Costs associated with insufficient physical activity in Germany: cross-sectional results from the baseline examination of the German national cohort (NAKO).

BACKGROUND: Insufficient physical activity (PA) is a leading risk factor for non-communicable diseases posing a significant economic burden to healthcare systems and societies. The study aimed to examine the differences in healthcare and indirect costs between sufficient and insufficient PA and the cost differences between PA intensity groups. METHODS: The cross-sectional analysis was based on data from 157,648 participants in the baseline examination of the German National Cohort (NAKO) study. Healthcare and indirect costs were calculated based on self-reported information on health-related resource use and productivity losses. PA in the domains leisure, transport, and work was assessed by the Global Physical Activity Questionnaire and categorized into sufficient/insufficient and intensity levels (very low/low/medium/high) based on PA recommendations of the World Health Organization. Two-part models adjusted for relevant covariates were used to estimate mean costs for PA groups. RESULTS AND CONCLUSION: Insufficiently active people had higher average annual healthcare costs (Δ €188, 95% CI [64, 311]) and healthcare plus indirect costs (Δ €482, 95% CI [262, 702]) compared to sufficiently active people. The difference was especially evident in the population aged 60 + years and when considering only leisure PA. An inverse association was observed between leisure PA and costs, whereas a direct association was found between PA at work and costs. Adjusting for the number of comorbidities reduced the differences between activity groups, but the trend persisted. The association between PA and costs differed in direction between PA domains. Future research may provide further insight into the temporal relationship between PA and costs.

Large-scale assessment of physical activity in a population using high-resolution hip-worn accelerometry: the German National Cohort (NAKO).

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

Health Service Use Among Migrants in the German National Cohort-The Role of Birth Region and Language Skills.

Objective: To compare health service use (HSU) between migrants and non-migrants in Germany. Methods: Using data from the population-based German National Cohort (NAKO), we compared the HSU of general practitioners, medical specialists, and psychologists/psychiatrists between six migrant groups of different origins with the utilization of non-migrants. A latent profile analysis (LPA) with a subsequent multinomial regression analysis was conducted to characterize the HSU of different groups. Additionally, separate regression models were calculated. Both analyses aimed to estimate the direct effect of migration background on HSU. Results: In the LPA, the migrant groups showed no relevant differences compared to non-migrants regarding HSU. In separate analyses, general practitioners and medical specialists were used comparably to slightly more often by first-generation migrants from Eastern Europe, Turkey, and resettlers. In contrast, the use of psychologists/psychiatrists was substantially lower among those groups. Second-generation migrants and migrants from Western countries showed no differences in their HSU compared to non-migrants. Conclusion: We observed a low mental HSU among specific migrant groups in Germany. This indicates the existence of barriers among those groups that need to be addressed.

Predictors of non-recovery from fatigue and cognitive deficits after COVID-19: a prospective, longitudinal, population-based study.

Background: Despite the high prevalence and major disability associated with fatigue and cognitive deficits after SARS-CoV-2 infection, little is known about long-term trajectories of these sequelae. We aimed to assess long-term trajectories of these conditions and to identify risk factors for non-recovery. Methods: We analyzed longitudinal data from the population-based COVIDOM/NAPKON-POP cohort in Germany. Participants with confirmed SARS-CoV-2 infection were assessed at least 6 months (baseline) and again at least 18 months (follow-up) after infection using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue) Scale (cutoff ≤ 30) and the Montreal Cognitive Assessment (MoCA, cutoff ≤ 25). Predictors of recovery from fatigue or cognitive deficits between assessments were identified through univariate and multivariable logistic regression models. The COVIDOM study is registered at the German registry for clinical studies (DRKS00023742) and at ClinicalTrials.gov (NCT04679584). Findings: Between 15 November 2020 and 9 May 2023, a total of 3038 participants were assessed at baseline (median 9 months after infection) and 83% responded to invitations for follow-up (median 26 months after infection). At baseline, 21% (95% confidence interval (CI) [20%, 23%]) had fatigue and 23% (95% CI [22%, 25%]) had cognitive deficits according to cutoff scores on the FACIT-Fatigue or MoCA. Participants with clinically relevant fatigue (at baseline) showed significant improvement in fatigue scores at follow-up (Hedges' g [95% CI] = 0.73 [0.60, 0.87]) and 46% (95% CI [41%, 50%]) had recovered from fatigue. Participants with cognitive deficits showed a significant improvement in cognitive scores (g [95% CI] = 1.12 [0.90, 1.33]) and 57% (95% CI [50%, 64%]) had recovered from cognitive deficits. Patients with fatigue exhibiting a higher depressive symptom burden and/or headache at baseline were significantly less likely to recover. Significant risk factors for cognitive non-recovery were male sex, older age and <12 years of school education. Importantly, SARS-CoV-2 reinfection had no significant impact on recovery from fatigue or cognitive deficits. Interpretation: Fatigue and cognitive deficits are common sequelae after SARS-CoV-2 infection. These syndromes improved over time and about half of the patients recovered within two years. The identified risk factors for non-recovery from fatigue and cognitive deficits could play an important role in shaping targeted strategies for treatment and prevention. Funding: Funded by the German Federal Ministry of Education and Research (BMBF; grant number 01KX2121) and German Research Foundation (DFG) Excellence Cluster "Position Medicine in Information".

