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BACKGROUND: Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10−18 years with an increased risk of developing type 1 diabetes. METHODS: Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes. FINDINGS: Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5–20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34–0·95) for each 1-year increment of diagnosis age (Pearson’s correlation coefficient 0·88, 95% CI 0·50–0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1–1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6–9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86–95) sensitive, with a positive predictive value of 66% (60–72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89–97) but lowered the positive predictive value to 55% (49–60). INTERPRETATION: Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials. FUNDING: JDRF International.

Long covid follows 10-20% of first-time SARS-CoV-2 infections, but the societal burden of long covid and risk factors for the condition are not well-understood. Here, we report findings about self-reported sick leave and risk factors thereof from a hybrid survey and register study, which included 37,482 RT- PCR confirmed SARS-CoV-2 cases and 51,336 test-negative controls who were tested during the index and alpha waves. An additional 33 individuals per 1000 took substantial sick leave following acute infection compared to persons with no known history of infection, where substantial sick leave was defined as >1 month of sick leave within the period 1-9 months after the RT-PCR test date. Being female, ≥50 years, and having certain pre-existing conditions such as fibromyalgia increased risks for taking substantial sick leave. Further research exploring this heterogeneity is urgently needed and may provide important evidence for more targeted preventative strategies.

BackgroundPost-acute symptoms are not uncommon after SARS-CoV-2 infection with pre-Omicron variants. How the Omicron variant and COVID-19 booster vaccination influences the risk of post-acute symptoms is less clear.ObjectivesTo evaluate the effects of the Omicron variant and COVID-19 booster vaccination on post-acute symptoms, four months after infection with SARS-CoV-2.MethodsA nationwide Danish questionnaire study comprising 44,004 individuals aged 15 years or older with outcomes on post-acute symptoms and new-onset general health problems, four months after testing. Risk differences (RDs) were estimated by comparing Omicron -cases to controls, Omicron to Delta -cases, and Omicron vaccinated cases with three to -two doses, adjusted for age, sex, BMI, self-reported chronic diseases, Charlson comorbidity index, healthcare occupation, and vaccination status.ResultsFour months after testing for SARS-CoV-2 during the Omicron period, the largest RD comparing Omicron cases to controls was observed for memory issues (RD=7.2%, 95% CI: 6.4 to 8.1). Compared to cases from the Delta period, Omicron cases reported reduced risks of post-acute dysosmia (RD=-15.5%, 95% CI: -17.5 to -13.4) and dysgeusia (RD=-11.8%, 95% CI: -13.9 to -9.8). Cases vaccinated with three doses prior to Omicron infection reported reduced risk of 13/26 post-acute symptoms and of 4/5 new-onset general health problems, compared to those vaccinated with two doses.ConclusionsCases infected during the Omicron period experienced substantial post-acute symptoms and new-onset health problems, four months after testing, although milder than Delta cases. Booster vaccination was associated with fewer post-acute symptoms and new-onset health problems, four months after Omicron infection, compared to two doses of COVID-19 vaccine.

