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. 2021 Jan 12;16(1):e0245318. doi: 10.1371/journal.pone.0245318

Factors associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases: A nationwide population-based observational study

Nobuhiro Sato 1,*, Reo Takaku 2, Hidenori Higashi 3, Alan Kawarai Lefor 4, Takashi Shiga 5
Editor: Ho Ting Wong6
PMCID: PMC7802939  PMID: 33434216

Abstract

Although it is essential to shorten the interval to initial treatment in the care of acute ischemic stroke, some hospitals in Japan reject requests for hospital acceptance from on-scene emergency medical service personnel because of limited resources, which can cause delays in care. We aimed to assess the risk factors for difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases. We conducted a retrospective analysis of the national ambulance records of the Fire and Disaster Management Agency in Japan in 2016. Multivariable logistic regression analysis was used to assess the association between difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases and prehospital factors. During the study period, a total of 222,926 patients were included, and 5283 patients (2.4%) experienced difficulties in hospital acceptance. In multivariable analysis, nights (adjusted odds ratio [AOR] 1.54, 95% confidence interval [CI] 1.45–1.64), weekends (AOR 1.32, 95% CI 1.24–1.40), <25 percentile ratio of emergency physicians and neurosurgeons to all physicians (AOR 1.13, 95% CI 1.03–1.23) (AOR 1.36, 95% CI 1.25–1.48), and mean age of physicians (AOR 1.06, 95% CI 1.05–1.07) were significantly associated with difficulties of hospital acceptance of patients suspected to have cerebrovascular disease. There was a marked regional variation in the difficulties of hospital acceptance. Among the national ambulance records of patients suspected to have cerebrovascular diseases, certain prehospital factors such as weekends were positively associated with difficulty of hospital acceptance. A comprehensive strategy for hospital acceptance of patients with cerebrovascular diseases considering regional variation is required.

Introduction

The number of ambulance dispatches in Japan increased to 6,340,000 in 2017 [1]. The emergency medical system in Japan is different from that in other countries. The medical staff in charge of a hospital emergency department can decide whether to accept or to reject a request for patient acceptance from the on-scene emergency medical service (EMS) personnel [2, 3]. The person who responds to the phone call (physician, nurse, or other staff) from EMS personnel depends on the hospital. In reality, emergency departments often decline to accept patients because of limited resources, e.g., no available hospital beds or the absence of specialists appropriate for the patient’s symptoms [4]. A total of 16.4% of requests for patient acceptance from the on-scene EMS personnel were rejected by medical staff in 2017 [1]. One criterion to define difficulty in hospital acceptance, used by the Fire and Disaster Management Agency (FDMA), is ≥4 phone calls by EMS personnel to hospitals before obtaining acceptance from a destination hospital [2]. The proportion of ≥4 phone calls by EMS personnel to hospitals until acceptance was 2.4% (137,833/5,736,086 patients) in 2017 [1]. An increased number of phone calls to hospitals from ambulances leads to delays in hospital arrival time [2, 3, 5, 6]. As a result, the patient’s transport to the hospital may be critically delayed. Prior studies showed that ≥5 phone calls led to more than 16 minutes prolongation compared with one phone call [2, 5, 6].

Stroke is one of the leading causes of death and long-term disability and is the fourth leading cause of death in Japan [7]. As a stroke progresses, neurons are rapidly and irretrievably lost. The typical patient loses 1.9 million neurons each minute that a stroke is untreated [8]. Indeed, in patients with acute ischemic stroke, early administration of intravenous recombinant tissue plasminogen activator within 4.5 hours improves neurological outcomes [9, 10]. When using endovascular therapy, time to revascularization remains the most critical metric for improved clinical outcomes [11, 12]. Every effort should be made to shorten delay in initiation of treatment to improve the outcomes. Therefore, EMS personnel must transport patients suspected to have an acute stroke to hospitals which can manage stroke as soon as possible. Despite their clinical and public health importance, there is a dearth of research to investigate prehospital factors associated with the difficulty of hospital acceptance among patients with stroke in Japan [6].

We hypothesized that patient characteristics such as age, time of day and number of physicians per population would influence hospital acceptance of patients with stroke and there would be regional variations. The aim of this study was to investigate the factor associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases.

Materials and methods

Study design and participants

This study analyzed the national ambulance records of the FDMA in Japan from 1 January 2016 to 31 December 2016. These data include all emergency transports throughout Japan except for Tokyo prefecture because fire stations in Tokyo are managed by an organization independent of the national government, and are not included in the FDMA database [13]. The data were collected by EMS personnel, in cooperation with the physicians overseeing the patient’s care. This study included all patients ≧15 years of age suspected to have cerebrovascular diseases by physicians at hospitals, who called an ambulance and were transported to a hospital. Hemiplegia, dysarthria, ataxia or severe headache were classified as cerebrovascular disease-related symptoms. We excluded patients with cardio-pulmonary arrest at the hospital and who had missing data. This study was approved by the Ethics Committee of the International University of Health and Welfare (5-19-46), and the requirement for patient informed consent was waived.

Setting

Japan has an area of 378,000 km2 divided into 47 prefectures and the population was approximately 127 million in 2016 [14]. The EMS system in Japan has been described elsewhere [15]. There were 733 fire stations with dispatch centers in 2016; EMS at these fire stations is provided by municipal governments [16]. In most cases, an ambulance has a crew of three providers, including at least one emergency lifesaving technician, a person who has undergone extensive training in the provision of pre-hospital care [17, 18]. Using the protocol established by each municipal fire department, EMS ambulance crews at the scene or emergency dispatchers select an appropriate hospital for emergency care according to medical urgency or the patient’s symptoms.

Data collection

Demographic factors (age and gender), chronological factors (date and time), severity, location, and prefecture were extracted from available data. The severity is classified into 4 categories: dead, severe, moderate and mild [13]. Severe patients are those expected to be hospitalized for over 3 weeks, and moderate patients are those expected to be hospitalized for 3 weeks or less. If patients are not likely to require hospitalization, they are categorized as mild. The location where the emergency occurred is also recorded using the following 5 categories: patient’s home, public area, workplace, road and others. We defined seven geographic regions (Hokkaido-Tohoku, Kanto, Chubu, Kansai, Chugoku, Shikoku, and Kyushu-Okinawa), based on previous studies [19, 20]. To characterize regional (secondary health care area) level effects in these analyses, we obtained data on the following variables at the prefecture level from the Japanese National Survey as well as Japan national physician database provided by Nihon Ultmarc INC: numbers of physicians, female physicians, emergency physicians, and neurosurgeon, population in the area covered by each municipal fire department, the number of elderly people (≧65 years of age), young people (< 15 years of age) per capita income, industry ratio, and mean age of physicians in each area.

