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Long Covid is a public health concern that needs defining, quantifying, and describing. We aimed to explore the initial and ongoing symptoms of Long Covid following SARS-CoV-2 infection and describe its impact on daily life. We collected self-reported data through an online survey using convenience non-probability sampling. The survey enrolled adults who reported lab-confirmed (PCR or antibody) or suspected COVID-19 who were not hospitalised in the first two weeks of illness. This analysis was restricted to those with self-reported Long Covid. Univariate comparisons between those with and without confirmed COVID-19 infection were carried out and agglomerative hierarchical clustering was used to identify specific symptom clusters, and their demographic and functional correlates. We analysed data from 2550 participants with a median duration of illness of 7.6 months (interquartile range (IQR) 7.1-7.9). 26.5% reported lab-confirmation of infection. The mean age was 46.5 years (standard deviation 11 years) with 82.8% females and 79.9% of participants based in the UK. 89.5% described their health as good, very good or excellent before COVID-19. The most common initial symptoms that persisted were exhaustion, chest pressure/tightness, shortness of breath and headache. Cognitive dysfunction and palpitations became more prevalent later in the illness. Most participants described fluctuating (57.7%) or relapsing symptoms (17.6%). Physical activity, stress, and sleep disturbance commonly triggered symptoms. A third (32%) reported they were unable to live alone without any assistance at six weeks from start of illness. 16.9% reported being unable to work solely due to COVID-19 illness. 37.0% reported loss of income due to illness, and 64.4% said they were unable to perform usual activities/duties. Acute systems clustered broadly into two groups: a majority cluster (n = 2235, 88%) with cardiopulmonary predominant symptoms, and a minority cluster (n = 305, 12%) with multisystem symptoms. Similarly, ongoing symptoms broadly clustered in two groups; a majority cluster (n = 2243, 88.8%) exhibiting mainly cardiopulmonary, cognitive symptoms and exhaustion, and a minority cluster (n = 283, 11.2%) exhibiting more multisystem symptoms. Belonging to the more severe multisystem cluster was associated with more severe functional impact, lower income, younger age, being female, worse baseline health, and inadequate rest in the first two weeks of the illness, with no major differences in the cluster patterns when restricting analysis to the lab-confirmed subgroup. This is an exploratory survey of Long Covid characteristics. Whilst this is a non-representative population sample, it highlights the heterogeneity of persistent symptoms, and the significant functional impact of prolonged illness following confirmed or suspected SARS-CoV-2 infection. To study prevalence, predictors and prognosis, research is needed in a representative population sample using standardised case definitions.

Better understanding is essential for health system planning

Stigma can be experienced as perceived or actual disqualification from social and institutional acceptance on the basis of one or more physical, behavioural or other attributes deemed to be undesirable. Long Covid is a predominantly multisystem condition that occurs in people with a history of SARSCoV2 infection, often resulting in functional disability. This study aimed to develop and validate a Long Covid Stigma Scale (LCSS); and to quantify the burden of Long Covid stigma. Data from the follow-up of a co-produced community-based Long Covid online survey using convenience non-probability sampling was used. Thirteen questions on stigma were designed to develop the LCSS capturing three domains-enacted (overt experiences of discrimination), internalised (internalising negative associations with Long Covid and accepting them as self-applicable) and anticipated (expectation of bias/poor treatment by others) stigma. Confirmatory factor analysis tested whether LCSS consisted of the three hypothesised domains. Model fit was assessed and prevalence was calculated. 966 UK-based participants responded (888 for stigma questions), with mean age 48 years (SD: 10.7) and 85% female. Factor loadings for enacted stigma were 0.70-0.86, internalised 0.75-0.84, anticipated 0.58-0.87, and model fit was good. The prevalence of experiencing stigma at least 'sometimes' and 'often/always' was 95% and 76% respectively. Anticipated and internalised stigma were more frequently experienced than enacted stigma. Those who reported having a clinical diagnosis of Long Covid had higher stigma prevalence than those without. This study establishes a scale to measure Long Covid stigma and highlights common experiences of stigma in people living with Long Covid.

