European Journal of Epidemiology

ObjectivesWe studied post-COVID-19 condition by investigating health-related quality of life and fatigue in the general Dutch population in the early phase of the pandemic, including symptomatic and asymptomatic infections among unvaccinated individuals. Methods(Still) unvaccinated participants aged [≥]15 years were selected from the February 2021 round of the nationwide seroepidemiological PIENTER Corona cohort study. We assessed associations between the time since serologically-identified SARS-CoV-2 infection and four outcome measures: health utility (Short-Form 6 Dimensions), mental health and physical health (Short Form Health Survey 12) and fatigue (Checklist Individual Strength subscale fatigue). Per outcome, cutoff points were selected at each 5% increment (5-75%) along the cumulative distribution of those uninfected. At each cutoff, multivariable logistic regression models (score below cutoff yes/no) were fitted adjusted for infection history, age, sex, education level, comorbidities, and restriction intensity. ResultsAt the cutoff of the lowest 15th percentile among uninfected, significant differences between uninfected (n=4,569) and infected [≤]4 months ago (n=351) were observed for health utility (OR [95%CI]: 1.6 [1.2-2.2]), physical health (1.9 [1.5-2.5]) and fatigue (1.6 [1.3-2.1]), but not for mental health (1.2 [0.9-1.6]). There were no significant differences between uninfected and infected >4 months ago (n=327) for all outcomes at any cutoff of the cumulative distribution, with post-hoc analysis showing a power to detect prevalence differences as low as 7%. ConclusionsIn the first year of the pandemic, data from this Dutch population-based seroepidemiological cohort showed that unvaccinated individuals with a SARS-CoV-2 infection [≤]4 months ago reported poorer health utility and physical health, and more severe fatigue compared to those uninfected. Interestingly, for those infected >4 months ago differences remained below the detection limit, suggesting a lower population prevalence of post-COVID-19 condition than currently found in literature for this period.

Background and objectiveThere has been an increasing interest in using life expectancy metrics, such as years of life lost (YLL), to explore epidemiological associations. YLL is easier to understand for both healthcare professionals and the lay people and has become a common measure for evaluating public health priorities. As the literature presents a range of approaches to estimate it, this review aims to: (1) summarise the key methods; (2) show how to implement them using current software; (3) apply them in a real-world example. MethodsWe investigated simpler nonparametric as well as parametric, model-based methods to estimate of YLL, including: (1) Years of potential life lost (YPLL); (2) Global Burden of Disease (GBD) approach; (3) Chiangs life tables; (4) Epi-demographic approach; and (5) Flexible Royston-Parmar parametric survival model. We used data from the UK Biobank with baseline measures collected in 2006-2010 and linkage to mortality records. We selected 36 chronic conditions: participants with two or more conditions were categorised as having multimorbidity. ResultsFor the YPLL and GBD method, the analytical procedures allow only to quantify the average YLL within each group (with and without multimorbidity) and, from them, their difference. Conversely, for the Chiangs life tables, the epi-demographic approach, and the Royston-Parmar survival model, both the remaining life expectancy within each group and the YLL could be estimated. In 499,992 UK Biobank participants (white ethnicity, 94%; women, 55%) with a median (IQR) age of 58 (50-63) years, 98,605 (20%) had multimorbidity and 11,871 deaths occurred during the follow-up. The YLLs comparing subjects with vs without multimorbidity varied significantly according to the technique and the modelling approach used: from a longer life expectancy in subjects with multimorbidity using the YPLL and the GBD method to a shorter one using the other three methods (i.e., at 65 years, the YLL were 1.8, 1.3, and 4.6 years using Chiangs, epi-demographic, and Royston-Parmar approach, respectively). ConclusionsWhen comparing the burden of a disease on life expectancy across studies caution is needed as methods may estimate different quantities. While deciding among different methods to estimate YLL, researchers should consider such differences in relation to the purpose of the research and the type of available data. O_TEXTBOXSUMMARY BOXO_LIThe concept of years of life lost (YLL) is easier to understand compared to traditional estimates from survival analysis, such as hazard ratios, but very few studies report it. C_LIO_LIA range of different methods of estimating YLL are reviewed: from basic methods - such as life tables - to most recent and advanced methods using statistical modelling. C_LIO_LIUsing the example of multimorbidity, the estimated numbers of YLL differs between methods, as each method focused on estimating different quantities. C_LIO_LIThis review will help promote a better understanding and use of life expectancy and YLL metrics in a wide range of studies in health care research. C_LI C_TEXTBOX

