Evidence map›Paper›PMID 35687648›Full record

ArticlePloS one2022

Rate of diagnosed seasonal influenza in children with influenza-like illness: A cross-sectional study.

Mitsuo Uchida, Takenori Yamauchi

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

2 authors.

Mitsuo UchidaDepartment of Public Health, Graduate School of Medicine, Gunma University, Maebashi, Gunma, Japan.ORCID 0000-0002-9928-8897
Takenori YamauchiDepartment of Hygiene, Public Health and Preventive Medicine, Faculty of Medicine, Showa University, Tokyo, Japan.ORCID 0000-0002-1877-3234

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAlthough influenza surveillance systems have been used to monitor influenza epidemics, these systems generally evaluate diagnostic information obtained from medical institutions and they do not include patients who have not been examined. In contrast, community based epidemiological studies target people with influenza-like illness (ILI) that self-reported influenza-like symptoms whether they have medical examinations or not. Because the criteria for influenza surveillance systems and ILI differ, there is a gap between them. The purpose of this study was to clarify this gap using school-based survey data.

methodsQuestionnaires about both ILI and the influenza diagnosis history during the 2018/19 season were administered to the guardians of 11,684 elementary schoolchildren in a single city in Japan. Based on their responses, a Bayesian model was constructed to estimate the probability of infection, ILI onset, and diagnosis at medical institutions.

resultsResponses were obtained from guardians of 10,309 children (88.2%). Of these, 3,380 children (32.8%) had experienced ILI, with 2,380 (23.1%) diagnosed as influenza at a medical institution. Bayesian estimation showed that the probability of influenza cases being diagnosed among ILI symptomatic children was 70% (95% credible interval, 69-71%). Of the infected children, 5% were without ILI symptoms, with 11% of these patients diagnosed with influenza.

conclusionsThis epidemiological study clarified the proportion gap between ILI and influenza diagnosis among schoolchildren. These results may help to establish epidemic control measures and secure sufficient medical resources.

Indexed as

EpidemicsInfluenza, HumanVirus DiseasesBayes TheoremChildCross-Sectional StudiesHumansSeasons

Identifiers

PMID35687648
PMCPMC9187082

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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.