Characteristics of post-acute COVID-19 presentations and healthcare use in Australian general practice and comparisons to those with acute COVID-19 and upper respiratory tract infection

Characteristics of post-acute COVID-19 presentations and healthcare use in Australian general practice and comparisons to those with acute COVID-19 and upper respiratory tract infection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characteristics of post-acute COVID-19 presentations and healthcare use in Australian general practice and comparisons to those with acute COVID-19 and upper respiratory tract infection Emmae Ramsay, Nigel Stocks, Carla Bernando, Fernanda Nobre, Sandrine Stepien, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7561005/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Post-acute COVID-19 syndrome (long COVID) is recognised internationally as a significant public health issue. Despite a large international literature on these conditions, there are still many gaps in understanding key aspects of their epidemiology and health system impact. This study examined patient characteristics, common symptoms, and healthcare utilisation associated with post-acute COVID-19 syndrome and compared this to acute COVID-19 and other upper respiratory tract infections (URTI) presenting to primary care. Methods We used MedicineInsight, a national general practice database, to identify adults diagnosed with post-acute COVID syndrome, acute COVID-19, or URTI between January and July 2022. Patients were required to be regular attenders with at least two GP visits in the previous two years. Sociodemographic and clinical characteristics were described, and multilevel Poisson regression was used for comparisons between groups. Symptoms, prescriptions, pathology, and imaging requests were examined for the 12 weeks before and after diagnosis. Results Among 102,907 patients, 701 were diagnosed with post-acute COVID syndrome, 74,486 with acute COVID-19, and 27,720 with URTI. Compared with acute COVID-19, individuals with post-acute COVID were more often female (RR 1.17 (95% CI:1.01–1.36)), aged 35–64 years and had higher rates of chronic respiratory disease and depression/anxiety. Compared with those with URTI, they were older, more often female (RR 1.22 (95% CI:1.05–1.42)), and less likely to smoke. Following diagnosis, patients with post-acute COVID experienced marked increases in fatigue (RR 3.37(95% CI:2.21, 5.14)), cough (RR 3.67(95% CI:2.19, 6.13)), and chest pain (RR 2.50(95% CI:1.28, 4.87)). Pathology requests doubled or tripled, while imaging increased sharply, including a six-fold rise in chest X-rays, exceeding increases observed in comparator groups. Conclusions Post-acute COVID syndrome in Australian general practice is most common among middle-aged women and those with respiratory or mental health comorbidities. Diagnosis is associated with substantially greater use of investigations than for acute COVID-19 or URTI, highlighting the diagnostic complexity and additional health system burden. Primary care data provides crucial insights into post-acute COVID-19 syndrome and can guide future health resource planning. Post-acute COVID-19 syndrome long COVID-19 primary health care general practice Australia electronic health records Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background SARS-CoV-2, the virus that causes COVID-19, is known to result in long-term health sequelae. These sequelae are highly variable and have been described not only among those with severe acute infections but also those with mild disease. They are often grouped together under the term used by the World Health Organisation as post-acute COVID-19 conditions or colloquially as long COVID (1, 2). Despite a large international literature on these conditions (3, 4), there are still many gaps in understanding key aspects of their epidemiology and health system impact. This is particularly the case for infections with the SARS-CoV-2 Omicron subvariants and among people who have been vaccinated and not hospitalised with COVID-19. In Australia, general practice is often the initial point of care for patients experiencing persistent symptoms following COVID-19. General practitioners (GPs) have the difficult task of distinguishing post-acute COVID syndrome from other conditions. This process is complicated by the variety and overlap of symptoms, which can range from cardio-respiratory, gastrointestinal or neurological complaints to non-specific symptoms such as fatigue. The absence of pathognomonic features and the need to exclude alternative conditions mean that a range of investigations, including pathology, imaging requests, and other diagnostic tests are often required(5). Few studies in Australia have analysed post-COVID conditions in primary care data(6, 7), and none have used national data to compare patients with post-acute COVID syndrome to those diagnosed with other respiratory infections. This type of comparison is needed to determine how COVID-19 may differ from other viral respiratory illnesses. This study aimed to use a large database of Australian general practice records (MedicineInsight (8)) to firstly describe the characteristics of adults presenting with post-acute COVID syndrome, assess the common symptoms recorded, and then make comparisons between patients with acute COVID-19 and those with other upper respiratory tract infections (URTI). Secondly, we described the prescriptions, pathology, and imaging requests associated with the diagnosis of post-acute COVID syndrome and compared these to the patterns in other patient groups. Methods Data Source and study population MedicineInsight is a database containing de-identified, patient-level data from 8% of general practices across Australia, of varying sizes, service types and geographical locations. The characteristics of patients in the MedicineInsight database resemble the Australian population(8). Standard data collection began in 2011 and covers a wide range of routine healthcare information, including patient demographics (sex, year of birth, postcode), diagnosis, reasons for encounters, prescribed medications and their indications, pathology and imaging requests, and clinical measurements (e.g. weight, height, blood pressure). A detailed description of the MedicineInsight database has been previously published(8, 9). We had full data from MedicineInsight up until 30 th September 2022. We used data from January to September 2022; during this time, there were nearly 7 million visits recorded in the MedicineInsight database for 1.2 million regular patients across 326 practices(8). We restricted our analyses to patients 18 years or over who were diagnosed with post-acute COVID syndrome, acute COVID-19 or URTI between 1 st January 2022 and 8 th July 2022 to ensure a full 12-week follow-up following diagnosis. The observation period corresponded to when mostly the SARS-CoV-2 Omicron sub-variants BA.1, BA.2 and some BA.4/5 circulated(10) and more than 90% of the adult Australian population was estimated to have completed the primary course of COVID-19 vaccination(11). We included only patients who were regular attenders to one of the practices within MedicineInsight to ensure that we had longitudinal information on symptoms and comorbidities. A regular attender for this study was defined as having at least two recorded GP consultations in the 2 years prior to their index date, with at least one consultation in each year prior. If patients had a COVID-19 diagnosis that was identified in the data prior to 2022, they were excluded from the study. Study definitions We defined three target populations of interest using terms specified in at least one of the three main text fields (diagnosis, reason for encounter, or reason for prescription). For all conditions, where available, standardised algorithms based on UK phenotypes were used along with consultation with clinicians to guide the search terms (12, 13). Regular expressions were used to capture misspellings and variations in phrasing. Post-acute COVID syndrome Patients with post-acute COVID syndrome were classified as such if (1) the diagnosis of "long COVID" or "post-COVID syndrome" was recorded in any of these fields, regardless of whether they had a prior COVID-19 diagnosis or (2) these fields included terms like "post COVID”, "post-COVID + infection”, or "post-COVID + symptoms” preceded by at least 12 weeks with a confirmed “COVID-19” diagnosis recorded (i.e. acute infection). Their index date for the SARS-CoV-2 infection was defined as either a) the first recorded date of a COVID-19 diagnosis (i.e. acute infection), or b) 12 weeks before the date of the first post-acute COVID syndrome diagnosis in those cases when there was no record of a COVID-19 diagnosis. This is in line with the common definition of post-acute COVID syndrome (i.e. persistence of symptoms for 12 weeks or more after a COVID-19 infection)(5). Acute COVID-19 Patients with acute COVID-19 were classified as such if: 1) terms such as “COVID” (excluding those with ‘vaccination’, ‘eligible’, ‘possible’, or ‘contact’) were recorded in any of the fields; and/or 2) they had a positive pathology test result for COVID-19, and/or 3) they were prescribed COVID-specific antivirals (nirmatrelvir/ritonavir or molnupiravir) and 4) were not classified as having post-acute COVID syndrome. The index date was the first COVID-19 diagnosis date or positive pathology test, or the date of antiviral prescription. Other upper respiratory tract infection (URTI) Patients with other upper respiratory tract infections (URTI) were classified if terms such as “URTI”, “upper respiratory”, or “respiratory infection” were recorded in any of the fields, but were not already classified as having post-acute COVID syndrome or acute COVID-19. Their index date was the date of their first URTI diagnosis in 2022. Variables of interest Sociodemographic characteristics included age, sex, remoteness of practice using the Australian Statistical Geography Standard (ASGS), which considers population size and distance to services(14), state of GP practice, Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) quintiles derived for GP practice and patient postcode(15). Clinical history included patient smoking status and the number of recorded GP consultations in the year prior to the index date. Chronic comorbidities were identified in the 2 years prior to the index date. They included chronic respiratory disease, cardiovascular disease, diabetes, liver disease, chronic kidney disease, neurological conditions, dementia, depression/anxiety, sleep issues, autoimmune diseases, and post-viral syndromes (Epstein-Barr virus (EBV) infection, chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME) and postural orthostatic tachycardia (POTS)). These were extracted by looking for terms associated with these conditions in the