Notes

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high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas.
Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity.

In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant v performance.
Research on risk for child pedestrian injury risk focuses primarily on cognitive risk factors, but emotional states such as fear may also be relevant to injury risk. The current study examined children's perception of fear in various traffic situations and the relationship between fear perception and pedestrian decisions.

150 children aged 6-12-years old made pedestrian decisions using a table-top road model. Their perceived fear in the pedestrian context was assessed.

Children reported greater emotional fear when they faced quicker traffic, shorter distances from approaching traffic, and red rather than green traffic signals. Children who were more fearful made safer pedestrian decisions in more challenging traffic situations. However, when the least risky traffic situation was presented, fear was associated with more errors in children's pedestrian decisions fearful children failed to cross the street when they could have done so safely. Perception of fear did not vary by child age, although safe pedestrian decisions were more common among the older children.

Children's emotional fear may predict risk-taking in traffic. When traffic situations are challenging to cross within, fear may appropriately create safer decisions. However, when the traffic situation is less risky, feelings of fear could lead to excessive caution and inefficiency. Practical applications Child pedestrian safety interventions may benefit by incorporating activities that introduce realistic fear of traffic risks into broader safety lessons.
Children's emotional fear may predict risk-taking in traffic. When traffic situations are challenging to cross within, fear may appropriately create safer decisions. However, when the traffic situation is less risky, feelings of fear could lead to excessive caution and inefficiency. Practical applications Child pedestrian safety interventions may benefit by incorporating activities that introduce realistic fear of traffic risks into broader safety lessons.
Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources.

This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity whindings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.
In this study we explore the added value of bicycle crash descriptions from open text fields in hospital records from the Aarhus municipality in Denmark. We also explore how bicycle crash data from the hospital complements crash data registered by the police in the same area and time period.

The study includes 5,313 Danish bicycle crashes, of which 4,205 were registered at the hospital and 1,078 by the police. All crashes occurred from 2010 to 2015. We performed an in-depth analysis of the open text fields on hospital records to identify factors associated with each crash using four categories bicyclist, road, bicycle, and the other party. We employed the chi-squared test to compare the distribution of variables between crashes registered at the hospital and by the police. this website A binary logit model was used to estimate the probability that a crash factor is identified, and that each crash factor is associated with a single-bicycle crash.

The open-ended text fields in hospital records provide detailed informaut crash-associated factors as well as information about a larger number of bicycle crashes, particularly single-bicycle crashes. Practical implication Efforts to improve access to detailed information about bicycle crashes are needed to provide a better basis for bicycle crash prevention.
Falls among older adults are a significant health concern affecting more than a quarter of older adults (age 65+). Certain fall risk factors, such as medication use, increase fall risk among older adults (age 65+).

The aim of this study is to examine the association between antidepressant-medication subclass use and self-reported falls in community-dwelling older adults.

This analysis used the 2009-2013 Medicare Current Beneficiary Survey, a nationally representative panel survey. A total of 8,742 community-dwelling older adults, representing 40,639,884 older Medicare beneficiaries, were included. We compared self-reported falls and psychoactive medication use, including antidepressant subclasses. These data are controlled for demographic, functional, and health characteristics associated with increased fall risk. Descriptive analyses and multivariate logistic regression analyses were conducted using SAS 9.4 and Stata 15 software.

The most commonly used antidepressant subclass were selective serotoninards reducing fall risk among their older patients by minimizing the use of certain medications when potential risks outweigh the benefits.
SSRI and SNRI are associated with increased risk of falling after adjusting for important confounders. Medication use is a modifiable fall risk factor in older adults and can be targeted to reduce risk of falls. Practical Applications Use of selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors increased the risk of falling in older adults by approximately 30%, even after controlling for demographic, functional, and health characteristics, including depression. Health care providers can work towards reducing fall risk among their older patients by minimizing the use of certain medications when potential risks outweigh the benefits.
National estimates for nonfatal self-directed violence (SDV) presenting at EDs are calculated from the National Electronic Injury Surveillance System - All Injury Program (NEISS-AIP). In 2005, the Centers for Disease Control and Prevention and Consumer Product Safety Commission added several questions on patient characteristics and event circumstances for all intentional, nonfatal SDV captured in NEISS-AIP. In this study, we evaluated these additional questions along with the parent NEISS-AIP, which together is referred to as NEISS-AIP SDV for study purposes.

We used a mixed methods design to evaluate the NEISS-AIP SDV as a surveillance system through an assessment of key system attributes. We reviewed data entry forms, the coding manual, and training materials to understand how the system functions. To identify strengths and weaknesses, we interviewed multiple key informants. Finally, we analyzed the NEISS-AIP SDV data from 2018-the most recent data year available-to assess data quality by examining the nd characteristics associated with nonfatal SDV that are not regularly available through other data sources. With some modifications to data fields and yearly analysis of the additional SDV questions, NEISS-AIP SDV can be a valuable tool for informing suicide prevention. Practical Applications NEISS-AIP may consider updating the SDV questions and responses and analyzing SDV data on a regular basis. Findings from analyses of the SDV data may lead to improvements in ED care.
Reducing the likelihood of freeway secondary crashes will provide significant safety, operational and environmental benefits. This paper presents a method for assessing the likelihood of freeway secondary crashes with Adaptive Signal Control Systems (ASCS) deployed on alternate routes that are typically used by diverted freeway traffic to avoid any delay or congestion due to a freeway primary crash.

The method includes four steps (1) identification of secondary crashes, (2) verification of alternate routes, (3) assessment of the likelihood of secondary crashes for freeways with ASCS deployed on alternate routes and non-ASCS (i.e. pre-timed, semi- or fully-actuated) alternate routes, and (4) investigation of unobserved heterogeneity of the likelihood of freeway secondary crashes. Four freeway sections (i.e., two with ASCS deployed on alternate routes and two non-ASCS alternate routes) in South Carolina are considered.

Findings from the logistic regression modeling reveal significant reduction in the likes Based on the findings, it is recommended that the South Carolina Department of Transportation (SCDOT) considers deploying ASCS on alternate routes parallel to freeway sections where high percentages of secondary crashes are found.
Adaptive Signal Control System (ASCS) can improve both operational and safety benefits at signalized corridors.

This paper develops a series of models accounting for model forms and possible predictors and implements these models in Empirical Bayes (EB) and Fully Bayesian (FB) frameworks for ASCS safety evaluation studies. Different models are validated in terms of the ability to reduce the potential bias and variance of prediction and improve the safety effectiveness estimation accuracy using real-world crash data from non-ASCS sites. This paper then develops the safety effectiveness of ASCS at six different corridors with a total of 65 signalized intersections with the same type of ASCS, in South Carolina.

Validation results show that the FB model that accounts for traffic volume, roadway geometric features, year factor, and spatial effects shows the best performance among all models. The study findings reveal that ASCS reduces crash frequencies in the total crash, fatal and injury crash, and angle crash for most of the intersections.
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