Evidence map›Paper›PMID 32691877›Full record

ArticleJournal of medical virology2021

Ambient air pollution, meteorology, and COVID-19 infection in Korea.

Tung Hoang, Tho Thi Anh Tran

Open access · bronzeAbstract read
In one paragraph

Article in Journal of medical virology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
6.0field-weighted citation impact, top 3% 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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Effects of fine particulate matter (PMCase studies in chemical and environmental engineering · 2023
    Article
  6. Modeling the Climatic Suitability of COVID-19 Cases in Brazil.Tropical medicine and infectious disease · 2023
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning.International journal of environmental research and public health · 2022
    Article
  17. Article
  18. Article
  19. Article
  20. 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

2 authors at 2 institutions in 3 countries.

Tung HoangDepartment of Cancer Biomedical Science, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Korea.ORCID 0000-0001-6653-3406
Tho Thi Anh TranDepartment of Gastroenterology and Hepatology, Nghe An Oncology Hospital, Nghe An, Vietnam.ORCID 0000-0002-4712-257X
Duy Tan University · VNKhanh Hoa General Hospital · VN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of novel pneumonia coronavirus disease has become a public health concern worldwide. Here, for the first time, the association between Korean meteorological factors and air pollutants and the COVID-19 infection was investigated. Data of air pollutants, meteorological factors, and daily COVID-19 confirmed cases of seven metropolitan cities and nine provinces were obtained from 3 February 2020 to 5 May 2020 during the first wave of pandemic across Korea. We applied the generalized additive model to investigate the temporal relationship. There was a significantly nonlinear association between daily temperature and COVID-19 confirmed cases. Each 1°C increase in temperature was associated with 9% (lag 0-14; OR = 1.09; 95% CI = 1.03-1.15) increase of COVID-19 confirmed cases when the temperature was below 8°C. A 0.01 ppm increase in NO

Indexed as

PandemicsAir PollutantsAir PollutionCarbon MonoxideCitiesCOVID-19HumansMeteorologyNitrogen DioxideParticulate MatterRepublic of KoreaSARS-CoV-2Sulfur DioxideTemperatureAir PollutantsCarbon MonoxideNitrogen DioxideParticulate MatterSulfur Dioxideair pollutionCOVID-19generalized additive modelKorea

Identifiers

PMID32691877
PMCPMC7405165
OpenAlexW3044282681

What OpenQuestion holds

Textmetadata
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.