Evidence map›Paper›PMID 38816697›Full record

ArticleBMC infectious diseases2024

Assessment of fever screening at airports in detecting domestic passengers infected with SARS-CoV-2, 2020-2022, Okinawa prefecture, Japan.

Yoshihiro Takayama, Yining S Xu, Yusuke Shimakawa, Gerardo Chowell, Masahiro Kozuka, Ryosuke Omori, Ryota Matsuyama, Taro Yamamoto, Kenji Mizumoto

Erratum issuedAbstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Yoshihiro TakayamaOkinawa Prefecture Commission for Epidemiological and Statistical Analysis, Naha-shi, Okinawa, Japan.
Yining S XuGraduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, 1, Yoshida-Nakaadachi-Cho, Sakyo-Ku, Kyoto, 606-8306, Japan.
Yusuke ShimakawaOkinawa Prefecture Commission for Epidemiological and Statistical Analysis, Naha-shi, Okinawa, Japan.
Gerardo ChowellSchool of Public Health, Georgia State University, 33 Gilmer Street SE, Atlanta, GA, 30303, USA.
Masahiro KozukaGraduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, 1, Yoshida-Nakaadachi-Cho, Sakyo-Ku, Kyoto, 606-8306, Japan.
Ryosuke OmoriOkinawa Prefecture Commission for Epidemiological and Statistical Analysis, Naha-shi, Okinawa, Japan.
Ryota MatsuyamaOkinawa Prefecture Commission for Epidemiological and Statistical Analysis, Naha-shi, Okinawa, Japan.
Taro YamamotoDepartment of International Health and Medical Anthropology, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan.
Kenji MizumotoOkinawa Prefecture Commission for Epidemiological and Statistical Analysis, Naha-shi, Okinawa, Japan. mizumoto.kenji.5a@kyoto-u.ac.jp.

Funding

Japan Science and Technology Agency JPMJCR20H1Japan Science and Technology Agency JPMJSC21U4Japan Society for the Promotion of Science 20H03940Japan Society for the Promotion of Science 21K10416Japan Society for the Promotion of Science 21K17250
6 · The paper itself

Abstract

backgroundWhile airport screening measures for COVID-19 infected passengers at international airports worldwide have been greatly relaxed, observational studies evaluating fever screening alone at airports remain scarce. The purpose of this study is to retrospectively assess the effectiveness of fever screening at airports in preventing the influx of COVID-19 infected persons.

methodsWe conducted a retrospective epidemiological analysis of fever screening implemented at 9 airports in Okinawa Prefecture from May 2020 to March 2022. The number of passengers covered during the same period was 9,003,616 arriving at 9 airports in Okinawa Prefecture and 5,712,983 departing passengers at Naha Airport. The capture rate was defined as the proportion of reported COVID-19 cases who would have passed through airport screening to the number of suspected cases through fever screening at the airport, and this calculation used passengers arriving at Naha Airport and surveillance data collected by Okinawa Prefecture between May 2020 and March 2021.

resultsFrom May 2020 to March 2021, 4.09 million people were reported to pass through airports in Okinawa. During the same period, at least 122 people with COVID-19 infection arrived at the airports in Okinawa, but only a 10 suspected cases were detected; therefore, the capture rate is estimated to be up to 8.2% (95% CI: 4.00-14.56%). Our result of a fever screening rate is 0.0002% (95%CI: 0.0003-0.0006%) (10 suspected cases /2,971,198 arriving passengers). The refusal rate of passengers detected by thermography who did not respond to temperature measurements was 0.70% (95% CI: 0.19-1.78%) (4 passengers/572 passengers).

conclusionsThis study revealed that airport screening based on thermography alone missed over 90% of COVID-19 infected cases, indicating that thermography screening may be ineffective as a border control measure. The fact that only 10 febrile cases were detected after screening approximately 3 million passengers suggests the need to introduce measures targeting asymptomatic infections, especially with long incubation periods. Therefore, other countermeasures, e.g. preboarding RT-PCR testing, are highly recommended during an epidemic satisfying World Health Organization (WHO) Public Health Emergency of International Concern (PHEIC) criteria with pathogen characteristics similar or exceeding SARS-CoV-2, especially when traveling to rural cities with limited medical resources.

Indexed as

AirportsCOVID-19FeverMass ScreeningSARS-CoV-2AdultFemaleHumansJapanMaleRetrospective StudiesTravelAirportCOVID-19Fever screeningPrevention

Identifiers

PMID38816697
PMCPMC11138063

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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.