Causal Relationship Research Articles

Associations of schizophrenia with arrhythmic disorders and electrocardiogram traits: genetic exploration of population samples

Background An important contributor to the decreased life expectancy of individuals with schizophrenia is sudden cardiac death. Arrhythmic disorders may play an important role herein, but the nature of the relationship between schizophrenia and arrhythmia is unclear. Aims To assess shared genetic liability and potential causal effects between schizophrenia and arrhythmic disorders and electrocardiogram (ECG) traits. Method We leveraged summary-level data of large-scale genome-wide association studies of schizophrenia (53 386 cases, 77 258 controls), arrhythmic disorders (atrial fibrillation, 55 114 cases, 482 295 controls; Brugada syndrome, 2820 cases, 10 001 controls) and ECG traits (heart rate (variability), PR interval, QT interval, JT interval and QRS duration, n = 46 952–293 051). We examined shared genetic liability by assessing global and local genetic correlations and conducting functional annotation. Bidirectional causal relations between schizophrenia and arrhythmic disorders and ECG traits were explored using Mendelian randomisation. Results There was no evidence for global genetic correlation, except between schizophrenia and Brugada syndrome (rg = 0.14, 95% CIs = 0.06–0.22, P = 4.0E−04). In contrast, strong positive and negative local correlations between schizophrenia and all cardiac traits were found across the genome. In the most strongly associated regions, genes related to immune and viral response mechanisms were overrepresented. Mendelian randomisation indicated that liability to schizophrenia causally increases Brugada syndrome risk (beta = 0.14, CIs = 0.03–0.25, P = 0.009) and heart rate during activity (beta = 0.25, CIs = 0.05–0.45, P = 0.015). Conclusions Despite little evidence for global genetic correlation, specific genomic regions and biological pathways emerged that are important for both schizophrenia and arrhythmia. The putative causal effect of liability to schizophrenia on Brugada syndrome warrants increased cardiac monitoring and early medical intervention in people with schizophrenia.

Detecting, Interpreting and Modifying the Heterogeneous Causal Network in Multi‐Source Event Sequences

AbstractUncovering causal relations from event sequences to guide decision‐making has become an essential task across various domains. Unfortunately, this task remains a challenge because real‐world event sequences are usually collected from multiple sources. Most existing works are specifically designed for homogeneous causal analysis between events from a single source, without considering cross‐source causality. In this work, we propose a heterogeneous causal analysis algorithm to detect the heterogeneous causal network between high‐level events in multi‐source event sequences while preserving the causal semantic relationships between diverse data sources. Additionally, the flexibility of our algorithm allows to incorporate high‐level event similarity into learning model and provides a fuzzy modification mechanism. Based on the algorithm, we further propose a visual analytics framework that supports interpreting the causal network at three granularities and offers a multi‐granularity modification mechanism to incorporate user feedback efficiently. We evaluate the accuracy of our algorithm through an experimental study, illustrate the usefulness of our system through a case study, and demonstrate the efficiency of our modification mechanisms through a user study.

A Novel One-Sample Mendelian Randomization Approach for Count-Type Outcomes That Is Robust to Correlated and Uncorrelated Pleiotropic Effects.

We propose two novel one-sample Mendelian randomization (MR) approaches to causal inference from count-type health outcomes, tailored to both equidispersion and overdispersion conditions. Selecting valid single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) poses a key challenge for MR approaches, as it requires meeting the necessary IV assumptions. To bolster the proposed approaches by addressing violations of IV assumptions, we incorporate a process for removing invalid SNPs that violate the assumptions. In simulations, our proposed approaches demonstrate robustness to the violations, delivering valid estimates, and interpretable type-I errors and statistical power. This increases the practical applicability of the models. We applied the proposed approaches to evaluate the causal effect of fetal hemoglobin (HbF) on the vaso-occlusive crisis and acute chest syndrome (ACS) events in patients with sickle cell disease (SCD) and revealed the causal relation between HbF and ACS events in these patients. We also developed a user-friendly Shiny web application to facilitate researchers' exploration of causal relations.