Outcome measures

The primary outcome was the difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases [6]. The definition of difficulty of hospital acceptance was the requirement for ≥4 phone calls by EMS personnel to hospitals before obtaining acceptance from destination hospitals, based on reports from the FDMA [2]. The secondary outcome was transportation time from arrival at the scene to arrival at the hospital.

Statistical analysis

Continuous data with skewed distributions was shown as medians and interquartile range (IQR), and categorical data as frequencies and proportions. Bivariate analyses were performed with chi-squared tests for dichotomous variables and the Mann-Whitney U test used for continuous variables. While we use the Mann-Whitney U test to address skewed distribution of some variables, note that the results are almost the same with those obtained using the t-test. For example, the distribution of transportation time is slightly skewed (e.g. skewness = 2.35) but we confirmed that the distribution is sufficiently normal around the mean.

Multivariable analyses were used to assess factors associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases using logistic regression models, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Multivariate linear regression analysis was used to investigate the association between the factors and a reduction in pre-hospital transportation time. We selected covariates based on biological plausibility and previous studies in the multivariable analysis [2, 6, 21]. These variables included age (15–64 years, 65–84 years, ≧85 years), gender (male, female), time of day (daytime [09.00–16.59], night time [17.00–08.59]), day of the week (weekday, weekend), severity (mild, moderate, severe), location (home, public space, workplace, road, others), region (Hokkaido-Tohoku, Kanto, Chubu, Kansai, Chugoku, Shikoku, and Kyushu-Okinawa), month (January, February, March, April, May, June, July, August, September, October, November, December), the number of physicians per population in the area covered by each municipal fire department (<25 percentile, 25–74 percentile, ≧75 percentile), the proportion of emergency physicians to all physicians in the area (<25 percentile, 25–74 percentile, ≧75 percentile), the proportion of neurosurgeon to all physicians in the area (<25 percentile, 25–74 percentile, ≧75 percentile), population in the area, the proportion of older people to all people in the area, the proportion of younger people to all people in the area, per capita income in the area, industry ratio in the area (primary, secondary, tertiary), mean age of physicians in the area, and the proportion of female physicians in the area. In subgroup analyses, we stratified the model according to severity.

Data were analyzed using Stata version 14 (College Station, TX). All tests were two-tailed, and p values <0.05 were statistically significant.

Results

From January to December 2016, 4,805,224 ambulances were dispatched. Of these, 222,926 patients were eligible for inclusion in this study (Fig 1).

Fig 1. Study patient flow.

Fig 1

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Patient characteristics

Patient characteristics according to the number of phone calls to hospitals by emergency medical service personnel are listed in Table 1. A total of 5283 (2.4%) patients had difficulty obtaining hospital acceptance. There were differences in the groups regarding age, gender, time, day of the week, severity, location, region, number of physicians per population, proportion of emergency physician and neurosurgeon, and proportion of female physician. The mean age of physicians in the area was similar among the groups. The median transportation time was 36 (IQR 29–45) minutes during the night and 34 (28–43) minutes during daytime (Table 2). The Kanto region had the longest transportation times.

Table 1. Basic characteristics and number of phone calls to hospitals by emergency medical service.

All number of phone calls to hospitals by EMS
<4 ≥4
variables n = 217643 % n = 5283 % p value
Age, median (IQR) 76(65–84) 75(63–83) <0.001*
Age <0.001†
    15–64 53,721 24.7 1,433 27.1
    65–84 118,512 54.5 2,704 51.2
    85- 50,693 23.3 1,146 21.7
Gender 0.015†
    Male 120,143 55.2 2,937 55.6
    Female 102,783 47.2 2,346 44.4
Time <0.001†
    Daytime (9:00–16:59) 84,429 38.8 1,506 28.5
    Night (17:00–8:59) 138,497 63.6 3,777 71.5
Day of the week <0.001†
    weekday 159,841 73.4 3,468 65.6
    weekend 63,085 29.0 1,815 34.4
Severity <0.001†
    Minor 54,302 25.0 1,202 22.8
    Moderate 126,750 58.2 2,959 56.0
    Severe 41,874 19.2 1,122 21.2
Location <0.001†
    Home 156,742 72.0 3,848 72.8
    Public space 46,191 21.2 946 17.9
    Workplace 7,299 3.4 110 2.1
    Road 7,325 3.4 176 3.3
    Others 5,370 2.5 203 3.8
Region <0.001†
    Hokkaido—Tohoku 30,912 14.2 838 15.9
    Chubu 48,256 22.2 1,502 28.4
    Kanto 34,844 16.0 366 6.9
    Kansai 39,508 18.2 1,511 28.6
    Chugoku 17,874 8.2 429 8.1
    Shikoku 11,452 5.3 227 4.3
    Kyusyu, Okinawa 40,081 18.4 410 7.8
Month
    January 21,281 9.8 985 18.6
    February 18,108 8.3 604 11.4
    March 19,240 8.8 552 10.5
    April 17,863 8.2 393 7.4
    May 17,479 8.0 393 7.4
    June 16,707 7.7 299 5.7
    July 16,502 7.6 317 6.0
    August 16,499 7.6 292 5.5
    September 17,141 7.9 321 6.1
    October 18,368 8.4 342 6.5
    November 18,606 8.6 372 7.0
    December 19,849 9.1 413 7.8
Number of physicians per population <0.001†
    < 25 percentile 59,291 27.2 1,402 26.5
    25–74 percentile 100,741 46.3 2,219 42.0
    ≧ 75 percentile 62,893 28.9 1,662 31.5
Proportion of emergency physicians <0.001†
    < 25 percentile 63,707 29.3 1,353 25.6
    25–74 percentile 105,287 48.4 2,479 46.9
    ≧ 75 percentile 53,932 24.8 1,451 27.5
Proportion of neurosurgeons <0.001†
    < 25 percentile 57,941 26.6 1,310 24.8
    25–74 percentile 104,123 47.8 2,780 52.6
    ≧ 75 percentile 60,862 28.0 1,193 22.6
Mean age of physicians 49.94 50.00 0.319*
Proportion of female physicians 20% 21% <0.001*
Transportation time (from arrival at the scene to arrival at the hospital), median 8.40 8.65 <0.001*
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EMS, emergency medical service; IQR, interquartile range