BackgroundBoth enhancing life expectancy as well as diminishing inequalities in lifespan among social groups represent significant goals for public policy. However, there is a lack of methodological tools to simultaneously monitor progress in both dimensions. Additionally, there is a consensus that absolute and relative inequalities in lifespan must be scrutinized together. MethodsWe introduce a novel graphical representation that combines national mortality rates with social inequalities, considering both absolute and relative measures. We use French and German data stratified by place of residence to illustrate this representation. ResultsFor all-age mortality we detect for France a rather continuous pace of decline in both mortality levels and variation. In Germany, substantial progress was made in the 1990s, which was mostly driven by convergence between eastern and western Germany, followed by a period with less progress. Age-specific analyses reveal for Germany some worrying regional divergence trends at ages 35-74 in recent years. This is particularly pronounced among women. ConclusionOur novel visual approach allows evaluating easily the dynamics of societal progress in terms of longevity, and facilitates meaningful comparisons between populations, even when their current mortality rates differ. The methods we employ can be reproduced easily in any country with longitudinal mortality data stratified by relevant socio-economic information or regions. It is both useful for scientific analyses as well as policy advice. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSImproving life expectancy as well as reducing social inequalities in longevity are major public policy objectives. However, there is a lack of proper methodological tools to evaluate progress on these objectives. What this study addsThis study proposes an innovative graphical representation that combines national mortality and social inequalities in both absolute and relative terms in order to assess the dynamics of societal progress in longevity and make relevant comparisons between populations whose mortality rates are not at the same level nowadays. How this study might affect research, practice or policyMethods are freely and easily reproducible for all countries with longitudinal mortality data stratified by socio-economic information or geographic regions.

BackgroundThe negative correlation between life purpose levels and subsequent morbidity and mortality is interpreted as evidence that a higher sense of life purpose causes healthier and longer lives. Causation, however, could run the other direction as a decline in health is, by definition, associated with greater morbidity and mortality risk and may also cause a decline in life purpose. We examine the relation between objective measures of changes in health and changes in purpose to better understand the causal mechanisms linking purpose to health and mortality. MethodsProspective cohort sample of 12 745 individuals aged 50 and older who were eligible to participate in the 2006, 2010, or 2014 Health and Retirement Study Psychosocial and Lifestyle questionnaire. The final sample consists of 15 034 observations measured over three four-year periods from 5 147 individuals. Controlling for standard covariates, we examined the relation between changes in purpose and 14 contemporaneous and subsequent objectively measured changes in health--lung function, grip strength, walking speed, balance, and physician diagnoses of hypertension, diabetes, cancer, lung disease, heart condition, stroke, psychiatric problem, arthritis, dementia, and Alzheimers disease. FindingsThere is strong evidence that negative health shocks cause a decline in life purpose as individuals who suffer a negative health shock experience a statistically meaningful contemporaneous decline in life purpose for 12 of the 14 changes in health metrics. In contrast, there is relatively weak evidence that a decline in purpose contributes to a deterioration of future health. InterpretationMuch of the relation between life purpose levels and mortality risk arises from reverse causation--a decline in health causes both increased mortality risk and lower life purpose. There is little evidence that life purpose interventions would alter future morbidity or mortality. FundingNone. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and Google Scholar with no language or date restriction for the term "life purpose" and found four comprehensive reviews of the life purpose or psychological well-being (which included life purpose in the set of psychological well-being metrics) literatures in the last three years and a 2016 meta-analysis of the relation between life purpose and mortality. Although acknowledging the possibility that reverse causation plays a role in linking life purpose levels to subsequent morbidity and mortality, the prevalent view appears to be that even when controlling for current health levels, higher life purpose causes behavioral, biological, or stress buffering changes that, in turn, cause lower future morbidity and mortality. Added value of this studyBy focusing on changes in health, changes in life purpose, and a longer horizon, we find strong evidence that changes in health cause changes in life purpose, but, contrary to the conclusions of most previous work, there is little evidence changes in life purpose cause changes in behavior, biology, or stress-buffering that, in turn, cause changes in future health. Implications of all the available evidenceAlthough life purpose intervention--either at the provider level or in public policy--may have benefits, there is little evidence to suggest it will cause greater longevity or lower future illness.

