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Background Reliability of measurements is a prerequisite of medical research. For nominal data, Fleiss’ kappa (in the following labelled as Fleiss’ K) and Krippendorff’s alpha provide the highest flexibility of the available reliability measures with respect to number of raters and categories. Our aim was to investigate which measures and which confidence intervals provide the best statistical properties for the assessment of inter-rater reliability in different situations. Methods We performed a large simulation study to investigate the precision of the estimates for Fleiss’ K and Krippendorff’s alpha and to determine the empirical coverage probability of the corresponding confidence intervals (asymptotic for Fleiss’ K and bootstrap for both measures). Furthermore, we compared measures and confidence intervals in a real world case study. Results Point estimates of Fleiss’ K and Krippendorff’s alpha did not differ from each other in all scenarios. In the case of missing data (completely at random), Krippendorff’s alpha provided stable estimates, while the complete case analysis approach for Fleiss’ K led to biased estimates. For shifted null hypotheses, the coverage probability of the asymptotic confidence interval for Fleiss’ K was low, while the bootstrap confidence intervals for both measures provided a coverage probability close to the theoretical one. Conclusions Fleiss’ K and Krippendorff’s alpha with bootstrap confidence intervals are equally suitable for the analysis of reliability of complete nominal data. The asymptotic confidence interval for Fleiss’ K should not be used. In the case of missing data or data or higher than nominal order, Krippendorff’s alpha is recommended. Together with this article, we provide an R-script for calculating Fleiss’ K and Krippendorff’s alpha and their corresponding bootstrap confidence intervals.

Background Projection of future cancer incidence is an important task in cancer epidemiology. The results are of interest also for biomedical research and public health policy. Age-Period-Cohort (APC) models, usually based on long-term cancer registry data (> 20 yrs), are established for such projections. In many countries (including Germany), however, nationwide long-term data are not yet available. General guidance on statistical approaches for projections using rather short-term data is challenging and software to enable researchers to easily compare approaches is lacking. Methods To enable a comparative analysis of the performance of statistical approaches to cancer incidence projection, we developed an R package (incAnalysis), supporting in particular Bayesian models fitted by Integrated Nested Laplace Approximations (INLA). Its use is demonstrated by an extensive empirical evaluation of operating characteristics (bias, coverage and precision) of potentially applicable models differing by complexity. Observed long-term data from three cancer registries (SEER-9, NORDCAN, Saarland) was used for benchmarking. Results Overall, coverage was high (mostly > 90%) for Bayesian APC models (BAPC), whereas less complex models showed differences in coverage dependent on projection-period. Intercept-only models yielded values below 20% for coverage. Bias increased and precision decreased for longer projection periods (> 15 years) for all except intercept-only models. Precision was lowest for complex models such as BAPC models, generalized additive models with multivariate smoothers and generalized linear models with age x period interaction effects. Conclusion The incAnalysis R package allows a straightforward comparison of cancer incidence rate projection approaches. Further detailed and targeted investigations into model performance in addition to the presented empirical results are recommended to derive guidance on appropriate statistical projection methods in a given setting.

