Correlates of stress are interactive and not unidimensional: Evidence from U.S. college students early in the COVID-19 pandemic.

Studies have investigated various aspects of how the COVID-19 pandemic has impacted college students' well-being. However, the complex relationships between stress and its correlates have received limited attention. Thus, the main objective of this study is to evaluate multiplicative associations between stress and demographic, lifestyle, and other negative emotion factors during the pandemic. We used data from a survey with 2,534 students enrolled in seven U.S. universities and analyzed such data with generalized additive Tobit models and pairwise interaction terms. The results highlighted associations and interactions between myriad factors such as students' social class, income, parental education, body mass index (BMI), amount of exercise, and knowing infected people in the student's communities. For instance, we found that the associations between feeling irritable and sad due to the pandemic were interactive, resulting in higher associated stress for students with higher levels of parents' education. Furthermore, associations between taking precautionary actions (i.e., avoiding travel and large gatherings) and stress varied with the intensity of negative feelings (i.e., sadness and irritability). Considering these interaction terms, the results highlighted a great inequality in pandemic-related stress within low income, lower social class, and higher BMI students. This study is among the earliest that employed a stratified approach with numerous interaction terms to better understand the multiplicative associations between different factors during the COVID-19 pandemic.

The Associations Between Death Anxiety, Supernatural Beliefs, Caring for Loved Ones and Attachments.

Despite extensive efforts in a better understanding of associations between death anxiety and various factors, efforts studying the complex associations across those variables are still limited. This study was conducted to better understand the possible complexity between death anxiety and myriad of factors, by first extracting the most important features, and then assessing the complexity of variables by checking all pairwise interaction terms. We found most of associated factors of death anxiety are related to the concept of attachment or caring for loved ones. Ill-effect attachment with positive associations with death anxiety included factors such as attachment to the physical side of oneself, being alone before death, and the possibility of death being the end of us. On the other hand, supernatural conceptions of worldviews such as believing in God, believing that the soul is separate from body, and being religious buffer against the death anxiety.

The mixed-mixed multinomial logit model for identification of factors to the passengers' seatbelt use.

A better understanding of the underlying factors to the choice of seatbelt use could contribute to the policy solutions, which consequently enhance the rate of seatbelt usage. To achieve that goal, it is important to obtain unbiased and reliable results by employing a valid statistical technique. In this paper, the latent class (LC) model was extended to account for unobserved heterogeneity across parameters within the same class. The random parameter latent class, or mixed-mixed (MM) model, is an extension of the mixed and LC models by adding another layer to the LC model, with an objective of accounting for heterogeneity within a same class. The results indicated that although the LC model outperformed the mixed model, the standard LC model did not account for the whole heterogeneity in the dataset and adding an extra layer for changing the parameter across the observations result in an improvement in a model fit. The results indicated that seatbelt status of the driver, vehicle type, day of a week, and driver gender are some of factors impacting whether or not passengers would wear their seatbelts. It was also observed that accounting for day of a week, drivers' gender, and type of vehicle heterogeneities in the second layer of the MM model result in a better fit, compared with the LC technique. The results of this study expand our understanding about factors to the choice of seatbelt use while capturing extra heterogeneity of the front-seat passengers' choice of seatbelt use. This is one of the earliest studies implemented the technique in the context of the traffic safety, with individual-specific observations.

Associated Factors of Worrying About Loneliness Before Death.

Human beings' welfare is closely related to their social relationships, and lack of that relationship could result in social isolation and loneliness. Various types of loneliness have been discussed in the literature, but a sense of loneliness before death has received no attention. Thus, this study is conducted to better understand the mechanism and nature of that type of loneliness. The response of this study was "Do you worry that you may be alone when you are dying?". After highlighting the most important features, to examine the true associations between various factors and a sense of loneliness, the pairwise interactions across various variables were considered. It was found, for instance, while attending religious service, having higher subjective life condition, and being older are negatively associated with the response, belief that success depends on God and higher income are positively associated with that feeling. Also, while married people are less worried about loneliness before death, this interacts with a myriad of factors. Similarly, positive associations were found across responses and a higher concern that those close with us won't be with us, or those who we care about might not remember us after death.