Multilevel causal mechanisms in social entrepreneurship: the enabling role of social capital

ABSTRACT We take a new mechanism-based approach to explain how social entrepreneurship emerges from the interaction of multilevel elements, based on case study evidence from China. Informed by Coleman’s ‘boat model’ of social mechanisms, social capital theory and a critical realist ontology, we highlight three mechanisms – the sparking, manifesting and scaling mechanisms – which collectively generate the social entrepreneurship phenomenon. When enabled by social capital, these mechanisms explain the causal relations between the multilevel elements of social entrepreneurship: social needs, social entrepreneurial ideas and practice, market creation and social impact. This framework generates novel insights into the multilevel nature of social entrepreneurship, and the central role of social capital in enabling its underlying mechanisms.

Genome Instability Precedes Viral Integration in Human Papilloma Virus Transformed Tonsillar Keratinocytes.

Approximately 70% of oropharyngeal squamous carcinomas (OPSCC) are associated with human papillomavirus (HPV). While patients with HPV-positive tumors generally have better outcomes than those with HPV-negative tumors, a subset of HPV-positive patients do have poor outcomes. Our previous work suggested that tumors with integrated virus exhibit significantly greater genome wide genomic instability than those with only episomal viral genomes and patients with HPV+ OPSCC with episomal viral genomes had better outcomes. To explore the causal relation between viral integration and genomic instability, we have examined the time course of viral integration and genetic instability in tonsillar keratinocytes transformed with HPV16. HPV-infected human tonsil keratinocyte cell lines were continuously passaged and every fifth passage some cells were retained for genomic analysis. Whole genome sequencing and optical genomic mapping confirmed that virus integrated in five of six cell lines while remaining episomal in the sixth. In all lines genome instability occurred during early passages, but essentially ceased following viral integration but continued to occur later passages in the episomal line. To test tumorigenicity of the cell lines, cells were injected subcutaneously into the flanks of nude mice. A cell line with the integrated virus induced tumors following injection in the nude mouse while that with the episomal virus did not. Implications: Genomic instability in HPV OPSCC tumors is not the result of viral integration but likely promotes integration. Moreover, transformants with episomal virus appear to be less tumorigenic than those with integrated virus.

Do CEOs' Industry Tournament Incentives Affect Stock Liquidity?

ABSTRACTResearch Question/IssueWe examine the relationship between CEOs' industry tournament incentives (CITI) and stock liquidity in the United States. We also examine if the effect of CITI on stock liquidity varies depending on the information‐asymmetry and corporate governance mechanisms.Research Findings/InsightsWe find that firms with stronger CITI are associated with greater stock liquidity. Exploiting the enforceability of executive noncompetition agreements across the states in the United States as a quasi‐natural experiment, we find that firms headquartered in states that introduce these agreements on average experience lower stock liquidity, suggesting a causal relation. We also find that the effect of CITI on stock liquidity is stronger among firms with severe information‐asymmetry problems, but weaker among firms with strong governance mechanisms.Theoretical/Academic ImplicationsWe extend research that examines the impacts of CEO industry tournament incentives on corporate outcomes and strategies. Our paper shows that CEO industry tournament incentives matter for stock liquidity. Our paper contributes to a large literature on the roles of CEO styles and behaviors in shaping corporate policies.Practitioner/Policy ImplicationsOur findings have several practical implications for investors, policymakers, and financial analysts. For example, our findings can help investors better understand how CEOs' compensation‐based incentives impact stock liquidity. Similarly, policymakers can use our findings to design policies that encourage CEOs to act in the best interest of their shareholders and promote market efficiency.

Causal relationships between diseases mined from the literature improve the use of polygenic risk scores.

