Evidence map›Paper›PMID 35324863›Full record

ArticleVeterinary sciences2022

Spatial Variation in Risk for Highly Pathogenic Avian Influenza Subtype H5N6 Viral Infections in South Korea: Poultry Population-Based Case-Control Study.

Saleem Ahmad, Kye-Young Koh, Dae-Sung Yoo, Jae-Il Lee

Open access · goldAbstract read
In one paragraph

Article in Veterinary sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 43% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
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

4 authors at 2 institutions in 1 country.

Saleem AhmadVeterinary Public Health Lab, College of Veterinary Medicine, Chonnam National University, Gwangju 61186, Korea.
Kye-Young KohVeterinary Public Health Lab, College of Veterinary Medicine, Chonnam National University, Gwangju 61186, Korea.
Dae-Sung YooAnimal and Plant Quarantine Agency, Gimcheon 39660, Korea.
Jae-Il LeeVeterinary Public Health Lab, College of Veterinary Medicine, Chonnam National University, Gwangju 61186, Korea.
Chonnam National University · KRAnimal and Plant Quarantine Agency · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Given the substantial economic damage caused by the continual circulation of highly pathogenic avian influenza (HPAI) outbreaks since 2003, identifying high-risk locations associated with HPAI infections is essential. In this study, using affected and unaffected poultry farms' locations during an HPAI H5N6 epidemic in South Korea, we identified places where clusters of HPAI cases were found. Hotspots were defined as regions having clusters of HPAI cases. With the help of the statistical computer program R, a kernel density estimate and a spatial scan statistic were employed for this purpose. A kernel density estimate and detection of significant clusters through a spatial scan statistic both showed that districts in the Chungcheongbuk-do, Jeollabuk-do, and Jeollanam-do provinces are more vulnerable to HPAI outbreaks. Prior to the migration season, high-risk districts should implement particular biosecurity measures. High biosecurity measures, as well as improving the cleanliness of the poultry environment, would undoubtedly aid in the prevention of HPAIV transmission to poultry farms in these high-risk regions of South Korea.

Indexed as

clustershighly pathogenic avian influenzahotspotskernel density estimatespatial scan statistic

Identifiers

PMID35324863
PMCPMC8952335
OpenAlexW4221075037

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.