Factors associated with subjective state of health in college students.

Although the COVID-19 pandemic has been deeply painful, it has provided a rare opportunity to study the behavioral responses of individuals in adapting to an unprecedented life event. An analysis of participants' subjective health ratings during the COVID-19 pandemic was conducted by utilizing data from a survey of college students across seven universities in the US. In this study, we challenged the unidimensional factors to the subjective wellbeing by considering all multiplicative associations of those factors. Considering the interaction terms is especially important as not considering those impacts might obscure our understanding regarding the real associations. It was found that while higher screen hours, BMI, and various negative feelings are negatively associated with higher subjective health, higher family income, social class, and students' and their mothers' educations are associated with a higher subjective well-being. However, the impacts of the majority variables are interactive. For instance, the impact of mother's education varies based on the genders of students, or the impact of screen hours differs based on family income. In addition, the degree students limit of exercise at home or gym changes based on the negative feeling they experience during the pandemic. Remarkably, during the pandemic while irrationally limiting exercise at home was associated with a lower subjective health, limiting exercise at gym was positively associated with the response.

Behavioral and emotional adaptations of obese and underweight students in response to the COVID-19 pandemic.

Previous studies have investigated the effects of COVID-19 on the general population of college students. However, research evaluating the complex behavioral and psychological impact of the pandemic on both obese and underweight students is currently limited. We used data from a survey conducted from March to April 2020 across 2534 students enrolled in seven US universities. We examined whether the associations between taking various behaviors and being obese and underweight students are unidimensional, or varies based on various negative emotions, and their sociodemographic characteristics. Also, we checked whether emotions of obese and underweight groups are impaired, which consequently might impact taking various cautionary behaviors. The results highlight complex relationships between being obese and underweight students and other considered variables. For instance, despite the associated risk, it was found that obese students are associated with less fear, guilt and irritability due to the pandemic. However, those associations vary based on factors such as level of educations. In addition, while obese students are less likely to avoid a large group of people, the impact changes based on gender. Lack of precautions and emotions is despite the increased risk of hospitalization and death associated with obese and underweight groups. Finally, it was found that there are negative and stable associations between higher social class, income, and the lower likelihood of being obese or underweight. Significant differences and similarities were also found across factors to obese and underweight students.

The Interactive Factors Contributing to Fear of Death.

Despite the importance of the topic of death, a limited comprehensive statistical analysis conducted highlighting the complex association between fear of death and various variables. Thus, this study is conducted to account for the possible complexity by considering all interaction terms after reducing the dimensionality of a dataset by means of recursive feature elimination, followed by the removal of the multi-collinear variables. The results highlighted, for instance, although being married, older and female offset the negative associations of fear of death, their impacts are multiplicative. Also, those who think cryonics is desirable are associated with higher fear of death. For instance, while belief in cryonics is positively associated with fear of death, its association varies depending on the trouble that individuals experience that someday they would not be alive and their marital status.

Contributory factors to the severity of single-vehicle rollover crashes on a mountainous area, generalized additive model.

The purpose of this study was to identify contributory factors to severity of rollover crashes in the mountainous state of Wyoming. These crashes account for more than half of all roadway fatalities in Wyoming, compared with the average of the U.S. rollover-related fatality crashes, which stands at 33%. In this study, the standard generalized linear model (GLM) was extended to the method of generalized additive model (GAM) to determine if giving more flexibility provides more realistic point estimates of the factors to the rollover crash severity. The results highlighted the superiority of the GAM compared with the GLM in terms of confusion matrix accuracy and Akaike Information Criterion (AIC). The results of the GAM highlighted that the majority of important factors that contribute to rollover crash severity are related to drivers' characteristics such as driving while under influence of drugs, being under an emotional condition, driving with no valid driver license, and driving with suspended drivers' license. Also, it was found that the impact of passenger vehicles on the severity of rollover crashes is not stable and varies based on the gender of drivers. Only two predictors were considered based on the smooth functions including posted speed limit and drivers' age. We accounted for non-linearity of those two predictors by means of cubic spline smooth function.

