ImportanceSome individuals experience persistent symptoms after initial symptomatic SARS-CoV-2 infection (often referred to as Long COVID).ObjectiveTo estimate the proportion of males and females with COVID-19, younger or older than 20 years of age, who had Long COVID symptoms in 2020 and 2021 and their Long COVID symptom duration.Design, Setting, and ParticipantsBayesian meta-regression and pooling of 54 studies and 2 medical record databases with data for 1.2 million individuals (from 22 countries) who had symptomatic SARS-CoV-2 infection. Of the 54 studies, 44 were published and 10 were collaborating cohorts (conducted in Austria, the Faroe Islands, Germany, Iran, Italy, the Netherlands, Russia, Sweden, Switzerland, and the US). The participant data were derived from the 44 published studies (10 501 hospitalized individuals and 42 891 nonhospitalized individuals), the 10 collaborating cohort studies (10 526 and 1906), and the 2 US electronic medical record databases (250 928 and 846 046). Data collection spanned March 2020 to January 2022.ExposuresSymptomatic SARS-CoV-2 infection.Main Outcomes and MeasuresProportion of individuals with at least 1 of the 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after SARS-CoV-2 infection in 2020 and 2021, estimated separately for hospitalized and nonhospitalized individuals aged 20 years or older by sex and for both sexes of nonhospitalized individuals younger than 20 years of age.ResultsA total of 1.2 million individuals who had symptomatic SARS-CoV-2 infection were included (mean age, 4-66 years; males, 26%-88%). In the modeled estimates, 6.2% (95% uncertainty interval [UI], 2.4%-13.3%) of individuals who had symptomatic SARS-CoV-2 infection experienced at least 1 of the 3 Long COVID symptom clusters in 2020 and 2021, including 3.2% (95% UI, 0.6%-10.0%) for persistent fatigue with bodily pain or mood swings, 3.7% (95% UI, 0.9%-9.6%) for ongoing respiratory problems, and 2.2% (95% UI, 0.3%-7.6%) for cognitive problems after adjusting for health status before COVID-19, comprising an estimated 51.0% (95% UI, 16.9%-92.4%), 60.4% (95% UI, 18.9%-89.1%), and 35.4% (95% UI, 9.4%-75.1%), respectively, of Long COVID cases. The Long COVID symptom clusters were more common in women aged 20 years or older (10.6% [95% UI, 4.3%-22.2%]) 3 months after symptomatic SARS-CoV-2 infection than in men aged 20 years or older (5.4% [95% UI, 2.2%-11.7%]). Both sexes younger than 20 years of age were estimated to be affected in 2.8% (95% UI, 0.9%-7.0%) of symptomatic SARS-CoV-2 infections. The estimated mean Long COVID symptom cluster duration was 9.0 months (95% UI, 7.0-12.0 months) among hospitalized individuals and 4.0 months (95% UI, 3.6-4.6 months) among nonhospitalized individuals. Among individuals with Long COVID symptoms 3 months after symptomatic SARS-CoV-2 infection, an estimated 15.1% (95% UI, 10.3%-21.1%) continued to experience symptoms at 12 months.Conclusions and RelevanceThis study presents modeled estimates of the proportion of individuals with at least 1 of 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after symptomatic SARS-CoV-2 infection.

Background National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to guiding investment in more effective preparedness and response for future pandemics. MethodsDaily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation's modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates. Findings The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5•4% [4•0-7•9] of variation), GDP per capita (4•2% [1•8-6•6] of variation), and the proportion of infections attributable to seasonality (2•1% [95% uncertainty interval 1•7-2•7] of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46•7% [18•4-67•6] of variation), GDP per capita (3•1% [0•3-8•6] of variation), and national mean BMI (1•1% [0•2-2•6] of variation). 44•4% (29•2-61•7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as wel...

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Aims It has been proposed that an increase of myocardial adiposity is related to left ventricular (LV) diastolic dysfunction. The specific roles of myocardial steatosis including epicardial fat and intramyocardial fat for diastolic function are unknown in those patients suffering heart failure (HF) with reduced (HFrEF) or preserved ejection fraction (HFpEF). This study aims to determine the complex relationship between myocardial adiposity in patients with HFrEF or HFpEF. Methods and results Using cardiac magnetic resonance imaging (CMRI), myocardial steatosis was measured in 305 subjects (34 patients with HFrEF, 163 with HFpEF, and 108 non‐HF controls). We also evaluated cardiac structure and diastolic and systolic function by echocardiography and CMRI. Patients with HFpEF had significantly more intramyocardial fat than HFrEF patients or non‐HF controls [intramyocardial fat content (%), 1.56 (1.26, 1.89) vs. 0.75 (0.50, 0.87) and 1.0 (0.79, 1.15), P < 0.05]. Intramyocardial fat amount (%) was higher in HFpEF women than in men [1.91% (1.17%, 2.32%) vs. 1.22 (0.87%, 2.02%), P = 0.01]. When estimated by CMRI (left ventricular peak filling rate), echocardiographic E/e′ level, or left atrial volume index, intramyocardial fat correlated with LV diastolic dysfunction parameters in HFpEF patients, and this was independent of age, co‐morbidities, body mass index, gender, and myocardial fibrosis (standardized coefficient: β = −0.34, P = 0.03; β = 0.29, P = 0.025; and β = 0.25, P = 0.02, respectively). Conclusions Patients with HFpEF had significantly more intramyocardial fat than HFrEF patients or non‐HF controls. Independent of risk factors or gender, intramyocardial fat correlated with LV diastolic dysfunction parameters in HFpEF patients.