Evidence map›Paper›PMID 28054599›Full record

ArticleScientific reports2017

Estimating Risks of Inapparent Avian Exposure for Human Infection: Avian Influenza Virus A (H7N9) in Zhejiang Province, China.

Erjia Ge, Renjie Zhang, Dengkui Li, Xiaolin Wei, Xiaomeng Wang, Poh-Chin Lai

Abstract read
In one paragraph

Article in Scientific reports, 2017. 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

6 authors.

Erjia GeDivision of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Renjie ZhangZhejiang Provincial Center for Disease Prevention &Control, Hangzhou, P.R. China.
Dengkui LiSchool of Mathematics &Statistics, Xi'an Jiaotong University, Xi'an, P.R. China.
Xiaolin WeiDivision of Clinical Public Health and Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Xiaomeng WangZhejiang Provincial Center for Disease Prevention &Control, Hangzhou, P.R. China.
Poh-Chin Lai

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inapparent avian exposure was suspected for the sporadic infection of avian influenza A(H7N9) occurring in China. This type of exposure is usually unnoticed and difficult to model and measure. Infected poultry with avian influenza H7N9 virus typically remains asymptomatic, which may facilitate infection through inapparent poultry/bird exposure, especially in a country with widespread practice of backyard poultry. The present study proposed a novel approach that integrated ecological and case-control methods to quantify the risk of inapparent avian exposure on human H7N9 infection. Significant associations of the infection with chicken and goose densities, but not with duck density, were identified after adjusting for spatial clustering effects of the H7N9 cases across multiple geographic scales of neighborhood, community, district and city levels. These exposure risks varied geographically in association with proximity to rivers and lakes that were also proxies for inapparent exposure to avian-related environment. Males, elderly people, and farmers were high-risk subgroups for the virus infection. These findings enable health officials to target educational programs and awareness training in specific locations to reduce the risks of inapparent exposure.

Indexed as

Environmental ExposureAge FactorsAnimalsChickensChinaDucksGeeseGeographyHumansInfluenza A Virus, H7N9 SubtypeInfluenza, HumanInfluenza in BirdsRisk AssessmentSex Factors

Identifiers

PMID28054599
PMCPMC5214706

What OpenQuestion holds

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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.