The Impact of Aging Drivers and Vehicles on the Injury Severity of Crash Victims.

Against a general trend of increasing driver longevity, the injuries suffered by vehicle occupants in Spanish road traffic crashes are analyzed by the level of severity of their bodily injuries (BI). Generalized linear mixed models are applied to model the proportion of non-serious, serious, and fatal victims. The dependence between vehicles involved in the same crash is captured by including random effects. The effect of driver age and vehicle age and their interaction on the proportion of injured victims is analyzed. We find a nonlinear relationship between driver age and BI severity, with young and older drivers constituting the riskiest groups. In contrast, the expected severity of the crash increases linearly up to a vehicle age of 18 and remains constant thereafter at the highest level of BI severity. No interaction between the two variables is found. These results are especially relevant for countries such as Spain with increasing driver longevity and an aging car fleet.

Hospitalizations from covid-19: a health planning tool.

OBJECTIVE: Estimate the future number of hospitalizations from Covid-19 based on the number of diagnosed positive cases. METHOD: Using the covid-19 Panel data recorded in Spain at the Red Nacional de Vigilancia Epidemiológica, Renave (Epidemiological Surveillance Network), a regression model with multiplicative structure is adjusted to explain and predict the number of hospitalizations from the lagged series of positive cases diagnosed from May 11, 2020 to September 20, 2021. The effect of the time elapsed since the vaccination program starting on the number of hospitalizations is reviewed. RESULTS: Nine days is the number of lags in the positive cases series with greatest explanatory power on the number of hospitalizations. The variability of the number of hospitalizations explained by the model is high (adjusted R2: 96.6%). Before the vaccination program starting, the expected number of hospitalizations on day t was 20.2% of the positive cases on day t-9 raised to 0.906. After the vaccination program started, this percentage was reduced by 0.3% a day. Using the same model, we find that in the first pandemic wave the number of positive cases was more than six times that reported on official records. CONCLUSIONS: Starting from the covid-19 cases detected up to a given date, the proposed model allows estimating the number of hospitalizations nine days in advance. Thus, it is a useful tool for forecasting the hospital pressure that health systems shall bear as a consequence of the disease.

Hospitalizations from covid-19: a health planning tool / Ingresos hospitalarios por covid-19: una herramienta de planificación sanitaria

ABSTRACT OBJECTIVE Estimate the future number of hospitalizations from Covid-19 based on the number of diagnosed positive cases. METHOD Using the covid-19 Panel data recorded in Spain at the Red Nacional de Vigilancia Epidemiológica, Renave (Epidemiological Surveillance Network), a regression model with multiplicative structure is adjusted to explain and predict the number of hospitalizations from the lagged series of positive cases diagnosed from May 11, 2020 to September 20, 2021. The effect of the time elapsed since the vaccination program starting on the number of hospitalizations is reviewed. RESULTS Nine days is the number of lags in the positive cases series with greatest explanatory power on the number of hospitalizations. The variability of the number of hospitalizations explained by the model is high (adjusted R2: 96.6%). Before the vaccination program starting, the expected number of hospitalizations on day t was 20.2% of the positive cases on day t-9 raised to 0.906. After the vaccination program started, this percentage was reduced by 0.3% a day. Using the same model, we find that in the first pandemic wave the number of positive cases was more than six times that reported on official records. CONCLUSIONS Starting from the covid-19 cases detected up to a given date, the proposed model allows estimating the number of hospitalizations nine days in advance. Thus, it is a useful tool for forecasting the hospital pressure that health systems shall bear as a consequence of the disease.

