Exploring Determinants of Urban Motorcycle Accident Severity: The Case of Barcelona

Public authorities and road users alike are increasingly concerned by recent trends in road safety outcomes in Barcelona, which is the European city with the highest number of registered Powered Two-Wheel (PTW) vehicles per inhabitant,. In this study we explore the determinants of motorcycle and moped accident severity in a large urban area, drawing on Barcelona’s local police database (2002-2008). We apply non-parametric regression techniques to characterize PTW accidents and parametric methods to investigate the factors influencing their severity. Our results show that PTW accident victims are more vulnerable, showing greater degrees of accident severity, than other traffic victims. Speed violations and alcohol consumption provide the worst health outcomes. Demographic and environment-related risk factors, in addition to helmet use, play an important role in determining accident severity. Thus, this study furthers our understanding of the most vulnerable vehicle types, while our results have direct implications for local policy makers in their fight to reduce the severity of PTW accidents in large urban areas.

An Introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions

We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.

Estimation of parametric and nonparametric models for univariate claim severity distributions - an approach using R

This paper presents an analysis of motor vehicle insurance claims relating to vehicle damage and to associated medical expenses. We use univariate severity distributions estimated with parametric and non-parametric methods. The methods are implemented using the statistical package R. Parametric analysis is limited to estimation of normal and lognormal distributions for each of the two claim types. The nonparametric analysis presented involves kernel density estimation. We illustrate the benefits of applying transformations to data prior to employing kernel based methods. We use a log-transformation and an optimal transformation amongst a class of transformations that produces symmetry in the data. The central aim of this paper is to provide educators with material that can be used in the classroom to teach statistical estimation methods, goodness of fit analysis and importantly statistical computing in the context of insurance and risk management. To this end, we have included in the Appendix of this paper all the R code that has been used in the analysis so that readers, both students and educators, can fully explore the techniques described

Does Warfare Matter? Severity, Duration, and Outcomes of Civil Wars

Importa si una guerra civil es combat com un conflicte irregular, convencional o simètric no convencional? En altres paraules, tenen les “tecnologies de la rebel·lió” un impacte sobre la gravetat d’una guerra, la seva durada o el seu resultat? Aquest treball mostra que els conflictes irregulars duren més que els altres tipus de conflicte, mentre els convencionals tendeixen a ser més greus en termes de letalitat al camp de batalla. D’altra banda, els conflictes irregulars tendeixen a ser guanyats pels governs, mentre els altres són més propensos a acabar en empat. Substancialment, aquests resultats ens ajuden a donar sentit a l’evolució de les guerres civils, les quals tendeixen a ser més curtes, més intenses i més difícils per als governs. Teòricament, aquests resultats donen suport a la importància de la tecnologia de rebel·lió a l’estudiar la gravetat, la durada i els resultats de les guerres civils; a més, contribueixen a una millor comprensió de la contribució històrica de la guerra irregular a la construcció de l’Estat i al canvi social.

Crucial role of CB(2) cannabinoid receptor in the regulation of central immune responses during neuropathic pain

Neuropathic pain is a clinical manifestation of nerve injury difficult to treat even with potent analgesic compounds. Here, we used different lines of genetically modified mice to clarify the role played by CB2 cannabinoid receptors in the regulation of the central immune responses leading to the development of neuropathic pain. CB2 knock-out mice and wild-type littermates were exposed to sciatic nerve injury, and both genotypes developed a similar hyperalgesia and allodynia in the ipsilateral paw. Most strikingly, knock-outs also developed a contralateral mirror image pain, associated with an enhanced microglial and astrocytic expression in the contralateral spinal horn. In agreement, hyperalgesia, allodynia, and microglial and astrocytic activation induced by sciatic nerve injury were attenuated in transgenic mice overexpressing CB2 receptors. These results demonstrate the crucial role of CB2 cannabinoid receptor in modulating glial activation in response to nerve injury. The enhanced manifestations of neuropathic pain were replicated in irradiated wild-type mice reconstituted with bone marrow cells from CB2 knock-outs, thus demonstrating the implication of the CB2 receptor expressed in hematopoietic cells in the development of neuropathic pain at the spinal cord.

Interferon-gamma is a critical modulator of CB(2) cannabinoid receptor signaling during neuropathic pain

