Evidence map›Paper›PMID 40352103›Full record

ArticleArchives of academic emergency medicine2025

Predicting the Number of Consultations by Emergency Medical Teams During Disasters Using a Constant Attenuation Model: Analyzing the Data of 6 Disasters in Japan and Mozambique Between 2016-2020.

Takahito Yoshida, Tomohito Hayashi, Odgerel Chimed-Ochir, Yui Yumiya, Ami Fukunaga, Akihiro Taji, Takashi Nakano, Yoichi Ikeda, Kenji Sasaki, Matchecane Cossa and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Archives of academic emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Takahito YoshidaDepartment of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-0037, Japan.
Tomohito HayashiDepartment of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-0037, Japan.
Odgerel Chimed-OchirDepartment of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-0037, Japan.
Yui YumiyaDepartment of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-0037, Japan.
Ami FukunagaDepartment of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-0037, Japan.
Akihiro TajiDepartment of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-0037, Japan.
Takashi NakanoCenter for Infectious Disease Education and Research, Osaka University, 2-1 Yamadaoka Suita city, Osaka 565-0871, Japan.
Yoichi IkedaCenter for Infectious Disease Education and Research, Osaka University, 2-1 Yamadaoka Suita city, Osaka 565-0871, Japan.
Kenji SasakiCenter for Infectious Disease Education and Research, Osaka University, 2-1 Yamadaoka Suita city, Osaka 565-0871, Japan.
Matchecane CossaNational Program of Surgery, Ministry of Health of Mozambique, Maputo, Mozambique; Avenida Eduardo Mondlane Maputo 1008, Mozambique.
Isse UsseneNational Program of Surgery, Ministry of Health of Mozambique, Maputo, Mozambique; Avenida Eduardo Mondlane Maputo 1008, Mozambique.
Ryoma KayanoWorld Health Organization Centre for Health Development (WHO Kobe Centre), I.H.D. Centre Bldg. 9F, 1-5-1, Wakinohama-Kaigandori, Chuo-ku, Kobe-shi, Hyogo, 651-0073, Japan.
Flavio SalioEmergency Medical Teams, World Health Organization, Geneva, Switzerland, Avenue Appia 20, 1211 Geneva, Switzerland.
Kouki AkahoshiNational Hospital Organization Headquarters DMAT Secretariat MHLW Japan, 3256 Midori-cho Tachikawa, Tokyo 190-8579, Japan.
Yoshiki ToyokuniNational Hospital Organization Headquarters DMAT Secretariat MHLW Japan, 3256 Midori-cho Tachikawa, Tokyo 190-8579, Japan.
Kayako ChishimaNational Hospital Organization Headquarters DMAT Secretariat MHLW Japan, 3256 Midori-cho Tachikawa, Tokyo 190-8579, Japan.
Seiji MimuraNational Hospital Organization Headquarters DMAT Secretariat MHLW Japan, 3256 Midori-cho Tachikawa, Tokyo 190-8579, Japan.
Akinori WakaiNational Hospital Organization Headquarters DMAT Secretariat MHLW Japan, Houenzaka 2-1-14 Chuo-ku Osaka City, Osaka 540-0006, Japan.
Hisayoshi KondoNational Hospital Organization Headquarters DMAT Secretariat MHLW Japan, 3256 Midori-cho Tachikawa, Tokyo 190-8579, Japan.
Yuichi KoidoNational Hospital Organization Headquarters DMAT Secretariat MHLW Japan, 3256 Midori-cho Tachikawa, Tokyo 190-8579, Japan.
Tatsuhiko KuboDepartment of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-0037, Japan.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Introduction: Predicting the number of emergency medical team (EMT) consultations that are needed following a natural or man-made disaster can help improve decisions regarding the dispatch and withdrawal of these teams. This study aimed to predict the number of consultations by EMTs using the Methods: Data were collected using the Japan-Surveillance in Post-Extreme Emergencies and Disasters (J-SPEED) and Minimum Data Set (MDS) for five disasters in Japan and one disaster in Mozambique. We compared the number of consultations, which was predicted based on Results: The total number of EMT consultations per disaster ranged from 684 to 18,468. The predicted curve and actual Conclusions: The

Indexed as

DatasetDisastersEmergency Medical ServicesModelsPrediction AlgorithmsStatistical

Identifiers

PMID40352103
PMCPMC12065031

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.