Evidence map›Paper›PMID 40801065›Full record

ArticleArchives of academic emergency medicine2025

Assessing Data Completeness in Emergency Medical Team Reports: Analysis of the Response to Cyclone Idai in Mozambique using the WHO Minimum Data Set.

Odgerel Chimed-Ochir, Inn-Kynn Khaing, Ami Fukunaga, Takahito Yoshida, Yuki Takamura, Yui Yumiya, Matchecane Cossa, Isse Ussene, Salio Flavio, Ryoma Kayano and 1 more

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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

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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

11 authors.

Odgerel Chimed-OchirGraduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Inn-Kynn KhaingGraduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Ami FukunagaGraduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Takahito YoshidaGraduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Yuki TakamuraGraduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Yui YumiyaGraduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Matchecane CossaMinistry of Health of Mozambique, Maputo, 1100, Mozambique.
Isse UsseneMinistry of Health of Mozambique, Maputo, 1100, Mozambique.
Salio FlavioEmergency Medical Teams, World Health Organization, 1211 Geneva, Switzerland.
Ryoma KayanoWorld Health Organization Centre for Health Development (WHO Kobe Centre), Kobe 651-0073, Japan.
Tatsuhiko KuboGraduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Introduction: In 2017, WHO endorsed the Emergency Medical Team (EMT) Minimum Data Set (MDS) for real-time data collection during health emergencies. It was first activated during Cyclone Idai in Mozambique in 2019. The objective of the study is to evaluate the completeness of data collected by EMTs during the Cyclone Idai response in Mozambique. Methods: This study evaluated data completeness from Cyclone Idai, analyzing 277 daily reports with 18,468 patient consultations from 13 international teams between 27 March and 12 July, 2019. Completeness of team information, demographics, health events, disaster relation, and outcomes were compared across EMT types and classifications using box plots, Kruskal-Wallis, t-tests, and multivariable logistic regression. Results: During the 110-day response, 13 EMTs submitted 277 daily reports on patient information. Findings showed that, out of the 277 daily reports, demographic information was complete in 92.8% of reports, health event information in 62.1%, information on the relation of health events to disaster in 57.4%, and outcome data in 50.2%. Type 2 EMTs exhibited higher data completeness, likely due to greater resources and personnel, compared to Type 1 Mobile and Type 1 Fixed EMTs. Type 1 Fixed EMTs demonstrated lower completeness for outcomes, health events, and disaster relation, potentially due to heavier workloads. Type 1 Mobile EMTs likely benefited from enhanced training and frequent interactions with data managers, which may have contributed to their higher data completeness compared to Type 1 Fixed EMTs. Classified EMTs performed better overall. Conclusion: This study underscores the need for standardized training, and the data collection applications that enable the automatic inclusion of information such as geotags.

Indexed as

Data collectionData qualityDisastersEmergency medical servicesInformation management

Identifiers

PMID40801065
PMCPMC12341006

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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.