RESUMEN OBJETIVO Predecir el número futuro de hospitalizaciones por covid-19 a partir del número de casos positivos diagnosticados. MÉTODO Usando datos del Panel covid-19 registrados en España en la Red Nacional de Vigilancia Epidemiológica (Renave), se ajusta un modelo de regresión con estructura multiplicativa para explicar y predecir el número de hospitalizaciones a partir de la serie retardada de casos positivos diagnosticados durante el periodo entre el 11 de mayo de 2020 y el 20 de septiembre de 2021. Se analiza el efecto sobre el número de hospitalizaciones del tiempo transcurrido desde el inicio del programa de vacunación. RESULTADOS El número de retardos de la serie de casos positivos que mayor capacidad explicativa tiene sobre el número de hospitalizaciones es de nueve días. La variabilidad del número de hospitalizaciones explicada por el modelo es elevada (R2 ajustado: 96,6%). Antes del inicio del programa de vacunación, el número esperado de ingresos hospitalarios en el día t era igual al 20,2% de los casos positivos del día t-9 elevado a 0,906. Iniciado el programa de vacunación, este porcentaje se redujo un 0,3% diario. Con el mismo modelo se obtiene que en la primera ola de la pandemia el número de casos positivos fue más de seis veces el que figura en los registros oficiales. CONCLUSIONES Partiendo de los casos de covid-19 detectados hasta una fecha, el modelo propuesto permite estimar el número de hospitalizaciones con nueve días de antelación. Ello lo convierte en una herramienta útil para prever con cierta anticipación la presión hospitalaria que el sistema sanitario tendrá que soportar como consecuencia de la enfermedad.

Differences in the risk profiles of drunk and drug drivers: Evidence from a mandatory roadside survey.

BACKGROUND: The study assesses the prevalence rates of alcohol- and drug-involved driving in Catalonia (Spain). METHOD: Drivers were randomly selected for roadside testing using a stratified random sampling procedure representative of all vehicles circulating on non-urban roads. Mandatory alcohol and drug tests were performed during autumn 2017. A sample of 6860 drivers were tested for alcohol use, of these 671 were also tested for drugs. Standard procedures were employed by traffic officers to detect alcohol and drug use. Alcohol breath tests were performed with breathalyser devices and on-site drug screening systems were used to test for drugs. RESULTS: The prevalence of alcohol use above the legal limit and drug use were 1.2 % (95 % CI: 0.9-1.5 %) and 8.3 % (95 % CI: 5.8-11.2 %), respectively. The most frequent drugs detected were THC (5.6 %, 95 % CI: 3.7-8.0 %), cocaine (3.5 %, 95 % CI: 2.0-5.5 %) and amphetamines (1.6 %, 95 % CI: 0.6-3.4 %). Alcohol use was detected more frequently on conventional roads, at weekends and during night-time hours. Drug use was detected more frequently in young males during daytime hours. CONCLUSIONS: Driver risk profiles associated with alcohol use and drug use differ. Positive alcohol use is not a predictor of drug use when controlling for all other factors.

Does longevity impact the severity of traffic crashes? A comparative study of young-older and old-older drivers.

INTRODUCTION: This article analyzes the effect of driver's age in crash severity with a particular focus on those over the age of 65. The greater frequency and longevity of older drivers around the world suggests the need to introduce a possible segmentation within this group at risk, thus eliminating the generic interval of 65 and over as applied today in road safety data and in the automobile insurance sector. METHOD: We investigate differences in the severity of traffic crashes among two subgroups of older drivers -young-older (65-75) and old-older (75+), and findings are compared with the age interval of drivers under 65. Here, we draw on data for 2016 provided by Spanish Traffic Authority. Parametric and semi-parametric regression models are applied. RESULTS: We identified the factors related to the crash, vehicle, and driver that have a significant impact on the probability of the crash being slight, serious, or fatal for the different age groups. CONCLUSIONS: We found that crash severity and the expected costs of crashes significantly increase when the driver is over the age of 75. Practical Applications: Our results have obvious implications for regulators responsible for road safety policies - most specifically as they consider there should be specific driver licensing requirements and driving training for elderly - and for the automobile insurance industry, which to date has not examined the impact that the longevity of drivers is likely to have on their balance sheets.

A discrete mixture regression for modeling the duration of non-hospitalization medical leave of motor accident victims.