Depression and fatigue six months post-COVID-19 disease are associated with overlapping symptom constellations: A prospective, multi-center, population-based cohort study.

BACKGROUND: Depression and fatigue are commonly observed sequelae following viral diseases such as COVID-19. Identifying symptom constellations that differentially classify post-COVID depression and fatigue may be helpful to individualize treatment strategies. Here, we investigated whether self-reported post-COVID depression and post-COVID fatigue are associated with the same or different symptom constellations. METHODS: To address this question, we used data from COVIDOM, a population-based cohort study conducted as part of the NAPKON-POP platform. Data were collected in three different German regions (Kiel, Berlin, Würzburg). We analyzed data from >2000 individuals at least six months past a PCR-confirmed COVID-19 disease, using elastic net regression and cluster analysis. The regression model was developed in the Kiel data set, and externally validated using data sets from Berlin and Würzburg. RESULTS: Our results revealed that post-COVID depression and fatigue are associated with overlapping symptom constellations consisting of difficulties with daily activities, perceived health-related quality of life, chronic exhaustion, unrestful sleep, and impaired concentration. Confirming the overlap in symptom constellations, a follow-up cluster analysis could categorize individuals as scoring high or low on depression and fatigue but could not differentiate between both dimensions. LIMITATIONS: The data presented are cross-sectional, consisting primarily of self-reported questionnaire or medical records rather than biometric data. CONCLUSIONS: In summary, our results suggest a strong link between post-COVID depression and fatigue, highlighting the need for integrative treatment approaches.

Differences in Anthropometric Measures Based on Sex, Age, and Health Status: Findings From the German National Cohort (NAKO).

BACKGROUND: Obesity is a worldwide health problem. We conducted detailed analyses of anthropometric measures in a comprehensive, population-based, current cohort in Germany. METHODS: In the German National Cohort (NAKO), we analyzed cross-sectional data on body-mass index (BMI), waist and hip circumference, subcutaneous (SAT) and visceral adipose tissue (VAT) as measured by ultrasound, and body fat percentage. The data were stratified by sex, age, and self-reported physicians' diagnoses of cardiovascular diseases (CVD), metabolic diseases (MetD), cardiometabolic diseases (CMD), and cancer. RESULTS: Data were available from 204 751 participants (age, 49.9 ± 12.8 years; 50.5% women). Body size measures generally increased with age. Men had a higher BMI, larger waist circumference, and more VAT than women, while women had a larger hip circumference, more SAT, and a higher body fat percentage than men. For example, the mean BMI of participants over age 60 was 28.3 kg/m2 in men and 27.6 kg/m2 in women. CVD, MetD, and CMD were associated with higher anthropometric values, while cancer was not. For example, the mean BMI was 25.3 kg/m2 in healthy women, 29.4 kg/m2 in women with CMD, and 25.4 kg/m2 in women with cancer. CONCLUSION: Obesity is widespread in Germany, with notable differences between the sexes in anthropometric values. Obesity was more common in older participants and those with chronic diseases other than cancer. Elevated values were especially common in multimorbid individuals.

Changes in physical activity and sedentary behavior during the first COVID-19 pandemic- restrictions in Germany: a nationwide survey : Running head: physical activity during the COVID-19 restrictions.