three main text fields. An explanation of the methods used to extract clinical history data can be found in earlier publications (16, 17). The list of terms and algorithms that were used for data extraction is available from authors upon request. Outcomes of interest We examined several outcomes, which are detailed below, in the three populations of interest (i.e. post-acute COVID syndrome, acute COVID-19 or URTI groups). Symptoms Based on the available literature, we selected the most common symptoms described as associated with post-acute COVID syndrome, which could be identified in the diagnosis, reason for encounter and reason for prescription fields of the MedicineInsight database. These included fatigue, headache/migraine, shortness of breath (dyspnoea, breathlessness, orthopnoea), chest pain, sleep disorders/insomnia, cough (18-20) and memory issues (memory loss, concentration difficulties, brain fog, confusion, learning difficulties)(2, 21, 22). Prescriptions, pathology and imaging requests Prescriptions of sedatives, anxiolytics, antidepressants, antibiotics and analgesics were extracted from the script item field based on active ingredients or brand names. Pathology requests for full blood count, C-reactive protein (CRP), electrolytes, iron studies and B12/folate and imaging requests for chest x-ray and chest CT scans derived from the pathology request fields. Data Analysis For the three patient groups (post-acute COVID syndrome, acute COVID-19 and URTI), we compared baseline sociodemographic and clinical characteristics of patients and the practice they attended. The index date was used to determine the timing of variables of interest. For the post-acute COVID syndrome group, the COVID-19 diagnosis date (or proxy, as described earlier) was used to ensure comparability at the same point in the acute COVID-19 disease trajectory. We compared patients diagnosed with post-acute COVID syndrome to those with either acute COVID-19 only or URTI (comparator groups), using multilevel Poisson regression with robust standard errors. The outcome was the diagnostic group (post-acute COVID vs comparator), and explanatory variables included baseline patient and general practice characteristics. Both univariate models (each covariate separately) and multivariate models (adjusting for all covariates simultaneously) were fitted. Medical practice was included as a random effect to account for clustering of patients, as different data recording and diagnosis practices may occur within a practice. Relative risks (RR) and 95% confidence intervals were reported from these models. We then examined symptoms recorded, medicines prescribed, pathology and imaging requests in each of the three groups within 0-12 weeks prior to their index date and 0-12 weeks after their index date. Due to the lack of a definitive diagnostic method for identifying post-acute COVID syndrome and the broad spectrum of symptoms, we only examined a limited number of commonly reported symptoms that could be identified in the database. Given the differences in baseline characteristics across diagnosis groups, we conducted these analyses within each group separately. To assess changes in healthcare use before and after the index date, we used multilevel Poisson regression with robust standard errors to compare binary outcomes; symptoms, prescriptions, pathology requests, and imaging requests, between the 12 weeks prior to and the 12 weeks following the index date. Random effects were included for GP practice (to account for clustering of patients within practices) and for individual patients (to account for repeated measures). To assess changes in healthcare use before and after the index date, crude and model-adjusted percentages, relative risks and corresponding 95% confidence intervals were presented. All analyses were performed in STATA 18 (StataCorp, College Station, Texas, USA). Results Cohort demographics During January to July, 701 patients were diagnosed with post-acute COVID syndrome, 74,486 with acute COVID-19 and no subsequent report of post-acute COVID, and 27,720 with an upper respiratory tract infection (URTI). The post-acute COVID syndrome group had a higher proportion of females (491; 70%) compared to the COVID-19 only (48,048; 65%) and the URTI group (17,357; 63%). The most common age group at diagnosis was 35-64 years for both the post-acute syndrome group (417; 60%) and the COVID-19 only group (37,182; 50%), while the URTI group were younger, 16,825 (61%) aged 18-49 years. All patient groups were more likely to be seen in practices in higher socioeconomic areas, but the proportion was higher for those with post-acute COVID and acute COVID. The distribution of other characteristics is described in Table 1. Post-acute COVID syndrome vs COVID-19 only group The multivariate model adjusting for age, sex and other factors (Figure 1) showed that compared to those diagnosed with only acute COVID-19, individuals with post-acute COVID syndrome were more likely to be aged 35-49 years (RR 1.45 (95% CI:1.14, 1.86)) and 50-64 years (RR 1.44 (95% CI:1.13, 1.84)). Women were also more likely than men to be in the post-acute group (RR 1.17 (95% CI:1.01, 1.36)). Those in the post-acute COVID syndrome group were also more likely to have had a prior chronic respiratory disease (RR 1.36 (95% CI:1.15, 1.61)) and depression/anxiety diagnosis (RR 1.42 (95% CI:1.23, 1.63)) and less likely to have had a diabetes diagnosis (RR 0.73 (95% CI:0.57, 0.93)). Post-acute COVID syndrome vs URTI group The results from the multivariate model adjusting for age, sex and other factors comparing the post-COVID syndrome to the URTI group are presented in Figure 2. There were differences in the sociodemographic and geographical location of the GP practices. Individuals with post-acute COVID syndrome were older, more likely to be women than those with other URTIs (RR 1.22 (95% CI:1.05, 1.42)). They were also less likely to be smokers (RR 0.55 (95% CI:0.41, 0.74)) but more likely to have had a prior chronic respiratory disease (RR 1.27 (95% CI:1.08, 1.50)) and depression/anxiety diagnosis (RR 1.39 (95% CI:1.19, 1.62)) and less likely to have chronic kidney disease (RR 0.70 (95% CI:0.50, 0.98)). Symptoms, prescriptions, pathology requests and imaging requests Figures 3-5 show comparisons of symptoms, prescriptions and pathology/imaging requests in the 0-12 weeks prior and post diagnosis within each diagnosis group. For the post-acute COVID syndrome group (Figure 3), symptoms that were reported significantly more frequently 0-12 weeks post diagnosis compared to 0-12 weeks prior to diagnosis were: fatigue (13.1% vs 3.9% : RR 3.37 (95% CI:2.21, 5.14)), a cough (8.1% vs 2.2% : RR 3.67 (95% CI:2.19, 6.13)) and chest pain (4.3% vs 1.7% : RR 2.50 (95% CI:1.28, 4.87)). There was an increase in some medicines prescribed; sedatives (10.3% vs 7.3%: RR 1.41 (95% CI:1.08, 1.83)), antibiotics (25.0% vs 14.8%: RR 1.68 (95% CI:1.32, 2.15)) and analgesics (20.7% vs 16.2%: RR 1.28 (95% CI:1.06, 1.55)). All pathology requests examined increased 2 to 3-fold in the post-diagnosis period, as did imaging; chest x-ray (15.8% vs 2.4%: RR 6.47 (95% CI:4.02, 10.40)) and chest CT scan (2.1% vs 0.7%: RR 3.00 (95% CI:1.05, 8.55)). For the acute COVID-19 only group (Figure 4), changes in reported symptoms were less than those observed for the post-acute COVID syndrome group, although there was still a 3-fold increase in likelihood of reporting cough in the 12 weeks following diagnosis. Similar to the post-acute COVID group, there were small increases in all prescriptions. For pathology and radiology requests, there were also significant increases, but the magnitude of that increase was substantially smaller than for the post-acute COVID group with only the frequency of chest x-ray increasing two-fold (3.3% vs 1.5%: RR 2.27 (95% CI:2.10, 2.45)). For the URTI group (Figure 5), in 0-12 weeks post diagnosis there were significant increases in reporting of shortness of breath (0.2% vs 0.1%: RR 2.32 (95% CI:1.55, 3.49)), cough (5.6% vs 1.8%: RR 3.17 (95% CI:2.86, 3.51)) and in chest pain (1.2% vs 1.0%: RR 1.28 (95% CI:1.10, 1.49)). There was a large increase in prescriptions for antibiotics (40.9% vs 11.8%: RR 3.46 (95% CI:3.29, 3.65)) and a small increase in sedatives (5.4% vs 5.0%: RR 1.07 (95% CI:1.01, 1.14)), antidepressants (13.1% vs 11.9%: RR 1.10 (95% CI:1.06, 1.14)) and analgesics (13.7% vs 11.5%: RR 1.19 (95% CI:1.14, 1.24)). Most pathology tests saw a small increase in the post-diagnosis period full blood count (21.6% vs 19.3%: RR 1.12 (95% CI:1.08, 1.16)), C-reactive protein (7.6% vs 5.7%: RR 1.34 (95% CI:1.26, 1.43)), electrolytes (19.2% vs 17.1%: RR 1.12 (95% CI:1.08, 1.16)), iron studies (10.6% vs 9.9%: RR 1.08 (95% CI:1.02, 1.13)). Both the frequency of chest x-rays and chest CT scans increased at least 2-fold. For the post-acute COVID syndrome group, most of the pathology and imaging requests and prescriptions in the 12-week period post-index date occurred later in the period, around week 12. At the same time, they were more evenly distributed across all weeks in the acute COVID and URTI groups, except for antibiotics, which for the URTI group occurred around the week of diagnosis. The reporting of symptoms in the post-acute COVID group was also often higher in the latter part of the 12-week period post-index date, while the symptom of cough was reported in the 1-2 week period post-index date for those with URTI (see Figure 6). Discussion Our findings suggest that individuals diagnosed with post-acute COVID syndrome were more likely to be female, middle-aged, and have pre-existing chronic respiratory disease or depression/anxiety, compared to those with acute COVID-19 only. Additionally, those with post-acute COVID syndrome were less likely to be smokers than those with other URTIs. We also found that those with post-acute COVID syndrome had 2-3-fold increases in pathology testing and up to 6-fold increases in imaging tests following their diagnosis. These were generally higher than the post-diagnosis increases we observed for those with acute COVID-19 and other URTIs. Our results are consistent with international literature, where key risk factors for post-acute COVID or “long COVID” consistently include female sex, being middle-aged, and pre-existing comorbidities (23, 24). More recently, results from a secondary analysis of the Anti-Coronavirus Therapies trials identified similar risk factors, with additional findings suggesting lower rates among smokers and people with diabetes mellitus, which is consistent with our results(25). A preprint of an updated systematic review and meta-analysis including 442 studies found the three most consistent predictors of post-acute COVID-19 were being unvaccinated, having pre-existing comorbidities, and female sex; memory problems were reported as the most common symptom (26). However, the