Identifying causal relations between diseases allows for the study of shared pathways, biological mechanisms, and inter-disease risks. Such causal relations can facilitate the identification of potential disease precursors and candidates for drug re-purposing. However, computational methods often lack access to these causal relations. Few approaches have been developed to automatically extract causal relationships between diseases from unstructured text, but they are often only focused on a small number of diseases, lack validation of the extracted causal relations, or do not make their data available. We automatically mined statements asserting a causal relation between diseases from the scientific literature by leveraging lexical patterns. Following automated mining of causal relations, we mapped the diseases to the International Classification of Diseases (ICD) identifiers to allow the direct application to clinical data. We provide quantitative and qualitative measures to evaluate the mined causal relations and compare to UK Biobank (UKB) diagnosis data as a completely independent data source. The validated causal associations were used to create a directed acyclic graph that can be used by causal inference frameworks. We demonstrate the utility of our causal network by performing causal inference using the do-calculus, using relations within the graph to construct and improve polygenic risk scores, and disentangle the pleiotropic effects of variants. The data is available through https://github.com/bio-ontology-research-group/causal-relations-between-diseases. Supplementary data are available at Bioinformatics online.

Exploring the potential link between gut microbiota and chronic kidney disease in causality: A 2-sample Mendelian randomization study.

Increasing evidence indicates a significant correlation between gut microbiota (GM) and susceptibility to chronic kidney disease (CKD). However, causal relationship presence remains uncertain. Mendelian randomization (MR) was applied to evaluate potential causal relation from GM to CKD. Genomic association analysis aggregates publicly online databases, utilizing Genome-Wide Association Study (GWAS) database focused on GM and CKD. For examination of potential causal connection from GM to CKD, a 2-way, 2-sample Mendelian randomization (MR) method was applied. Sensitivity analyses were utilized to scrutinize for heterogeneity, horizontal pleiotropy, MR outcomes resilience. Result from inverse variance weighting (IVW) method revealed that 10 microbiotas such as Porphyromonadaceae (OR = 1.351, 95% CI: 1.114-1.638, P = .002), Dorea (OR = 1.236, 95% CI: 1.040-1.468, P = .016), Ruminococcus torques group (OR = 1.290, 95% CI: 1.035-1.608, P = .024) are potential CKD risk factors. Five microbiotas, including the Prevotellaceae (OR = 0.814, 95% CI: 0.719-0.922, P = .001) are potential CKD protective factors. Sensitivity analyses reveal no horizontal pleiotropy or heterogeneity. Additionally, reverse MR results unveiled potential relation between CKD and disorders in 3 microbiotas, including Senegalimassilia. According to the investigation, MR method was employed to delve into reciprocal causal connection from GM to CKD. Our findings identified 15 types of GM causally linked to CKD, as well as CKD demonstrating causal associations with 3 types of GM. Further exploration of these associated GM types is hopeful to raise novel insights, for CKD preventing and early monitoring.

Reconstructing Molecular Networks by Causal Diffusion Do-Calculus Analysis with Deep Learning.

Quantifying molecular regulations between genes/molecules causally from observed data is crucial for elucidating the molecular mechanisms underlying biological processes at the network level. Presently, most methods for inferring gene regulatory and biological networks rely on association studies or observational causal-analysis approaches. This study introduces a novel approach that combines intervention operations and diffusion models within a do-calculus framework by deep learning, i.e., Causal Diffusion Do-calculus (CDD) analysis, to infer causal networks between molecules. CDD can extract causal relations from observed data owing to its intervention operations, thereby significantly enhancing the accuracy and generalizability of causal network inference. Computationally, CDD has been applied to both simulated data and real omics data, which demonstrates that CDD outperforms existing methods in accurately inferring gene regulatory networks and identifying causal links from genes to disease phenotypes. Especially, compared with the Mendelian randomization algorithm and other existing methods, the CDD can reliably identify the disease genes or molecules for complex diseases with better performances. In addition, the causal analysis between various diseases and the potential factors in different populations from the UK Biobank database is also conducted, which further validated the effectiveness of CDD.