Studies focusing on taxonomic compositions of the gut microbiota are plentiful, whereas its functional capabilities are still poorly understood. Specific key functions deserve detailed investigations, as they regulate microbiota-host interactions and promote host health and disease. The production of butyrate is among the top targets since depletion of this microbe-derived metabolite is linked to several emerging noncommunicable diseases and was shown to facilitate establishment of enteric pathogens by disrupting colonization resistance. In this study, we established a workflow to investigate in detail the composition of the polyphyletic butyrate-producing community from omics data extracting its biochemical and taxonomic diversity. By combining information from various publicly available data sets, we identified universal ecological key features of this functional group and shed light on its role in health and disease. Our results will assist the development of precision medicine to combat functional dysbiosis. Given the key role of butyrate for host health, understanding the ecology of intestinal butyrate-producing communities is a top priority for gut microbiota research. To this end, we performed a pooled analysis on 2,387 metagenomic/transcriptomic samples from 15 publicly available data sets that originated from three continents and encompassed eight diseases as well as specific interventions. For analyses, a gene catalogue was constructed from gene-targeted assemblies of all genes from butyrate synthesis pathways of all samples and from an updated reference database derived from genome screenings. We demonstrate that butyrate producers establish themselves within the first year of life and display high abundances (>20% of total bacterial community) in adults regardless of origin. Various bacteria form this functional group, exhibiting a biochemical diversity including different pathways and terminal enzymes, where one carbohydrate-fueled pathway was dominant with butyryl coenzyme A (CoA):acetate CoA transferase as the main terminal enzyme. Subjects displayed a high richness of butyrate producers, and 17 taxa, primarily members of the Lachnospiraceae and Ruminococcaceae along with some Bacteroidetes , were detected in >70% of individuals, encompassing ~85% of the total butyrate-producing potential. Most of these key taxa were also found to express genes for butyrate formation, indicating that butyrate producers occupy various niches in the gut ecosystem, concurrently synthesizing that compound. Furthermore, results from longitudinal analyses propose that diversity supports functional stability during ordinary life disturbances and during interventions such as antibiotic treatment. A reduction of the butyrate-producing potential along with community alterations was detected in various diseases, where patients suffering from cardiometabolic disorders were particularly affected. IMPORTANCE Studies focusing on taxonomic compositions of the gut microbiota are plentiful, whereas its functional capabilities are still poorly understood. Specific key functions deserve detailed investigations, as they regulate microbiota-host interactions and promote host health and disease. The production of butyrate is among the top targets since depletion of this microbe-derived metabolite is linked to several emerging noncommunicable diseases and was shown to facilitate establishment of enteric pathogens by disrupting colonization resistance. In this study, we established a workflow to investigate in detail the composition of the polyphyletic butyrate-producing community from omics data extracting its biochemical and taxonomic diversity. By combining information from various publicly available data sets, we identified universal ecological key features of this functional group and shed light on its role in health and disease. Our results will assist the development of precision medicine to combat functional dysbiosis.

Objective To revise the German guidelines and recommendations for ensuring Good Epidemiological Practice (GEP) that were developed in 1999 by the German Society for Epidemiology (DGEpi), evaluated and revised in 2004, supplemented in 2008, and updated in 2014. Methods The executive board of the DGEpi tasked the third revision of the GEP. The revision was arrived as a result of a consensus-building process by a working group of the DGEpi in collaboration with other working groups of the DGEpi and with the German Association for Medical Informatics, Biometry and Epidemiology , the German Society of Social Medicine and Prevention (DGSMP), the German Region of the International Biometric Society (IBS-DR), the German Technology, Methods and Infrastructure for Networked Medical Research (TMF), and the German Network for Health Services Research (DNVF). The GEP also refers to related German Good Practice documents (e.g. Health Reporting, Cartographical Practice in the Healthcare System, Secondary Data Analysis). Results The working group modified the 11 guidelines (after revision: 1 ethics, 2 research question, 3 study protocol and manual of operations, 4 data protection, 5 sample banks, 6 quality assurance, 7 data storage and documentation, 8 analysis of epidemiological data, 9 contractual framework, 10 interpretation and scientific publication, 11 communication and public health) and modified and supplemented the related recommendations. All participating scientific professional associations adopted the revised GEP. Conclusions The revised GEP are addressed to everyone involved in the planning, preparation, execution, analysis, and evaluation of epidemiological research, as well as research institutes and funding bodies.

AimsObservational evidence suggests that physical activity (PA) is inversely and sedentarism positively related with cardiovascular disease risk. We performed a two-sample Mendelian randomization (MR) analysis to examine whether genetically predicted PA and sedentary behavior are related to coronary artery disease, myocardial infarction, and ischemic stroke.Methods and resultsWe used single nucleotide polymorphisms (SNPs) associated with self-reported moderate to vigorous PA (n = 17), accelerometer based PA (n = 7) and accelerometer fraction of accelerations > 425 milli-gravities (n = 7) as well as sedentary behavior (n = 6) in the UK Biobank as instrumental variables in a two sample MR approach to assess whether these exposures are related to coronary artery disease and myocardial infarction in the CARDIoGRAMplusC4D genome-wide association study (GWAS) or ischemic stroke in the MEGASTROKE GWAS. The study population included 42,096 cases of coronary artery disease (99,121 controls), 27,509 cases of myocardial infarction (99,121 controls), and 34,217 cases of ischemic stroke (404,630 controls). We found no associations between genetically predicted self-reported moderate to vigorous PA, accelerometer-based PA or accelerometer fraction of accelerations > 425 milli-gravities as well as sedentary behavior with coronary artery disease, myocardial infarction, and ischemic stroke.ConclusionsThese results do not support a causal relationship between PA and sedentary behavior with risk of coronary artery disease, myocardial infarction, and ischemic stroke. Hence, previous observational studies may have been biased.Graphic abstract

