Evidence map›Paper›PMID 38816720›Full record

ArticleBMC emergency medicine2024

Medical needs during the Kumamoto heavy rain 2020: analysis from emergency medical teams' responses.

Akihiro Taji, Yui Yumiya, Odgerel Chimed-Ochir, Ami Fukunaga, Yoko Tsurugi, Koji Kiwaki, Kouki Akahoshi, Yoshiki Toyokuni, Kayako Chishima, Seiji Mimura and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC emergency medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
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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

14 authors.

Akihiro TajiDepartment of Public Health and Health Policy Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, 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, 734-0037, Japan. yumiya@hiroshima-u.ac.jp.
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, 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, 734-0037, Japan.
Yoko TsurugiAso Public Health Center, Kumamoto Prefecture Government, 2402 Miyaji, Ichinomiya-machi, Aso City, Kumamoto, 869-2612, Japan.
Koji KiwakiHitoyoshi Public Health Center, Kumamoto Prefectural Government, 86-1 Nishiaidashimo-machi, Hitoyoshi-city, Kumamoto, 868-8503, Japan.
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, 2-1-14 Houenzaka 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, 734-0037, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRainfall-induced floods represented 70% of the disasters in Japan from 1985 to 2018 and caused various health problems. To improve preparedness and preventive measures, more information is needed on the health problems caused by heavy rain. However, it has proven challenging to collect health data surrounding disasters due to various inhibiting factors such as environmental hazards and logistical constraints. In response to the Kumamoto Heavy Rain 2020, Emergency Medical Teams (EMTs) used J-SPEED (Japan-Surveillance in Post Extreme Emergencies and Disasters) as a daily reporting tool, collecting patient data and sending it to an EMTCC (EMT Coordination Cell) during the response. We performed a descriptive epidemiological analysis using J-SPEED data to better understand the health problems arising from the Kumamoto Heavy Rain 2020 in Japan.

methodsDuring the Kumamoto Heavy Rain 2020 from July 5 to July 31, 2020, 79 EMTs used the J-SPEED form to submit daily reports to the EMTCC on the number and types of health problems they treated. We analyzed the 207 daily reports, categorizing the data by age, gender, and time period.

resultsAmong the 816 reported consultations, women accounted for 51% and men accounted for 49%. The majority of patients were elderly (62.1%), followed by adults (32.8%), and children (5%). The most common health issues included treatment interruption (12.4%), hypertension (12.0%), wounds (10.8%), minor trauma (9.6%), and disaster-related stress symptoms (7.4%). Consultations followed six phases during the disaster response, with the highest occurrence during the hyperacute and acute phases. Directly disaster-related events comprised 13.9% of consultations, indirectly related events comprised 52.0%, and unrelated events comprised 34.0%. As the response phases progressed, the proportions of directly and indirectly related events decreased while that of unrelated events increased.

conclusionBy harnessing data captured by J-SPEED, this research demonstrates the feasibility of collecting, quantifying, and analyzing data using a uniform format. Comparison of the present findings with those of two previous analyses of J-SPEED data from other disaster scenarios that varied in time, location, and/or disaster type showcases the potential to use analysis of past experiences to advancing knowledge on disaster medicine and disaster public health.

Indexed as

RainAdolescentAdultAgedAged, 80 and overChildChild, PreschoolDisaster PlanningDisastersEmergency Medical ServicesFemaleFloodsHealth Services Needs and DemandHumansInfantInfant, NewbornDisasterEmergency medical teamEmergency medical team minimum data setJapanJ-SPEEDKumamoto heavy rain

Identifiers

PMID38816720
PMCPMC11141056

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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.