* Mann-Whitney U test

† chi-squared test

Table 2. Basic characteristics and EMS transportation times.

transportation times
variables median IQR
Age
    15–64 34 28–44
    65–84 35 29–45
    85- 35 28–44
Gender
    Male 35 28–44
    Female 35 28–44
Time
    Daytime (9:00–16:59) 34 28–43
    Night (17:00–8:59) 36 29–45
Day of the week
    weekday 35 28–44
    weekend 35 28–45
Severity
    Minor 34 28–43
    Moderate 35 28–45
    Severe 36 29–46
Location
    Home 36 29–45
    Public space 33 27–41
    Workplace 32 26–41
    Road 34 27–44
    Others 38 31–48
Region
    Hokkaido, Tohoku 36 29–47
    Tokai, Chubu, Hokuriku 33 27–42
    Kanto 39 32–49
    Kansai 35 28–43
    Chugoku 36 29–46
    Shikoku 34 27–43
    Kyusyu, Okinawa 31 25–40
Month
    January 36 29–46
    February 36 29–45
    March 35 28–44
    April 35 28–44
    May 35 28–44
    June 35 28–44
    July 35 28–44
    August 35 28–44
    September 35 28–44
    October 35 28–44
    November 35 28–45
    December 35 29–45
Number of physicians per population
    < 25 percentile 37 29–46
    25–74 percentile 35 28–43
    ≧ 75 percentile 32 26–40
Proportion of emergency physicians
    < 25 percentile 37 29–47
    25–74 percentile 36 29–46
    ≧ 75 percentile 35 28–44
Proportion of neurosurgeons
    < 25 percentile 36 29–46
    25–74 percentile 35 29–44
    ≧ 75 percentile 35 28–44
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EMS, emergency medical service; IQR, interquartile range

Risk factors for difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases

In multivariable logistic analysis, a positive association was observed between difficulty of hospital acceptance and night hours (adjusted OR [AOR] 1.54, 95% CI 1.45–1.64), weekend day (AOR 1.32, 95% CI 1.24–1.40), <25 percentile ratio of emergency physicians and neurosurgeons to all physicians (AOR 1.13, 95% CI 1.03–1.23) (AOR 1.36, 95% CI 1.25–1.48), and mean age of physicians in the area (AOR 1.06, 95% CI 1.05–1.07) (Table 3). Conversely, patients 65–84 years of age (AOR 0.86, 95% CI 0.80–0.91), moderate severity (AOR 0.87, 95% CI 0.81–0.94), public space (AOR 0.91, 95% CI 0.85–0.99), and workplace (AOR 0.62, 95% CI 0.51–0.75) were negatively associated with difficulty obtaining hospital acceptance. In addition, there was a high degree of variation in difficulty of hospital acceptance across regions (Fig 2). In multivariate linear regression analysis, similar associations were observed between an increase in pre-hospital transportation time and night hours, weekend days, 25–74 percentile ratio of neurosurgeons to all physicians and mean age of physicians in the area, while the associations were observed between a reduction in pre-hospital transportation time and public space, and workplace. In subgroup analysis, similar associations among the above factors except for public space and 25–74 percentile ratio of neurosurgeons to all physicians were observed.

Table 3. Factors associated with difficulty of hospital acceptance and transportation time among patients suspected to have cerebrovascular diseases.

All patients
Number of phone calls to hospitals by EMS Transportation time
OR 95% CI Time Difference 95% CI
Age
    15–64 reference reference
    65–84 0.86*** 0.80–0.91 -0.17 -0.44–0.09
    85- 0.93* 0.86–1.01 -0.60*** -0.96 - -0.24
Female 0.96 0.91–1.02 -0.29*** -0.39 - -0.19
Night 1.54*** 1.45–1.64 1.31*** 1.03–1.58
Weekend 1.32*** 1.24–1.40 0.67*** 0.41–0.92
Severity
    Mild reference reference
    Moderate 0.87*** 0.81–0.94 0.32 -0.26–0.91
    Severe 0.92** 0.84–1.00 0.53 -0.30–1.35
Location
    Home reference reference
    Public space 0.91** 0.85–0.99 -1.31*** -1.66 - -0.97
    Workplace 0.62*** 0.51–0.75 -2.35*** -2.70 - -2.00
    Road 1.00 0.85–1.16 -0.93*** -1.38 - -0.49
    Others 1.45*** 1.25–1.70 0.78 -0.87–2.44
Region
    Hokkaido—Tohoku 0.69*** 0.61–0.77 -3.58 -8.37–1.21
    Chubu 0.35*** 0.31–0.40 -5.69** -10.33 - -1.04
    Kanto reference reference
    Kansai 1.10* 1.00–1.20 -4.00** -7.83 - -0.16
    Chugoku 0.81*** 0.71–0.92 -3.94 -8.97–1.09
    Shikoku 0.74*** 0.63–0.87 -6.57*** -11.30 - -1.83
    Kyusyu, Okinawa 0.22*** 0.19–0.26 -8.39*** -13.72 - -3.05
Month
    January reference reference
    February 0.73*** 0.65–0.81 -0.34* -0.71–0.03
    March 0.62*** 0.56–0.69 -0.91*** -1.27 - -0.55
    April 0.47*** 0.42–0.53 -1.11*** -1.53 - -0.69
    May 0.47*** 0.42–0.53 -1.30*** -1.74 - -0.85
    June 0.38*** 0.33–0.43 -1.60*** -2.10 - -1.11
    July 0.40*** 0.36–0.46 -1.75*** -2.35 - -1.15
    August 0.37*** 0.32–0.42 -1.73*** -2.25 - -1.20
    September 0.40*** 0.35–0.45 -1.41*** -1.98 - -0.83
    October 0.39*** 0.35–0.45 -1.36*** -1.92 - -0.79
    November 0.43*** 0.38–0.48 -1.06*** -1.63 - -0.49
    December 0.45*** 0.40–0.51 -1.03*** -1.54 - -0.52
Number of physicians per population
    < 25 percentile 1.09 0.95–1.24 2.81* -0.16–5.79
    25–74 percentile 0.95 0.85–1.04 1.33 -0.91–3.56
    ≧ 75 percentile reference reference
Proportion of emergency physicians
    < 25 percentile 1.13*** 1.03–1.23 0.86 -0.37–2.08
    25–74 percentile 1.17*** 1.08–1.26 0.657 -0.87–2.18
    ≧ 75 percentile reference reference
Proportion of neurosurgeons
    < 25 percentile 1.36*** 1.25–1.48 1.362** 0.10–2.61
    25–74 percentile 1.32*** 1.22–1.44 1.432*** 0.46–2.39
    ≧ 75 percentile reference reference
Mean age of physicians 1.06*** 1.05–1.07 0.29*** 0.09–0.48
Proportion of female physicians 1.38 0.76–2.53 -2.24 -10.28–5.80
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EMS, emergency medical service; OR, odds ratio; CI, confidence interval

* p < 0.10

** p < 0.05

*** p < 0.01

Nagelkerke pseudo R2 = 0.053

Fig 2. Regional variation of the odds ratio of number of phone calls to hospitals by emergency medical service personnel.

