Evidence map›Paper›PMID 28750032›Full record

ArticlePloS one2017

Spatial characteristics and the epidemiology of human infections with avian influenza A(H7N9) virus in five waves from 2013 to 2017 in Zhejiang Province, China.

Haocheng Wu, XinYi Wang, Ming Xue, Melanie Xue, Chen Wu, Qinbao Lu, Zheyuan Ding, Xiaoping Xv, Junfen Lin

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

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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

9 authors.

Haocheng WuZhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.ORCID http://orcid.org/0000-0003-1851-3704
XinYi WangZhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
Ming XueHangzhou Centre for Disease Control and Prevention, Hangzhou, Zhejiang, Province, China.
Melanie XueKingston University UK, London, United Kingdom.
Chen WuZhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
Qinbao LuZhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
Zheyuan DingZhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
Xiaoping XvZhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
Junfen LinZhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe five-wave epidemic of H7N9 in China emerged in the second half of 2016. This study aimed to compare the epidemiological characteristics among the five waves, estimating the possible infected cases and inferring the extent of the possible epidemic in the areas that have not reported cases before.

methodsThe data for the H7N9 cases from Zhejiang Province between 2013 and 2017 was obtained from the China Information Network System of Disease Prevention and Control. The start date of each wave was 16 March 2013, 1 July 2013, 1 July 2014, 1 July 2015 and 1 July 2016. The F test or Pearson's chi-square test were used to compare the characteristics of the five waves. Global and local autocorrelation analysis was carried out to identify spatial autocorrelations. Ordinary kriging interpolation was analyzed to estimate the number of human infections with H7N9 virus and to infer the extent of infections in the areas with no cases reported before.

resultThere were 45, 94, 45, 34 and 80 cases identified from the first wave to the fifth, respectively. The death rate was significantly different among the five waves of epidemics (χ2 = 10.784, P = 0.029). The age distribution (F = 0.903, P = 0.462), gender (χ2 = 2.674, P = 0.614) and occupation(χ2 = 19.764, P = 0.407) were similar in each period. Most of the cases were males and farmers. A significant trend (χ2 = 70.328, P<0.001) was identified that showed a growing proportion of rural cases. There were 31 high-high clusters and 3 high-low clusters at the county level among the five waves and 12, 8, 2, 9 and 3 clusters in each wave, respectively. The total cases infected with the H7N9 virus were far more than those that have been reported now, and the affected areas continue to expand. The epidemic in the north of Zhejiang Province persisted in all five waves. Since the second wave, the virus spread to the south areas and central areas. There was an obvious decline in the infected cases in the urban areas, and the epidemics mostly occurred in the rural areas after the fourth wave. The epidemic was relatively dispersed since the third wave had fewer than two cases in most of the areas and showed a reinforcing trend again in the fifth wave.

conclusionsThe study revealed that there were few differences in the epidemiologic characteristics among the five waves of the epidemic. However, the areas where the possible epidemic circulated was larger than reported. The epidemic cross-regional expansion continued and mostly occurred in rural areas. Continuous closure of the live poultry market (LPM) is strongly recommended in both rural and urban areas. Illegal and scattered live poultry trading, especially in rural areas, must be forbidden. It is suggested too that a more rigorous management be performed on live poultry trade and wholesale across the area. Health education, surveillance of cases and pathogenicity should also be strengthened.

Indexed as

ChinaCluster AnalysisConfidence IntervalsGeographyHumansInfluenza A Virus, H7N9 SubtypeInfluenza, Human

Identifiers

PMID28750032
PMCPMC5531501

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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.