Background It is becoming evident that certain features of human microbiota, encoded by distinct autochthonous taxa, promote disease. As a result, borders between the so-called opportunistic pathogens, pathobionts, and commensals are increasingly blurred, and specific targets for manipulating microbiota to improve host health are becoming elusive. Results In this study, we focus on the functions of host bacterial communities that have the potential to cause disease, proposing the term “pathogenic function (pathofunction)”. The concept is presented via three distinct examples, namely, the formation of (i) trimethylamine, (ii) secondary bile acids, and (iii) hydrogen sulfide, which represent metabolites of the gut microbiota linked to the development of non-communicable diseases. Using publicly available metagenomic and metatranscriptomic data ( n  = 2975), we quantified those pathofunctions in health and disease and exposed the key players. Pathofunctions were ubiquitously present with increased abundances in patient groups. Overall, the three pathofunctions were detected at low mean concentrations (< 1% of total bacteria carried respective genes) and encompassed various taxa, including uncultured members. Conclusions We outline how this function-centric approach, where all members of a community exhibiting a particular pathofunction are redundant, can contribute to risk assessment and the development of precision treatment directing gut microbiota to increase host health.

Background Neurofilament light chain (NfL) is a cytoskeletal protein component whose release into blood is indicative of neuronal damage. Tau is a microtubule-associated protein in neurons and strongly associated with overall brain degeneration. NfL and tau levels are associated with mortality in different neurological diseases, but studies in the general population are missing. We investigated whether NfL and tau serum levels could serve as prognostic markers for overall mortality in elderly individuals without pre-defined neurological conditions. Further, we investigated the cross-sectional associations between NfL, tau, neuropsychological functioning, and brain structures. Methods In 1997, 385 inhabitants of Augsburg who were aged 65 years and older were included in the Memory and Morbidity in Augsburg Elderly (MEMO) study. They participated in a face-to-face medical interview including neuropsychological tests and magnetic resonance imaging (MRI) of the brain. NfL and tau were measured from non-fasting blood samples using highly sensitive single molecule array assays. To assess the prognostic accuracy of the biomarkers, concordance statistics based on the predicted 5-year survival probabilities were calculated for different Cox regression models. Associations between the biomarkers and the neuropsychological test scores or brain structures were investigated using linear or logistic regression. Results NfL (HR 1.27, 95% CI [1.14–1.42]) and tau (1.20 [1.07–1.35]) serum levels were independently associated with all-cause mortality. NfL, but not tau, increased the prognostic accuracy when added to a model containing sociodemographic characteristics (concordance statistic 0.684 [0.612–0.755] vs. 0.663 [0.593–0.733]), but not when added to a model containing sociodemographic characteristics and brain atrophy or neuropsychological test scores. NfL serum levels were cross-sectionally associated with neuropsychological test scores and brain structures. Conclusions The association between NfL serum levels and brain atrophy and neuropsychological performance in individuals without overt neurological disease is similar to that seen in patients with neurodegenerative diseases. These findings support the concept of a continuum of physiological aging and incipient, subclinical pathology, and manifest disease. NfL, but not tau, serum levels might serve as a prognostic marker for all-cause mortality if no other clinical information is available.