Fig 2

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Discussion

Using data from a national ambulance database in Japan, we observed that nights, weekends, and a higher mean age of physicians in the area are associated with increased difficulty of hospital acceptance and an increased transportation time of patients suspected to have cerebrovascular diseases. These data also demonstrate there is a marked regional variation in the difficulties associated with hospital acceptance.

Results in context

To our knowledge, this is the first study to document the difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases [6]. A previous study in Osaka, Japan reported that elderly patients, foreigners, unconsciousness, nights and weekends/holidays were associated with difficulty of hospital acceptance at the scene requiring EMS personnel to make ≥5 phone calls to hospitals until the patient was accepted for transport [6]. Another study showed, among older patients, more advanced age, nights, weekend days and gastrointestinal related symptoms were more associated with difficulties of hospital acceptance while patients with cardiac arrest, acute coronary syndrome and stroke-related symptoms were less likely to have difficulties of hospital acceptance [2]. The results of the present nationwide study demonstrate a positive association between chronological factors such as nights and weekends and difficulties of hospital acceptance, consistent with previous studies. In Japan, the number of medical facilities and staff that can treat emergency patients during nights, weekends or holidays is low [6]. In addition, few hospitals in Japan have physicians who work in shifts, and they usually continue to work for long hours regardless of the time of day [2]. Furthermore, this study underscored that a higher mean age of physicians in the area is associated with greater difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases. It might be challenging for senior physicians to work longer hours to accept emergency patients without working in shifts. Therefore, centralization of medical resources such as physicians, other specialized staff, and equipment to prevent unbalanced seniority of physicians and longer work hours, using a dedicated emergency physician model of emergency care which allows emergency physicians to work in shifts, might be helpful to facilitate acceptance of the patients during nights or weekends [21].

There was a high degree of variation in difficulties associated with hospital acceptance across regions. In particular, Hokkaido-Tohoku, Chubu, Chugoku, Shikoku, and Kyushu-Okinawa were negatively associated with difficulties of hospital acceptance compared with Kanto and Kansai which include big cities like Yokohama, Osaka and Kyoto. This is consistent with a prior report which showed that serial rejection of a patient by several hospitals is more frequent in urban areas, where there are many hospitals, but also many patients [4]. To address this issue, a regionalized stroke system which includes establishing primary stroke centers that can deliver intravenous alteplase and better access to those centers is effective [22, 23]. In addition, a law which punishes hospitals if a patient is declined entry to an emergency department for screening and stabilization might be useful, similar to the Emergency Medical Treatment and Active Labor Act (EMTALA) in the United States [24].

Limitations

The present study has several acknowledged limitations. First, we were unable to obtain information regarding in-hospital outcomes and treatment of patients after arrival at the hospital. Therefore, we did not classify cerebrovascular diseases as acute ischemic stroke or cerebral hemorrhage. Second, the actual severity of a patient’s condition might not be reflected by the severity judged at the time when the ambulance was called, because further assessment of the patient’s condition was conducted upon arrival at the hospital. Some patients might deteriorate during transport and be assessed as critically ill on arrival at the hospital. Third, we could not analyze information regarding consciousness, such as Glasgow Coma Scale or Japan Coma Scale because the data were not available. Fourth, we did not consider national holidays, which were included as weekdays. Finally, despite adjusting for potential covariates, we did not exclude other possible residual confounding factors that might affect difficulties associated with hospital acceptance of patients suspected to have cerebrovascular diseases, such as the area’s hospital bed capacity or occupancy rates.

Conclusion

The results of this study show that prehospital factors of nights, weekends, and a higher mean age of physicians in the area are associated with greater difficulty of hospital acceptance and increased transportation time for patients suspected to have cerebrovascular diseases and that there is a marked regional variation. A comprehensive strategy to facilitate hospital acceptance of patients suspected to have cerebrovascular diseases considering regional variation is required.

Acknowledgments

We would like to thank all of the emergency medical services personnel.

Abbreviations

AOR

adjusted odds ratio

CI

confidence interval

EMS

emergency medical service

FDMA

Fire and Disaster Management Agency

IQR

interquartile range; OR: odds ratio

Data Availability

Due to restrictions on the availability of data due to consent agreements for data security as well as IRB approval, data is available on request. The Japanese government owns the data and interested researchers can contact Ministry of Internal Affairs and Communications Fire and Disaster Management Agency Ambulance Service Planning Office. phone number: +81-3-5253-7529. The data from the Japanese National Survey could be accessed by contacting ministry of health labor and welfare (phone number: +81-3-5253-1111) for researchers who meet the criteria for access to confidential data. The data from Nihon Ultmarc INC could be accessed by contacting https://www.ultmarc.co.jp/mdb/index.html for researchers who meet the criteria for access to confidential data.

Funding Statement

The authors received no specific funding for this work.

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Decision Letter 0

Ho Ting Wong

18 May 2020

PONE-D-20-03553

Factors associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases: A nationwide population-based observational study

PLOS ONE

Dear Dr Sato,

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I think you can add more content to the introduction section.

For table 2, please provide p-values, Nagelkerke pseudo R2, and the information on how you code the variables.

Why are some variables, such as “month,” missing from the table?

In line 162, you mentioned that the variable “month” was considered in the regression analysis.

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

Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #2: Yes

**********

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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: In the present study, the authors aimed to investigate the factor associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases. When reviewing this manuscript, I am worrying that hospitals in Japan should be increasingly turning away sick people as the country struggles with surging coronavirus infections and its emergency medical system collapses.

Overall, this manuscript is well written. I only have the following minor comments:

1. In Line 103, “we excluded patients who were children (< 15 years old)…” And in Line 136, they said “…young people (< 15 years of age).” I suggest the authors to use a consistent term for < 15 years of age.

2. Line 124: In addition to the 4 categories of severity, can the authors accessed other variables (e.g. Japan Coma Scale) in their data set?

