Essa Tawfiq, Rosalie Chen, Damian Alexander Honeyman, Rebecca Dawson, Mohana Kunasekaran, Adriana Notaras, Deepti Gurdasani, Helen Skouteris, Darshini Ayton, Chandini Raina MacIntyre
{"title":"2023年澳大利亚高度接种疫苗人群中的长冠状病毒症状群、相关因素和预测因素","authors":"Essa Tawfiq, Rosalie Chen, Damian Alexander Honeyman, Rebecca Dawson, Mohana Kunasekaran, Adriana Notaras, Deepti Gurdasani, Helen Skouteris, Darshini Ayton, Chandini Raina MacIntyre","doi":"10.1111/hex.70273","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Limited data exists regarding long Covid burden following Omicron infection in highly vaccinated populations.</p>\n </section>\n \n <section>\n \n <h3> Objective</h3>\n \n <p>To (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.</p>\n </section>\n \n <section>\n \n <h3> Design</h3>\n \n <p>Anonymous, online, cross-sectional survey.</p>\n </section>\n \n <section>\n \n <h3> Setting</h3>\n \n <p>January 2023, Australia.</p>\n </section>\n \n <section>\n \n <h3> Participants</h3>\n \n <p>Residents aged ≥ 18 years with self-reported history of test-positive Covid-19.</p>\n \n <p>The main variables studied were socio-demographic characteristics, Covid-19 risk factors, vaccination, infection history and experiences with long Covid.</p>\n </section>\n \n <section>\n \n <h3> Main Outcome Measures</h3>\n \n <p>Long 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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Overall, 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.</p>\n \n <p>We identified two long Covid clusters (pauci-symptomatic, <i>n</i> = 170, vs. polysymptomatic, <i>n</i> = 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]).</p>\n </section>\n \n <section>\n \n <h3> Discussion</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>One-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.</p>\n </section>\n \n <section>\n \n <h3> Patient or Public Contribution</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":55070,"journal":{"name":"Health Expectations","volume":"28 3","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/hex.70273","citationCount":"0","resultStr":"{\"title\":\"Long Covid Symptom Clusters, Correlates and Predictors in a Highly Vaccinated Australian Population in 2023\",\"authors\":\"Essa Tawfiq, Rosalie Chen, Damian Alexander Honeyman, Rebecca Dawson, Mohana Kunasekaran, Adriana Notaras, Deepti Gurdasani, Helen Skouteris, Darshini Ayton, Chandini Raina MacIntyre\",\"doi\":\"10.1111/hex.70273\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Limited data exists regarding long Covid burden following Omicron infection in highly vaccinated populations.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>To (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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Design</h3>\\n \\n <p>Anonymous, online, cross-sectional survey.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Setting</h3>\\n \\n <p>January 2023, Australia.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Participants</h3>\\n \\n <p>Residents aged ≥ 18 years with self-reported history of test-positive Covid-19.</p>\\n \\n <p>The main variables studied were socio-demographic characteristics, Covid-19 risk factors, vaccination, infection history and experiences with long Covid.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Main Outcome Measures</h3>\\n \\n <p>Long 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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Overall, 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]). 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Our findings can inform policies for protecting vulnerable populations and frameworks for long Covid risk assessment and management.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>One-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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Patient or Public Contribution</h3>\\n \\n <p>We did not involve patients or the public in the design of the questionnaire. 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Long Covid Symptom Clusters, Correlates and Predictors in a Highly Vaccinated Australian Population in 2023
Background
Limited data exists regarding long Covid burden following Omicron infection in highly vaccinated populations.
Objective
To (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.
Design
Anonymous, online, cross-sectional survey.
Setting
January 2023, Australia.
Participants
Residents 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 Measures
Long 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.
Results
Overall, 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.
Conclusions
One-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.
期刊介绍：
Health Expectations promotes critical thinking and informed debate about all aspects of patient and public involvement and engagement (PPIE) in health and social care, health policy and health services research including:
• Person-centred care and quality improvement
• Patients'' participation in decisions about disease prevention and management
• Public perceptions of health services
• Citizen involvement in health care policy making and priority-setting
• Methods for monitoring and evaluating participation
• Empowerment and consumerism
• Patients'' role in safety and quality
• Patient and public role in health services research
• Co-production (researchers working with patients and the public) of research, health care and policy
Health Expectations is a quarterly, peer-reviewed journal publishing original research, review articles and critical commentaries. It includes papers which clarify concepts, develop theories, and critically analyse and evaluate specific policies and practices. The Journal provides an inter-disciplinary and international forum in which researchers (including PPIE researchers) from a range of backgrounds and expertise can present their work to other researchers, policy-makers, health care professionals, managers, patients and consumer advocates.
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