Failure to routinely assess the state of knowledge as new studies accumulate results in 1) non-use of effective interventions, 2) continued use of ineffective or harmful interventions, and 3) unnecessary research. We use a published cumulative meta-analysis of interventions to reduce the harms of acute myocardial infarctions (1966-1992), and applied population attributable risk to assess the mortality consequences of the failure to cumulatively assess the state of knowledge. Failure to use knowledge that would have been available with cumulative meta-analysis may have resulted in annual estimated mortality: 41,000 deaths from non-use of intravenous dilators, 35,000 deaths from non-use of aspirin, and 37,000 deaths annually from non-use of {beta}-blockers. Continued use of Class 1 anti-arrhythmic drugs, which would have been found to be harmful in 1981, resulted 39,000 deaths annually. Failure to routinely update the state knowledge can have large health consequences. The process of building knowledge and practice in medicine and public health needs fundamental revision.

ObjectivesTo quantify the contribution of cardiorespiratory fitness (CRF) genetics in common non-communicable disease (NCD) and mortality risk and to assess whether health discrepancies exist between "inherited" and "gained" CRF. MethodsWe used a validated SBayesR-based genome-wide polygenic score, leveraging information from 905,707 single-nucleotide polymorphisms, to measure CRF genetics (PGS CRF). Associations with register-based incident cardiovascular disease, cancer, pulmonary disease, type 2 diabetes (T2D), and all-cause mortality were analysed using Cox proportional hazards models in the FinnGen cohort (N=262,137; 53.5-y at baseline, 52.0% women) and replicated in the HUNT3 cohort (N=26,115; 59.0-y, 52.4% women). In HUNT3, we also compared the health characteristics and disease risk of individuals having age- and sex-specific high CRF ([V]O2max) and high PGS CRF (group "inherited" CRF) to those having high CRF but low PGS CRF (group "gained" CRF), N=375 vs. 279, respectively. ResultsHigher PGS CRF was associated with lower risk of lung cancer (hazard ratio [HR] 0.95, 95% confidence interval 0.93-0.98), chronic obstructive pulmonary disease (HR 0.98, 0.96-1.00), T2D (HR 0.98, 0.96-0.99), and all-cause mortality (HR 0.98, 0.98-0.99) in the most adjusted model per each standard deviation increase in PGS. In sensitivity analyses including never-smokers, the association between PGS CRF and T2D remained statistically significant. Replication analyses supported main observations. No differences in health outcomes were observed between individuals with "inherited" and "gained" CRF. ConclusionsPGS CRF requires further development, but the findings suggest genetic predisposition accounts for some, albeit a limited proportion, of the public health benefits observed with CRF. Summary boxO_ST_ABSWhat is already known on this topicC_ST_ABSCardiorespiratory fitness (CRF) is a well-known correlate of health and longevity, with an expected strong genetic component. However, it is not known to what extent genetics explain the health benefits of CRF or if "inherited" CRF is more health-protective than CRF "gained" through exercise. What this study addsWe found that current polygenic metrics for CRF show modest protective associations against type 2 diabetes, but not against cardiovascular disease, cancers, pulmonary disease or all-cause mortality after controlling for potential covariates. We did not observe differential associations between "inherited" and "gained" CRF with health outcomes. How this study might affect research, practice or policyGenetic confounding is expected to play a limited role in the relationship between CRF and the risk of common NCDs and mortality. Therefore, promotion of CRF remains a viable public health strategy.