BackgroundA considerable number of individuals infected with SARS-CoV-2 continue to experience symptoms after the acute phase. More information on duration and prevalence of these symptoms in non-hospitalized populations is needed.MethodsWe conducted a nationwide cross-sectional study including 152 880 individuals aged 15-years or older, consisting of RT-PCR confirmed SARS-CoV-2 cases between September 2020-April 2021 (N=61 002) and a corresponding test-negative control group (N=91 878). Data were collected 6, 9 or 12 months after the test using web-based questionnaires. The questionnaire covered acute and post-acute symptoms, selected diagnoses, sick leave and general health, together with demographics and life style at baseline. Risk differences (RDs) between test-positives and -negatives were reported, adjusted for age, sex, single comorbidities, Charlson comorbidity score, obesity and healthcare-occupation.FindingsSix to twelve months after the test date, the risks of 18 out of 21 physical symptoms were elevated among test-positives and one third (29.6%) of the test-positives experienced at least one physical post-acute symptom. The largest risk differences were observed for dysosmia (RD = 10.92%, 95%CI 10.68-11.21%), dysgeusia (RD=8.68%, 95%CI 8.43-8.93%), fatigue/exhaustion (RD=8.43%, 95%CI 8.14-8.74%), dyspnea (RD=4.87%, 95%CI 4.65-5.09%) and reduced strength in arms/legs (RD=4.68%, 95%CI 4.45-4.89%). More than half (53.1%) of test-positives reported at least one of the following conditions: concentration difficulties (RD=28.34%, 95%CI 27.34-28.78%), memory issues (RD=27.25%, 95%CI 26.80-27.71%), sleep problems (RD=17.27%, 95%CI 16.81-17.73%), mental (RD=32.58%, 95%CI 32.11-33.09%) or physical exhaustion (RD=40.45%, 95%CI 33.99-40.97%), compared to 11.5% of test-negatives. New diagnoses of anxiety (RD=1.15%, 95%CI 0.95-1.34%) or depression (RD=1.00%, 95%CI 0.81-1.19%) were also more common among test-positives.InterpretationAt the population-level, where the majority of test-positives (96.0%) were not hospitalized during acute infection, a considerable proportion experience post-acute symptoms and sequelae 6-12 months after infection.FundingNoneResearch in contextEvidence before the studyTo identify existing studies on the epidemiology and clinical nature of post-acute COVID-19 symptoms, we searched PubMed for articles published until January 4, 2022 using the search string (((SARS-CoV-2[Title/Abstract]) OR (COVID-19[Title/Abstract]) OR (coronavirus[Title/Abstract])) AND ((post-acute[Title/Abstract]) OR (“post acute”[Title/Abstract]) OR (“long haul*”[Title/Abstract]) OR (“long-term symptoms”[Title/Abstract]) OR (“long-term disease”[Title/Abstract]) OR (“long-term illness”[Title/Abstract]) OR (“persistent symptoms”[Title/Abstract]) OR (“persistent disease”[Title/Abstract]) OR (“persistent illness”[Title/Abstract]) OR (“prolonged symptoms”[Title/Abstract]) OR (“prolonged disease”[Title/Abstract]) OR (“prolonged illness”[Title/Abstract]))) OR (long-covid[Title/Abstract]) OR (“Post-COVID-19 syndrome”[Title/Abstract]) OR (“Post-COVID-19 condition”[Title/Abstract]) OR (“Post-COVID-19 symptoms”[Title/Abstract]). This resulted in 870 articles. When screening these, we focused on articles covering symptoms comprehensively or a broader area, e.g. mental health problems, rather than in-depth studies of symptoms within a single area, case stories or studies focusing on clinical management.A very wide range of post-acute symptoms originating from many different organ systems have been reported. This includes pulmonary, cardiovascular, hematologic, gastrointestinal, renal, endocrine, dermatologic, neurological and cognitive symptoms, as well as more general health problems, in particular fatigue.Based on two systematic reviews covering the period December 2019-March 2021, the majority of studies of persistent COVID-19 symptoms had until then been conducted among hospitalized patients and thus were not representative of the general population, where the majority will only have suffered mild or moderate disease. Since then some larger register-based studies as well as some smaller questionnaire- or interview-based studies have been conducted among non-hospitalized patients. Both types of studies offer advantages and disadvantages in relation to obtaining the full overview of long-term effects. Register-based studies are best suited for capturing more severe conditions confirmed by a trained physician and defined by diagnostic classification schemes, whereas questionnaires including self-reported symptoms are able to capture symptoms and health outcomes that do not easily confirm to disease diagnoses, but which are nevertheless critical to our understanding of the burden of post-acute symptoms.The number of sstudies of post-acute conditions among non-hospitalized patients with a follow-up time of more than 6 months is still limited. Some of the major remaining knowledge gaps regarding post-acute symptoms are: 1) What is the prevalence and variety of post-acute symptoms in the general population of infected persons, where the majority will only have suffered mild or moderate disease, 2) For how long do post-acute symptoms persist, and 3) Which subgroups of individuals, if any, are at higher risk of post-acute symptoms.Added value of this studyThe present nationwide questionnaire-study is based on a large, mainly adult study population (N=152 880), where all individuals in Denmark, who tested positive during the study period, were invited to participate along with comparable test-negative controls.Marked levels of post-acute symptoms and conditions were reported with changes in sense of smell and taste being the most frequently reported single physical symptoms. As many as half (53.1%) of the participants report having experienced general health problems in the form of either mental or physical exhaustion, sleep problems or cognitive problems, compared to 11.5% of control persons 6 to 12 months after the test. Our results suggest that a considerable proportion of the general population, who did not experience severe disease, are still affected 6 to 12 months after infection and that post-acute symptoms are more often experienced by females and middle-aged individuals.Implications of all available evidenceDiverse post-acute symptoms following infection with SARS-CoV-2 occur frequently. Even up to 12 months after the onset of infection, a considerable proportion of individuals, who did not experience severe disease, continue to experience symptoms. Post-acute symptoms are generally more often reported by females than males, whereas the influence of age remains unclear.