3. Line 129-130, they defined seven geographic regions (Hokkaido-Tohoku, Kanto, Chubu, Kansai, Chugoku, Shikoku, and Kyushu-Okinawa). But In Table 1, the name of the seven regions did not 100% match those written in Line 129-130. It’s better to revise it.

4. Line 164-168: “…the number of physicians per population in the area covered by each municipal fire department (<25 percentile, 25-75 percentile, 75 percentile≦), the proportion of emergency physicians to all physicians in the area (<25 percentile, 25-75 percentile, 75 percentile≦), the proportion of neurosurgeon to all physicians in the area (<25 percentile, 25-75 percentile, 75 percentile≦),…” Should “75 percentile≦” better be “76 percentile≦”? That’s because “25-75 percentile” already included 75.

5. As above, the use of “25-75 percentile” as reference was less intuitive and would made the results difficult to interpret. I suggest to use “76 percentile≦” as a reference.

6. Line 262: May add a limitation that the authors did not include national holidays, in addition to weekend, into analysis.

Keep fingers crossed and hope that Japan, as well as the whole world, will get through the tough situation right now.

Reviewer #2: This study expanded the scope of prior research on "Factors associated with the difficulty in hospital acceptance at the scene by emergency medical service personnel" and “Evaluation of factors associated with the difficulty in finding receiving hospitals for traffic accident patients” by examining similar factors associated with nationwide ambulance diversion or so-called “difficulty of hospital acceptance.” The outcome is measured as four or more phone calls to destination hospitals prior to the arrival. The topic is important given the high prevalence of patients being declined for emergency medical services in Japan; understanding the prehospital factors associated with ambulance diversion may help identify at-risk vulnerable populations and further provide policy measures to address the difficulties. Several concerns dampen my enthusiasm for the overall merit of the paper. I hope my comments and suggestions below help the authors as they move forth with this research.

• Lack of contribution: Authors did a great job discussing the importance of timely access to treatments among patients with stroke. However, besides the essential role of EMS and timely hospital acceptance on stroke care, this paper would benefit from a brief discussion on why and how authors would hypothesize different findings of prehospital factors associated with the difficulty in hospital acceptance between trauma care and stroke care. In other words, what makes stroke patients special in terms of prehospital factors for hospital acceptance. The findings are mostly consistent with previous studies, making the contribution of this new article unclear.

• the term, “difficulty of hospital acceptance”: is it different from ambulance diversion? the noun phrase difficulty seems to be missing a determiner before it. Authors might consider adding an article before ‘difficulty’ throughout the paper.

• Hospital bed capacity as a confounder: surprisingly, authors noted hospital bed capacity as one key reason for the difficulty in hospital ER acceptance but did not examine this factor in the study. An area’s hospital bed capacity or occupancy rates would be a confounder for the relationship between provider supply and outcomes.

• Page 5, Line 84: “early administration of intravenous recombinant tissue plasminogen activator within 4.5 hours” is interesting, as the study found a minimal difference in median transportation time (<1 minute) between patients in the group of less than 4 phone calls to be accepted by a hospital and their counterparts with 4+ phone calls.

• Descriptive statistics for transportation times by prehospital factors would help readers interpret the model coefficients.

• Student’s t-test statistics should not be used for skewed data even though those are continuous variables. Consider using Wilcoxon tests for the comparisons between medians of the two groups.

• Regression analysis of transportation times: authors might want to clarify whether any clustering effects were controlled in the multivariate logistic and linear regression analyses, given the nature of multiple ecological levels of factors (patient, area, and region). Also, how many patients had multiple ambulance services during the study period?

• Nonlinear associations for transportation times by factors examined: since authors report medians of transportation times in Table 2, it is reasonable to expect a skewed distribution of this outcome. How did authors address the violation of homoscedasticity assumptions and normal distributions of the error terms in the multivariate linear model?

Minor comments:

• Page 5, Line 71: authors stated that “in reality, emergency departments often decline to accept patients because of limited resources.” this would have been a stronger statement if authors can quantify how often this phenomenon was in 2017.

• Page 5, Line 77: the unit of the rate is needed here. Was it 137,833 per 5,736,086 patients?

• Page 5, Line 83: ‘among’ patients with acute ischemic stroke

• Page 6, Line 90-93: these two sentences are a bit repetitive. Authors might consider reconcile these.

• Page 6, Line 101: why was age at 15 chosen as a cutoff?

• Page 7, Line 103-105: the inclusion and exclusion criteria read redundant. Authors may first describe inclusion criteria and state that they also exclude those with cardio-pulmonary arrest at the hospital and who had missing data on which factors.

• Page 7, Line 117-118, “an appropriate hospital” requires an explanation. It’d be great if authors can discuss the protocol of the hospital selections. Were those by proximity from an onset location to a hospital? by hospital bed capacity? or other criteria?

• Page 8, Line 150: “univariate” should be bivariate.

• Table 1 and Table 2: the third quartiles were listed as equal or greater than 75 percentile but 75 percentile has been included in the second categories.

• Table 1: authors may consider presenting both median and interquartiles for transportation times. This may also apply for the new table with the statistics of transportation times by factors.

• Page 14, Line 207-208: it is unclear what the legend in Figure 2 means. why not the predicted rate after controlling for patient and area factors?

• Pape 14, Line 209-214: this sentence was written with ambiguity. Authors stated that similar associations were observed between a “reduction” in prehospital transportation time and night hours, weekend days,… but the table presents opposite for some variables.

• Page 18, Line 258-259: rather than saying the present findings should encourage healthcare providers and policy makers to decrease these regional variations, authors might use existing evidence to support how interventions should be implemented to address the difficulty facing hospital acceptance. Several examples would be a stroke care regionalization, and EMTALA law in the US.

**********

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Reviewer #1: Yes: Cheng-Yang Hsieh

Reviewer #2: No

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Attachment

Submitted filename: PONE-D-20-03553_reviewer.docx

Click here for additional data file. (13.7KB, docx)
PLoS One. 2021 Jan 12;16(1):e0245318. doi: 10.1371/journal.pone.0245318.r002

Author response to Decision Letter 0

9 Jul 2020

Re: PONE-D-20-03553

Dear Editors and Reviewers:

Thank you for your thorough review of PONE-D-20-03553, “Factors associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases: a nationwide population-based observational study”. Please find our responses to the Editors’ and Reviewers’ comments shown in Bold in the following response letter.

Sincerely,

Nobuhiro Sato

Response to the Editor:

Thank you for your thorough reviews and suggestions. Our responses to your queries follow.