BACKGROUND: The COVID-19 pandemic restrictions posed challenges to maintaining healthy lifestyles and physical well-being. During the first mobility restrictions from March to mid-July 2020, the German population was advised to stay home, except for work, exercise, and essential shopping. Our objective was to comprehensively assess the impact of these restrictions on changes in physical activity and sedentary behavior to identify the most affected groups. METHODS: Between April 30, 2020, and May 12, 2020, we distributed a COVID-19-specific questionnaire to participants of the German National Cohort (NAKO). This questionnaire gathered information about participants' physical activity and sedentary behavior currently compared to the time before the restrictions. We integrated this new data with existing information on anxiety, depressive symptoms, and physical activity. The analyses focused on sociodemographic factors, social relationships, physical health, and working conditions. RESULTS: Out of 152,421 respondents, a significant proportion reported altered physical activity and sedentary behavioral patterns due to COVID-19 restrictions. Over a third of the participants initially meeting the WHO's physical activity recommendation could no longer meet the guidelines during the restrictions. Participants reported substantial declines in sports activities (mean change (M) = -0.38; 95% CI: -.390; -.378; range from -2 to + 2) and reduced active transportation (M = -0.12; 95% CI: -.126; -.117). However, they also increased recreational physical activities (M = 0.12; 95% CI: .117; .126) while engaging in more sedentary behavior (M = 0.24; 95% CI: .240; .247) compared to pre-restriction levels. Multivariable linear and log-binomial regression models indicated that younger adults were more affected by the restrictions than older adults. The shift to remote work, self-rated health, and depressive symptoms were the factors most strongly associated with changes in all physical activity domains, including sedentary behavior, and the likelihood to continue following the physical activity guidelines. CONCLUSIONS: Mobility patterns shifted towards inactivity or low-intensity activities during the nationwide restrictions in the spring of 2020, potentially leading to considerable and lasting health risks.

Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study.

In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73-100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, 'structured noise maximum' was the strongest predictor for the technologists' choice, followed by 'N/2 ghosting average'. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists' choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers.

Associations of Migration, Socioeconomic Position and Social Relations With Depressive Symptoms - Analyses of the German National Cohort Baseline Data.

Objectives: We analyze whether the prevalence of depressive symptoms differs among various migrant and non-migrant populations in Germany and to what extent these differences can be attributed to socioeconomic position (SEP) and social relations. Methods: The German National Cohort health study (NAKO) is a prospective multicenter cohort study (N = 204,878). Migration background (assessed based on citizenship and country of birth of both participant and parents) was used as independent variable, age, sex, Social Network Index, the availability of emotional support, SEP (relative income position and educational status) and employment status were introduced as covariates and depressive symptoms (PHQ-9) as dependent variable in logistic regression models. Results: Increased odds ratios of depressive symptoms were found in all migrant subgroups compared to non-migrants and varied regarding regions of origins. Elevated odds ratios decreased when SEP and social relations were included. Attenuations varied across migrant subgroups. Conclusion: The gap in depressive symptoms can partly be attributed to SEP and social relations, with variations between migrant subgroups. The integration paradox is likely to contribute to the explanation of the results. Future studies need to consider heterogeneity among migrant subgroups whenever possible.

90K/LGALS3BP expression is upregulated in COVID-19 but may not restrict SARS-CoV-2 infection.

Glycoprotein 90K, encoded by the interferon-stimulated gene LGALS3BP, displays broad antiviral activity. It reduces HIV-1 infectivity by interfering with Env maturation and virion incorporation, and increases survival of Influenza A virus-infected mice via antiviral innate immune signaling. Its antiviral potential in SARS-CoV-2 infection remains largely unknown. Here, we analyzed the expression of 90K/LGALS3BP in 44 hospitalized COVID-19 patients at multiple levels. We quantified 90K protein concentrations in serum and PBMCs as well as LGALS3BP mRNA levels. Complementary, we analyzed two single cell RNA-sequencing datasets for expression of LGALS3BP in respiratory specimens and PBMCs from COVID-19 patients. Finally, we analyzed the potential of 90K to interfere with SARS-CoV-2 infection of HEK293T/ACE2, Calu-3 and Caco-2 cells using authentic virus. 90K protein serum concentrations were significantly elevated in COVID-19 patients compared to uninfected sex- and age-matched controls. Furthermore, PBMC-associated concentrations of 90K protein were overall reduced by SARS-CoV-2 infection in vivo, suggesting enhanced secretion into the extracellular space. Mining of published PBMC scRNA-seq datasets uncovered monocyte-specific induction of LGALS3BP mRNA expression in COVID-19 patients. In functional assays, neither 90K overexpression in susceptible cell lines nor exogenous addition of purified 90K consistently inhibited SARS-CoV-2 infection. Our data suggests that 90K/LGALS3BP contributes to the global type I IFN response during SARS-CoV-2 infection in vivo without displaying detectable antiviral properties in vitro.

Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort.

This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)-based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of >0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index-related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies.

Persistent symptoms and risk factors predicting prolonged time to symptom-free after SARS­CoV­2 infection: an analysis of the baseline examination of the German COVIDOM/NAPKON-POP cohort.

PURPOSE: We aimed to assess symptoms in patients after SARS-CoV-2 infection and to identify factors predicting prolonged time to symptom-free. METHODS: COVIDOM/NAPKON-POP is a population-based prospective cohort of adults whose first on-site visits were scheduled ≥ 6 months after a positive SARS-CoV-2 PCR test. Retrospective data including self-reported symptoms and time to symptom-free were collected during the survey before a site visit. In the survival analyses, being symptom-free served as the event and time to be symptom-free as the time variable. Data were visualized with Kaplan-Meier curves, differences were tested with log-rank tests. A stratified Cox proportional hazard model was used to estimate adjusted hazard ratios (aHRs) of predictors, with aHR < 1 indicating a longer time to symptom-free. RESULTS: Of 1175 symptomatic participants included in the present analysis, 636 (54.1%) reported persistent symptoms after 280 days (SD 68) post infection. 25% of participants were free from symptoms after 18 days [quartiles: 14, 21]. Factors associated with prolonged time to symptom-free were age 49-59 years compared to < 49 years (aHR 0.70, 95% CI 0.56-0.87), female sex (aHR 0.78, 95% CI 0.65-0.93), lower educational level (aHR 0.77, 95% CI 0.64-0.93), living with a partner (aHR 0.81, 95% CI 0.66-0.99), low resilience (aHR 0.65, 95% CI 0.47-0.90), steroid treatment (aHR 0.22, 95% CI 0.05-0.90) and no medication (aHR 0.74, 95% CI 0.62-0.89) during acute infection. CONCLUSION: In the studied population, COVID-19 symptoms had resolved in one-quarter of participants within 18 days, and in 34.5% within 28 days. Over half of the participants reported COVID-19-related symptoms 9 months after infection. Symptom persistence was predominantly determined by participant's characteristics that are difficult to modify.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.

BACKGROUND: As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. OBJECTIVES: The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. METHODS: All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. RESULTS: Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. CONCLUSION: An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

Grip strength values and cut-off points based on over 200,000 adults of the German National Cohort - a comparison to the EWGSOP2 cut-off points.

BACKGROUND: The European Working Group on Sarcopenia in Older People (EWGSOP) updated in 2018 the cut-off points for low grip strength to assess sarcopenia based on pooled data from 12 British studies. OBJECTIVE: Comparison of the EWGSOP2 cut-off points for low grip strength to those derived from a large German sample. METHODS: We assessed the grip strength distribution across age and derived low grip strength cut-off points for men and women (peak mean -2.5 × SD) based on 200,389 German National Cohort (NAKO) participants aged 19-75 years. In 1,012 Cooperative Health Research in the Region of Augsburg (KORA)-Age participants aged 65-93 years, we calculated the age-standardised prevalence of low grip strength and time-dependent sensitivity and specificity for all-cause mortality. RESULTS: Grip strength increased in the third and fourth decade of life and declined afterwards. Calculated cut-off points for low grip strength were 29 kg for men and 18 kg for women. In KORA-Age, the age-standardised prevalence of low grip strength was 1.5× higher for NAKO-derived (17.7%) compared to EWGSOP2 (11.7%) cut-off points. NAKO-derived cut-off points yielded a higher sensitivity and lower specificity for all-cause mortality. CONCLUSIONS: Cut-off points for low grip strength from German population-based data were 2 kg higher than the EWGSOP2 cut-off points. Higher cut-off points increase the sensitivity, thereby suggesting an intervention for more patients at risk, while other individuals might receive additional diagnostics/treatment without the urgent need. Research on the effectiveness of intervention in patients with low grip strength defined by different cut-off points is needed.

Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model.