Globally, the lifespan of populations increases but the healthspan is lagging behind. Previous research showed that survival into extreme ages (longevity) clusters in families as illustrated by the increasing lifespan of study participants with each additional long-lived family member. Here we investigate whether the healthspan in such families follows a similar quantitative pattern using three-generational data from two databases, LLS (Netherlands), and SEDD (Sweden). We study healthspan in 2,143 families containing index persons and two ancestral generations, comprising 17,539 persons with 25 follow-up years. Our results provide strong evidence that an increasing number of long-lived ancestors associates with up to a decade of healthspan extension. Further evidence indicates that members of long-lived families have a delayed onset of medication use, multimorbidity and, in mid-life, healthier metabolomic profiles than their partners. We conclude that in longevity families, both lifespan and healthspan are quantitatively linked to ancestral longevity, making such families highly suitable to identify protective mechanisms of multimorbidity.

BackgroundPolygenic indices (PGIs) of various traits abound, but knowledge remains limited on how they predict wide-ranging health indicators, including the risk of death. We investigated the associations between mortality and 35 different PGIs related to social, psychological, and behavioural traits, and typically non-fatal health conditions. MethodsData consist of Finnish adults from population-representative genetically informed epidemiological surveys (FINRISK 1992-2012, Health 2000/2011, FinHealth 2017), linked to administrative registers (N: 40,097 individuals, 5948 deaths). Within-sibship analysis was complemented with dizygotic twins from Finnish twin study cohorts (N: 10,174 individuals, 2116 deaths). We estimated Cox proportional hazards models with mortality follow-up 1995-2019. ResultsPGIs most strongly predictive of all-cause mortality were ever smoking (hazard ratio [HR]=1.12, 95% confidence interval [95% CI] 1.09; 1.14 per one standard deviation larger PGI), self-rated health (HR=0.90, 95% CI 0.88; 0.93), body mass index (HR=1.10, 95% CI 1.07; 1.12), educational attainment (HR=0.91, 95% CI 0.89; 0.94), depressive symptoms (HR=1.07, 95% CI 1.04; 1.10), and alcohol drinks per week (HR=1.06, 95% CI 1.04; 1.09). Within-sibship estimates were approximately consistent with the population analysis, suggesting no evidence for inflation of PGI-mortality associations by population phenomena. The investigated PGIs were typically more predictive for external than for natural causes of death. PGIs were more strongly associated with death occurring at younger ages, while among those who survived to age 80, the PGI-mortality associations were negligible. ConclusionsPGIs related to the best-established mortality risk phenotypes had the strongest associations with mortality. They offer moderate additional prediction even when mutually adjusting with their phenotype.

BackgroundGenome wide association studies (GWAS) of specific diseases are central to scientific discovery. Bias from inevitably recruiting only survivors of genetic make-up and disease specific competing risk has not been comprehensively considered.\n\nMethodsWe identified sources of bias using directed acyclic graphs, and tested for them in the UK Biobank GWAS by making comparisons across the survival distribution, proxied by age at recruitment.\n\nResultsAssociations of genetic variants with some diseases depended on their effect on survival. Variants associated with common harmful diseases had weaker or reversed associations with subsequent diseases that shared causes.\n\nConclusionGenetic studies of diseases that involve surviving other common diseases are open to selection bias that can generate systematic type 2 error. GWAS ignoring such selection bias are most suitable for monogenetic diseases. Genetic effects on age at recruitment may indicate potential bias in disease-specific GWAS and relevance to population health.