A proportion of people infected with SARS-CoV-2 develop post-COVID-19 condition (also known as long COVID), a predominantly multisystem condition resulting in varying degrees of functional disability limiting day-to-day activities. We aimed to describe the impact of long COVID on work. We co-produced baseline and follow-up online surveys with people with lived experience of long COVID (including three of the co-authors). Respondents were aged 18 years and older with self-reported long COVID following confirmed or suspected COVID-19 infection who were not hospitalised in the first 2 weeks of illness. The baseline survey was administered in November, 2020, using convenience non-probability sampling through social media. Following informed consent, participants completed a follow-up survey at 1 year (November, 2021). Ethics approval was granted by the University of Southampton. Of 2210 invited, 1153 (52%) participants responded to the survey (mean age of 47·7 years [SD 10·6], 965 [84%] female, 1096 [95%] White, and 892 [78%] holding a university degree). 54 participants (4·7%) reported recovery at follow-up. Median duration of illness was 19·8 months (IQR 19·3–20·1) at follow-up. An equal proportion reported being unable to work at baseline (20·4%, n=235) and follow-up (20·6%, n=237). However, a higher proportion reported being made redundant or taking early retirement at follow-up (8·9%, n=102) than at baseline (2·2%, n=25). 209 (18·1%) reported losing or resigning or leaving their job due to long COVID at follow-up compared with 170 (14·8%) participants at baseline. 307 (26·6%) participants reported not taking time off-sick due to long COVID at baseline, decreasing to 122 (10·6%) at follow-up. Of the 656 individuals reporting length of time off-sick, 354 (54%) were off-sick for more than 3 months, with 113 (17·2%) off-sick for more than 12 months. Nearly half (47%, n=538) reported a loss in income. The convenience non-probability sampling limits generalisability. Research is needed in a representative population sample to characterise the effect on working patterns in people with long COVID, particularly in those with less flexible and more physically demanding occupations who may be less able to take time off to recover. None.

Left ventricular assist device (LVAD) implantation significantly impacts on a recipient's symptoms and quality of life. Capturing their experiences and post implant journey is an important part of clinical practice, research and device design evolution. Patient reported outcome measures (PROMs) are a useful tool for capturing that experience. However, patient reported outcome measures need to reflect recipients' experiences. Discussions with a patient partner group found that none of the frequently used cardiology PROMs captured their unique experiences. To capture the experiences and important issues for LVAD recipients. Develop a conceptual map of domains and items that should be reflected in patient reported outcomes. Group concept mapping (GCM) web-based software was used to remotely capture and structure recipients' experiences across a wide geographical area. GCM is a semi-quantitative mixed method consisting of 3 stages: item generation, item sorting and rating (importance, relevance and frequency). Patient partners were involved in all aspects of the study design and development. 18 LVAD recipients consented to take part. 101 statements were generated and multi-dimensional scaling, and hierarchical cluster analysis identified 9 clusters. Cluster themes included: Activities, Partner/family support, Travel, Mental wellbeing, Equipment and clothing, Physical and cognitive limitations, LVAD Restrictions, LVAD Challenges and positive impact of the LVAD (LVAD Positives). LVAD Positives were scored highest across all the rating variables, e.g., frequency (2.85), relevance (2.44) and importance (2.21). Other domains rated high for importance included physical and cognitive limitations (2.19), LVAD restrictions (2.11), Partner/family support (2.02), and Equipment and clothing (2.01). Online GCM software facilitated the inclusion of geographically dispersed recipients and provided useful insights into the experiences of LVAD recipients. The conceptual framework identifies important domains and items that should be prioritised and included in patient reported outcomes in future research, LVAD design evolution, and clinical practice.

