Evidence map›Paper›PMID 23509772›Full record

ArticleBioMed research international2013

Prescription surveillance and polymerase chain reaction testing to identify pathogens during outbreaks of infection.

Hiroaki Sugiura, Tsuguto Fujimoto, Tamie Sugawara, Nozomu Hanaoka, Masami Konagaya, Kiyoshi Kikuchi, Eisuke Hanada, Nobuhiko Okabe, Yasushi Ohkusa

Abstract read
In one paragraph

Article in BioMed research international, 2013. 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

9 authors.

Hiroaki SugiuraSugiura Clinic, 2-8-3 Imaichi-Kita, Honmachi, Shimane, Izumo 693-0002, Japan. tomomarie@smn.enjoy.ne.jp
Tsuguto Fujimoto
Tamie Sugawara
Nozomu Hanaoka
Masami Konagaya
Kiyoshi Kikuchi
Eisuke Hanada
Nobuhiko Okabe
Yasushi Ohkusa

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Syndromic surveillance, including prescription surveillance, offers a rapid method for the early detection of agents of bioterrorism and emerging infectious diseases. However, it has the disadvantage of not considering definitive diagnoses. Here, we attempted to definitively diagnose pathogens using polymerase chain reaction (PCR) immediately after the prescription surveillance system detected an outbreak. Specimens were collected from 50 patients with respiratory infections. PCR was used to identify the pathogens, which included 14 types of common respiratory viruses and Mycoplasma pneumoniae. Infectious agents including M. pneumoniae, respiratory syncytial virus (RSV), rhinovirus, enterovirus, and parainfluenza virus were detected in 54% of patients. For the rapid RSV diagnosis kit, sensitivity was 80% and specificity was 85%. For the rapid adenovirus diagnosis kit, no positive results were obtained; therefore, sensitivity could not be calculated and specificity was 100%. Many patients were found to be treated for upper respiratory tract infections without the diagnosis of a specific pathogen. In Japan, an outbreak of M. pneumoniae infection began in 2011, and our results suggested that this outbreak may have included false-positive cases. By combining syndromic surveillance and PCR, we were able to rapidly and accurately identify causative pathogens during a recent respiratory infection outbreak.

Indexed as

Disease OutbreaksEpidemiological MonitoringAdolescentAdultChildChild, PreschoolDrug PrescriptionsEnterovirusHumansInfantJapanMycoplasma pneumoniaePolymerase Chain ReactionRespiratory Syncytial VirusesRespiratory Tract InfectionsRespirovirus

Identifiers

PMID23509772
PMCPMC3581269

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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.