Evidence map›Paper›PMID 38873305›Full record

ArticleFrontiers in public health2024

Factors associated with difficulty in hospital acceptance during the COVID-19 pandemic period in Osaka Prefecture, Japan: a population-based study.

Yusuke Katayama, Kenta Tanaka, Shunichiro Nakao, Jotaro Tachino, Tomoya Hirose, Hisaya Dohmi, Tetsuhisa Kitamura, Jun Oda, Tetsuya Matsuoka

Abstract read
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Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yusuke KatayamaThe Working Group to Analyze the Emergency Medical Care System in Osaka Prefecture, Osaka, Japan.
Kenta TanakaDepartment of Social and Environmental Medicine, Division of Environmental Medicine and Population Sciences, Osaka University Graduate School of Medicine, Suita, Japan.
Shunichiro NakaoDepartment of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Suita, Japan.
Jotaro TachinoDepartment of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Suita, Japan.
Tomoya HiroseDepartment of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Suita, Japan.
Hisaya DohmiThe Working Group to Analyze the Emergency Medical Care System in Osaka Prefecture, Osaka, Japan.
Tetsuhisa KitamuraThe Working Group to Analyze the Emergency Medical Care System in Osaka Prefecture, Osaka, Japan.
Jun OdaDepartment of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Suita, Japan.
Tetsuya MatsuokaThe Working Group to Analyze the Emergency Medical Care System in Osaka Prefecture, Osaka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In many countries, emergency medical systems were responsible for initial treatment of patients with COVID-19. Generally, acceptance by medical institutions may not be sufficient, and it may take much time to determine the medical institution to which to transport the patient. This problem is termed "difficulty in hospital acceptance (DIH)," and it is used as a key performance indicator in the assessment of the EMS in Japan. The purpose of this study was to reveal the factors associated with the DIH during the COVID-19 pandemic using dataset in the ORION (Osaka emergency information Research Intelligent Operation Network system). Methods: This was a retrospective descriptive study with a 3-year study period from January 1, 2019 to December 31, 2021. We included patients who were recorded in the ORION system during the study period. The primary endpoint was defined as DIH. Multivariable logistic regression model was used to assess factors associated with DIH during the COVID-19 pandemic and calculated their adjusted odds ratio (AOR) and associated 95% confidence interval (CI). Results: 1,078,850 patients included in this study. Of them, 41,140 patients (3.8%) experienced DIH and 1,037,710 patients (96.2%) did not experience DIH. The median age was 71 years (IQR: 45-82), and 543,760 patients (50.4%) were male. In this study, SpO Conclusion: In this study, we revealed factors associated with the DIH during the COVID-19 pandemic. As various factors are involved in the spread of an unknown infectious disease, it is necessary not only to plan in advance but also to take appropriate measures according to the situation in order to smoothly accept emergency patients.

Indexed as

COVID-19AdultAgedAged, 80 and overEmergency Medical ServicesFemaleHumansJapanMaleMiddle AgedPandemicsPatient Acceptance of Health CareRetrospective StudiesSARS-CoV-2COVID-19emergency medical serviceepidemiologypandemicprognosispublic health

Identifiers

PMID38873305
PMCPMC11171416

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.