Real-time quaking-induced conversion (RT-QuIC) allows the amplification of miniscule amounts of scrapie prion protein (PrP Sc ). Recent studies applied the RT-QuIC methodology to cerebrospinal fluid (CSF) for diagnosing human prion diseases. However, to date, there has not been a formal multi-centre assessment of the reproducibility, validity and stability of RT-QuIC in this context, an indispensable step for establishment as a diagnostic test in clinical practice. In the present study, we analysed CSF from 110 prion disease patients and 400 control patients using the RT-QuIC method under various conditions. In addition, “blinded” ring trials between different participating sites were performed to estimate reproducibility. Using the previously established cut-off of 10,000 relative fluorescence units (rfu), we obtained a sensitivity of 85 % and a specificity of 99 %. The multi-centre inter-laboratory reproducibility of RT-QuIC revealed a Fleiss’ kappa value of 0.83 (95 % CI: 0.40–1.00) indicating an almost perfect agreement. Moreover, we investigated the impact of short-term CSF storage at different temperatures, long-term storage, repeated freezing and thawing cycles and the contamination of CSF with blood on the RT-QuIC seeding response. Our data indicated that the PrP Sc seed in CSF is stable to any type of storage condition but sensitive to contaminations with blood (>1250 erythrocytes/μL), which results in a false negative RT-QuIC response. Fresh blood-contaminated samples (3 days) can be rescued by removal of erythrocytes. The present study underlines the reproducibility and high stability of RT-QuIC across various CSF storage conditions with a remarkable sensitivity and specificity, suggesting RT-QuIC as an innovative and robust diagnostic method.

Background The effect of contact reduction measures on infectious disease transmission can only be assessed indirectly and with considerable delay. However, individual social contact data and population mobility data can offer near real-time proxy information. The aim of this study is to compare social contact data and population mobility data with respect to their ability to reflect transmission dynamics during the first wave of the SARS-CoV-2 pandemic in Germany. Methods We quantified the change in social contact patterns derived from self-reported contact survey data collected by the German COVIMOD study from 04/2020 to 06/2020 (compared to the pre-pandemic period from previous studies) and estimated the percentage mean reduction over time. We compared these results as well as the percentage mean reduction in population mobility data (corrected for pre-pandemic mobility) with and without the introduction of scaling factors and specific weights for different types of contacts and mobility to the relative reduction in transmission dynamics measured by changes in R values provided by the German Public Health Institute. Results We observed the largest reduction in social contacts (90%, compared to pre-pandemic data) in late April corresponding to the strictest contact reduction measures. Thereafter, the reduction in contacts dropped continuously to a minimum of 73% in late June. Relative reduction of infection dynamics derived from contact survey data underestimated the one based on reported R values in the time of strictest contact reduction measures but reflected it well thereafter. Relative reduction of infection dynamics derived from mobility data overestimated the one based on reported R values considerably throughout the study. After the introduction of a scaling factor, specific weights for different types of contacts and mobility reduced the mean absolute percentage error considerably; in all analyses, estimates based on contact data reflected measured R values better than those based on mobility. Conclusions Contact survey data reflected infection dynamics better than population mobility data, indicating that both data sources cover different dimensions of infection dynamics. The use of contact type-specific weights reduced the mean absolute percentage errors to less than 1%. Measuring the changes in mobility alone is not sufficient for understanding the changes in transmission dynamics triggered by public health measures.

Background Many European countries experienced outbreaks of mpox in 2022, and there was an mpox outbreak in 2023 in the Democratic Republic of Congo. There were many apparent differences between these outbreaks and previous outbreaks of mpox; the recent outbreaks were observed in men who have sex with men after sexual encounters at common events, whereas earlier outbreaks were observed in a wider population with no identifiable link to sexual contacts. These apparent differences meant that data from previous outbreaks could not reliably be used to parametrise infectious disease models during the 2022 and 2023 mpox outbreaks, and modelling efforts were hampered by uncertainty around key transmission and immunity parameters. Methods We developed a stochastic, discrete-time metapopulation model for mpox that allowed for sexual and non-sexual transmission and the implementation of non-pharmaceutical interventions, specifically contact tracing and pre- and post-exposure vaccinations. We calibrated the model to case data from Berlin and used Sobol sensitivity analysis to identify parameters that mpox transmission is especially sensitive to. We also briefly analysed the sensitivity of the effectiveness of non-pharmaceutical interventions to various efficacy parameters. Results We found that variance in the transmission probabilities due to both sexual and non-sexual transmission had a large effect on mpox transmission in the model, as did the level of immunity to mpox conferred by a previous smallpox vaccination. Furthermore, variance in the number of pre-exposure vaccinations offered was the dominant contributor to variance in mpox dynamics in men who have sex with men. If pre-exposure vaccinations were not available, both the accuracy and timeliness of contact tracing had a large impact on mpox transmission in the model. Conclusions Our results are valuable for guiding epidemiological studies for parameter ascertainment and identifying key factors for success of non-pharmaceutical interventions.