Studies analyzing the temporary repercussions of motor vehicle accidents are scarcer than those analyzing permanent injuries or mortality. A regression model to evaluate the risk factors affecting the duration of temporary disability after injury in such an accident is constructed using a motor insurance dataset. The length of non-hospitalization medical leave, measured in days, following a motor accident is used here as a measure of the severity of temporary disability. The probability function of the number of days of sick leave presents spikes in multiples of five (working week), seven (calendar week) and thirty (month), etc. To account for this, a regression model based on finite mixtures of multiple discrete distributions is proposed to fit the data properly. The model provides a very good fit when the multiples for the working week, week, fortnight and month are taken into account. Victim characteristics of gender and age and accident characteristics of the road user type, vehicle class and the severity of permanent injuries were found to be significant when accounting for the duration of temporary disability.

Prevalence of drug use among drivers based on mandatory, random tests in a roadside survey.

BACKGROUND: In the context of road safety, this study aims to examine the prevalence of drug use in a random sample of drivers. METHODS: A stratified probabilistic sample was designed to represent vehicles circulating on non-urban roads. Random drug tests were performed during autumn 2014 on 521 drivers in Catalonia (Spain). Participation was mandatory. The prevalence of drug driving for cannabis, methamphetamines, amphetamines, cocaine, opiates and benzodiazepines was assessed. RESULTS: The overall prevalence of drug use is 16.4% (95% CI: 13.9; 18.9) and affects primarily younger male drivers. Drug use is similarly prevalent during weekdays and on weekends, but increases with the number of occupants. The likelihood of being positive for methamphetamines is significantly higher for drivers of vans and lorries. CONCLUSIONS: Different patterns of use are detected depending on the drug considered. Preventive drug tests should not only be conducted on weekends and at night-time, and need to be reinforced for drivers of commercial vehicles. Active educational campaigns should focus on the youngest age-group of male drivers.

Circular con tasa de alcohol superior a la legal: caracterización del conductor según la vía de circulación / Driving with alcohol content above the legal limits: profile of the driver according to the type of road

Se realiza un estudio transversal de los controles policiales para la medición de la tasa de alcohol en aire espirado (AAE) llevados a cabo en Cataluña en el año 2013. La muestra consta de 464.134 pruebas, de las cuales el 66% se realizaron en vías interurbanas y el 34% en vías urbanas. Método: Se mide si el conductor sobrepasa el límite legal en miligramos de alcohol por litro de aire espirado. En el caso de conductores noveles o profesionales el límite legal es de 0,15 mg/l y para el resto de conductores es de 0,25 mg/l. Se realiza un análisis descriptivo del porcentaje de conductores detectados por encima del límite legalmente permitido en vías interurbanas y en vías urbanas, según características del conductor y del vehículo, motivo de la prueba, y momento en que se lleva a cabo. En una segunda parte, se ajustan dos modelos lineales generalizados con vínculo logarítmico y familia binomial, según si la prueba se realiza en vía interurbana o en vía urbana. Resultados: La edad del conductor, la nacionalidad o la franja horaria en la que se realiza la prueba inciden de forma diferente en la probabilidad de sobrepasar el límite legal de alcohol, dependiendo de si la prueba se realiza en vía interurbana o urbana. Conclusión: Diseñar políticas de seguridad vial específicas según el tipo de vía puede ayudar a reducir la proporción de conductores que superan los límites legales de alcohol en aire espirado y, por tanto, la accidentalidad

A cross-sectional study of the alcohol breath tests carried out by police officers in Catalonia in 2013 is performed. The sample consists of 464,134 breath tests, of which 66% were held on interurban roads and 34% on urban roads. Method: We measure whether the driver exceeds the legal limit in milligrams of alcohol per litre of exhaled air. For novice or professional drivers the legal limit is 0.15 mg/l, while for other drivers it is 0.25 mg/l. First, a descriptive analysis of the percentage of drivers detected above the legally permitted limit on interurban and urban roads is performed. It takes into account the characteristics of the driver and the vehicle, the reason for the test, and the timeframe. Afterwards, two generalized linear models with binomial family and logarithmic link are adjusted, depending on whether the test is conducted on interurban or urban roads. Results: Driver age, nationality or timeframe affect the probability of exceeding the alcohol legal limit differently, depending on whether the test is conducted on interurban or urban roads. Conclusion: Designing road safety policies adapted to the type of road can help reduce alcohol-impaired driving and therefore accident rates

Copula-based regression modeling of bivariate severity of temporary disability and permanent motor injuries.