EXPRESS: When is a causal illusion an illusion? Separating discriminability and bias in human contingency judgements.

Humans often behave as if unrelated events are causally related. As the name suggests, such causal illusions imply failures to detect the absence of a causal relation. Taking a signal-detection approach, we asked whether causal illusions indeed reflect failures of discriminability, or whether they reflect a general bias to behave as if events are causally related. Participants responded in a discrete-trials procedure in which point gains, point losses, or no change in points occurred dependent on or independently of responding. Participants reported whether each event was response-dependent or response-independent by choosing between two stimuli, one corresponding to reporting "I did it" and the other to "I didn't do it". Overall, participants responded accurately in about 80% of trials, and were biased to report that events depended on responding. This bias was strongest after point gains, and for higher-performing participants. Such differences in event-specific biases were not related to response rates; instead, they appear to reflect more fundamental differences in the effects of appetitive and aversive events. These findings demonstrate that people can judge causality relatively well, but are biased to attribute events to their own behaviour, particularly when those events are desirable. This highlights discriminability and bias as separable aspects of causal learning, and suggests that some causal illusions may not really be "illusions" at all - they may simply reflect a bias to report causal relations.

Mother-Led Science: Rethinking Science-Based Social Movements Through Temporality and Care

This article explores the subjective and temporal modes of organizing underlying science-based social movements through an analysis of two mother-led movements in Argentina. The Abuelas de Plaza de Mayo appropriated forensic anthropology and nascent DNA technologies to identify their disappeared grandchildren during the 1976–83 dictatorship. The Madres de Ituzaingó Anexo developed community-led epidemiology studies and agrochemical contamination mapping to argue for a causal relation between intensive pesticide use and high rates of childhood illnesses. We focus on how these movements delineate visions of time, responsibility, and collective action by closely examining the underlying histories and practices of each social movement as they politicized motherhood and appropriated scientific practice. We offer the concept of mother-led science through the temporal registers of constancy, desgaste, and durability to illuminate the iterative relationship between care work, creating sustainable communities and institutions, and the fragile processes of stabilizing facts. Mother-led science, with its dual claims to scientific authority in an epistemic register and maternal authority in an affective register, articulates a potent form of scientific organizing. We suggest that these affective temporalities may be present across all science-based social movements but can be obscured by narratives of linear progress toward objective truths.

Perturbation graphs, invariant causal prediction and causal relations in psychology.

Networks (graphs) in psychology are often restricted to settings without interventions. Here we consider a framework borrowed from biology that involves multiple interventions from different contexts (observations and experiments) in a single analysis. The method is called perturbation graphs. In gene regulatory networks, the induced change in one gene is measured on all other genes in the analysis, thereby assessing possible causal relations. This is repeated for each gene in the analysis. A perturbation graph leads to the correct set of causes (not nec-essarily direct causes). Subsequent pruning of paths in the graph (called transitive reduction) should reveal direct causes. We show that transitive reduction will not in general lead to the correct underlying graph. We also show that invariant causal prediction is a generalisation of the perturbation graph method and does reveal direct causes, thereby replacing transitive re-duction. We conclude that perturbation graphs provide a promising new tool for experimental designs in psychology, and combined with invariant causal prediction make it possible to re-veal direct causes instead of causal paths. As an illustration we apply these ideas to a data set about attitudes on meat consumption and to a time series of a patient diagnosed with major depression disorder.

Natural variation in the chickpea metabolome under drought stress.