Background This article investigates the impact of a non-mandatory and age-specific social distancing recommendation on isolation behaviours and disease outcomes in Sweden during the first wave of the coronavirus disease 2019 (COVID-19) pandemic (March to July 2020). The policy stated that people aged 70 years or older should avoid crowded places and contact with people outside the household. Methods We used a regression discontinuity design—in combination with self-reported isolation data from COVID Symptom Study Sweden (n = 96 053; age range: 39–79 years) and national register data (age range: 39–100+ years) on severe COVID-19 disease (hospitalization or death, n = 21 804) and confirmed cases (n = 48 984)—to estimate the effects of the policy. Results Our primary analyses showed a sharp drop in the weekly number of visits to crowded places (−13%) and severe COVID-19 cases (−16%) at the 70-year threshold. These results imply that the age-specific recommendations prevented approximately 1800–2700 severe COVID-19 cases, depending on model specification. Conclusions It seems that the non-mandatory, age-specific recommendations helped control COVID-19 disease during the first wave of the pandemic in Sweden, as opposed to not implementing a social distancing policy aimed at older adults. Our study provides empirical data on how populations may react to non-mandatory, age-specific social distancing policies in the face of a novel virus.

BackgroundThe response of the Swedish authorities to the COVID-19 pandemic was less restrictive than in most countries during the first year, with infection and death rates substantially higher than in neighbouring Nordic countries. Because access to PCR testing was limited during the first wave (February to June 2020) and regional data were reported with delay, adequate monitoring of community disease spread was hampered. The app-based COVID Symptom Study was launched in Sweden to disseminate real-time estimates of disease spread and to collect prospective data for research. The aim of this study was to describe the research project, develop models for estimation of COVID-19 prevalence and to evaluate it for prediction of hospital admissions for COVID-19. MethodsWe enrolled 143 531 study participants ([≥]18 years) throughout Sweden, who contributed 10{middle dot}6 million daily symptom reports between April 29, 2020 and February 10, 2021. Data from 19 161 self-reported PCR tests were used to create a symptom-based algorithm to estimate daily prevalence of symptomatic COVID-19. The prediction model was validated using external datasets. We further utilized the model estimates to forecast subsequent new hospital admissions. FindingsA prediction model for symptomatic COVID-19 based on 17 symptoms, age, and sex yielded an area under the ROC curve of 0{middle dot}78 (95% CI 0{middle dot}74-0{middle dot}83) in an external validation dataset of 943 PCR-tested symptomatic individuals. App-based surveillance proved particularly useful for predicting hospital trends in times of insufficient testing capacity and registration delays. During the first wave, our prediction model estimates demonstrated a lower mean error (0{middle dot}38 average new daily hospitalizations per 100 000 inhabitants per week (95% CI 0{middle dot}32, 0{middle dot}45)) for subsequent hospitalizations in the ten most populated counties, than a model based on confirmed case data (0{middle dot}72 (0{middle dot}64, 0{middle dot}81)). The model further correctly identified on average three out of five counties (95% CI 2{middle dot}3, 3{middle dot}7) with the highest rates of hospitalizations the following week during the first wave and four out of five (3{middle dot}0, 4{middle dot}6) during the second wave. InterpretationThe experience of the COVID Symptom Study highlights the important role citizens can play in real-time monitoring of infectious diseases, and how app-based data collection may be used for data-driven rapid responses to public health challenges. FundingSwedish Heart-Lung Foundation (20190470, 20140776), Swedish Research Council (EXODIAB, 2009-1039; 2014-03529), European Commission (ERC-2015-CoG - 681742 NASCENT), and Swedish Foundation for Strategic Research (LUDC-IRC, 15-0067) to MG or PF. European Research Council Starting Grant (801965) to TF. NO was financially supported by the Knut and Alice Wallenberg Foundation as part of the National Bioinformatics Infrastructure Sweden at SciLifeLab. ATC was supported in this work through the Massachusetts Consortium on Pathogen Readiness (MassCPR). ZOE Limited provided in-kind support for all aspects of building, running and supporting the app and service to all users worldwide. Support for this study for KCL researchers was provided by the National Institute for Health Research (NIHR)-funded Biomedical Research Centre based at Guys and St Thomas (GSTT) NHS Foundation Trust. This work was supported by the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare. Investigators also received support from the Wellcome Trust, Medical Research Council (MRC), British Heart Foundation (BHF), Alzheimers Society, European Union, NIHR, COVID-19 Driver Relief Fund (CDRF) and the NIHR-funded BioResource, Clinical Research Facility and Biomedical Research Centre (BRC) based at GSTT NHS Foundation Trust in partnership with KCL. ZOE Limited developed the app for data collection as a not-for-profit endeavour. None of the funding entities had any role in study design, data analysis, data interpretation, or the writing of this manuscript.