authors noted that populations from Oceania were underrepresented. This highlights the need for more Australian studies, as we had a unique pandemic trajectory, with prolonged border closures and high vaccination coverage prior to major community transmission, predominantly involving the Omicron variant (27). While several Australian studies have explored long COVID, few have used primary care data. One study using general practice records from New South Wales and Victoria compared patients with post-acute COVID with a population without COVID-19 and reported findings broadly consistent with the international evidence (7). Our study builds on this by comparing individuals with post-acute COVID to both those with acute COVID only and those with other respiratory infections, rather than just comparing to a general population of healthcare users. There is limited Australian research assessing healthcare utilisation following a COVID-19 diagnosis. Preliminary work by the Australian Department of Health and Aged Care suggested increased use of GP services, chest X-rays, blood tests, and pulmonary rehabilitation following a COVID-19 diagnosis, though case ascertainment was limited due to small sample sizes (28). The increase in pathology and imaging requests in those with post-acute COVID syndrome that we observed is expected, given that the condition requires exclusion of other conditions (5, 29). Understanding how post-acute COVID diagnoses affect health service use is essential in the Australian context, where healthcare structures and pandemic dynamics differ from those in Europe and North America. Our quantification with up to 6-fold increases in some imaging requests suggests considerable additional healthcare costs for this population. Quantifying excess health system burden due to post-acute COVID will help inform the cost-effectiveness of prevention strategies and guide future resource allocation(29). Several limitations must be acknowledged. Firstly, in this database, there was no consistent coding for diagnoses of COVID-19, post-acute COVID, or URTI. In our analysis, the ratio of people with a post-acute COVID diagnosis to those with acute COVID-19 was less than 1:100, and this is low compared to estimates from some studies (6). However, it has been suggested that estimates based on surveys may be biased(30) and our study was done during the circulation of the Omicron variant in a highly vaccinated population with more than 90% of Australians having received their primary course (31). Hence, it would be expected that rates of post-acute COVID syndromes would be lower due to less severe disease(31). Second, we could have missed people who did not inform their GP of their COVID-19 diagnosis or people who were severely unwell and were managed in hospital. We know that post-acute COVID syndromes may be more common in people with severe disease. Third, similar to diagnoses, in the MedicineInsight data, there is also no consistent coding for symptoms and medicines prescribed and while pathology requests are generally well captured, there is limited information on test results. These issues would have led to under ascertainment of these variables and might explain why symptoms such as fatigue, that have been widely reported in post-acute covid cases(2), was only reported among 13% of those with post-acute COVID. However, for the pre-post comparisons presented here we used consistent definitions so that any misclassification should be consistent between the comparison periods. Additionally, we only examined common symptoms that have been described in the literature for post-acute COVID syndrome (2, 18-22). Conclusions This study enabled the identification of key risk factors associated with post-acute COVID syndrome. These findings may support GPs in recognising patients at higher risk of developing post-acute COVID syndrome earlier, allowing for timely management and intervention strategies aimed at improving patient outcomes. By highlighting the types of medications prescribed, pathology tests ordered, and imaging studies requested, our findings offer a different perspective from many existing studies that focus primarily on symptoms and clinical characteristics and can help inform the additional healthcare use for those with post-acute COVID syndrome. This study also highlights the importance of using primary care data for public health research (32). It demonstrated that the utilisation of general practice data leads to greater understanding of the patient experience, treatment pathways which can support GPs to diagnose complex conditions such as post-acute COVID syndrome as well as providing quantitative evidence to support health system needs. This reinforces the value of investing in robust, representative primary care datasets in Australia. Doing so will support timely, evidence-based care for emerging conditions and strengthen the role of GPs in managing population health. Declarations Ethics approval and consent to participate The Royal Australian College of General Practitioners (RACGP) National Research and Evaluation Ethics Committee (NREEC) granted ethics approval (NREEC 17-017) for the standard operation and use of the NPS MedicineInsight programme, waiving the need for a consent to participate from patients. MedicineInsight used an opt-out approach instead, by a process handled independently at the practice, with the display of posters to inform them that MedicineInsight was collecting their de-identified data. Research was conducted in accordance with the Declaration of Helsinki. Reference: Busingye D, Gianacas C, Pollack A, Chidwick K, Merrifield A, Norman S, Mullin B, Hayhurst R, Blogg S, Havard A, Stocks N. Data Resource Profile: MedicineInsight, an Australian national primary health care database. Int J Epidemiol. 2019 Dec 1;48(6):1741-1741h. doi: 10.1093/ije/dyz147. Consent for publication Not applicable Availability of data and materials Researchers can apply to access the MedicineInsight data independently from the Australian Commission on Safety and Quality in Health Care. Competing interests James Wood is a member of the Australian Technical Advisory Group on Immunisation (ATAGI) and a past unpaid member of the of a Moderna committee from April 2021 to April 2022. Other authors declare that they have no competing interests. Funding Australian Medical Research Future Fund (MRFF) Authors' contributions ER conducted analysis and drafted the manuscript. BL and SS conceived the initial study design and contributed to the writing of the manuscript. All authors contributed to the study design, analysis, interpretation of results and revisions of the paper. All authors read and approved the final manuscript. Acknowledgements We acknowledge John Rule and Michelle Thompson, who have provided consumer input. Authors' information (optional) References World Health Organization. Post COVID-19 condition (Long COVID). 2022. https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition. Accessed 21 August 2025. Groff D, Sun A, Ssentongo AE, Ba DM, Parsons N, Poudel GR, et al. Short-term and Long-term Rates of Postacute Sequelae of SARS-CoV-2 Infection: A Systematic Review. JAMA Netw Open. 2021;4(10):e2128568. Davis HE, McCorkell L, Vogel JM, Topol EJ. Long COVID: major findings, mechanisms and recommendations. Nature Reviews Microbiology. 2023;21(3):133-46. Greenhalgh T, Sivan M, Perlowski A, Nikolich JŽ. Long COVID: a clinical update. The Lancet. 2024;404(10453):707-24. NICE. COVID-19 rapid guideline: managing the long term effects of COVID-19. In: Excellence NIfHaC, editor. 2024. Holmes A, Emerson L, Irving LB, Tippett E, Pullin JM, Young J, et al. Persistent symptoms after COVID-19: an Australian stratified random health survey on long COVID. Med J Aust. 2024;221 Suppl 9:S12-s7. Kamalakkannan A, Prgomet M, Thomas J, Pearce C, McGuire P, Mackintosh F, Georgiou A. Factors associated with general practitioner-led diagnosis of long COVID: an observational study using electronic general practice data from Victoria and New South Wales, Australia. Med J Aust. 2024;221 Suppl 9:S18-s22. Busingye D, Gianacas C, Pollack A, Chidwick K, Merrifield A, Norman S, et al. Data Resource Profile: MedicineInsight, an Australian national primary health care database. Int J Epidemiol. 2019;48(6):1741-h. González-Chica DA, Vanlint S, Hoon E, Stocks N. Epidemiology of arthritis, chronic back pain, gout, osteoporosis, spondyloarthropathies and rheumatoid arthritis among 1.5 million patients in Australian general practice: NPS MedicineWise MedicineInsight dataset. BMC Musculoskelet Disord. 2018;19(1):20. Epidemiology C-, Surveillance T. COVID-19 Australia: Epidemiology Report 69 Reporting period ending 18 December 2022. Communicable Diseases Intelligence. 2023;47. COVID-19 Vaccine Rollout. Australian Government; 2022. https://www.health.gov.au/sites/default/files/documents/2022/01/covid-19-vaccine-rollout-update-31-january-2022.pdf. Accessed 21 August 2025. Kuan V, Denaxas S, Gonzalez-Izquierdo A, Direk K, Bhatti O, Husain S, et al. A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digit Health. 2019;1(2):e63-e77. HDR UK Phenotype Library. https://phenotypes.healthdatagateway.org/. Accessed 21 August 2025. Australian Bureau of Statistics. Remoteness Structure. Canberra: ABS; jul2021-jun2026; 2021. https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/remoteness-structure. Accessed 21 August 2025. Australian Bureau of Statistics. Socio-Economic Indexes for Areas (SEIFA), Australia. Canberra; 2021. https://www.abs.gov.au/statistics/people/people-and-communities/socio-economic-indexes-areas-seifa-australia/latest-release. Accessed 21 August 2025. Roseleur J, Gonzalez-Chica DA, Bernardo CO, Geisler BP, Karnon J, Stocks NP. Blood pressure control in Australian general practice: analysis using general practice records of 1.2 million patients from the MedicineInsight database. J Hypertens. 2021;39(6):1134-42. Woods A, Begum M, Gonzalez-Chica D, Bernardo C, Hoon E, Stocks N. Long-term benzodiazepines and z-drug prescribing in Australian general practice between 2011 and 2018: A national study. Pharmacol Res Perspect. 2022;10(1):e00896. Alkodaymi MS, Omrani OA, Ashraf N, Shaar BA, Almamlouk R, Riaz M, et al. Prevalence of post-acute COVID-19 syndrome symptoms at different follow-up periods: a systematic review and meta-analysis. Clin Microbiol Infect. 2022;28(5):657-66. Almas T, Malik J, Alsubai AK, Jawad Zaidi SM, Iqbal R, Khan K, et al. Post-acute COVID-19 syndrome and its prolonged effects: An updated systematic review. Ann Med Surg (Lond). 2022;80:103995. Kelly JD, Curteis T, Rawal A, Murton M, Clark LJ, Jafry Z, et al. SARS-CoV-2 post-acute sequelae in previously hospitalised patients: systematic literature review and meta-analysis. Eur Respir Rev. 2023;32(169). Han Q, Zheng B, Daines L, Sheikh A. Long-Term Sequelae of COVID-19: A Systematic Review and Meta-Analysis of One-Year Follow-Up Studies on Post-COVID Symptoms. Pathogens. 