Evidence map›Paper›PMID 37009520›Full record

ArticleChina CDC weekly2023

Epidemic Features of COVID-19 and Potential Impact of Hospital Strain During the Omicron Wave - Australia, 2022.

Jingli Yang, Hannah McClymont, Liping Wang, Sotiris Vardoulakis, Wenbiao Hu

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in China CDC weekly, 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 21% 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. 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 at 2 institutions in 1 country.

Jingli YangSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Hannah McClymontSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Liping WangDivision of Infectious Diseases, Chinese Center for Diseases Control and Prevention, Beijing Municipality, China.
Sotiris VardoulakisNational Centre for Epidemiology and Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia.
Wenbiao HuSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Queensland University of Technology · AUAustralian National University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

What is already known about this topic?: Hospitals have experienced a surge in admissions due to the increasing number of Omicron cases. Understanding the epidemiological features of coronavirus disease 2019 (COVID-19) and the strain it places on hospitals will provide scientific evidence to help policymakers better prepare for and respond to future outbreaks. What is added by this report?: The case fatality rate of COVID-19 was 1.4 per 1,000 persons during the Omicron wave. Over 90% of COVID-19-related deaths occurred in individuals aged 60 years or older, with pre-existing chronic conditions such as cardiac conditions and dementia, particularly among males aged 80 years or older. What are the implications for public health practice?: Public health policy is essential for preparing and preserving medical resource capacity, as well as recruiting additional clinicians and front-line staff in hospitals to address the increased demand. High-risk individuals should be prioritized for healthcare, vaccines, and targeted interventions.

Indexed as

AustraliaCovid-19 omicron waveEpidemic featuresExperience and lessons

Identifiers

PMID37009520
PMCPMC10061764
OpenAlexW4321216733

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.