Evidence map›Paper›PMID 40342248›Full record

ArticleHealth expectations : an international journal of public participation in health care and health policy2025

Long Covid Symptom Clusters, Correlates and Predictors in a Highly Vaccinated Australian Population in 2023.

Essa Tawfiq, Rosalie Chen, Damian Alexander Honeyman, Rebecca Dawson, Mohana Kunasekaran, Adriana Notaras, Deepti Gurdasani, Helen Skouteris, Darshini Ayton, Chandini Raina MacIntyre

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health expectations : an international journal of public participation in health care and health policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Essa TawfiqBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, The University of New South Wales, Sydney, Australia.
Rosalie ChenBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, The University of New South Wales, Sydney, Australia.
Damian Alexander HoneymanBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, The University of New South Wales, Sydney, Australia.ORCID 0000-0002-4339-1801
Rebecca DawsonBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, The University of New South Wales, Sydney, Australia.
Mohana KunasekaranBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, The University of New South Wales, Sydney, Australia.
Adriana NotarasBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, The University of New South Wales, Sydney, Australia.
Deepti GurdasaniBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, The University of New South Wales, Sydney, Australia.
Helen SkouterisHealth and Social Care Unit, School of Public Health and Preventive Medicine, Monash University in collaboration with Monash Health, Melbourne, Australia.
Darshini AytonHealth and Social Care Unit, School of Public Health and Preventive Medicine, Monash University in collaboration with Monash Health, Melbourne, Australia.ORCID 0000-0002-2754-2024
Chandini Raina MacIntyreBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, The University of New South Wales, Sydney, Australia.ORCID 0000-0002-3060-0555

Funding

E.T., R.C., D.A.H., R.D., M.K., A.N. and D.G. are supported by the Balvi Filantropic Fund. C.R.M. is funded by NHMRC Investigator Grant 2016907. D.A.H. and A.N. are funded through MRFF (grant ID 2017048). The funding sources had no role in the study design, data collection, analysis or interpretation, reporting or publication of this work.
6 · The paper itself

Abstract

backgroundLimited data exists regarding long Covid burden following Omicron infection in highly vaccinated populations.

objectiveTo (1) characterise long Covid prevalence and predictors and (2) identify key symptom clusters and their correlates among long Covid patients, during an Omicron-predominant period in a highly vaccinated population.

designAnonymous, online, cross-sectional survey.

settingJanuary 2023, Australia.

participantsResidents aged ≥ 18 years with self-reported history of test-positive Covid-19. The main variables studied were socio-demographic characteristics, Covid-19 risk factors, vaccination, infection history and experiences with long Covid.

main outcome measuresLong Covid symptoms. Symptom-based clustering was used to identify long Covid symptom clusters and their functional correlates. Predictors of long Covid occurrence and severity were assessed using multivariable logistic regression.

resultsOverall, 240/1205 participants (19.9%) reported long Covid. Long Covid risk was significantly higher for women OR 1.71 (95% CI: 1.17-2.51), people with comorbidities 2.19 (95% CI: 1.56-3.08) and those using steroid inhalers for Covid-19 treatment (2.34 [95% CI: 1.29-4.24]). Long-Covid risk increased with increasing Covid-19 infection severity (moderately severe symptoms: 2.23 [95% CI: 1.50-3.30], extremely severe symptoms: 5.80 [95% CI: 3.48-9.66], presented to ED/hospitalised: 7.22 [95% CI: 3.06-17.03]). We found no significant difference in the likelihood of long Covid between the Omicron and pre-Omicron periods, vaccination status and participant age. We identified two long Covid clusters (pauci-symptomatic, n = 170, vs. polysymptomatic, n = 66). Polysymptomatic cluster membership was associated with worse functioning (impacts on work, moderate activity, emotions and energy). Severity acute infection was strongly predictive of polysymptomatic cluster membership (5.72 [2.04-17.58]). Monoclonal antibody treatment was strongly associated with pauci-symptomatic cluster membership (0.02 [0.00-0.13]). DISCUSSION: Our study shows that long Covid is an important health burden in Australia, including during the Omicron era, and identifies several risk factors. We found a subgroup of patients characterised by more symptoms and worse functional outcomes. Our findings can inform policies for protecting vulnerable populations and frameworks for long Covid risk assessment and management.

conclusionsOne-in-five people may suffer long Covid after acute Covid-19 infection, with similar risk across age groups. Omicron variants appear not to have a lower risk compared to earlier variants in our study. A cumulative number of symptoms can help triage long Covid patients. PATIENT OR PUBLIC CONTRIBUTION: We did not involve patients or the public in the design of the questionnaire. However, after a soft launch, we revised some survey questions by reviewing early responses from patients and the public.

Indexed as

COVID-19COVID-19 VaccinesAdolescentAdultAgedAustraliaCross-Sectional StudiesFemaleHumansMaleMiddle AgedPrevalenceRisk FactorsSARS-CoV-2Severity of Illness IndexVaccinationCOVID-19 VaccinesAustraliaCovid‐19long CovidOmicronSARS‐CoV‐2

Identifiers

PMID40342248
PMCPMC12059467

What Socratic holds

Textmetadata
LicenceCC BY
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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.