Evidence map›Paper›PMID 28036373›Full record

ArticlePloS one2016

Coherence of Influenza Surveillance Data across Different Sources and Age Groups, Beijing, China, 2008-2015.

Zhenyu Wu, Xiaoyu Sun, Yanhui Chu, Jingyi Sun, Guoyou Qin, Lin Yang, Jingning Qin, Zheng Xiao, Jian Ren, Di Qin and 2 more

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  3. Review
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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

12 authors.

Zhenyu WuDepartment of Biostatistics, School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China.
Xiaoyu SunXicheng District Centers for Disease Control and Prevention, Beijing, China.
Yanhui ChuXicheng District Centers for Disease Control and Prevention, Beijing, China.
Jingyi SunXicheng District Centers for Disease Control and Prevention, Beijing, China.
Guoyou QinDepartment of Biostatistics, School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China.
Lin YangSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Jingning QinXicheng District Centers for Disease Control and Prevention, Beijing, China.
Zheng XiaoXicheng District Centers for Disease Control and Prevention, Beijing, China.
Jian RenXicheng District Centers for Disease Control and Prevention, Beijing, China.
Di QinXicheng District Centers for Disease Control and Prevention, Beijing, China.
Xiling WangDepartment of Biostatistics, School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China.
Xueying ZhengDepartment of Biostatistics, School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Influenza is active during the winter and spring in the city of Beijing, which has a typical temperate climate with four clear distinct seasons. The clinical and laboratory surveillance data for influenza have been used to construct critical indicators for influenza activities in the community, and previous studies have reported varying degrees of association between laboratory-confirmed influenza specimens and outpatient consultation rates of influenza-like illness in subtropical cities. However, few studies have reported on this issue for cities in temperate regions, especially in developing countries. Furthermore, the mechanism behind age-specific seasonal epidemics remains unresolved, although it has been widely discussed. We utilized a wavelet analysis method to monitor the coherence of weekly percentage of laboratory-confirmed influenza specimens with the weekly outpatient consultation rates of influenza-like illness in Beijing, China. We first examined the seasonal pattern of laboratory-confirmed cases of influenza A (subtyped into seasonal A(H1N1) and A(H3N2) and pandemic virus A(H1N1) pdm09) and influenza B separately within the period from 2008-2015; then, we detected the coherence of clinical and laboratory surveillance data in this district, specially examining weekly time series of age-specific epidemics of influenza-like illnesses in the whole study period for three age categories (age 0-5, 5-15 and 25-60). We found that influenza A and B were both active in winter but were not always seasonally synchronous in Beijing. Synchronization between age ranges was found in most epidemic peaks from 2008-2015. Our findings suggested that peaks of influenza-like illness in individuals aged 0-5 and 5-15 years consistently appeared ahead of those of adults, implying the possibility that schoolchildren may lead epidemic fluctuations.

Indexed as

Population SurveillanceAdolescentAdultAnimalsBeijingCell LineChildChild, PreschoolChinaClimateDogsEpidemicsHumansInfantInfant, NewbornInfluenza A Virus, H1N1 Subtype

Identifiers

PMID28036373
PMCPMC5201231

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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.