Loneliness and social isolation are important public health concerns due to their associations with a range of health outcomes. However, it is difficult to ascertain whether loneliness and social isolation cause those outcomes or whether the observed associations are biased by confounding and reverse causation. In this study we used a triangulation approach combining observational analysis, sibling control design, and Mendelian Randomisation (a genetically informed causal inference approach), to draw robust conclusions about these relationships. Using a combination of publicly available genome-wide association study (N= 17,526 to 2,083,151) and UK Biobank data (N= 8,075 to 414,432), we examined relationships between loneliness and social isolation and outcomes related to physical health, mental health and wellbeing and general health (reflecting both physical and mental health e.g., multimorbidity). Our results provide evidence for causal effects of loneliness and social isolation on poorer mental health and wellbeing and of loneliness on poorer general health. Evidence was generally stronger for loneliness compared to social isolation. We do not find evidence of effects on specific physical health outcomes; however, we cannot definitively rule out causal relationships. Interventions targeting loneliness and social isolation may be effective strategies for improving general health, mental health and wellbeing outcomes.

BackgroundLow muscle strength predicts premature mortality. We determined whether genetic liability to muscle strength is associated with mortality and whether this association is influenced by long-term leisure-time physical activity (PA). Methods and ResultsWe estimated the effects of a polygenic score for hand grip strength (PGS HGS) on all-cause and cardiovascular disease (CVD) mortality risk in the older Finnish Twin Cohort (N=8815, 53% women). National registries provided dates and causes of death. PA volume was assessed longitudinally in 1975, 1981, and 1990 using validated questionnaires. During the 16.9-year median follow-up time (143,723 person-years), 2896 deaths occurred, of which 1089 were due to CVD. We found a significant interaction between sex and PGS HGS (P=0.016) for predicting all-cause mortality. In men, one standard deviation increase in the PGS HGS was associated with a decreased risk both of all-cause (hazard ratio, HR [95% confidence interval, CI]): 0.93 [0.89-0.98] and CVD mortality (HR 0.88 [0.81-0.96]). Associations persisted after adjusting for PA, but only with CVD mortality after adjusting for other lifestyle covariates (HR 0.85 [0.76-0.96]). The cumulative incidence rates by age 75 years were 4.3% lower for all-cause mortality and 2.1% lower for CVD mortality in the highest PGS HGS quintile compared to the lowest quintile. No PGS HGSxPA interactions were found. PGS HGS was not associated with mortality in women. ConclusionsHigher PGS HGS was associated with a decreased risk of all-cause and CVD mortality in men; however, long-term PA in adulthood did not potentiate this association. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LITo the best of our knowledge, this is the first study to use a genome-wide polygenic score for hand grip strength to investigate whether the association between genetic liability to muscle strength and lifespan is affected by physical activity. C_LIO_LIOur results suggest that individuals with a genetic predisposition for higher muscle strength have a modest decreased risk of cardiovascular disease mortality, independent of their lifestyle. C_LI What Are the Clinical Implications?O_LIPolygenic scores for muscle strength require further development but may help identify individuals who represent extreme ends of genetic predisposition and vulnerability to premature death. C_LI

The ageing society and worldwide rise of chronic disease make adequate early identification of at-risk individuals and preventive intervention highly relevant to public health. Molecular indicators of global health have been developed, such as metabolomics-based MetaboHealth. A shortcoming of molecular biomarkers may be their lack of integration of lifestyle and environmental factors relevant for health span. Hence, we explored the MetaboHealth biomarker and a range of health- and lifestyle factors, including plant based diet index, physical activity, alcohol use, smoking, medication use, 25(OH)D status and socioeconomic position and education in a subpopulation (n=35,192, mean age=56 years) from the UK Biobank cohort. We analysed which of these factors associated independently with mortality; which associated with the MetaboHealth score and which of the independent factors improve mortality prediction by MetaboHealth. By applying multivariate Cox regression modelling we found that all factors associated independently with prospective survival, except for physical activity and education level. Sex, smoking and income were most strongly associated with both mortality and the MetaboHealth score. By cross-validation we subsequently assessed contribution of all independent health- and lifestyle-related factors to MetaboHealth-based mortality prediction and computed a weighted score. We found income and medication intake to be the most and diet the least prominently adding contributors. In conclusion, MetaboHealth partly reflects the effect of health- and lifestyle-related factors, while identification of at-risk individuals is improved by the information on income and medication use. Insights in these factors can be attained non-intrusively and may therefore be taken into account in the context of population health management.