Additional Editor Comments:

I think you can add more content to the introduction section.

We appreciate your suggestions. As suggested, we have added more content to the Introduction section (Page 5-6 Para 65-98).

For table 2, please provide p-values, Nagelkerke pseudo R2, and the information on how you code the variables.

As requested, we have added information to table 3 and added a new table as table 2.

Why are some variables, such as “month,” missing from the table?

In line 162, you mentioned that the variable “month” was considered in the regression analysis.

We thank the reviewer for the careful review of the manuscript. We have added this information to table 3 (we have made a new table as table 2) as above.

 

Response to Reviewer 1:

Reviewer #1: In the present study, the authors aimed to investigate the factor associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases. When reviewing this manuscript, I am worrying that hospitals in Japan should be increasingly turning away sick people as the country struggles with surging coronavirus infections and its emergency medical system collapses.

Overall, this manuscript is well written. I only have the following minor comments:

Thank you for your thorough reviews and suggestions. Our responses to your queries follow.

1. In Line 103, “we excluded patients who were children (< 15 years old)…” And in Line 136, they said “…young people (< 15 years of age).” I suggest the authors to use a consistent term for < 15 years of age.

We appreciate your suggestions. As pointed by reviewer 2 as well, we have removed <15 years old.

2. Line 124: In addition to the 4 categories of severity, can the authors accessed other variables (e.g. Japan Coma Scale) in their data set?

Unfortunately, the database we used did not include the Japan Coma Scale or Glasgow Coma Scale. Therefore, we have acknowledged this issue in the Limitations (Page 23 Para 282-284).

3. Line 129-130, they defined seven geographic regions (Hokkaido-Tohoku, Kanto, Chubu, Kansai, Chugoku, Shikoku, and Kyushu-Okinawa). But In Table 1, the name of the seven regions did not 100% match those written in Line 129-130. It’s better to revise it.

We thank the reviewer for the careful review. We have revised Table 1 as you suggested.

4. Line 164-168: “…the number of physicians per population in the area covered by each municipal fire department (<25 percentile, 25-75 percentile, 75 percentile≦), the proportion of emergency physicians to all physicians in the area (<25 percentile, 25-75 percentile, 75 percentile≦), the proportion of neurosurgeon to all physicians in the area (<25 percentile, 25-75 percentile, 75 percentile≦),…” Should “75 percentile≦” better be “76 percentile≦”? That’s because “25-75 percentile” already included 75.

We meant 25-75 percentile as 25≦ and <75. Therefore, we have revised to 25-74 percentile in the Materials and Methods, table 1 and table 3 (we have made a new table as table 2) (Page 10-11 Para 166-180).

5. As above, the use of “25-75 percentile” as reference was less intuitive and would made the results difficult to interpret. I suggest to use “76 percentile≦” as a reference.

As requested, we have used 75 percentile≦ as a reference in table 3 (we have made a new table as table 2) .

6. Line 262: May add a limitation that the authors did not include national holidays, in addition to weekend, into analysis.

We appreciate your suggestions. As suggested, we have added the issue to the Limitation section (Page 23 Para 284-285).

Keep fingers crossed and hope that Japan, as well as the whole world, will get through the tough situation right now.

We appreciate your help.

 

Response to Reviewer 2:

Reviewer #2: This study expanded the scope of prior research on "Factors associated with the difficulty in hospital acceptance at the scene by emergency medical service personnel" and “Evaluation of factors associated with the difficulty in finding receiving hospitals for traffic accident patients” by examining similar factors associated with nationwide ambulance diversion or so-called “difficulty of hospital acceptance.” The outcome is measured as four or more phone calls to destination hospitals prior to the arrival. The topic is important given the high prevalence of patients being declined for emergency medical services in Japan; understanding the prehospital factors associated with ambulance diversion may help identify at-risk vulnerable populations and further provide policy measures to address the difficulties. Several concerns dampen my enthusiasm for the overall merit of the paper. I hope my comments and suggestions below help the authors as they move forth with this research.

Thank you for your thorough review and suggestions. Our responses to your queries follow.

• Lack of contribution: Authors did a great job discussing the importance of timely access to treatments among patients with stroke. However, besides the essential role of EMS and timely hospital acceptance on stroke care, this paper would benefit from a brief discussion on why and how authors would hypothesize different findings of prehospital factors associated with the difficulty in hospital acceptance between trauma care and stroke care. In other words, what makes stroke patients special in terms of prehospital factors for hospital acceptance. The findings are mostly consistent with previous studies, making the contribution of this new article unclear.

We thank the reviewer for these insightful comments. We considered that delay of transportation to the hospital for patients with acute stroke was an important problem in public health and clinical situation. One of the main reasons is that stroke was fourth leading cause of death (7.9%) in 2018 in Japan, while the death ratio of accidents from leading causes of death was 3.0% (1). Another reason is that time is brain (2). Human nervous tissue is rapidly and irretrievably lost as stroke progresses. The typical patient loses 1.9 million neurons each minute in which stroke is untreated. In addition, as shown in the Materials and methods, we used nationwide database and characterized regional level effects unlike the previous studies which we believe is a new contribution of this article. We have revised the Introduction (Page 5-6 Para 65-98).

• the term, “difficulty of hospital acceptance”: is it different from ambulance diversion? the noun phrase difficulty seems to be missing a determiner before it. Authors might consider adding an article before ‘difficulty’ throughout the paper

We appreciate the author's insightful comments. Ambulance diversion is often used to describe the practice of temporarily closing a facility, typically an emergency department, to incoming ambulances in the United states (3). In Japan, an emergency department can choose to accept an incoming ambulance on a single case basis. Therefore, we have used the word “difficulty of hospital acceptance” instead of ambulance diversion. We have inserted a reference article throughout the paper per the reviewer's suggestion.

• Hospital bed capacity as a confounder: surprisingly, authors noted hospital bed capacity as one key reason for the difficulty in hospital ER acceptance but did not examine this factor in the study. An area’s hospital bed capacity or occupancy rates would be a confounder for the relationship between provider supply and outcomes

Unfortunately, we did not have the data of an area’s hospital bed capacity or occupancy rates. Therefore, we have added the issue in the Limitation section (Page 23 Para 285-288).

• Page 5, Line 84: “early administration of intravenous recombinant tissue plasminogen activator within 4.5 hours” is interesting, as the study found a minimal difference in median transportation time (<1 minute) between patients in the group of less than 4 phone calls to be accepted by a hospital and their counterparts with 4+ phone calls.