Abstract Background Little is known about the risk of long COVID following reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We estimated the likelihood of new-onset, self-reported long COVID after a second SARS-CoV-2 infection, compared to a first infection. Methods We included UK COVID-19 Infection Survey participants who tested positive for SARS-CoV-2 between 1 November 2021 and 8 October 2022. The primary outcome was self-reported long COVID 12–20 weeks after each infection. Separate analyses were performed for those <16 years and ≥16 years. We estimated adjusted odds ratios (aORs) for new-onset long COVID using logistic regression, comparing second to first infections, controlling for sociodemographic characteristics and calendar date of infection, plus vaccination status in participants ≥16 years of age. Results Overall, long COVID was reported by those ≥16 years after 4.0% and 2.4% of first and second infections, respectively; the corresponding estimates among those aged <16 years were 1.0% and 0.6%. The aOR for long COVID after second compared to first infections was 0.72 (95% confidence interval [CI], .63–.81) for those ≥16 years and 0.93 (95% CI, .57–1.53) for those <16 years. Conclusions The risk of new-onset long COVID after a second SARS-CoV-2 infection is lower than that after a first infection for persons aged ≥16 years, though there is no evidence of a difference in risk for those <16 years. However, there remains some risk of new-onset long COVID after a second infection, with around 1 in 40 of those aged ≥16 years and 1 in 165 of those <16 years reporting long COVID after a second infection. In this community-based cohort study, the risk of new-onset long COVID was 28% lower after a second infection compared with a first infection in participants aged ≥16 years. There was no evidence of a difference in risk for those <16 years.

The reality of Long COVID emerged soon after the beginning of the COVID-19 pandemic. More than five years later, thousands of articles have been published with multiple case definitions, heterogenous populations, and numerous measurement instruments, yielding a massive amount of evidence. Health outcome measurement is vital for identifying health challenges, changes in health status and predicting future health states for people with Long COVID. Nevertheless, distinct issues of measurement require attention in the context of Long COVID. In this commentary, we discuss complexities, challenges and considerations associated with health outcome measurement in research and clinical practice with people with Long COVID. Specifically, we address: (i) identifying the population in the context of variable terminology, definitions and symptoms affecting people with Long COVID; (ii) identifying the complexity of health constructs, often multidimensional, to measure with numerous health-related consequences associated with Long COVID; (iii) identifying the purpose of measurement while taking into account the dynamic nature of Long COVID and (iv) identifying appropriate outcome measures used with people with Long COVID and their limitations. We highlight important considerations for measurement in research and clinical practice, including the impacts of the various symptoms and the dynamic nature of Long COVID. We provide examples of outcome measures used to date in the context of Long COVID to illustrate the complexities throughout, with a glimpse at wider consequences. We conclude with a brief discussion of considerations to help pave the way forward for the improvement in health outcomes measurement in Long COVID research and clinical practice. Advancing knowledge on Long COVID requires a return to the fundamentals of measurement science. It is critical to appropriately assess the measurement properties of existing instruments for their ability to accurately and reliably measure health-related constructs associated with this condition. Identifying limitations of currently used tools is also essential to prevent perpetuation of issues in the development of condition-specific measurement instruments for Long COVID. This, in turn, will help pave the way for more robust measurement and improved data interpretation in the context of Long COVID.

Background A substantial portion of people with COVID-19 subsequently experience lasting symptoms including fatigue, shortness of breath, and neurological complaints such as cognitive dysfunction many months after acute infection. Emerging evidence suggests that this condition, commonly referred to as long COVID but also known as post-acute sequelae of SARS-CoV-2 infection (PASC) or post-COVID-19 condition , could become a significant global health burden. Main text While the number of studies investigating the post-COVID-19 condition is increasing, there is no agreement on how this new disease should be defined and diagnosed in clinical practice and what relevant outcomes to measure. There is an urgent need to optimise and standardise outcome measures for this important patient group both for clinical services and for research and to allow comparing and pooling of data. Conclusions A Core Outcome Set for post-COVID-19 condition should be developed in the shortest time frame possible, for improvement in data quality, harmonisation, and comparability between different geographical locations. We call for a global initiative, involving all relevant partners, including, but not limited to, healthcare professionals, researchers, methodologists, patients, and caregivers. We urge coordinated actions aiming to develop a Core Outcome Set (COS) for post-COVID-19 condition in both the adult and paediatric populations.