Researchers have long studied allostatic (over)load as an estimated measure of individual cumulative stress over a lifetime. Often called the overall wear and tear from social and environmental stressors, allostatic (over)load shows promise as a practical indicator of general health trends in community settings. This data processing workflow aims to document our overall approach and reasoning when calculating allostatic (over)load for data analysis and knowledge sharing. The included repository features an R script for generating datasets using this workflow from the following data sources: O_LIAll of Us Research Program data repository C_LIO_LIHealth and Retirement Study (HRS) C_LIO_LINational Health and Nutrition Examination Survey (NHANES) C_LI Our allostatic (over)load measurement process, along with the linked repository, provides a reproducible workflow to process secondary data and offers insights into protocol-driven measurement practices in community environments.

BackgroundChronic disease microsimulation models often simulate disease incidence as a function of risk factors that evolve over time (e.g., blood pressure increasing with age) in order to facilitate decision analyses of different disease screening and prevention strategies. Existing models typically rely on incidence rates estimated with standard survival analysis techniques (e.g., proportional hazards from baseline data) that are not designed to be continually updated each model cycle. We introduce the use of joint longitudinal and time-to-event to parameterize microsimulations to avoid potential issues from using these existing methods. These joint models include random effects regressions to estimate the risk factor trajectories and a survival model to predict disease risk based on those estimated trajectories. In a case study on cardiovascular disease (CVD), we compare the validity of microsimulation models parameterized with this joint model approach to those parameterized with the standard approaches. MethodsA CVD microsimulation model was constructed that modeled the trajectory of seven CVD risk factors/predictors as a function of age (smoking, diabetes, systolic blood pressure, antihypertensive medication use, total cholesterol, HDL, and statin use) and predicted yearly CVD incidence as a function of these predictors, plus age, sex, and race. We parameterized the model using data from the Atherosclerosis Risk in Communities study (ARIC). The risk of CVD in the microsimulation was parameterized with three approaches: (1) joint longitudinal and time-to-event model, (2) proportional hazards model estimated using baseline data, and (3) proportional hazards model estimated using time-varying data. We accounted for non-CVD mortality across all the parameterization approaches. We simulated risk factor trajectories and CVD incidence from age 70y to 85y for an external test set comprised of individuals from the Multi-Ethnic Study of Atherosclerosis (MESA). We compared the simulated to observed incidence using both average survival curves and the E50 and E90 calibration metrics (the median and 90th percentile absolute difference between observed and predicted incidence) to measure the validity of each parameterization approach. ResultsThe average CVD survival curve estimated by the microsimulation model parameterized with the joint model approach matched the observed curve from the test set relatively closely. The other parameterization methods generally performed worse, especially the proportional hazards model estimated using baseline data. Similar results were observed for the calibration metrics, with the joint model performing particularly well on the E90 metric compared to the other models. ConclusionsUsing a joint longitudinal and time-to-event model to parameterize a CVD simulation model produced incidence predictions that more accurately reflected observed data than a model parameterized with standard approaches. This parameterization approach could lead to more reliable microsimulation models, especially for models that evaluate policies which depend on tracking dynamic risk factors over time. Beyond this single case study, more work is needed to identify the specific circumstances where the joint model approach will outperform existing methods.

