Pandemics at Work: Convergence of Epidemiology and Ethics

Like COVID-19, new infectious disease outbreaks emerge almost annually, and studies predict that this trend will continue due to a variety of factors, including an aging population, ease of travel, and globalization of the economy. In response to episodic public health crises, governments and organizations develop, implement, and enforce policies, procedures, protocols, and programs. The epidemiological triad is both a model of disease causation and fundamentally used to design and deploy such control measures. Here we adapt this model to the workplace setting and use the epidemiological triad to characterize the related ethical challenges in implementing the control measures employers face as a guide for a workplace intervention framework. Through this approach, our aim is to show how an integrated ethical framework, grounded in epidemiological principles, has important implications for how we categorize, understand, and resolve the difficult decisions that emerge in the workplace under pandemic conditions.

Evaluation of gene validity for CPVT and short QT syndrome in sudden arrhythmic death

Aims Catecholaminergic polymorphic ventricular tachycardia (CPVT) and short QT syndrome (SQTS) are inherited arrhythmogenic disorders that can cause sudden death. Numerous genes have been reported to cause these conditions, but evidence supporting these gene–disease relationships varies considerably. To ensure appropriate utilization of genetic information for CPVT and SQTS patients, we applied an evidence-based reappraisal of previously reported genes. Methods and results Three teams independently curated all published evidence for 11 CPVT and 9 SQTS implicated genes using the ClinGen gene curation framework. The results were reviewed by a Channelopathy Expert Panel who provided the final classifications. Seven genes had definitive to moderate evidence for disease causation in CPVT, with either autosomal dominant (RYR2, CALM1, CALM2, CALM3) or autosomal recessive (CASQ2, TRDN, TECRL) inheritance. Three of the four disputed genes for CPVT (KCNJ2, PKP2, SCN5A) were deemed by the Expert Panel to be reported for phenotypes that were not representative of CPVT, while reported variants in a fourth gene (ANK2) were too common in the population to be disease-causing. For SQTS, only one gene (KCNH2) was classified as definitive, with three others (KCNQ1, KCNJ2, SLC4A3) having strong to moderate evidence. The majority of genetic evidence for SQTS genes was derived from very few variants (five in KCNJ2, two in KCNH2, one in KCNQ1/SLC4A3). Conclusions Seven CPVT and four SQTS genes have valid evidence for disease causation and should be included in genetic testing panels. Additional genes associated with conditions that may mimic clinical features of CPVT/SQTS have potential utility for differential diagnosis.

Is Multiple System Atrophy a Prion-like Disorder?

Multiple system atrophy (MSA) is a rapidly progressive, fatal neurodegenerative disease of uncertain aetiology that belongs to the family of α-synucleinopathies. It clinically presents with parkinsonism, cerebellar, autonomic, and motor impairment in variable combinations. Pathological hallmarks are fibrillary α-synuclein (αSyn)-rich glial cytoplasmic inclusions (GCIs) mainly involving oligodendroglia and to a lesser extent neurons, inducing a multisystem neurodegeneration, glial activation, and widespread demyelinization. The neuronal αSyn pathology of MSA has molecular properties different from Lewy bodies in Parkinson’s disease (PD), both of which could serve as a pool of αSyn (prion) seeds that could initiate and drive the pathogenesis of synucleinopathies. The molecular cascade leading to the “prion-like” transfer of “strains” of aggregated αSyn contributing to the progression of the disease is poorly understood, while some presented evidence that MSA is a prion disease. However, this hypothesis is difficult to reconcile with postmortem analysis of human brains and the fact that MSA-like pathology was induced by intracerebral inoculation of human MSA brain homogenates only in homozygous mutant 53T mice, without production of disease-specific GCIs, or with replication of MSA prions in primary astrocyte cultures from transgenic mice expressing human αSyn. Whereas recent intrastriatal injection of Lewy body-derived or synthetic human αSyn fibrils induced PD-like pathology including neuronal αSyn aggregates in macaques, no such transmission of αSyn pathology in non-human primates by MSA brain lysate has been reported until now. Given the similarities between αSyn and prions, there is a considerable debate whether they should be referred to as “prions”, “prion-like”, “prionoids”, or something else. Here, the findings supporting the proposed nature of αSyn as a prion and its self-propagation through seeding as well as the transmissibility of neurodegenerative disorders are discussed. The proof of disease causation rests on the concordance of scientific evidence, none of which has provided convincing evidence for the classification of MSA as a prion disease or its human transmission until now.

894Molecular epidemiology: building causal evidence within highly dimensioned studies

Focus of Presentation This presentation provides a practical perspective on molecular epidemiologic analysis within a single high dimensional study to consider a causal question. Findings The work steps include special issues. Those include composite exposures and understanding of collinearity across predictors. Laboratory artefact needs to be minimised. Non-causal confounding is also important. This can occur when a factor is a determinant of outcome and the underlying association between exposure and the factor is non-causal. That is, the association arises due to chance, confounding or other bias rather than reflecting that exposure and the factor are causally related. The influence of laboratory processing features must be accounted for. This can be achieved by pre-processing measures to remove artefact or controlling for process factors. The work plan should allow the evaluation of alternative non-causal explanations for observed exposure-disease associations and strategies to obtain the highest level of causal inference possible within the study. Conclusions/Implications A systematic approach is required to work through a question a question set and obtain insights on not only the exposure-disease association but the multifactorial causal structure of the underlying data where possible. The appropriate inclusion of molecular findings will enhance the quest to better understand multifactorial disease causation in modern observational epidemiological studies. Key messages A systematic set of work steps is outlined to assess whether detected exposure-disease associations have a low and high likelihood of being attributed to non-causal and causal explanations respectively.