Chickpea is the world's fourth largest grown legume crop, which significantly contributes to food security by providing calories and dietary protein globally. However, the increased frequency of drought stress has significantly reduced chickpea production in recent years. Here, we have performed a field experiment with 36 diverse chickpea genotypes to evaluate grain yield, photosynthetic activities and molecular traits related to drought stress. For metabolomics analysis, leaf tissue was collected at three time points representing different pod-filling stages. We identified L-threonic acid, fructose and sugar alcohols involved in chickpea adaptive drought response within the mid-pod-filling stage. A stress susceptibility index for each genotype was calculated to identify tolerance capacity under drought, distributing the 36 genotypes into four categories from best to worst performance. To understand how biochemical mechanisms control different traits for genetic improvement, we performed a differential Jacobian analysis, which unveiled the interplay between various metabolic pathways across three time points, including higher flux towards inositol interconversions, glycolysis for high-performing genotypes, fumarate to malate conversion, and carbon and nitrogen metabolism perturbations. Metabolic GWAS (mGWAS) analysis uncovered gene candidates involved in glycolysis and MEP pathway corroborating with the differential biochemical Jacobian results. Accordingly, this proposed data analysis strategy bridges the gap from pure statistical association to causal biochemical relations by exploiting natural variation. Our study offers new perspectives on the genetic and metabolic understanding of drought tolerance-associated diversity in the chickpea metabolome and led to the identification of metabolic control points that can be also tested in other legume crops.

Quantifying convergence and consistency.

The reproducibility crisis highlights several unresolved issues in science, including the need to develop measures that gauge both the consistency and convergence of data sets. While existing meta-analytic methods quantify the consistency of evidence, they do not quantify its convergence: the extent to which different types of empirical methods have provided evidence to support a hypothesis. To address this gap in meta-analysis, we and colleagues developed a summary metric-the cumulative evidence index (CEI)-which uses Bayesian statistics to quantify the degree of both consistency and convergence of evidence regarding causal hypotheses between two phenomena. Here, we outline the CEI's underlying model, which quantifies the extent to which studies of four types-positive intervention, negative intervention, positive non-intervention and negative non-intervention-lend credence to any of three types of causal relations: excitatory, inhibitory or no-connection. Along with p-values and other measures, the CEI can provide a more holistic perspective on a set of evidence by quantitatively expressing epistemic principles that scientists regularly employ qualitatively. The CEI can thus address the reproducibility crisis by formally demonstrating how convergent evidence across multiple study types can yield progress toward scientific consensus, even when an individual type of study fails to yield reproducible results.

Partnership quality and maternal depressive symptoms in the transition to parenthood: a prospective cohort study

BackgroundPregnancy and childbirth are critical life events which lead to significant changes in family structures and roles, thus having a substantial impact on partner relationship and maternal wellbeing. A dysfunctional partnership during this critical time of life has been associated with maternal depressiveness. However, sub-components of partnership quality and the causal relation with maternal symptoms of depression in the perinatal period have been sparsely studied so far. The current study aims to longitudinally assess the course of relationship quality and its sub-components from pregnancy to postpartum and to test a potential causal association with maternal symptoms of depression in the perinatal period.MethodsDiffering from previous studies, partnership quality and symptoms of depression have been assessed prospectively and longitudinally from an early stage of pregnancy (second trimester) until six months postpartum. Cross-lagged panel models were applied to investigate a potential causal relationship between partnership quality and maternal depressive symptoms.ResultsRelationship quality decreased significantly during the transition to parenthood (p < .05) with the steepest decline referring to tenderness (p < .001). We also found a substantial association of relationship quality and maternal depressiveness, but no indication for a clear causal direction of this association.ConclusionsOur results suggest that relationship quality and maternal depressiveness are substantially related in the perinatal period, thus pointing to the need of early prevention and intervention programs for peripartum women and their partners to prevent adverse outcome for the couple and the family.

