Evidence map›Paper›PMID 38125847›Full record

ArticleFrontiers in public health2023

Was China's zero-COVID policy the right choice? The multiple factor analysis of variables that affected the course of COVID-19 pandemic in China.

Marharyta Sobczak, Rafał Pawliczak

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. 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
0.9field-weighted citation impact, top 28% 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, 4 citations in OpenAlex.

  1. Review
  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

2 authors at 1 institution in 1 country.

Marharyta SobczakDepartment of Immunopathology, Division of Biomedical Science, Faculty of Medicine, Medical University of Lodz, Łódź, Poland.
Rafał PawliczakDepartment of Immunopathology, Division of Biomedical Science, Faculty of Medicine, Medical University of Lodz, Łódź, Poland.
Medical University of Lodz · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: After 3 years of the COVID-19 pandemic and zero-COVID policy, a rapid increase in the number of daily COVID-19 infections was observed in China from November to December 2022. Therefore, we decided to analyze the factors that have been related to the COVID-19 pandemic in China. Methods: The multiple factor analysis was conducted, using the data from publicly available databases from the beginning of the COVID-19 pandemic to 30 January 2023. Results: Our study showed that each year of the pandemic in China had different profiles and can be described by different variables: year 2020 was characterized by restrictions, such as international travel controls, stay at home requirements, and health system policies including contact tracing and protection of older adults; year 2021 was characterized by Alpha, Beta, Gamma, and Delta variants; 2022 was characterized by new cases per million, Omicron lineages, and a few restrictions-related variables; and year 2023 was mainly described by the number of new deaths per million and Omicron variant 22B (BA.5) but also by testing and vaccination policies, as well as the number of people fully vaccinated per 100 and total boosters per 100. Conclusion: The COVID-19 pandemic has changed over time. Therefore, the anti-pandemic policies implemented must be dynamic and adapted to the current situation.

Indexed as

COVID-19AgedChinaFactor Analysis, StatisticalHealth PolicyHumansPandemicsSARS-CoV-2ChinaCOVID-19multiple factor analysispandemicSARS-CoV-2

Identifiers

PMID38125847
PMCPMC10731256
OpenAlexW4389393002

What OpenQuestion holds

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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.