Aim: To use a Delphi-panel-based assessment of the effectiveness of different non-pharmaceutical interventions (NPI) in order to retrospectively approximate and to prospectively predict the SARS-CoV-2 pandemic progression via a SEIR model (susceptible, exposed, infectious, removed). Methods: We applied an evidence-educated Delphi-panel approach to elicit the impact of NPIs on the SARS-CoV-2 transmission rate R0 in Germany. Effectiveness was defined as the product of efficacy and compliance. A discrete, deterministic SEIR model with time step of 1 day, a latency period of 1.8 days, duration of infectiousness of 5 days, and a share of the total population of 15% assumed to be protected by immunity was developed in order to estimate the impact of selected NPI measures on the course of the pandemic. The model was populated with the Delphi-panel results and varied in sensitivity analyses. Results: Efficacy and compliance estimates for the three most effective NPIs were as follows: test and isolate 49% (efficacy)/78% (compliance), keeping distance 42%/74%, personal protection masks (cloth masks or other face masks) 33%/79%. Applying all NPI effectiveness estimates to the SEIR model resulted in a valid replication of reported occurrence of the German SARS-CoV-2 pandemic. A combination of four NPIs at consented compliance rates might curb the CoViD-19 pandemic. Conclusion: Employing an evidence-educated Delphi-panel approach can support SARS-CoV-2 modelling. Future curbing scenarios require a combination of NPIs. A Delphi-panel-based NPI assessment and modelling might support public health policy decision making by informing sequence and number of needed public health measures. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-021-01566-2.

Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients.

Anonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks. However, the degree of protection must be balanced against the impact on statistical properties. We studied an extreme case of this trade-off: the statistical validity of an open medical dataset based on the German National Pandemic Cohort Network (NAPKON), which was prepared for publication using a strong anonymization procedure. Descriptive statistics and results of regression analyses were compared before and after anonymization of multiple variants of the original dataset. Despite significant differences in value distributions, the statistical bias was found to be small in all cases. In the regression analyses, the median absolute deviations of the estimated adjusted odds ratios for different sample sizes ranged from 0.01 [minimum = 0, maximum = 0.58] to 0.52 [minimum = 0.25, maximum = 0.91]. Disproportionate impact on the statistical properties of data is a common argument against the use of anonymization. Our analysis demonstrates that anonymization can actually preserve validity of statistical results in relatively low-dimensional data.

Fatigue and cognitive impairment after COVID-19: A prospective multicentre study.

Background: Reliable estimates of frequency, severity and associated factors of both fatigue and cognitive impairment after COVID-19 are needed. Also, it is not clear whether the two are distinct sequelae of COVID-19 or part of the same syndrome." Methods: In this prospective multicentre study, frequency of post-COVID fatigue and cognitive impairment were assessed in n = 969 patients (535 [55%] female) ≥6 months after SARS-CoV-2 infection with the FACIT-Fatigue scale (cut-off ≤30) and Montreal Cognitive Assessment (≤25 mild, ≤17 moderate impairment) between November 15, 2020 and September 29, 2021 at University Medical Center Schleswig-Holstein, Campus Kiel and University Hospital Würzburg in Germany. 969 matched non-COVID controls were drawn from a pre-pandemic, randomised, Germany-wide population survey which also included the FACIT-Fatigue scale. Associated sociodemographic, comorbid, clinical, psychosocial factors and laboratory markers were identified with univariate and multivariable linear regression models. Findings: On average 9 months after infection, 19% of patients had clinically relevant fatigue, compared to 8% of matched non-COVID controls (p < 0.001). Factors associated with fatigue were female gender, younger age, history of depression and the number of acute COVID symptoms. Among acute COVID symptoms, altered consciousness, dizziness and myalgia were most strongly associated with long-term fatigue. Moreover, 26% of patients had mild and 1% had moderate cognitive impairment. Factors associated with cognitive impairment were older age, male gender, shorter education and a history of neuropsychiatric disease. There was no significant correlation between fatigue and cognitive impairment and only 5% of patients suffered from both conditions. Interpretation: Fatigue and cognitive impairment are two common, but distinct sequelae of COVID-19 with potentially separate pathophysiological pathways. Funding: German Federal Ministry of Education and Research (BMBF).

The bias from heaping on risk estimation: effect of age at diagnosis of hypertension on risk of subsequent cardiovascular comorbidities.