Motivated by the ambiguity of operational case definitions for long COVID and the impact of the lack of a common causal language on long COVID research, in early 2023 we began developing a research framework on this post-acute infection syndrome. We used directed acyclic graphs (DAGs) and Bayesian networks (BNs) to depict the hypothesised mechanisms of long COVID in an agnostic fashion. The DAGs were informed by the evolving literature and subsequently refined following elicitation workshops with domain experts. The workshops were structured online sessions guided by an experienced facilitator. The causal DAGs aim to summarise the hypothesised pathobiological pathways from mild or severe COVID-19 disease to the development of pulmonary symptoms and fatigue over four different time points. The DAG was converted into a BN using qualitative parametrisation. These causal models aim to assist the identification of disease endotypes, as well as the design of randomised controlled trials and observational studies. The framework can also be extended to a range of other post-acute infection syndromes.

BackgroundSevere acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection has disproportionately affected older individuals and those with underlying medical conditions. Research has focused on short-term outcomes in hospital, and single organ involvement. Consequently, impact of long COVID (persistent symptoms three months post-infection) across multiple organs in low-risk individuals is yet to be assessed. MethodsAn ongoing prospective, longitudinal, two-centre, observational study was performed in individuals symptomatic after recovery from acute SARS-CoV-2 infection. Symptoms and organ function (heart, lungs, kidneys, liver, pancreas, spleen) were assessed by standardised questionnaires (EQ-5D-5L, Dyspnoea-12), blood investigations and quantitative magnetic resonance imaging, defining single and multi-organ impairment by consensus definitions. FindingsBetween April and September 2020, 201 individuals (mean age 44 (SD 11.0) years, 70% female, 87% white, 31% healthcare workers) completed assessments following SARS-CoV-2 infection (median 140, IQR 105-160 days after initial symptoms). The prevalence of pre-existing conditions (obesity: 20%, hypertension: 6%; diabetes: 2%; heart disease: 4%) was low, and only 18% of individuals had been hospitalised with COVID-19. Fatigue (98%), muscle aches (88%), breathlessness (87%), and headaches (83%) were the most frequently reported symptoms. Ongoing cardiorespiratory (92%) and gastrointestinal (73%) symptoms were common, and 42% of individuals had ten or more symptoms. There was evidence of mild organ impairment in heart (32%), lungs (33%), kidneys (12%), liver (10%), pancreas (17%), and spleen (6%). Single (66%) and multi-organ (25%) impairment was observed, and was significantly associated with risk of prior COVID-19 hospitalisation (p<0.05). InterpretationIn a young, low-risk population with ongoing symptoms, almost 70% of individuals have impairment in one or more organs four months after initial symptoms of SARS-CoV-2 infection. There are implications not only for burden of long COVID but also public health approaches which have assumed low risk in young people with no comorbidities. FundingThis work was supported by the UKs National Consortium of Intelligent Medical Imaging through the Industry Strategy Challenge Fund, Innovate UK Grant 104688, and also through the European Unions Horizon 2020 research and innovation programme under grant agreement No 719445.

Understanding how genetic influences on complex traits change with age is a fundamental question in genetic epidemiology. Both cross-sectional (between-subject) and longitudinal (within-subject) approaches can contribute to answering this question, but come with distinct strengths and limitations. Using data from 31 health-related phenotypes in the UK Biobank, we applied a two-stage genome-wide approach to identify genetic variants exhibiting age-dependent effects. To assess the robustness of these findings, we tested for variant-specific results across multiple analytical models, and linked these to differences in key methodological assumptions. Within this framework, we systematically compared the results from cross-sectional gene-by-age interaction models (up to 406,226 individuals) with those from genetic association tests on longitudinal change (up to 83,579 individuals with repeat measurements), and investigated potential sources of bias underlying any observed discrepancies. We found high concordance in the direction of age-varying genetic effects across the two designs (85.96% of the 57 identified variants), but only moderate agreement in magnitude of effect sizes (Pearson r = 0.74). Gene-by-birth year effects, which bias cross-sectional estimates, accounted for the largest proportion of variance in effect size differences across SNPs with age-varying effects between designs (53.1%). Participation bias accounted for an additional 13.3%, while unmodeled non-linear age trajectories contributed minimally to these differences (2.1%). Overall, our results demonstrate that both cross-sectional and longitudinal designs can yield different estimates of age-varying genetic effects, principally due to cohort confounding and participation bias. As neither approach is immune to bias, we recommend integrating both designs for robust inference, to help minimize bias and more accurately characterize how genetic effects on complex traits change over the lifespan.