Positive Mental Health and Meat Consumption: A Systematic and Meta-Analytic Review

In this systematic and meta-analytic review, we examined the current evidence on positive psychological variables between individuals who consumed meat and individuals who abstained from meat consumption. After systematically searching five online databases for primary research on positive psychological outcomes in meat consumers and meat abstainers, 19 studies with 94,204 participants (nmeat consumers = 82,449, nmeat abstainers = 9,964) met the inclusion/exclusion criteria. The primary outcomes were self-esteem, satisfaction with life, and positive mental health. The secondary outcomes were positive affect, psychological well-being, vigor, optimism, happiness, and meaning in life. Individuals who consumed meat had greater positive mental health (g = 0.21, 95% CI [0.08, 0.31], p = .001) than meat abstainers. No significant differences were found between the groups on self-esteem (g = 0.19, 95% CI [-0.01, 0.38], p = .06) and satisfaction with life (g = 0.02, 95% CI [-0.04, 0.07], p = .57). The majority of studies examining the secondary outcomes showed no group differences. The evidence was limited, requiring more studies to determine the role of study quality in diet-health relations. Study designs precluded inference of causal and temporal relations. With respect to clinical practice, our findings add to the current controversial diet-health debate.

Redress for the non-pecuniary damage in case of exceedingly long non-execution of the final judgement

The article is focused on studying the topical issue of determining the possibility of redress for the non-pecuniary damage in case of exceedingly long non-execution of the final judgement. The author of the article has stated that the right to judicial protection includes both holding a judgement and its timely execution. Timely and full execution of a judgement guarantees the realization of subjective rights recognized by the judgement and fulfillment of legal obligations confirmed by it. It has been proved that execution of judgements within civil legal proceeding should be carried out taking into account the European Convention on Human Rights and the European Court of Human Rights case-law. In particular, the ECtHR insists on the great importance of such a property of a judgement that has become final (res judicata), as the possibility and factual enforceability of its actual execution by the state. It has been noted in the paper that the adoption of a number of new legislative acts, which introduced a lot of amendments in the field of executive proceedings, foreground researches focused on levying of execution of judgements from a new angle, in particular, the topical issue on determining the possibility of redress for the non-pecuniary damage in case of exceedingly long non-execution of the final judgement. The requirements of reasonableness and fairness should be considered while determining the amount of compensation. Non-pecuniary damage is compensated regardless of the pecuniary damage that is subject to compensation and is not related to the amount of this compensation. Non- pecuniary damage is compensated on a once-only basis, unless otherwise stipulated by the contract or law. As a general rule, the infliction of the non- pecuniary damage to another person is the basis for the obligation to redress for the non- pecuniary damage. The obligation to redress for the non-pecuniary damage caused to a person by illegal decisions, action or act of omission of a state authority while exercising its powers arises under the following conditions: existence of non- pecuniary damage; wrongfulness of the person’s behavior who caused non-pecuniary damage; existence of causal relation between wrongfulness of the person’s behavior who caused non-pecuniary damage and its result - non-pecuniary damage. On the basis of the conducted research, the author has concluded that the possibility of redress for the non-pecuniary damage in case of exceedingly long non-execution of the final judgement supports the integrity of the legal system by ensuring the execution of judgements and maintaining trust. When judgements are executed with consequences, the authority of the judicial system is undermined and the effectiveness of the legal system is put at stake. The integrity of the legal system is preserved and public confidence in the judicial system is strengthened by upholding the authority of a judgement through the possibility of compensation. It is impossible to bear with non-execution, because it creates a dangerous precedent that undermines the foundation of an honest and fair legal system.

Forecasting malaria dynamics based on causal relations between control interventions, climatic factors, and disease incidence in western Kenya.