PURPOSE: To investigate (1) the bias in effect estimation due to heaping or digit preference, (2) the association between age at hypertension diagnosis and risk of cardiovascular comorbidities, and (3) the influence of heaping on risk estimates. METHODS: We performed a simulation study with various scenarios, binary outcome, and normal or lognormal distributed covariables. We calculated mean logistic coefficients under the original and heaped data and their relative deviation. The association of age at hypertension diagnosis and risk of ≥1 cardiovascular comorbidity was investigated using logistic regression among 50,858 participants in the NAKO Gesundheitsstudie (German National Cohort) who reported such diagnosis. We assessed the proportion of heaped observations and to what extent heaping may have influenced risk estimates. RESULTS: Based on the simulation study and assuming 50% of observations in the variable of interest to be heaped, relative bias was <6%. In NAKO, a 5-year younger age at hypertension diagnosis was associated with a 15% increased risk of having ≥1 cardiovascular comorbidity. Observed heaping in age at hypertension diagnosis was 12.6%, and bias of the risk estimate was 0.14%. CONCLUSIONS: Bias in effect estimation due to heaping is low in most common scenarios. Younger age at hypertension diagnosis is associated with a higher risk of cardiovascular comorbidities.

Severity, predictors and clinical correlates of Post-COVID syndrome (PCS) in Germany: A prospective, multi-centre, population-based cohort study.

Background: Post-COVID syndrome (PCS) is an important sequela of COVID-19, characterised by symptom persistence for >3 months, post-acute symptom development, and worsening of pre-existing comorbidities. The causes and public health impact of PCS are still unclear, not least for the lack of efficient means to assess the presence and severity of PCS. Methods: COVIDOM is a population-based cohort study of polymerase chain reaction (PCR) confirmed cases of SARS-CoV-2 infection, recruited through public health authorities in three German regions (Kiel, Berlin, Würzburg) between November 15, 2020 and September 29, 2021. Main inclusion criteria were (i) a PCR confirmed SARS-CoV-2 infection and (ii) a period of at least 6 months between the infection and the visit to the COVIDOM study site. Other inclusion criteria were written informed consent and age ≥18 years. Key exclusion criterion was an acute reinfection with SARS-CoV-2. Study site visits included standardised interviews, in-depth examination, and biomaterial procurement. In sub-cohort Kiel-I, a PCS (severity) score was developed based upon 12 long-term symptom complexes. Two validation sub-cohorts (Würzburg/Berlin, Kiel-II) were used for PCS score replication and identification of clinically meaningful predictors. This study is registered at clinicaltrials.gov (NCT04679584) and at the German Registry for Clinical Studies (DRKS, DRKS00023742). Findings: In Kiel-I (n = 667, 57% women), 90% of participants had received outpatient treatment for acute COVID-19. Neurological ailments (61·5%), fatigue (57·1%), and sleep disturbance (57·0%) were the most frequent persisting symptoms at 6-12 months after infection. Across sub-cohorts (Würzburg/Berlin, n = 316, 52% women; Kiel-II, n = 459, 56% women), higher PCS scores were associated with lower health-related quality of life (EQ-5D-5L-VAS/-index: r = -0·54/ -0·56, all p < 0·0001). Severe, moderate, and mild/no PCS according to the individual participant's PCS score occurred in 18·8%, 48·2%, and 32·9%, respectively, of the Kiel-I sub-cohort. In both validation sub-cohorts, statistically significant predictors of the PCS score included the intensity of acute phase symptoms and the level of personal resilience. Interpretation: PCS severity can be quantified by an easy-to-use symptom-based score reflecting acute phase disease burden and general psychological predisposition. The PCS score thus holds promise to facilitate the clinical diagnosis of PCS, scientific studies of its natural course, and the development of therapeutic interventions. Funding: The COVIDOM study is funded by the Network University Medicine (NUM) as part of the National Pandemic Cohort Network (NAPKON).

Statistical Analysis in the German National Cohort (NAKO) - Specific Aspects and General Recommendations.

The German National Cohort (NAKO) is an ongoing, prospective multicenter cohort study, which started recruitment in 2014 and includes more than 205,000 women and men aged 19-74 years. The study data will be available to the global research community for analyses. Although the ultimate decision about the analytic methods will be made by the respective investigator, in this paper we provide the basis for a harmonized approach to the statistical analyses in the NAKO. We discuss specific aspects of the study (e.g., data collection, weighting to account for the sampling design), but also give general recommendations which may apply to other large cohort studies as well.