2022;11(2). Marjenberg Z, Leng S, Tascini C, Garg M, Misso K, El Guerche Seblain C, Shaikh N. Risk of long COVID main symptoms after SARS-CoV-2 infection: a systematic review and meta-analysis. Sci Rep. 2023;13(1):15332. Subramanian A, Nirantharakumar K, Hughes S, Myles P, Williams T, Gokhale KM, et al. Symptoms and risk factors for long COVID in non-hospitalized adults. Nat Med. 2022;28(8):1706-14. Tsampasian V, Elghazaly H, Chattopadhyay R, Debski M, Naing TKP, Garg P, et al. Risk Factors Associated With Post−COVID-19 Condition: A Systematic Review and Meta-analysis. JAMA Internal Medicine. 2023;183(6):566-80. Lucas Etienne H, Sean W, Lizhen X, John E. Long COVID prevalence and risk factors in adults residing in middle- and high-income countries: secondary analysis of the multinational Anti-Coronavirus Therapies (ACT) trials. BMJ Global Health. 2025;10(4):e017126. Hou Y, Gu T, Ni Z, Shi X, Ranney ML, Mukherjee B. Global Prevalence of Long COVID, its Subtypes and Risk factors: An Updated Systematic Review and Meta-Analysis. medRxiv. 2025. Stobart A, Duckett S. Australia's Response to COVID-19. Health Econ Policy Law. 2022;17(1):95-106. Department of Health and Aged Care. Inquiry into Long COVID and Repeated COVID infections. Australian Government; 2022. https://www.aph.gov.au/Parliamentary_Business/Committees/House/Former_Committees/Health_Aged_Care_and_Sport/LongandrepeatedCOVID/Submissions. Accessed 21 August 2025. Tufts J, Guan N, Zemedikun DT, Subramanian A, Gokhale K, Myles P, et al. The cost of primary care consultations associated with long COVID in non-hospitalised adults: a retrospective cohort study using UK primary care data. BMC Primary Care. 2023;24(1):245. Curtis N. Long COVID in a highly vaccinated but largely unexposed Australian population following the 2022 SARS-CoV-2 Omicron wave. Med J Aust. 2025;222(7):372. Australian National Audit Office. Australia's COVID-19 Vaccine Rollout. 2022-23. Australian Institute of Health Welfare. COVID-19. Canberra: AIHW; 2024. https://www.aihw.gov.au/reports/australias-health/covid-19. Accessed 21 August 2025. Table 1 Table 1: GP Practice, baseline sociodemographic and clinical characteristics of patients Post-acute COVID COVID-19 only Upper respiratory tract infection (N=701) (N=74486) (N=27720) Median Index month April April May Practice characteristics Socioeconomic group* Very High 221 (31.5%) 22119 (29.7%) 6412 (23.2%) High 124 (17.7%) 14501 (19.5%) 5807 (21.0%) Middle 142 (20.3%) 12928 (17.4%) 4858 (17.5%) Low 129 (18.4%) 14103 (19.0%) 5749 (20.8%) Very Low 85 (12.1%) 10713 (14.4%) 4871 (17.6%) Geographical area Major Cities 447 (63.8%) 48057 (64.6%) 18474 (66.7%) Inner Regional 173 (24.7%) 19167 (25.8%) 6073 (21.9%) Outer/Remote/Very Remote 81 (11.6%) 7140 (9.6%) 3150 (11.4%) State NSW 242 (34.5%) 30220 (40.6%) 8258 (29.8%) VIC 179 (25.5%) 17716 (23.8%) 8711 (31.4%) QLD 107 (15.3%) 10045 (13.5%) 4870 (17.6%) WA 75 (10.7%) 6080 (8.2%) 2623 (9.5%) TAS 52 (7.4%) 6858 (9.2%) 2150 (7.8%) SA/ACT/NT 46 (6.6%) 3567 (4.8%) 1108 (4.0%) Patient characteristics Age(years) 18-34 106 (15.1%) 13894 (18.7%) 9108 (32.9%) 35-49 202 (28.8%) 18055 (24.2%) 7717 (27.8%) 50-64 215 (30.7%) 19127 (25.7%) 6245 (22.5%) 65-74 95 (13.6%) 11666 (15.7%) 2667 (9.6%) 75+ 83 (11.8%) 11744 (15.8%) 1983 (7.2%) Female 491 (70.0%) 48048 (64.5%) 17357 (62.6%) Smoker 51 (7.3%) 5765 (7.7%) 3596 (13.0%) Number of GP consultations in the year prior 1-4 159 (22.7%) 18668 (25.1%) 8597 (31.0%) 5-9 208 (29.7%) 24462 (32.8%) 9199 (33.2%) 10+ 334 (47.6%) 31356 (42.1%) 9924 (35.8%) Co-morbidities Chronic respiratory diseases 171 (24.4%) 13449 (18.1%) 4548 (16.4%) Cardiovascular disease 74 (10.6%) 8991 (12.1%) 1868 (6.7%) Diabetes 76 (10.8%) 10662 (14.3%) 3040 (11.0%) Chronic Kidney Disease 36 (5.1%) 6084 (8.2%) 1193 (4.3%) Neurological Disease 18 (2.6%) 1,719 (2.3%) 422 (1.5%) Dementia 9 (1.3%) 1,551 (2.1%) 199 (0.7%) Cancer 47 (6.7%) 5,805 (7.8%) 1,272 (4.6%) Depression/Anxiety 270 (38.5%) 21,288 (28.6%) 6,978 (25.2%) Sleep issues 87 (12.4%) 6,227 (8.4%) 2,311 (8.3%) Post viral infections 14 (2.0%) 667 (0.9%) 244 (0.9%) Autoimmune diseases 39 (5.6%) 3,436 (4.6%) 832 (3.0%) Additional Declarations Competing interest reported. James Wood is a member of the Australian Technical Advisory Group on Immunisation (ATAGI) and a past unpaid member of the of a Moderna committee from April 2021 to April 2022. Other authors declare that they have no competing interests. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 22 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Editor invited by journal 19 Sep, 2025 Submission checks completed at journal 19 Sep, 2025 First submitted to journal 19 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7561005","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523639205,"identity":"a055c319-6d4b-4d02-940b-15c9ecd9e01d","order_by":0,"name":"Emmae 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16:23:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1601107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultilevel Poisson regression comparing characteristics of patients with post-acute COVID-19 syndrome vs URTI\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7561005/v1/05553a34d4c2968a85e4482a.jpg"},{"id":92734694,"identity":"660a3de3-4cad-4793-b8b7-437c8a5746e2","added_by":"auto","created_at":"2025-10-03 16:23:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel-adjusted proportions of symptoms in the prior and post-diagnosis time periods\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7561005/v1/1b07bf5260e1d35a9f5bed4e.png"},{"id":92734708,"identity":"f4730aef-7c17-468c-85b7-b0c84b3bed01","added_by":"auto","created_at":"2025-10-03 16:23:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel-adjusted proportions of prescriptions in the prior and post-diagnosis time periods\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7561005/v1/430748a9b935e9e4e1702b72.png"},{"id":92734691,"identity":"90149c9b-d926-40cb-aef4-c80bf641c484","added_by":"auto","created_at":"2025-10-03 16:23:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45400,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel-adjusted proportions of pathology and imaging requests\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7561005/v1/bea6c139337ff8ec42900e06.png"},{"id":92737061,"identity":"d3269aa1-1be8-4bd8-afc5-bc38c7ae5306","added_by":"auto","created_at":"2025-10-03 16:39:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":26090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvent rates per 1000 per week in the prior and post-diagnosis time periods\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7561005/v1/dfb3f5af23b35645e93b1d72.png"},{"id":92737068,"identity":"e1eda38f-111b-4ae9-bea7-6c1d5a9dab49","added_by":"auto","created_at":"2025-10-03 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James Wood is a member of the Australian Technical Advisory Group on Immunisation (ATAGI) and a past unpaid member of the of a Moderna committee from April 2021 to April 2022.\n\nOther authors declare that they have no competing interests.","formattedTitle":"Characteristics of post-acute COVID-19 presentations and healthcare use in Australian general practice and comparisons to those with acute COVID-19 and upper respiratory tract infection","fulltext":[{"header":"Background","content":"\u003cp\u003eSARS-CoV-2, the virus that causes COVID-19, is known to result in long-term health sequelae. These sequelae are highly variable and have been described not only among those with severe acute infections but also those with mild disease. They are often grouped together under the term used by the World Health Organisation as post-acute COVID-19 conditions or colloquially as long COVID (1, 2). Despite a large international literature on these conditions (3, 4), there are still many gaps in understanding key aspects of their epidemiology and health system impact. This is particularly the case for infections with the SARS-CoV-2 Omicron subvariants and among people who have been vaccinated and not hospitalised with COVID-19.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Australia, general practice is often the initial point of care for patients experiencing persistent symptoms following COVID-19. General practitioners (GPs) have the difficult task of distinguishing post-acute COVID syndrome from other conditions. \u0026nbsp;This process is complicated by the variety and overlap of symptoms, which can range from cardio-respiratory, gastrointestinal or neurological complaints to non-specific symptoms such as fatigue. The absence of pathognomonic features and the need to exclude alternative conditions mean that a range of investigations, including pathology, imaging requests, and other diagnostic tests are often required(5). Few studies in Australia have analysed post-COVID conditions in primary care data(6, 7), and none have used national data to compare patients with post-acute COVID syndrome to those diagnosed with other respiratory infections. This type of comparison is needed to determine how COVID-19 may differ from other viral respiratory illnesses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study aimed to use a large database of Australian general practice records (MedicineInsight (8)) to firstly describe the characteristics of adults presenting with post-acute COVID syndrome, assess the common symptoms recorded, and then make comparisons between patients with acute COVID-19 and those with other upper respiratory tract infections (URTI). \u0026nbsp;Secondly, we described the prescriptions, pathology, and imaging requests associated with the diagnosis of post-acute COVID syndrome and compared these to the patterns in other patient groups. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eData Source and study population\u003c/h2\u003e\n\u003cp\u003eMedicineInsight is a database containing de-identified, patient-level data from 8% of general practices across Australia, of varying sizes, service types and geographical locations.\u0026nbsp;The characteristics of patients in the MedicineInsight database resemble the Australian population(8).\u0026nbsp;\u0026nbsp;Standard data collection began in 2011 and covers a wide range of routine healthcare information, including patient demographics (sex, year of birth, postcode), diagnosis, reasons for encounters, prescribed medications and their indications, pathology and imaging requests, and clinical measurements (e.g. weight, height, blood pressure). A detailed description of the MedicineInsight database has been previously published(8, 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe had full data from MedicineInsight up until 30\u003csup\u003eth\u003c/sup\u003e September 2022. We used data from January to September 2022; during this time, there were nearly 7 million visits recorded in the MedicineInsight database for 1.2 million regular patients across 326 practices(8). We restricted our analyses to patients 18 years or over who were diagnosed with post-acute COVID syndrome, acute COVID-19 or URTI between 1\u003csup\u003est\u003c/sup\u003e January 2022 and 8\u003csup\u003eth\u003c/sup\u003e July 2022 to ensure a full 12-week follow-up following diagnosis.