Malaria remains one of the deadliest diseases worldwide, especially among young children in sub-Saharan Africa. Predictive models are necessary for effective planning and resource allocation; however, statistical models suffer from association pitfalls. In this study, we used empirical dynamic modelling (EDM) to investigate causal links between climatic factors and intervention coverage with malaria for short-term forecasting. Based on data spanning the period from 2008 to 2022, we used convergent cross-mapping (CCM) to identify suitable lags for climatic drivers and investigate their effects, interaction strength, and suitability ranges on malaria incidence. Monthly malaria cases were collected at St. Elizabeth Lwak Mission Hospital. Intervention coverage and population movement data were obtained from household surveys in Asembo, western Kenya. Daytime land surface temperature (LSTD), rainfall, relative humidity (RH), wind speed, solar radiation, crop cover, and surface water coverage were extracted from remote sensing sources. Short-term forecasting of malaria incidence was performed using state-space reconstruction. We observed causal links between climatic drivers, bed net use, and malaria incidence. LSTD lagged over the previous month; rainfall and RH lagged over the previous two months; and wind speed in the current month had the highest predictive skills. Increases in LSTD, wind speed, and bed net use negatively affected incidence, while increases in rainfall and humidity had positive effects. Interaction strengths were more pronounced at temperature, rainfall, RH, wind speed, and bed net coverage ranges of 30-35°C, 30-120 mm, 67-80%, 0.5-0.7 m/s, and above 90%, respectively. Temperature and rainfall exceeding 35°C and 180 mm, respectively, had a greater negative effect. We also observed good short-term predictive performance using the multivariable forecasting model (Pearson correlation coefficient = 0.85, root mean square error = 0.15). Our findings demonstrate the utility of CCM in establishing causal linkages between malaria incidence and both climatic and non-climatic drivers. By identifying these causal links and suitability ranges, we provide valuable information for modelling the impact of future climate scenarios.

Ontology of active and passive environmental exposure

Exposure is a central concept of the health and behavioural sciences needed to study the influence of the environment on the health and behaviour of people within a spatial context. While an increasing number of studies measure different forms of exposure, including the influence of air quality, noise, and crime, the influence of land cover on physical activity, or of the urban environment on food intake, we lack a common conceptual model of environmental exposure that captures its main structure across all this variety. Against the background of such a model, it becomes possible not only to systematically compare different methodological approaches but also to better link and align the content of the vast amount of scientific publications on this topic in a systematic way. For example, an important methodical distinction is between studies that model exposure as an exclusive outcome of some activity versus ones where the environment acts as a direct independent cause (active vs. passive exposure). Here, we propose an information ontology design pattern that can be used to define exposure and to model its variants. It is built around causal relations between concepts including persons, activities, concentrations, exposures, environments and health risks. We formally define environmental stressors and variants of exposure using Description Logic (DL), which allows automatic inference from the RDF-encoded content of a paper. Furthermore, concepts can be linked with data models and modelling methods used in a study. To test the pattern, we translated competency questions into SPARQL queries and ran them over RDF-encoded content. Results show how study characteristics can be classified and summarized in a manner that reflects important methodical differences.

A study of the relation between traffic accidents and election posters

Inattention in traffic is a risk factor for the occurrence of accidents. Elements surrounding traffic could potentially be a safety risk for road users. This study investigates the relation between election posters and traffic accidents using a seasonal autoregressive-moving-average model with exogeneous variables (seasonal ARMAX) correcting for variations in meteorological conditions and general traffic volume indicated through public holidays. Danish traffic accident micro data are used to construct a time series for daily number of accidents in city zones adjusted for probable known accident causes, namely drunk driving, narcotics, drivers deemed unfit to drive, and accidents related to low visibility. Model parameters are determined from Maximum Likelihood estimation on the period from 1 January 1997 to 15 December 2009 with an indicator for general election periods. The presence of general election posters is associated with an increased risk of traffic accidents (p = 0.003), when correcting for meteorological variables and public school holidays. While a causal relation between traffic accidents and the presence of election posters cannot be concluded from the analysis, we find distraction from election posters to be the most likely explanation for the associated increase in traffic accidents. On average, the model indicates that 1.18 additional daily accidents occur for general elections in Denmark. In total, that amounts to 127 additional accidents for the investigated period of time. This study shows that such sudden changes in traffic surroundings are significantly related to increased safety risks which could lead to a better discussion of relevant traffic legislation.