admission. BMC Infect Dis. 2021; 21(1): 850. doi: 10.1186/s12879-021-06574-x . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref025] 25.Sattar N, McInnes IB, McMurray JJV. Obesity Is a Risk Factor for Severe COVID-19 Infection: Multiple Potential Mechanisms. Circulation. 2020; 142(1): 4–6. doi: 10.1161/CIRCULATIONAHA.120.047659 . [DOI] [PubMed] [Google Scholar]

[pone.0280291.ref026] 26.Huang Y, Lu Y, Huang YM, Wang M, Ling W, Sui Y, et al. Obesity in patients with COVID-19: a systematic review and meta-analysis. Metabolism. 2020; 113: 154378. doi: 10.1016/j.metabol.2020.154378 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280291.ref027] 27.Lee KC, Morgan AU, Chaiyachati KH, Asch DA, Xiong RA, Do D, et al. Pulse Oximetry for Monitoring Patients with Covid-19 at Home—A Pragmatic, Randomized Trial. N Engl J Med. 2022. doi: 10.1056/NEJMc2201541 . [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Clinical characteristics and the risk of hospitalization of patients with coronavirus disease 2019 quarantined in a designated hotel in Japan

Hiromasa Ito

Tadafumi Sugimoto

Yoshito Ogihara

Tairo Kurita

Masaki Tanabe

Masahiro Hirayama

Shuji Isaji

Kaoru Dohi

Roles

Abstract

Introduction

Materials and methods

Study design and study population

The accommodation program at a designated hotel

Hotel admission criteria

Definitions of patients’ characteristics

Recovery protocol at the hotel

Emergency protocol

Endpoint

Ethical considerations

Statistical analysis

Results

Outcome

Fig 1. Study population.

Number of COVID-19 patients

Fig 2. Changes in the number of COVID-19-infected persons, those who were quarantined in their own home or admitted to the designated hotel, and the hospital bed occupancy rate in Mie Prefecture.

Patients’ characteristics

Table 1. Patients’ characteristics and symptoms.

Risk of hospitalization

Table 2. Predictors of hospitalization on logistic regression analysis.

Table 3. Risk factors of hospitalization and adjustment factor points.

Fig 3. The hospitalization rate according to the risk scale score of COVID-19 severity identified in this study.

Fig 4. Calibration curve with bootstrap resampling validation for predicting the risk of hospitalization.

Discussion

Role and purpose of admission to the hotel

Safety of recovering at the hotel

Risk management of COVID-19 severity and hotel admission

Limitations

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Masaki Mogi

Roles

Author response to Decision Letter 0

Decision Letter 1

Masaki Mogi

Roles

Author response to Decision Letter 1

Decision Letter 2

Masaki Mogi

Roles

Acceptance letter

Masaki Mogi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases