Evidence map›Paper›PMID 41332799›Full record

ArticleJugan geon-gang gwa jilbyeong2024

[Introduction to the Research Results of Coronavirus Disease 2019 Big Data (K-COV-N) and the Corresponding Utilization Plan].

Hyeryeon Lee, Suhyeon Choi, Eunjin Eom, Gyeong Hee Song, Seong Sun Kim

Abstract readEnglish Abstract
In one paragraph

Article in Jugan geon-gang gwa jilbyeong, 2024. 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
–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

2 citing papers in PubMed.

  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

5 authors.

Hyeryeon Lee질병관리청 질병데이터과학분석관 역학데이터분석담당관.ORCID https://orcid.org/0000-0002-6920-2298
Suhyeon Choi질병관리청 질병데이터과학분석관 역학데이터분석담당관.ORCID https://orcid.org/0000-0002-6920-2298
Eunjin Eom질병관리청 질병데이터과학분석관 역학데이터분석담당관.ORCID https://orcid.org/0000-0002-6920-2298
Gyeong Hee Song질병관리청 질병데이터과학분석관 역학데이터분석담당관.ORCID https://orcid.org/0000-0002-6920-2298
Seong Sun Kim질병관리청 질병데이터과학분석관 역학데이터분석담당관.ORCID https://orcid.org/0000-0002-6920-2298

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As part of the effort to establish a scientific basis for quarantine policies aimed at responding to new infectious diseases based on the experience of responding to coronavirus disease 2019 (COVID-19), the Korea Disease Control and Prevention Agency (KDCA) and National Health Insurance Service promoted the establishment of health information big data and signed a business agreement (April 29, 2021). The big data created by combining the KDCA's COVID-19 confirmed cases and vaccination data with the National Health Insurance Service's national health information are known as K-COV-N (KDCA Covid-19 NHIS cohort). The big data constructed were opened to the private sector through the National Health Insurance Service's open platform to promote private research. A cumulative total of 211 cases have been approved since the opening of COVID-19 big data from April 2022 until October 2, 2024, and a total of 30 papers have been published in international journals. The main contents were research results such as COVID-19 infection risk factors, vaccination effects, and long COVID. In addition, after the opening of big data, we held workshops on practical networking for utilizing COVID-19 big data to share analyses and techniques for utilizing the data. Additionally, we established a public-private data analysis network to promote exchanges between practitioners. In addition, we are contributing to the activation of private research by expanding ties with the National Cancer Center and health-information-holding organizations. Accordingly, the KDCA plans to continue opening up infectious disease-related information to develop evidence-based quarantine policies and support expert decision-making.

Indexed as

COVID-19 infection risk factorsK-COV-NLong COVIDVaccination effect

Identifiers

PMID41332799
PMCPMC12480311

What Socratic holds

Textmetadata
LicenceCC BY-NC
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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 Socratic graph.