Understanding Holism in the light of principle underlying practice of Unani Medicine

The holistic Unani medicine is fundamentally different from the reductionist conventional medicine. It asserts the self-integration amongst its basic disciplines, without considering them underlying principles of Unani system of medicine cannot be understood. The diagnosis, selection of drugs, and plan of treatment is also overlooked. Unani scholars attribute health to the functions or actions of the body in a normal way. The constitution is considered as the result of need based on the amalgamation of Arkan (primordial essence). Umoor Tabiya (basic principles) interact at many levels and manifest into Kaifiyyat (Mizaj), Akhlat (Humour), Arwaah (Pneuma), Quwa (faculties), and Tabiyat (Physis) which need to be understood properly for effective management and diagnosis of disease in Unani medicine as well as its treatment. Ilmul Asbab is applied in the prevention of disease as well as in disease causation. In Unani medicine, there should be conformity in between, Asbab (causes), Alamaat (symptoms), and therapeutics. Therefore; the treatment strategy needs the knowledge of Ilmul Asbab. This paper will examine the basic relationship amongst disciplines i.e. Basic principles, diagnosis, and principles of treatment. It will attempt to illustrate the need for awareness of the basic principles of health and disease for a physician for effective management of disease which is what Unani medicine claims to be holistic.

Understanding interactions between risk factors, and assessing the utility of the additive and multiplicative models through simulations

Understanding the genetic background of complex diseases requires the expansion of studies beyond univariate associations. Therefore, it is important to use interaction assessments of risk factors in order to discover whether, and how genetic risk variants act together on disease development. The principle of interaction analysis is to explore the magnitude of the combined effect of risk factors on disease causation. In this study, we use simulations to investigate different scenarios of causation to show how the magnitude of the effect of two risk factors interact. We mainly focus on the two most commonly used interaction models, the additive and multiplicative risk scales, since there is often confusion regarding their use and interpretation. Our results show that the combined effect is multiplicative when two risk factors are involved in the same chain of events, an interaction called synergism. Synergism is often described as a deviation from additivity, which is a broader term. Our results also confirm that it is often relevant to estimate additive effect relationships, because they correspond to independent risk factors at low disease prevalence. Importantly, we evaluate the threshold of more than two required risk factors for disease causation, called the multifactorial threshold model. We found a simple mathematical relationship (square root) between the threshold and an additive-to-multiplicative linear effect scale (AMLES), where 0 corresponds to an additive effect and 1 to a multiplicative. We propose AMLES as a metric that could be used to test different effects relationships at the same time, given that it can simultaneously reveal additive, multiplicative and intermediate risk effects relationships. Finally, the utility of our simulation study was demonstrated using real data by analyzing and interpreting gene-gene interaction odds ratios from a rheumatoid arthritis case-control cohort.

Comprehensive Review of Cardiovascular Involvement in COVID-19

COVID-19 has emerged as one of the most devastating and clinically significant infectious diseases of the last decade. It has reached global pandemic status at an unprecedented pace and has placed significant demands on health care systems worldwide. Although COVID-19 primarily affects the lungs, epidemiologic reports have shown that the disease affects other vital organs of the body, including the heart, vasculature, kidneys, brain, and the hematopoietic system. Of importance is the emerging awareness of the effects of COVID-19 on the cardiovascular system. The current state of knowledge regarding cardiac involvement in COVID-19 is presented in this article, with particular focus on the cardiovascular manifestations and complications of COVID-19 infection. The mechanistic insights of disease causation and the relevant pathophysiology involved in COVID-19 as they affect the heart are explored and described. Relevant practice essentials and clinical management implications for patients with COVID-19 with a cardiac pathology are presented in light of recent evidence.

Gaussian graphical modeling of the serum exposome and metabolome reveals interactions between environmental chemicals and endogenous metabolites

Given the complex exposures from both exogenous and endogenous sources that an individual experiences during life, exposome-wide association studies that interrogate levels of small molecules in biospecimens have been proposed for discovering causes of chronic diseases. We conducted a study to explore associations between environmental chemicals and endogenous molecules using Gaussian graphical models (GGMs) of non-targeted metabolomics data measured in a cohort of California women firefighters and office workers. GGMs revealed many exposure-metabolite associations, including that exposures to mono-hydroxyisononyl phthalate, ethyl paraben and 4-ethylbenzoic acid were associated with metabolites involved in steroid hormone biosynthesis, and perfluoroalkyl substances were linked to bile acids—hormones that regulate cholesterol and glucose metabolism—and inflammatory signaling molecules. Some hypotheses generated from these findings were confirmed by analysis of data from the National Health and Nutrition Examination Survey. Taken together, our findings demonstrate a novel approach to discovering associations between chemical exposures and biological processes of potential relevance for disease causation.

The Perfect Storm Relationship Between Vaccination-induced Disorders and Illness Manifested by Post Covid-19 Long Haulers

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), the cause of coronavirus disease 2019 (Covid-19), has not only created an acute worldwide pandemic but also chronic adverse health effects in a substantial percentage of survivors. Hopefully, vaccines for Covid-19 will result in far fewer long haulers cases by reducing the severity of Covid-19 exposures. Acute and chronic vaccination-induced disorders attributable to Covid-19 immunizations need to be carefully assessed over intervals of several weeks because of the potential latency in mechanisms of disease causation that transcend anaphylaxis and allergic reactions.

Interpreting Results from Epidemiologic Studies

Epidemiology is the science of public health. The focus of this discussion is to present a brief overview of how epidemiology approaches questions of disease causation, including why it sometimes gets things wrong, and so to provide a framework for how we consume and use this type of research, particularly when it comes to patient care.