Students' negative emotions and their rational and irrational behaviors during COVID-19 outbreak.

The pandemic has posed an intense threat to the mental health of younger adults. Despite significant efforts in studying various aspects of COVID-19, there is a dearth of evidence on how negative emotions are associated with behaviors. A comparison across associated factors to different negative emotions by means of a unified model is especially missing from the literature. This study was conducted by using the results of a survey conducted across 2,534 students enrolled in 7 states in the US. Various feelings such as sad, irritable, stress and guilt were analyzed in a unified model by means of seemingly unrelated regression (SUR). Questions were asked related to rationally limiting the spread of virus, and questions related to behaviors that seem to be extreme. Irrational behaviors, such as limiting exercise at home due to COVID-19 could be due to experiencing negative emotions, which distort the meaning of events for the students. That behavior, for instance, was found to be positively associated with various negative feelings. In addition, the results highlighted significant differences across emotions in terms of demographic characteristics such as gender and age, and various precautionary actions that students take, such as limiting outdoor activities or limiting social gathering. For instance, it was highlighted while avoiding a large group of people, in spite of its importance in curbing the spread of virus, is negatively associated with various negative emotions, taking a possible nonconsequential precaution of completely limiting all people outside the immediate family result in a higher level of negative emotions. Also, it was found that having a higher body mass index (BMI), self-rated worse health conditions, and limiting outdoor activities have detrimental effects on the mental health of students.

Properties of a Transport Instrument for Measuring Psychological Impacts of Delay on Commuters, Mokken Scale Analysis.

This study assessed the validity of instrument including various negative psychological and physical behaviors of commuters due to the public transport delay. Instruments have been mostly evaluated by parametric method of item response theory (IRT). However, the IRT has been characterized by some restrictive assumptions about the data, focusing on detailed model fit evaluation. The Mokken scale analysis (MSA), as a scaling procedure is a non-parametric method, which does not require adherence to any distribution. The results of the study show that in most regards, our instrument meets the minimum requirements highlighted by the MSA. However, the instrument did not adhere to the minimum requirements of the "scalability" for two variables including "stomach pain" and "increased heart rate". So, modifications were proposed to address the violations. Although MSA technique has been used frequently in other fields, this is one of the earliest studies to implement the technique in the context of transport psychology.

Modeling crashes involving children, finite mixture cumulative link mixed model.

Despite the efforts in the literature review on the traffic safety of children, the majority of past studies mainly focused only on the child's seatbelt status, or its position while ignoring other underlying factors that might contribute to the severity of those crashes. Inclusion of ther factors is especially important for a mountainous state like Wyoming with one of the highest rates of children's traffic fatality in the country. Thus, this study is conducted to fill the gap by identifying important factors contributing to the severity of crashes involving children. Here child is defined as any passengers under 9 years old. A first step in identifying factors to the severity of crashes involving children is implementing a reliable statistical method that could account for heterogeneity across various observations. So, in this study, to account for the heterogeneity in the dataset, the standard cumulative link model (CLM) was extended to the random effect model, while instead of assigning the subjective attribute for random effect, an objective hierarchy through the finite mixture modeling (FMM) was used. The FMM was employed in the context of the CLM to prevent the loss of information due to disaggregation of the dataset into the homogeneous datasets. The comparison results highlighted that the random effect model by the objective hierarchy would result in a significant improvement in the model fit compared with the standard cumulative link model. The results highlighted factors such as safety equipment in use, type of collision, and various drivers' characteristics and actions such as belting condition, alcohol and drug involvement are some of the factors contributing to the severity of child crashes. As expected, the main findings of our results highlighted that various drivers' actions and behaviors are the main causes that children would undergo a higher severity level in crashes. An extensive discussion regarding the implications of the results and the implemented statistical method were given in the context of the manuscript.

Application of machine learning technique for optimizing roadside design to decrease barrier crash costs, a quantile regression model approach.