Re-examination of the large dataset collected and meta-analysed by Dr Chu and his colleagues contradicts their conclusions about the effects of separation distance on infection risk. Their conclusion was based on misunderstandings of the datasets. Each of these estimated risk relative to that incurred when touching infected individuals. Allowing for this suggests that the main advantage of social distancing, a perhaps 78% (95% CI 24, 92) reduction in risk of infection, occurs at distances below 1m. The data imply an 11% chance of further distances reducing the risk, with any effects likely to be small. However the limitations of the dataset do limit the strength of these conclusions.

Multi-state Markov models have been used to model the course of chronic disease. However, they are unsuitable to chronic disease where past and present states interplay and affect future states, and the estimated transition probabilities are time-specific which are not straightforward for public health interpretation. We have proposed a multi-state non-Markov framework that splits disease states into substates conditioning on past states. As the substates track past states and indicate multimorbidity, the estimated transition rates can be used to derive two summary estimates: Disease path which shows path of state transition, and multimorbidity-adjusted life year (MALY) which represents the adjusted life year in full health. In this paper, we showed the derivation of the two summary estimates and applied them to characterize the course of heart disease using data from the Atherosclerosis Risk in Communities Study (ARIC) study. The course of heart disease was modeled in five states, namely, healthy, at metabolic risk, coronary heart disease (CHD), heart failure, and mortality. In this mid- to old-age population, the estimated MALY was 24.13 (95% CI: 16.55, 32.06) years. For healthy participants at baseline, the most likely disease paths were: "Healthy [-&gt;] at metabolic risk [-&gt;] mortality" (37%), "Healthy [-&gt;] mortality" (21%), "Healthy [-&gt;] at metabolic risk [-&gt;] heart failure [-&gt;] mortality" (19%), and "Healthy [-&gt;] at metabolic risk [-&gt;] CHD [-&gt;] mortality" (8%). The MALY was higher among women than men and higher among Whites than Blacks. The distribution of disease path was similar across sex and race subgroups. In summary, MALY and disease path characterize the disease course in a summary manner and have potential use in chronic disease prevention.

During a pandemic, robust estimation of case fatality rates (CFRs) is essential to plan and control suppression and mitigation strategies. At present, estimates for the CFR of COVID-19 caused by SARS-CoV-2 infection vary considerably. Expert consensus of 0.1-1% covers in practical terms a range from normal seasonable Influenza to Spanish Influenza. In the following, I deduce a formula for an adjusted Infection Fatality Rate (IFR) to assess mortality in a period following a positive test adjusted for selection bias. Official datasets on cases and deaths were combined with data sets on number of tests. After data curation and quality control, a total of IFR (n=819) was calculated for 21 countries for periods of up to 26 days between registration of a case and death. Estimates for IRFs increased with length of period, but levelled off at >9days with a median for all 21 countries of 0.11 (95%-CI: 0.073-0.15). An epidemiologically derived IFR of 0.040 % (95%-CI: 0.029%- 0.055%) was determined for Iceland and was very close to the calculated IFR of 0.057% (95%-CI: 0.042- 0.078), but 2.7-6-fold lower than CFRs. IFRs, but not CFRs, were positively associated with increased proportions of elderly in age-cohorts (n=21, spearmans {rho}=.73, p =.02). Real-time data on molecular and serological testing may further displace classical diagnosis of disease and its related death. I will critically discuss, why, how and under which conditions the IFR, provides a more solid early estimate of the global burden of a pandemic than the CFR.