The analysis of factors influencing the severity of the personal injuries suffered by victims of motor accidents is an issue of major interest. Yet, most of the extant literature has tended to address this question by focusing on either the severity of temporary disability or the severity of permanent injury. In this paper, a bivariate copula-based regression model for temporary disability and permanent injury severities is introduced for the joint analysis of the relationship with the set of factors that might influence both categories of injury. Using a motor insurance database with 21,361 observations, the copula-based regression model is shown to give a better performance than that of a model based on the assumption of independence. The inclusion of the dependence structure in the analysis has a higher impact on the variance estimates of the injury severities than it does on the point estimates. By taking into account the dependence between temporary and permanent severities a more extensive factor analysis can be conducted. We illustrate that the conditional distribution functions of injury severities may be estimated, thus, providing decision makers with valuable information.

Prevalence of alcohol-impaired drivers based on random breath tests in a roadside survey in Catalonia (Spain).

Sobriety checkpoints are not usually randomly located by traffic authorities. As such, information provided by non-random alcohol tests cannot be used to infer the characteristics of the general driving population. In this paper a case study is presented in which the prevalence of alcohol-impaired driving is estimated for the general population of drivers. A stratified probabilistic sample was designed to represent vehicles circulating in non-urban areas of Catalonia (Spain), a region characterized by its complex transportation network and dense traffic around the metropolis of Barcelona. Random breath alcohol concentration tests were performed during spring 2012 on 7596 drivers. The estimated prevalence of alcohol-impaired drivers was 1.29%, which is roughly a third of the rate obtained in non-random tests. Higher rates were found on weekends (1.90% on Saturdays and 4.29% on Sundays) and especially at night. The rate is higher for men (1.45%) than for women (0.64%) and it shows an increasing pattern with age. In vehicles with two occupants, the proportion of alcohol-impaired drivers is estimated at 2.62%, but when the driver was alone the rate drops to 0.84%, which might reflect the socialization of drinking habits. The results are compared with outcomes in previous surveys, showing a decreasing trend in the prevalence of alcohol-impaired drivers over time.

Beyond Value-at-Risk: GlueVaR Distortion Risk Measures.

We propose a new family of risk measures, called GlueVaR, within the class of distortion risk measures. Analytical closed-form expressions are shown for the most frequently used distribution functions in financial and insurance applications. The relationship between GlueVaR, value-at-risk, and tail value-at-risk is explained. Tail subadditivity is investigated and it is shown that some GlueVaR risk measures satisfy this property. An interpretation in terms of risk attitudes is provided and a discussion is given on the applicability in nonfinancial problems such as health, safety, environmental, or catastrophic risk management.

Factors affecting hospital admission and recovery stay duration of in-patient motor victims in Spain.

Hospital expenses are a major cost driver of healthcare systems in Europe, with motor injuries being the leading mechanism of hospitalizations. This paper investigates the injury characteristics which explain the hospitalization of victims of traffic accidents that took place in Spain. Using a motor insurance database with 16,081 observations a generalized Tobit regression model is applied to analyse the factors that influence both the likelihood of being admitted to hospital after a motor collision and the length of hospital stay in the event of admission. The consistency of Tobit estimates relies on the normality of perturbation terms. Here a semi-parametric regression model was fitted to test the consistency of estimates, concluding that a normal distribution of errors cannot be rejected. Among other results, it was found that older men with fractures and injuries located in the head and lower torso are more likely to be hospitalized after the collision, and that they also have a longer expected length of hospital recovery stay.

Discrete distributions when modeling the disability severity score of motor victims.

Many European countries 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, standard and zero-altered discrete regression models are applied to model the disability severity score of victims. An application using data from Spain is provided in which the hurdle-Negative Binomial regression was the preferred method. The effects of victims' characteristics, type of road user and recovery duration are examined. The results suggest that the expected permanent disability severity is higher for older women with long recovery periods. The results provide traffic decision makers with a model to quantify the compensation cost savings due to disability severity reductions.