INTRODUCTION: In-transport vehicles often leave the travel lane and encroach onto natural objects on the roadsides. These types of crashes are called run-off the road crashes (ROR). Such crashes accounts for a significant proportion of fatalities and severe crashes. Roadside barrier installation would be warranted if they could reduce the severity of these types of crashes. However, roadside barriers still account for a significant proportion of severe crashes in Wyoming. The impact of the crash severity would be higher if barriers are poorly designed, which could result in override or underride barrier crashes. Several studies have been conducted to identify optimum values of barrier height. However, limited studies have investigated the monetary benefit associated with adjusting the barrier heights to the optimal values. In addition, few studies have been conducted to model barrier crash cost. This is because the crash cost is a heavily skewed distribution, and well-known distributions such as linear or poison models are incapable of capturing the distribution. A semi-parametric distribution such as asymmetric Laplace distribution can be used to account for this type of sparse distribution. METHOD: Interaction between different predictors were considered in the analysis. Also, to account for exposure effects across various barriers, barrier lengths and traffic volumes were incorporated in the models. This study is conducted by using a novel machine-learning-based cost-benefit optimization to provide an efficient guideline for decision makers. This method was used for predicting barrier crash costs without barrier enhancement. Subsequently the benefit was obtained by optimizing traffic barrier height and recalculating the benefit and cost. The trained model was used for crash cost prediction on barriers with and without crashes. RESULTS: The results of optimization clearly demonstrated the benefit of optimizing the heights of road barriers around the state. Practical Applications: The findings can be utilized by the Wyoming Department of Transportation (WYDOT) to determine the heights of which barriers should be optimized first. Other states can follow the procedure described in this paper to upgrade their roadside barriers.

Rail Transport Delay and Its Effects on the Perceived Importance of a Real-Time Information.

Various psychological feelings that commuters might experience due to the shortcomings of a public transport are a major concern for transport policy makers. Those shortcomings would be translated into various negative psychological feelings, which would consequently tarnish the perceived quality of the public transport system in terms of its characteristics, e.g., the perceived quality of the passengers' information system (PIS). A delay has often been defined as the difference between the real arrival of a transport and the scheduled arrival of based on the PIS. The main question this study seeks to answer is how passengers view the PIS while undergoing various psychological negative impacts due to delay? This is especially important when the PIS is not precise. Previous studies on the importance of real-time information mainly focus on the impact of PIS on the satisfaction of commuters, or the reliability of the public transport. However, they rarely consider the negative psychological impacts that delays might have on commuters, and how those negative feelings might be aggravated by providing inaccurate information for the commuters. The proposed study is based on completed questionnaires by 396 passengers waiting for a rail transport in Malaysia; the rail transport was experiencing frequent long delays due to various mechanical malfunctions. In addition, the PIS provided for the passengers were mainly imprecise, and was updated regularly. The relationship between various considered variables, and a related latent factor, were formed by means of factor analysis. The results of internal consistency and validity highlight acceptable factors to be considered for a structural equation modeling (SEM) model. Three latent factors were found to impact the latent factor of PIS. For instance, it was found that the relationship between motion sickness factor and the response of PIS is not by a direct relationship between those two factors, but through the mediation of a latent physiological factor. On the other hand, the impact of the psychological feelings of the commuter by PIS is higher than its physiological effects. The results of this study have an important managerial implication for policy makers that even if the delay is inevitable, an accurate PIS could be provided to reduce the associated negative feelings of delay. Extensive discussion has been had about identification of a best fit model and process of model's parameters' estimation.

Bayesian hierarchical modelling of traffic barrier crash severity.