\u0026nbsp;The observation period corresponded to when mostly the SARS-CoV-2 Omicron sub-variants BA.1, BA.2 and some \u0026nbsp;BA.4/5 circulated(10) and more than 90% of the adult Australian population was estimated to have completed the primary course of COVID-19 vaccination(11). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe included only patients who were regular attenders to one of the practices within MedicineInsight to ensure that we had longitudinal information on symptoms and comorbidities. A regular attender for this study was defined as having at least two recorded GP consultations in the 2 years prior to their index date, with at least one consultation in each year prior. If patients had a COVID-19 diagnosis that was identified in the data prior to 2022, they were excluded from the study.\u003c/p\u003e\n\u003ch2\u003eStudy definitions\u003c/h2\u003e\n\u003cp\u003eWe defined three target populations of interest using terms specified in at least one of the three main text fields (diagnosis, reason for encounter, or reason for prescription). \u0026nbsp;For all conditions, where available, standardised algorithms based on UK phenotypes were used along with consultation with clinicians to guide the search terms (12, 13). Regular expressions were used to capture misspellings and variations in phrasing.\u003c/p\u003e\n\u003ch2\u003ePost-acute COVID syndrome \u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003ePatients with post-acute COVID syndrome were classified as such if (1) the diagnosis of \"long COVID\" or \"post-COVID syndrome\" was recorded in any of these fields, regardless of whether they had a prior COVID-19 diagnosis or (2) these fields included terms like \"post COVID”, \"post-COVID + infection”, or \"post-COVID + symptoms” preceded by at least 12 weeks with a confirmed “COVID-19” diagnosis recorded (i.e. acute infection). \u0026nbsp;Their index date for the SARS-CoV-2 infection was defined as either a) the first recorded date of a COVID-19 diagnosis (i.e. acute infection), or b) 12 weeks before the date of the first post-acute COVID syndrome diagnosis in those cases when there was no record of a COVID-19 diagnosis. This is in line with the common definition of post-acute COVID syndrome (i.e. persistence of symptoms for 12 weeks or more after a COVID-19 infection)(5).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcute COVID-19\u003c/h2\u003e\n\u003cp\u003ePatients with acute COVID-19 were classified as such if: 1) terms such as “COVID” (excluding those with \u0026nbsp;‘vaccination’, ‘eligible’, ‘possible’, or ‘contact’) were recorded in any of the fields; and/or 2) they had a positive pathology test result for COVID-19, and/or 3) they were prescribed COVID-specific antivirals (nirmatrelvir/ritonavir or molnupiravir) and 4) were not classified as having post-acute COVID syndrome. The index date was the first COVID-19 diagnosis date or positive pathology test, or the date of antiviral prescription.\u003c/p\u003e\n\u003ch2\u003eOther upper respiratory tract infection (URTI)\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003ePatients with other upper respiratory tract infections (URTI) were classified if terms such as “URTI”, “upper respiratory”, or “respiratory infection” were recorded in any of the fields, but were not already classified as having post-acute COVID syndrome or acute COVID-19. Their index date was the date of their first URTI diagnosis in 2022.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eVariables of interest\u003c/h2\u003e\n\u003cp\u003eSociodemographic characteristics included age, sex, remoteness of practice using the Australian Statistical Geography Standard (ASGS), which considers population size and distance to services(14), state of GP practice, Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) quintiles derived for GP practice and patient postcode(15). Clinical history included patient smoking status and the number of recorded GP consultations in the year prior to the index date. Chronic comorbidities were identified in the 2 years prior to the index date. They included chronic respiratory disease, cardiovascular disease, diabetes, liver disease, chronic kidney disease, neurological conditions, dementia, depression/anxiety, sleep issues, autoimmune diseases, and post-viral syndromes (Epstein-Barr virus (EBV) infection, chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME) and postural orthostatic tachycardia (POTS)). These were extracted by looking for terms associated with these conditions in the three main text fields. \u0026nbsp;An explanation of the methods used to extract clinical history data can be found in earlier publications (16, 17). The list of terms and algorithms that were used for data extraction is available from authors upon request.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eOutcomes of interest\u003c/h2\u003e\n\u003cp\u003eWe examined several outcomes, which are detailed below, in the three populations of interest (i.e. post-acute COVID syndrome, acute COVID-19 or URTI groups).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSymptoms\u003c/h2\u003e\n\u003cp\u003eBased on the available literature, we selected the most common symptoms described as associated with post-acute COVID syndrome, which could be identified in the diagnosis, reason for encounter and reason for prescription fields of the MedicineInsight database. These included fatigue, headache/migraine, shortness of breath (dyspnoea, breathlessness, orthopnoea), chest pain, sleep disorders/insomnia, cough (18-20) and memory issues (memory loss, concentration difficulties, brain fog, confusion, learning difficulties)(2, 21, 22).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePrescriptions, pathology and imaging requests\u003c/h2\u003e\n\u003cp\u003ePrescriptions of sedatives, anxiolytics, antidepressants, antibiotics and analgesics were extracted from the script item field based on active ingredients or brand names. \u0026nbsp;Pathology requests for full blood count, C-reactive protein (CRP), electrolytes, iron studies and B12/folate and imaging requests for chest x-ray and chest CT scans derived from the pathology request fields.\u003c/p\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eFor the three patient groups (post-acute COVID syndrome, acute COVID-19 and URTI), we compared\u0026nbsp;baseline sociodemographic and clinical characteristics of patients and the practice they attended.\u0026nbsp;The index date was used to determine the timing of variables of interest. For the post-acute COVID syndrome group, the COVID-19 diagnosis date (or proxy, as described earlier) was used to ensure comparability at the same point in the acute COVID-19 disease trajectory.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe compared patients diagnosed with post-acute COVID syndrome to those with either acute COVID-19 only or URTI (comparator groups), using multilevel Poisson regression with robust standard errors. The outcome was the diagnostic group (post-acute COVID vs comparator), and explanatory variables included baseline patient and general practice characteristics. Both univariate models (each covariate separately) and multivariate models (adjusting for all covariates simultaneously) were fitted. Medical practice was included as a random effect to account for clustering of patients, as different data recording and diagnosis practices may occur within a practice. Relative risks (RR) and 95% confidence intervals were reported from these models.\u003c/p\u003e\n\u003cp\u003eWe then examined symptoms recorded, medicines prescribed, pathology and imaging requests in each of the three groups within 0-12 weeks prior to their index date and 0-12 weeks after their index date. Due to the lack of a definitive diagnostic method for identifying post-acute COVID syndrome and the broad spectrum of symptoms, we only examined a limited number of commonly reported symptoms that could be identified in the database. Given the differences in baseline characteristics across diagnosis groups, we conducted these analyses within each group separately. To assess changes in healthcare use before and after the index date, we used multilevel Poisson regression with robust standard errors to compare binary outcomes; symptoms, prescriptions, pathology requests, and imaging requests, between the 12 weeks prior to and the 12 weeks following the index date. Random effects were included for GP practice (to account for clustering of patients within practices) and for individual patients (to account for repeated measures). To assess changes in healthcare use before and after the index date, crude and model-adjusted percentages, relative risks and corresponding 95% confidence intervals were presented. \u0026nbsp; All analyses were performed in STATA 18 (StataCorp, College Station, Texas, USA).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eCohort demographics\u003c/h2\u003e\n\u003cp\u003eDuring January to July, 701 patients were diagnosed with post-acute COVID syndrome, 74,486 with acute COVID-19 and no subsequent report of post-acute COVID, and 27,720 with an upper respiratory tract infection (URTI). The post-acute COVID syndrome group had a higher proportion of females (491; 70%) compared to the COVID-19 only (48,048; 65%) and the URTI group (17,357; 63%). The most common age group at diagnosis was 35-64 years for both the post-acute syndrome group (417; 60%) and the COVID-19 only group (37,182; 50%), while the URTI group were younger, 16,825 (61%) aged 18-49 years. All patient groups were more likely to be seen in practices in higher socioeconomic areas, but the proportion was higher for those with post-acute COVID and acute COVID. The distribution of other characteristics is described in Table 1.\u003c/p\u003e\n\u003ch2\u003ePost-acute COVID syndrome vs COVID-19 only group\u003c/h2\u003e\n\u003cp\u003eThe multivariate model adjusting for age, sex and other factors (Figure 1) showed that compared to those diagnosed with only acute COVID-19, individuals with post-acute COVID syndrome were more likely to be aged 35-49 years (RR 1.45 (95% CI:1.14, 1.86)) and 50-64 years (RR 1.44 (95% CI:1.13, 1.84)). Women were also more likely than men to be in the post-acute group (RR 1.17 (95% CI:1.01, 1.36)). Those in the post-acute COVID syndrome group were also more likely to have had a prior chronic respiratory disease (RR 1.36 (95% CI:1.15, 1.61)) and depression/anxiety diagnosis (RR 1.42 (95% CI:1.23, 1.63)) and less likely to have had a diabetes diagnosis (RR 0.73 (95% CI:0.57, 0.93)).