Nerve injuries often lead to neuropathic pain syndrome. The mechanisms contributing to this syndrome involve local inflammatory responses, activation of glia cells, and changes in the plasticity of neuronal nociceptive pathways. Cannabinoid CB(2) receptors contribute to the local containment of neuropathic pain by modulating glial activation in response to nerve injury. Thus, neuropathic pain spreads in mice lacking CB(2) receptors beyond the site of nerve injury. To further investigate the mechanisms leading to the enhanced manifestation of neuropathic pain, we have established expression profiles of spinal cord tissues from wild-type and CB(2)-deficient mice after nerve injury. An enhanced interferon-gamma (IFN-gamma) response was revealed in the absence of CB(2) signaling. Immunofluorescence stainings demonstrated an IFN-gamma production by astrocytes and neurons ispilateral to the nerve injury in wild-type animals. In contrast, CB(2)-deficient mice showed neuronal and astrocytic IFN-gamma immunoreactivity also in the contralateral region, thus matching the pattern of nociceptive hypersensitivity in these animals. Experiments in BV-2 microglia cells revealed that transcriptional changes induced by IFN-gamma in two key elements for neuropathic pain development, iNOS (inducible nitric oxide synthase) and CCR2, are modulated by CB(2) receptor signaling. The most direct support for a functional involvement of IFN-gamma as a mediator of CB(2) signaling was obtained with a double knock-out mouse strain deficient in CB(2) receptors and IFN-gamma. These animals no longer show the enhanced manifestations of neuropathic pain observed in CB(2) knock-outs. These data clearly demonstrate that the CB(2) receptor-mediated control of neuropathic pain is IFN-gamma dependent.

Social factors related to the clinical severity of influenza cases in Spain during the A(H1N1)2009 virus pandemic

Background During the 2009 influenza pandemic, a change in the type of patients most often affected by influenza was observed. The objective of this study was to assess the role of individual and social determinants in hospitalizations due to influenza A (H1N1) 2009 infection. Methods We studied hospitalized patients (cases) and outpatients (controls) with confirmed influenza A (H1N1) 2009 infection. A standardized questionnaire was used to collect data. Variables that might be related to the hospitalization of influenza cases were compared by estimation of the odds ratio (OR) and 95% confidence intervals (CI) and the variables entered into binomial logistic regression models. Results Hospitalization due to pandemic A (H1N1) 2009 influenza virus infections was associated with non-Caucasian ethnicity (OR: 2.18, 95% CI 1.17 − 4.08), overcrowding (OR: 2.84, 95% CI 1.20 − 6.72), comorbidity and the lack of previous preventive information (OR: 2.69, 95% CI: 1.50 − 4.83). Secondary or higher education was associated with a lower risk of hospitalization (OR 0.56, 95% CI: 0.36 − 0.87) Conclusions In addition to individual factors such as comorbidity, other factors such as educational level, ethnicity or overcrowding were associated with hospitalization due to A (H1N1) 2009 influenza virus infections.

Stratification of the severity of critically ill patients with classification trees

Background: Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR). Methods: Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%). Results: CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69- 75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)). Conclusion: With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.

Social factors related to the clinical severity of influenza cases in Spain during the A(H1N1)2009 virus pandemic

Background During the 2009 influenza pandemic, a change in the type of patients most often affected by influenza was observed. The objective of this study was to assess the role of individual and social determinants in hospitalizations due to influenza A (H1N1) 2009 infection. Methods We studied hospitalized patients (cases) and outpatients (controls) with confirmed influenza A (H1N1) 2009 infection. A standardized questionnaire was used to collect data. Variables that might be related to the hospitalization of influenza cases were compared by estimation of the odds ratio (OR) and 95% confidence intervals (CI) and the variables entered into binomial logistic regression models. Results Hospitalization due to pandemic A (H1N1) 2009 influenza virus infections was associated with non-Caucasian ethnicity (OR: 2.18, 95% CI 1.17 − 4.08), overcrowding (OR: 2.84, 95% CI 1.20 − 6.72), comorbidity and the lack of previous preventive information (OR: 2.69, 95% CI: 1.50 − 4.83). Secondary or higher education was associated with a lower risk of hospitalization (OR 0.56, 95% CI: 0.36 − 0.87) Conclusions In addition to individual factors such as comorbidity, other factors such as educational level, ethnicity or overcrowding were associated with hospitalization due to A (H1N1) 2009 influenza virus infections.

Discrete distributions when modeling the disability severity score of motor victims

Many European states apply score systems to evaluate the disability severity of non-fatal motor victims under the law of third-party liability. The score is a non-negative integer with an upper bound at 100 that increases with severity. It may be automatically converted into financial terms and thus also reflects the compensation cost for disability. In this paper, discrete regression models are applied to analyze the factors that influence the disability severity score of victims. Standard and zero-altered regression models are compared from two perspectives: an interpretation of the data generating process and the level of statistical fit. The results have implications for traffic safety policy decisions aimed at reducing accident severity. An application using data from Spain is provided.

Modelling the disability severity score in motor insurance claims: an application to the Spanish case

Bodily injury claims have the greatest impact on the claim costs of motor insurance companies. The disability severity of motor claims is assessed in numerous European countries by means of score systems. In this paper a zero inflated generalized Poisson regression model is implemented to estimate the disability severity score of victims in-volved in motor accidents on Spanish roads. We show that the injury severity estimates may be automatically converted into financial terms by insurers at any point of the claim handling process. As such, the methodology described may be used by motor insurers operating in the Spanish market to monitor the size of bodily injury claims. By using insurance data, various applications are presented in which the score estimate of disability severity is of value to insurers, either for computing the claim compensation or for claim reserve purposes.