Severe vehicle crashes have resulted in a large-scale social and economic loss. As a result, the reduction of those crashes has become one of the key objectives of policy makers. Although traffic barriers have been utilized to reduce the run-of-road crash severity, still those crashes account for a high number of severe crashes. Previous studies of traffic barrier crashes often either ignore the heterogeneity across different traffic barrier types, or just focus on specific types of traffic barriers. Thus, this study developed a Bayesian Hierarchical model (BHM) to identify the contributory factors impacting the severity of traffic barrier crashes while accounting for that heterogeneity. The assessment of model fit, inter-class correlation (ICC) coefficient, and deviance information criterion (DIC) all favoured the use of the BHM. Besides accounting for the heterogeneity between barrier types, the interaction across variables shoulder width and traffic barrier height were incorporated into the analysis. Due to the differences in traffic barrier design and vehicle performance across different roadway classifications, only Wyoming interstate traffic barrier were considered. Results indicated that there is an important interaction term between traffic barrier height and shoulder width so that the impact of these two predictors should not be separated. In addition, having a citation record, negotiating a curve, being a female driver, non-speed compliance, alcohol involvement, and showing emotional signs at the time of crash were factors increasing the severity of traffic barrier crashes. On the other hand, having a turn before hitting a traffic barrier, being a younger driver, and driving in adverse weather conditions were factors that significantly decrease the severity of traffic barrier crashes.

Application of Quantile Mixed Model for modeling Traffic Barrier Crash Cost.

Run-off the road crashes account for a significant proportion of severe injuries to vehicle occupants. Traffic barriers have been installed with an objective to keep vehicles on the roadway, and prevent them from hitting natural obstacles like trees or boulders. However, still injuries and fatalities of barrier crashes account for high proportion of fatalities on roadway. Due to challenging geometrics characteristics of Wyoming's roadway, a high mileage of barriers has been installed in the state. The high mileages of barriers result in a high number of barrier crashes in terms of crash frequency and severity due to high exposure. Previous studies mainly focused on crash frequency or individual crash severity. However, it has been recognized the importance of accounting for both aspects of crash severity, and crash frequency. So, in this study, crashes are aggregated across different barriers, and those crashes were converted into costs by considering the impacts of both crash severity and frequency. However, one of the main challenges of this type of dataset is highly skewness of crash data due to its sparseness nature. An improper use of model distribution of crash cost would result in biased estimations of the covariates, and erroneous results. Thus, in order to address this issue, a semi-parametric method of quantile regression technique was implemented to account for the skewness of the response by relaxing model distribution parameters. Also, to account for the heterogeneity in the dataset due to barriers' types, a random intercept model accounting for the structure of the data was implemented. In addition, interaction terms between significant predictors were considered. Understanding what factors with which magnitude contribute to the barrier crash costs is crucial for the future barriers' optimization process. Thus, contributory factors to barriers crash cost with high, medium, and low values, corresponding to 95th, 70th, and 60th percentiles were considered, and a comparison was made across these models. It was found, for instance, that although factors such as rollover, driving under the influence, and presence of heavy truck all have contributory impacts on the cost of crashes, their impacts are greater on higher quantiles, or higher barriers' costs. These models were compared from various perspectives such as intra class correlation (ICC), and standard error of coefficients. This study highlights the changes in coefficient estimates while modeling crash costs.

Application of multi-group structural equation modelling for investigation of traffic barrier crash severity.

The severity of traffic barrier in the literature has been modelled considering different factors including human, environmental and road/traffic barrier characteristics. However, all these factors are interacting in a complicated way, and a real relationship between these factors is still unclear. A structural equation modelling (SEM) can be adopted to capture the intricate relationships between the contributory factors and latent (unseen) factors. This study was conducted by adopting multi-group SEM to unlock the complicated relationship between confounding factors and traffic barrier crash severity by considering differences across two important groups. Due to the possible difference across different highway systems, multi-group SEM was used instead of standard SEM to account for the differences across highway and interstate roadway system. SEM is a combination of confirmatory and path analysis, which could examine relationship between different observed and latent factors. Besides using factor analysis for identification of latent factors, item/variable cluster analysis was conducted to identify all the latent factors. Although cluster analysis often has been used in other fields, this is the first time this method has been applied in transportation problems for SEM modeling. The inclusion of the factors identified by cluster analysis show an improvement in goodness of fit. This study was conducted to evaluate the traffic barrier crash severity in terms of death, injury and severity of crashes. It examined the nature and causes of severe traffic barrier in Wyoming. The results indicated that different factors contribute to the severity size of traffic barrier crashes including different traffic barrier types, demographic characteristics, weather conditions, and indirect impact of force direction. The results indicated that collision force is a latent factor with highest impact on crash severity compared with other latent factors. Different models with different number of latent were compared based on different goodness-of-fit indices and a best model, with an acceptable model fit, was selected between them. A more discussion about the model presented in the manuscript.