\u003c/p\u003e\n\u003ch2\u003ePost-acute COVID syndrome vs URTI group\u003c/h2\u003e\n\u003cp\u003eThe results from the multivariate model adjusting for age, sex and other factors comparing the post-COVID syndrome to the URTI group are presented in Figure 2. There were differences in the sociodemographic and geographical location of the GP practices. Individuals with post-acute COVID syndrome were older, more likely to be women than those with other URTIs (RR 1.22 (95% CI:1.05, 1.42)). They were also less likely to be smokers (RR 0.55 (95% CI:0.41, 0.74)) but more likely to have had a prior chronic respiratory disease (RR 1.27 (95% CI:1.08, 1.50)) and depression/anxiety diagnosis (RR 1.39 (95% CI:1.19, 1.62)) and less likely to have chronic kidney disease (RR 0.70 (95% CI:0.50, 0.98)).\u003c/p\u003e\n\u003ch2\u003eSymptoms, prescriptions, pathology requests and imaging requests\u003c/h2\u003e\n\u003cp\u003eFigures 3-5 show comparisons of symptoms, prescriptions and pathology/imaging requests in the 0-12 weeks prior and post diagnosis within each diagnosis group. For the post-acute COVID syndrome group (Figure 3), symptoms that were reported significantly more frequently 0-12 weeks post diagnosis compared to 0-12 weeks prior to diagnosis were: fatigue (13.1% vs 3.9% : RR 3.37 (95% CI:2.21, 5.14)), a cough (8.1% vs 2.2% : RR 3.67 (95% CI:2.19, 6.13)) and chest pain (4.3% vs 1.7% : RR 2.50 (95% CI:1.28, 4.87)). There was an increase in some medicines prescribed; sedatives (10.3% vs 7.3%: RR 1.41 (95% CI:1.08, 1.83)), antibiotics (25.0% vs 14.8%: RR 1.68 (95% CI:1.32, 2.15)) and analgesics (20.7% vs 16.2%: RR 1.28 (95% CI:1.06, 1.55)). All pathology requests examined increased 2 to 3-fold in the post-diagnosis period, as did imaging; chest x-ray (15.8% vs 2.4%: RR 6.47 (95% CI:4.02, 10.40)) and chest CT scan (2.1% vs 0.7%: RR 3.00 (95% CI:1.05, 8.55)).\u003c/p\u003e\n\u003cp\u003eFor the acute COVID-19 only group (Figure 4), changes in reported symptoms were less than those observed for the post-acute COVID syndrome group, although there was still a 3-fold increase in likelihood of reporting cough in the 12 weeks following diagnosis. \u0026nbsp;Similar to the post-acute COVID group, there were small increases in all prescriptions. For pathology and radiology requests, there were also significant increases, but the magnitude of that increase was substantially smaller than for the post-acute COVID group with only the frequency of chest x-ray increasing two-fold (3.3% vs 1.5%: RR 2.27 (95% CI:2.10, 2.45)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the URTI group (Figure 5), in 0-12 weeks post diagnosis there were significant increases in reporting of shortness of breath (0.2% vs 0.1%: RR 2.32 (95% CI:1.55, 3.49)), cough (5.6% vs 1.8%: RR 3.17 (95% CI:2.86, 3.51)) and in chest pain (1.2% vs 1.0%: RR 1.28 (95% CI:1.10, 1.49)). There was a large increase in prescriptions for antibiotics (40.9% vs 11.8%: RR 3.46 (95% CI:3.29, 3.65)) and a small increase in sedatives (5.4% vs 5.0%: RR 1.07 (95% CI:1.01, 1.14)), antidepressants (13.1% vs 11.9%: RR 1.10 (95% CI:1.06, 1.14)) and analgesics (13.7% vs 11.5%: RR 1.19 (95% CI:1.14, 1.24)). Most pathology tests saw a small increase in the post-diagnosis period full blood count (21.6% vs 19.3%: RR 1.12 (95% CI:1.08, 1.16)), C-reactive protein (7.6% vs 5.7%: RR 1.34 (95% CI:1.26, 1.43)), electrolytes (19.2% vs 17.1%: RR 1.12 (95% CI:1.08, 1.16)), iron studies (10.6% vs 9.9%: RR 1.08 (95% CI:1.02, 1.13)). Both the frequency of chest x-rays and chest CT scans increased at least 2-fold. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the post-acute COVID syndrome group, most of the pathology and imaging requests and prescriptions in the 12-week period post-index date occurred later in the period, around week 12. At the same time, they were more evenly distributed across all weeks in the acute COVID and URTI groups, except for antibiotics, which for the URTI group occurred around the week of diagnosis. The reporting of symptoms in the post-acute COVID group was also often higher in the latter part of the 12-week period post-index date, while the symptom of cough was reported in the 1-2 week period post-index date for those with URTI (see Figure 6).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings suggest that individuals diagnosed with post-acute COVID syndrome were more likely to be female, middle-aged, and have pre-existing chronic respiratory disease or depression/anxiety, compared to those with acute COVID-19 only. Additionally, those with post-acute COVID syndrome were less likely to be smokers than those with other URTIs. We also found that those with post-acute COVID syndrome had 2-3-fold increases in pathology testing and up to 6-fold increases in imaging tests following their diagnosis. These were generally higher than the post-diagnosis increases we observed for those with acute COVID-19 and other URTIs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results are consistent with international literature, where key risk factors for post-acute COVID or “long COVID” consistently include female sex, being middle-aged, and pre-existing comorbidities (23, 24). \u0026nbsp;More recently, results from a secondary analysis of the Anti-Coronavirus Therapies trials identified similar risk factors, with additional findings suggesting lower rates among smokers and people with diabetes mellitus, which is consistent with our results(25). A preprint of an updated systematic review and meta-analysis including 442 studies found the three most consistent predictors of post-acute COVID-19 were being unvaccinated, having pre-existing comorbidities, and female sex; memory problems were reported as the most common symptom (26). However, the authors noted that populations from Oceania were underrepresented. This highlights the need for more Australian studies, as we had a unique pandemic trajectory, with prolonged border closures and high vaccination coverage prior to major community transmission, predominantly involving the Omicron variant (27).\u003c/p\u003e\n\u003cp\u003eWhile several Australian studies have explored long COVID, few have used primary care data. One study using general practice records from New South Wales and Victoria compared patients with post-acute COVID with a population without COVID-19 and reported findings broadly consistent with the international evidence (7). Our study builds on this by comparing individuals with post-acute COVID to both those with acute COVID only and those with other respiratory infections, rather than just comparing to a general population of healthcare users.\u003c/p\u003e\n\u003cp\u003eThere is limited Australian research assessing healthcare utilisation following a COVID-19 diagnosis. Preliminary work by the Australian Department of Health and Aged Care suggested increased use of GP services, chest X-rays, blood tests, and pulmonary rehabilitation following a COVID-19 diagnosis, though case ascertainment was limited due to small sample sizes (28). The increase in pathology and imaging requests in those with post-acute COVID syndrome that we observed is expected, given that the condition requires exclusion of other conditions (5, 29). Understanding how post-acute COVID diagnoses affect health service use is essential in the Australian context, where healthcare structures and pandemic dynamics differ from those in Europe and North America. Our quantification with up to 6-fold increases in some imaging requests suggests considerable additional healthcare costs for this population. Quantifying excess health system burden due to post-acute COVID will help inform the cost-effectiveness of prevention strategies and guide future resource allocation(29).\u003c/p\u003e\n\u003cp\u003eSeveral limitations must be acknowledged. Firstly, in this database, there was no consistent coding for diagnoses of COVID-19, post-acute COVID, or URTI. \u0026nbsp;In our analysis, the ratio of people with a post-acute COVID diagnosis to those with acute COVID-19 was less than 1:100, and this is low compared to estimates from some studies (6). However, it has been suggested that estimates based on surveys may be biased(30) and our study was done during the circulation of the Omicron variant in a highly vaccinated population with more than 90% of Australians having received their primary course (31). \u0026nbsp;Hence, it would be expected that rates of post-acute COVID syndromes would be lower due to less severe disease(31). Second, we could have missed people who did not inform their GP of their COVID-19 diagnosis or people who were severely unwell and were managed in hospital. We know that post-acute COVID syndromes may be more common in people with severe disease. Third, similar to diagnoses, in the MedicineInsight data, there is also no consistent coding for symptoms and medicines prescribed and while pathology requests are generally well captured, there is limited information on test results. These issues would have led to under ascertainment of these variables and might explain why symptoms such as fatigue, that have been widely reported in post-acute covid cases(2), was only reported among 13% of those with post-acute COVID. However, for the pre-post comparisons presented here we used consistent definitions so that any misclassification should be consistent between the comparison periods. Additionally, we only examined common symptoms that have been described in the literature for post-acute COVID syndrome (2, 18-22).\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study enabled the identification of key risk factors associated with post-acute COVID syndrome. These findings may support GPs in recognising patients at higher risk of developing post-acute COVID syndrome earlier, allowing for timely management and intervention strategies aimed at improving patient outcomes.\u003c/p\u003e\n\u003cp\u003eBy highlighting the types of medications prescribed, pathology tests ordered, and imaging studies requested, our findings offer a different perspective from many existing studies that focus primarily on symptoms and clinical characteristics and can help inform the additional healthcare use for those with post-acute COVID syndrome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study also highlights the importance of using primary care data for public health research (32). It demonstrated that the utilisation of general practice data leads to greater understanding of the patient experience, treatment pathways which can support GPs to diagnose complex conditions such as post-acute COVID syndrome as well as providing quantitative evidence to support health system needs. This reinforces the value of investing in robust, representative primary care datasets in Australia. Doing so will support timely, evidence-based care for emerging conditions and strengthen the role of GPs in managing population health.