• Descriptive statistics for transportation times by prehospital factors would help readers interpret the model coefficients.

We appreciate these helpful suggestions. As suggested, we have made a new table of descriptive statistics for transportation times by prehospital factors.

• Student’s t-test statistics should not be used for skewed data even though those are continuous variables. Consider using Wilcoxon tests for the comparisons between medians of the two groups.

We appreciate reviewer's insightful comments. We have used Mann-Whitney U test for skewed data for comparisons between medians of the two groups because two groups were independent. Therefore, we have revised Table 1 and Material and Methods (Page 9-10 Para153-159).

• Regression analysis of transportation times: authors might want to clarify whether any clustering effects were controlled in the multivariate logistic and linear regression analyses, given the nature of multiple ecological levels of factors (patient, area, and region). Also, how many patients had multiple ambulance services during the study period?

We appreciate the author's insightful comments. As shown in the Materials and methods, we adjusted factors of each patient and region with the national ambulance records and factors in each area with data at the prefecture level from the Japanese National Survey as well as Japan national physician database.

Unfortunately, the national data used in the study does not have personal identification information. It has information about each ambulance transport. Therefore, it is not possible to examine the effects of repeated transports of the same patients.

• Nonlinear associations for transportation times by factors examined: since authors report medians of transportation times in Table 2, it is reasonable to expect a skewed distribution of this outcome. How did authors address the violation of homoscedasticity assumptions and normal distributions of the error terms in the multivariate linear model?

We appreciate reviewer's insightful comments. The distribution of transportation time somewhat skewed as you pointed out, but it seems sufficiently normal around the mean. Also, it is well known that the normality assumption for linear regression applies to the errors, not to the outcome variable per se. Since we adjust many covariates, the error terms would be still normal even if the outcome variable exhibits a bit skewness. Anyway, we follow conventional wisdom that t tests will still provide good approximations if the distribution is not too grossly non-normal (and this is actually true for our outcome variable).

Minor comments:

• Page 5, Line 71: authors stated that “in reality, emergency departments often decline to accept patients because of limited resources.” this would have been a stronger statement if authors can quantify how often this phenomenon was in 2017.

16.4% of requests for patient acceptance from the on-scene EMS personnel was rejected by the medical staff once at least in 2017 (4). As suggested, we have added the information in the Introduction (Page 5 Para 71-72).

• Page 5, Line 77: the unit of the rate is needed here. Was it 137,833 per 5,736,086 patients?

As suggested, we have added the unit of the rate (Page 5 Para 75-76).

• Page 5, Line 83: ‘among’ patients with acute ischemic stroke

The prior study was about patients with acute myocardial infarction (5). There was no previous study of association between phone calls and transportation time among patients with acute ischemic stroke. We have revised it (Page 5 Para 79-80).

• Page 6, Line 90-93: these two sentences are a bit repetitive. Authors might consider reconcile these.

We thank the reviewer for the careful review. As suggested, we have revised them.

• Page 6, Line 101: why was age at 15 chosen as a cutoff?

In Japanese medical system, patients who are < 15 years of age are regarded as children while those who are ≧15 years of age are regarded as adults (6). Therefore, we chose age at 15 as a cutoff.

• Page 7, Line 103-105: the inclusion and exclusion criteria read redundant. Authors may first describe inclusion criteria and state that they also exclude those with cardio-pulmonary arrest at the hospital and who had missing data on which factors.

As requested, we have revised it (Page 7 Para 108-109).

• Page 7, Line 117-118, “an appropriate hospital” requires an explanation. It’d be great if authors can discuss the protocol of the hospital selections. Were those by proximity from an onset location to a hospital? by hospital bed capacity? or other criteria?

As shown in the Materials and methods, each municipal fire department has their own medical protocol, which helps emergency medical service decide to which hospital they transfer patients. For example, a stroke patient will be taken to the nearest designated stroke center. A severe trauma patient will be transported to the regional tertiary trauma center by aeromedical transport per the protocol.

• Page 8, Line 150: “univariate” should be bivariate.

As suggested, we have revised it (Page 9-10 Para 153-155).

• Table 1 and Table 2: the third quartiles were listed as equal or greater than 75 percentile but 75 percentile has been included in the second categories.

We thank the reviewer for the careful review. We have revised Table 1 and Table 3 (we have added a new table 2).

• Table 1: authors may consider presenting both median and interquartiles for transportation times. This may also apply for the new table with the statistics of transportation times by factors.

As requested, we have made the new table with the statistics of transportation times by factors.

• Page 14, Line 207-208: it is unclear what the legend in Figure 2 means. why not the predicted rate after controlling for patient and area factors?

We appreciate the author's insightful comments. Figure 2 was made from the actual rate of each region. Therefore, we used the odds ratio of number of phone calls to hospitals by emergency medical service personnel instead of predicted rate.

• Pape 14, Line 209-214: this sentence was written with ambiguity. Authors stated that similar associations were observed between a “reduction” in prehospital transportation time and night hours, weekend days,… but the table presents opposite for some variables.

We thank the reviewer for these insightful comments. As suggested, we have revised it (Page 17 Para 218-222).

• Page 18, Line 258-259: rather than saying the present findings should encourage healthcare providers and policy makers to decrease these regional variations, authors might use existing evidence to support how interventions should be implemented to address the difficulty facing hospital acceptance. Several examples would be a stroke care regionalization, and EMTALA law in the US.

We appreciate your suggestions. As suggested, we have added existing evidence to address the difficulty of hospital acceptance (Page 22 Para 267-272).

Reference

1. Ministry of Health Labour, and Welfare. Vital Statistics 2018. https://www.mhlw.go.jp/english/database/db-hw/dl/81-1a2en.pdf

2. Saver JL. Time is brain--quantified. Stroke. 2006;37(1):263-6.

3. Geiderman JM, Marco CA, Moskop JC, Adams J, Derse AR. Ethics of ambulance diversion. Am J Emerg Med. 2015;33(6):822-7.

4. Ambulance Service Planning Office of Fire and Disaster Management Agency of Japan: 2018 Effect of first aid for emergency patients. https://www.fdma.go.jp/publication/rescue/items/kkkg_h30_01_kyukyu.pdf.

5. Kitamura T, Iwami T, Kawamura T, Nishiyama C, Sakai T, Tanigawa-Sugihara K, et al. Ambulance calls and prehospital transportation time of emergency patients with cardiovascular events in Osaka City. Acute Med Surg. 2014;1(3):135-44.