Examination of the severity of two-lane highway traffic barrier crashes using the mixed logit model.

INTRODUCTION: Vehicles in transport sometimes leave the travel lane and encroach onto natural or artificial objects on the roadsides. These types of crashes are called run-off the road crashes, which account for a large proportion of fatalities and severe crashes to vehicle occupants. In the United States, there are about one million such crashes, with roadside features leading to one third of all road fatalities. Traffic barriers could be installed to keep vehicles on the roadways and to prevent vehicles from colliding with obstacles such as trees, boulder, and walls. The installation of traffic barriers would be warranted if the severity of colliding with the barrier would be less severe than colliding with other fix objects on the sides of the roadway. However, injuries and fatalities do occur when vehicle collide with traffic barriers. A comprehensive analysis of traffic barrier features is lacking due to the absence of traffic barrier features data. Previous research has focused on simulation studies or only a general evaluation of traffic barriers, without accounting for different traffic barrier features. METHOD: This study is conducted using an extensive traffic barrier features database for the purpose of investigating the impact of different environmental and traffic barrier geometry on this type of crash severity. This study only included data related to two-lane undivided roadway systems, which did not involve median barrier crashes. Crash severity is modeled using a mixed binary logistic regression model in which some parameters are fixed and some are random. RESULTS: The results indicated that the effects of traffic barrier height, traffic barrier offset, and shoulder width should not be separated, but rather considered as interactions that impact crash severity. Rollover, side slope height, alcohol involvement, road surface conditions, and posted speed limit are some factors that also impact the severity of these crashes. The effects of gender, truck traffic count, and time of a day were found to be best modeled with random parameters in this study. The effects of these risk factors are discussed in this paper. PRACTICAL APPLICATIONS: Results from this study could provide new guidelines for the design of traffic barriers based upon the identified roadway and traffic barrier characteristics.

Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming.

INTRODUCTION: The state of Wyoming, like other western United States, is characterized by mountainous terrain. Such terrain is well noted for its severe downgrades and difficult geometry. Given the specific challenges of driving in such difficult terrain, crashes with severe injuries are bound to occur. The literature is replete with research about factors that influence crash injury severity under different conditions. Differences in geometric characteristics of downgrades and mechanics of vehicle operations on such sections mean different factors may be at play in impacting crash severity in contrast to straight, level roadway sections. However, the impact of downgrades on injury severity has not been fully explored in the literature. This study is thus an attempt to fill this research gap. In this paper, an investigation was carried out to determine the influencing factors of crash injury severities of downgrade crashes. METHOD: Due to the ordered nature of the response variable, the ordered logit model was chosen to investigate the influencing factors of crash injury severities of downgrade crashes. The model was calibrated separately for single and multiple-vehicle crashes to ensure the different factors influencing both types of crashes were captured. RESULTS: The parameter estimates were as expected and mostly had signs consistent with engineering intuition. The results of the ordered model for single-vehicle crashes indicated that alcohol, gender, road condition, vehicle type, point of impact, vehicle maneuver, safety equipment use, driver action, and annual average daily traffic (AADT) per lane all impacted the injury severity of downgrade crashes. Safety equipment use, lighting conditions, posted speed limit, and lane width were also found to be significant factors influencing multiple-vehicle downgrade crashes. Injury severity probability plots were included as part of the study to provide a pictorial representation of how some of the variables change in response to each level of crash injury severity. CONCLUSION: Overall, this study provides insights into contributory factors of downgrade crashes. The literature review indicated that there are substantial differences between single- and multiple vehicle crashes. This was confirmed by the analysis which showed that mostly, separate factors impacted the crash injury severity of the two crash types. Practical applications: The results of this study could be used by policy makers, in other locations, to reduce downgrade crashes in mountainous areas.