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Royal Australian College of General Practitioners (RACGP) National Research and Evaluation Ethics Committee (NREEC) granted ethics approval (NREEC 17-017) for the standard operation and use of the NPS MedicineInsight programme, waiving the need for a consent to participate from patients. MedicineInsight used an opt-out approach instead, by a process handled independently at the practice, with the display of posters to inform them that MedicineInsight was collecting their de-identified data. Research was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eReference: Busingye D, Gianacas C, Pollack A, Chidwick K, Merrifield A, Norman S, Mullin B, Hayhurst R, Blogg S, Havard A, Stocks N. Data Resource Profile: MedicineInsight, an Australian national primary health care database. Int J Epidemiol. 2019 Dec 1;48(6):1741-1741h. doi: 10.1093/ije/dyz147.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers can apply to access the MedicineInsight data independently from the Australian Commission on Safety and Quality in Health Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJames Wood is a member of the Australian Technical Advisory Group on Immunisation (ATAGI) and a past unpaid member of the of a Moderna committee from April 2021 to April 2022.\u003c/p\u003e\n\u003cp\u003eOther authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAustralian Medical Research Future Fund (MRFF)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eER conducted analysis and drafted the manuscript. BL and SS conceived the initial study design and contributed to the writing of the manuscript. All authors contributed to the study design, analysis, interpretation of results and revisions of the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge John Rule and Michelle Thompson, who have provided consumer input.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information (optional)\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Post COVID-19 condition (Long COVID). 2022. https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition. Accessed 21 August 2025.\u003c/li\u003e\n\u003cli\u003eGroff D, Sun A, Ssentongo AE, Ba DM, Parsons N, Poudel GR, et al. Short-term and Long-term Rates of Postacute Sequelae of SARS-CoV-2 Infection: A Systematic Review. JAMA Netw Open. 2021;4(10):e2128568.\u003c/li\u003e\n\u003cli\u003eDavis HE, McCorkell L, Vogel JM, Topol EJ. Long COVID: major findings, mechanisms and recommendations. Nature Reviews Microbiology. 2023;21(3):133-46.\u003c/li\u003e\n\u003cli\u003eGreenhalgh T, Sivan M, Perlowski A, Nikolich JŽ. Long COVID: a clinical update. The Lancet. 2024;404(10453):707-24.\u003c/li\u003e\n\u003cli\u003eNICE. COVID-19 rapid guideline: managing the long term effects of COVID-19. In: Excellence NIfHaC, editor. 2024.\u003c/li\u003e\n\u003cli\u003eHolmes A, Emerson L, Irving LB, Tippett E, Pullin JM, Young J, et al. Persistent symptoms after COVID-19: an Australian stratified random health survey on long COVID. Med J Aust. 2024;221 Suppl 9:S12-s7.\u003c/li\u003e\n\u003cli\u003eKamalakkannan A, Prgomet M, Thomas J, Pearce C, McGuire P, Mackintosh F, Georgiou A. Factors associated with general practitioner-led diagnosis of long COVID: an observational study using electronic general practice data from Victoria and New South Wales, Australia. Med J Aust. 2024;221 Suppl 9:S18-s22.\u003c/li\u003e\n\u003cli\u003eBusingye D, Gianacas C, Pollack A, Chidwick K, Merrifield A, Norman S, et al. Data Resource Profile: MedicineInsight, an Australian national primary health care database. Int J Epidemiol. 2019;48(6):1741-h.\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez-Chica DA, Vanlint S, Hoon E, Stocks N. Epidemiology of arthritis, chronic back pain, gout, osteoporosis, spondyloarthropathies and rheumatoid arthritis among 1.5 million patients in Australian general practice: NPS MedicineWise MedicineInsight dataset. BMC Musculoskelet Disord. 2018;19(1):20.\u003c/li\u003e\n\u003cli\u003eEpidemiology C-, Surveillance T. COVID-19 Australia: Epidemiology Report 69 Reporting period ending 18 December 2022. Communicable Diseases Intelligence. 2023;47.\u003c/li\u003e\n\u003cli\u003eCOVID-19 Vaccine Rollout. Australian Government; 2022. https://www.health.gov.au/sites/default/files/documents/2022/01/covid-19-vaccine-rollout-update-31-january-2022.pdf. Accessed 21 August 2025.\u003c/li\u003e\n\u003cli\u003eKuan V, Denaxas S, Gonzalez-Izquierdo A, Direk K, Bhatti O, Husain S, et al. A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digit Health. 2019;1(2):e63-e77.\u003c/li\u003e\n\u003cli\u003eHDR UK Phenotype Library. https://phenotypes.healthdatagateway.org/. Accessed 21 August 2025.\u003c/li\u003e\n\u003cli\u003eAustralian Bureau of Statistics. Remoteness Structure. Canberra: ABS; jul2021-jun2026; 2021. https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/remoteness-structure. Accessed 21 August 2025.\u003c/li\u003e\n\u003cli\u003eAustralian Bureau of Statistics. Socio-Economic Indexes for Areas (SEIFA), Australia. Canberra; 2021. https://www.abs.gov.au/statistics/people/people-and-communities/socio-economic-indexes-areas-seifa-australia/latest-release. Accessed 21 August 2025.\u003c/li\u003e\n\u003cli\u003eRoseleur J, Gonzalez-Chica DA, Bernardo CO, Geisler BP, Karnon J, Stocks NP. Blood pressure control in Australian general practice: analysis using general practice records of 1.2 million patients from the MedicineInsight database. J Hypertens. 2021;39(6):1134-42.\u003c/li\u003e\n\u003cli\u003eWoods A, Begum M, Gonzalez-Chica D, Bernardo C, Hoon E, Stocks N. Long-term benzodiazepines and z-drug prescribing in Australian general practice between 2011 and 2018: A national study. Pharmacol Res Perspect. 2022;10(1):e00896.\u003c/li\u003e\n\u003cli\u003eAlkodaymi MS, Omrani OA, Ashraf N, Shaar BA, Almamlouk R, Riaz M, et al. Prevalence of post-acute COVID-19 syndrome symptoms at different follow-up periods: a systematic review and meta-analysis. Clin Microbiol Infect. 2022;28(5):657-66.\u003c/li\u003e\n\u003cli\u003eAlmas T, Malik J, Alsubai AK, Jawad Zaidi SM, Iqbal R, Khan K, et al. Post-acute COVID-19 syndrome and its prolonged effects: An updated systematic review. Ann Med Surg (Lond). 2022;80:103995.\u003c/li\u003e\n\u003cli\u003eKelly JD, Curteis T, Rawal A, Murton M, Clark LJ, Jafry Z, et al. SARS-CoV-2 post-acute sequelae in previously hospitalised patients: systematic literature review and meta-analysis. Eur Respir Rev. 2023;32(169).\u003c/li\u003e\n\u003cli\u003eHan Q, Zheng B, Daines L, Sheikh A. Long-Term Sequelae of COVID-19: A Systematic Review and Meta-Analysis of One-Year Follow-Up Studies on Post-COVID Symptoms. Pathogens. 2022;11(2).\u003c/li\u003e\n\u003cli\u003eMarjenberg Z, Leng S, Tascini C, Garg M, Misso K, El Guerche Seblain C, Shaikh N. Risk of long COVID main symptoms after SARS-CoV-2 infection: a systematic review and meta-analysis. Sci Rep. 2023;13(1):15332.\u003c/li\u003e\n\u003cli\u003eSubramanian A, Nirantharakumar K, Hughes S, Myles P, Williams T, Gokhale KM, et al. Symptoms and risk factors for long COVID in non-hospitalized adults. Nat Med. 2022;28(8):1706-14.\u003c/li\u003e\n\u003cli\u003eTsampasian V, Elghazaly H, Chattopadhyay R, Debski M, Naing TKP, Garg P, et al. Risk Factors Associated With Post\u0026minus;COVID-19 Condition: A Systematic Review and Meta-analysis. JAMA Internal Medicine. 2023;183(6):566-80.\u003c/li\u003e\n\u003cli\u003eLucas Etienne H, Sean W, Lizhen X, John E. Long COVID prevalence and risk factors in adults residing in middle- and high-income countries: secondary analysis of the multinational Anti-Coronavirus Therapies (ACT) trials. BMJ Global Health. 2025;10(4):e017126.\u003c/li\u003e\n\u003cli\u003eHou Y, Gu T, Ni Z, Shi X, Ranney ML, Mukherjee B. Global Prevalence of Long COVID, its Subtypes and Risk factors: An Updated Systematic Review and Meta-Analysis. medRxiv. 2025.\u003c/li\u003e\n\u003cli\u003eStobart A, Duckett S. Australia\u0026apos;s Response to COVID-19. Health Econ Policy Law. 2022;17(1):95-106.\u003c/li\u003e\n\u003cli\u003eDepartment of Health and Aged Care. Inquiry into Long COVID and Repeated COVID infections. Australian Government; 2022. https://www.aph.gov.au/Parliamentary_Business/Committees/House/Former_Committees/Health_Aged_Care_and_Sport/LongandrepeatedCOVID/Submissions. Accessed 21 August 2025.\u003c/li\u003e\n\u003cli\u003eTufts J, Guan N, Zemedikun DT, Subramanian A, Gokhale K, Myles P, et al. The cost of primary care consultations associated with long COVID in non-hospitalised adults: a retrospective cohort study using UK primary care data. BMC Primary Care. 2023;24(1):245.\u003c/li\u003e\n\u003cli\u003eCurtis N. Long COVID in a highly vaccinated but largely unexposed Australian population following the 2022 SARS-CoV-2 Omicron wave. Med J Aust. 2025;222(7):372.\u003c/li\u003e\n\u003cli\u003eAustralian National Audit Office. Australia\u0026apos;s COVID-19 Vaccine Rollout. 2022-23.\u003c/li\u003e\n\u003cli\u003eAustralian Institute of Health Welfare. COVID-19. Canberra: AIHW; 2024. https://www.aihw.gov.au/reports/australias-health/covid-19. Accessed 21 August 2025.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1: GP Practice, baseline sociodemographic and clinical characteristics of patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-acute COVID \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper respiratory tract infection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(N=701)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(N=74486)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(N=27720)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian Index month\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePractice characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eSocioeconomic group*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; Very High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e221 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e22119 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6412 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e124 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e14501 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5807 (21.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e142 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e12928 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4858 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e129 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e14103 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5749 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; Very Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e85 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e10713 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4871 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eGeographical area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; Major Cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e447 (63.