6. Katayama Y, Kitamura T, Kiyohara K, Iwami T, Kawamura T, Hayashida S, et al. Evaluation of factors associated with the difficulty in finding receiving hospitals for traffic accident patients at the scene treated by emergency medical services: a population-based study in Osaka City, Japan. Acute Med Surg. 2017;4(4):401-7.

 

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Attachment

Submitted filename: 20200710 Response to Reviewers.docx

Click here for additional data file. (76.8KB, docx)

Decision Letter 1

Ho Ting Wong

24 Nov 2020

PONE-D-20-03553R1

Factors associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases: a nationwide population-based observational study

PLOS ONE

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Reviewer #3: All comments have been addressed

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Reviewer #1: The authors have addressed all my comments adequately.

The manuscript now looks nice.

I have no further comments.

Reviewer #3: This study aimed to examine the predictors of difficulty of hospital acceptance of patients suspected to have cerebrovascular disease. It is unclear who and how to define those who were suspected to have cerebrovascular diseases? By the EMS personnel? Difficulty of acceptance was defined by having ≥4 phone calls by the EMS personnel to hospitals until acceptance. Who are the one responding to the phone calls? Triage nurses? Hospital administrators? Physicians? Symptom presentation is a key factor to urge patients to call for help, same as healthcare professionals. Did the EMS personnel inform the one responding to the phone calls the symptoms of patients? Did they know that the patients are suspected to have cerebrovascular diseases? Mean age of physicians is one of the factor associated with difficulty of hospital acceptance, was this variable refer to those who refused to admit the patients or those who finally admitted the patients? did these physicians involved in refusing to admit patients?

Minimal grammatical errors, but the manuscript requires some formatting to make it more readable, such as the “≦” should put in front of 75 percentile, Table 1, the fourth column heading should be ≥4

**********

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PLoS One. 2021 Jan 12;16(1):e0245318. doi: 10.1371/journal.pone.0245318.r004

Author response to Decision Letter 1

18 Dec 2020

Re: PONE-D-20-03553R1

Dear Editors and Reviewers:

Thank you for your thorough review of PONE-D-20-03553R 1, “Factors associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases: a nationwide population-based observational study”. Please find our responses to the Editors’ and Reviewers’ comments shown in Bold in the following response letter.

Sincerely,

Nobuhiro Sato

 

Reviewers' comments:

6. 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: The authors have addressed all my comments adequately.

The manuscript now looks nice.

I have no further comments.

Thank you for your thorough reviews and comments.

 

Reviewer #3: This study aimed to examine the predictors of difficulty of hospital acceptance of patients suspected to have cerebrovascular disease. It is unclear who and how to define those who were suspected to have cerebrovascular diseases? By the EMS personnel?

We appreciate your suggestions. Hemiplegia, dysarthria, ataxia or severe headache were classified as cerebrovascular disease-related symptoms. The data were collected by EMS personnel, in cooperation with the physicians overseeing the patient’s care. We added information to the Methods section (Page 7, Para 108-109 and 111-112).

Difficulty of acceptance was defined by having ≥4 phone calls by the EMS personnel to hospitals until acceptance. Who are the one responding to the phone calls? Triage nurses? Hospital administrators? Physicians?

We thank the reviewer for the careful review of the manuscript. The person who responds to the phone calls by EMS personnel depends on the hospital. While physicians respond to the phone calls in some hospitals, nurses or the other staff respond in the other hospitals. We have added a description about this in the Introduction (Page 5, Para 69-70).

Symptom presentation is a key factor to urge patients to call for help, same as healthcare professionals. Did the EMS personnel inform the one responding to the phone calls the symptoms of patients? Did they know that the patients are suspected to have cerebrovascular diseases?

We appreciate your suggestions. Yes, EMS personnel informed medical staff responding to the phone calls about the symptoms which led EMS personnel to suspected cerebrovascular diseases according to EMS protocols.

Mean age of physicians is one of the factor associated with difficulty of hospital acceptance, was this variable refer to those who refused to admit the patients or those who finally admitted the patients? did these physicians involved in refusing to admit patients?

We appreciate reviewers' insightful comments. In this study, we analyzed hospital acceptance of each ambulance transport as opposed to acceptance of emergency admission to an inpatient ward from the emergency department. The variable mean age of physicians refers to the mean age of physicians in the area that each patient was transported from. Therefore, this is the mean age of physicians in the area who refused to accept the EMS transportation. We have added “in the area” to the Results and the Discussion (Page 12, Para 201, Page 17, Para 214 and 222-223, Page 20, Para 234, Page 21, Para 255, Page 23, Para 294).

Minimal grammatical errors, but the manuscript requires some formatting to make it more readable, such as the “≦” should put in front of 75 percentile, Table 1, the fourth column heading should be ≥4

We thank the reviewer for the careful review. We have corrected the typos.

Attachment

Submitted filename: 20201219 Response to Reviewers.docx

Click here for additional data file. (16.7KB, docx)

Decision Letter 2

Ho Ting Wong

29 Dec 2020

Factors associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases: a nationwide population-based observational study

PONE-D-20-03553R2

Dear Dr. Sato,

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.

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Kind regards,

Ho Ting Wong, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Ho Ting Wong

4 Jan 2021

PONE-D-20-03553R2

Factors associated with difficulty of hospital acceptance of patients suspected to have cerebrovascular diseases: a nationwide population-based observational study

Dear Dr. Sato:

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on behalf of

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Academic Editor

PLOS ONE

Associated Data

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    Supplementary Materials

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    Submitted filename: PONE-D-20-03553_reviewer.docx

    Click here for additional data file. (13.7KB, docx)
    Attachment

    Submitted filename: 20200710 Response to Reviewers.docx

    Click here for additional data file. (76.8KB, docx)
    Attachment

    Submitted filename: 20201219 Response to Reviewers.docx

    Click here for additional data file. (16.7KB, docx)

    Data Availability Statement

    Due to restrictions on the availability of data due to consent agreements for data security as well as IRB approval, data is available on request. The Japanese government owns the data and interested researchers can contact Ministry of Internal Affairs and Communications Fire and Disaster Management Agency Ambulance Service Planning Office. phone number: +81-3-5253-7529. The data from the Japanese National Survey could be accessed by contacting ministry of health labor and welfare (phone number: +81-3-5253-1111) for researchers who meet the criteria for access to confidential data. The data from Nihon Ultmarc INC could be accessed by contacting https://www.ultmarc.co.jp/mdb/index.html for researchers who meet the criteria for access to confidential data.

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