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e48057 (64.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e18474 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; Inner Regional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e173 (24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e19167 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6073 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; Outer/Remote/Very Remote\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e81 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e7140 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3150 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eState\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; NSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e242 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e30220 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8258 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; VIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e179 (25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e17716 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8711 (31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; QLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e107 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e10045 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4870 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; WA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e75 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6080 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2623 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; TAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e52 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6858 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2150 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; SA/ACT/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e46 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3567 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1108 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; 18-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e106 (15.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e13894 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9108 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; 35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e202 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e18055 (24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7717 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; 50-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e215 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e19127 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6245 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; 65-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e95 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e11666 (15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2667 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; 75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e83 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e11744 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1983 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e491 (70.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e48048 (64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e17357 (62.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e51 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5765 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3596 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eNumber of GP consultations in the year prior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e159 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e18668 (25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8597 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; 5-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e208 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e24462 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9199 (33.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp; 10+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e334 (47.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e31356 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9924 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo-morbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eChronic respiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e171 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e13449 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4548 (16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e74 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8991 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1868 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e76 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e10662 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3040 (11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e36 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6084 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1193 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eNeurological Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e18 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1,719 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e422 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e9 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1,551 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e199 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e47 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5,805 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1,272 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eDepression/Anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e270 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e21,288 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e6,978 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eSleep issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e87 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6,227 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2,311 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003ePost viral infections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e14 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e667 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e244 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eAutoimmune diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e39 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3,436 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e832 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Post-acute COVID-19 syndrome, long COVID-19, primary health care, general practice, Australia, electronic health records","lastPublishedDoi":"10.21203/rs.3.rs-7561005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7561005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nPost-acute COVID-19 syndrome (long COVID) is recognised internationally as a significant public health issue. Despite a large international literature on these conditions, there are still many gaps in understanding key aspects of their epidemiology and health system impact. \u0026nbsp;This study examined patient characteristics, common symptoms, and healthcare utilisation associated with post-acute COVID-19 syndrome and compared this to acute COVID-19 and other upper respiratory tract infections (URTI) presenting to primary care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nWe used MedicineInsight, a national general practice database, to identify adults diagnosed with post-acute COVID syndrome, acute COVID-19, or URTI between January and July 2022. Patients were required to be regular attenders with at least two GP visits in the previous two years. \u0026nbsp;Sociodemographic and clinical characteristics were described, and multilevel Poisson regression was used for comparisons between groups. Symptoms, prescriptions, pathology, and imaging requests were examined for the 12 weeks before and after diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nAmong 102,907 patients, 701 were diagnosed with post-acute COVID syndrome, 74,486 with acute COVID-19, and 27,720 with URTI. Compared with acute COVID-19, individuals with post-acute COVID were more often female (RR 1.17 (95% CI:1.01–1.36)), aged 35–64 years and had higher rates of chronic respiratory disease and depression/anxiety. Compared with those with URTI, they were older, more often female (RR 1.22 (95% CI:1.05–1.42)), and less likely to smoke. Following diagnosis, patients with post-acute COVID experienced marked increases in fatigue (RR 3.37(95% CI:2.21, 5.14)), cough (RR 3.67(95% CI:2.19, 6.13)), and chest pain (RR 2.50(95% CI:1.28, 4.87)). Pathology requests doubled or tripled, while imaging increased sharply, including a six-fold rise in chest X-rays, exceeding increases observed in comparator groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cbr\u003e\nPost-acute COVID syndrome in Australian general practice is most common among middle-aged women and those with respiratory or mental health comorbidities. Diagnosis is associated with substantially greater use of investigations than for acute COVID-19 or URTI, highlighting the diagnostic complexity and additional health system burden. Primary care data provides crucial insights into post-acute COVID-19 syndrome and can guide future health resource planning.\u003c/p\u003e","manuscriptTitle":"Characteristics of post-acute COVID-19 presentations and healthcare use in Australian general practice and comparisons to those with acute COVID-19 and upper respiratory tract infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:23:42","doi":"10.21203/rs.3.rs-7561005/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-22T05:12:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T05:08:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-19T20:44:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-19T05:06:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-19T05:03:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c06835d9-34ac-418f-b68d-31ae52010c2f","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-03T16:23:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 16:23:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7561005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7561005","identity":"rs-7561005","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}