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The transition of control between partially automated vehicles and drivers is an important part of the operational design domain and poses unique and important design issues. One approach for enhancing the design of the transition of control mechanisms is to predict driver behavior during a takeover by analyzing his/her state before a takeover. Although there is a wealth of existing literature on modeling the prediction of driver behavior, little is known about the prediction of takeover performance (e.g., driver error) and its underlying data structure (e.g., window sizes or the inclusion of certain features). Thus, the goal of this study is to predict driver error during a takeover event using supervised machine learning algorithms for various window sizes. Three machine learning algorithms (i.e., decision tree, random forest, and support vector machine with a radial basis kernel) were applied to granular driving performance, physiological, and glance data from a driving simulator experiment examining automated vehicle driving. The results showed that a random forest algorithm with an area under the receiver operating characteristic curve of 0.72, trained on a 3 s window before the takeover time, had the best performance with regard to classifying driver error accurately. In addition, we identified the 10 most important predictors that resulted in the best error prediction performance. The results of this study could be useful in developing algorithms for driver state that could be integrated into highly automated systems and, potentially, improve the takeover process.

This systematic review documents current best practices, standards, and approaches for transportation safety data analytics. While standards exist for defining measures, there are few available standards or guides for processing driving and driver data. Standards are crucial for ensuring repeatability and appropriate cost-benefit decisions. The review identified 36 relevant studies describing behavioral and physiological measures. Most studies do not comprehensively report data processing steps. Of the studies that did report data processing steps, few analyzed the impact of decisions made during data processing on algorithm performance. Most studies were conducted in a controlled simulator environment and may not generalize to naturalistic settings. The findings show that driver behavior and physiological data show efficacy for detecting fatigue, distraction, stress, and driver errors. The results of these studies may necessitate additional data processing standards and future work should focus on measuring the effects of data decisions on model performance.

Previous research has shown that white males tend to perceive risks from environmental exposures as lower than women and members of minority populations, often referred to as the white male effect. However, this effect was mostly demonstrated without regard to the actual lived environment experienced by the study participants. There is growing evidence that differences in risk perceptions cannot be adequately explained through race or gender. This cross-sectional study collected survey data from residents of Manchester, a small neighborhood in Houston, Texas, characterized by industrial sites, unimproved infrastructure, nuisance flooding, and poor air quality. Trained community members attempted a complete census within the geographically compact neighborhood. Logistic regression was used to estimate the relative effect of race on environmental health perceptions adjusted for generational age. In contrast to previous research, our study (N = 109) showed that nonwhite individuals perceived a lower environmental health risk compared with their white counterparts. Comparing female and minority racial groups with white males showed that on most issues, white males had the highest perception of risk. For example, adjusted for age, nonwhite respondents perceived the risk of contact with standing water as significantly lower than white respondents (odds ratio = 0.34; 95% confidence interval = 0.12–0.93). This study supports the hypothesis that when environmental conditions experienced by individuals are the same, minority groups tend to underestimate their risk compared with white males. One possible explanation put forth is that communal norms are created within minority populations through generations of exposure to negative environmental conditions compared with white populations. [ABSTRACT FROM AUTHOR]

Objective. Acute exposure to pesticides is associated with nausea, headaches, rashes, eye irritation, seizures, and, in severe cases, death. We characterized pesticide-related hospitalizations in Texas among children and teenagers for 2004–2013 to characterize exposures in this population, which is less well understood than pesticide exposure among adults.Methods. We abstracted information on pesticide-related hospitalizations from hospitalization data using pesticide-related International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and E-codes. We calculated the prevalence of pesticide-related hospitalizations among children and teenagers aged ≤19 years for all hospitalizations, unintentional exposures, intentional exposures, pesticide classifications, and illness severity. We also calculated age- and sex-specific prevalence of pesticide-related hospitalizations among children.Results. The prevalence of pesticide-related hospitalizations among children and teenagers was 2.1 per 100,000 population. The prevalence of pesticide-related hospitalizations per 100,000 population was 2.7 for boys and 1.5 for girls. The age-specific prevalence per 100,000 population was 5.3 for children aged 0–4 years, 0.3 for children and teenagers aged 5–14 years, and 2.3 for teenagers aged 15–19 years. Children aged 0–4 years had the highest prevalence of unintentional exposures, whereas teenagers aged 15–19 years had the highest prevalence of intentional exposures. Commonly reported pesticide categories were organophosphates/carbamates, disinfectants, rodenticides, and other pesticides (e.g., pyrethrins, pyrethroids). Of the 158 pesticide-related hospitalizations, most were coded as having minor (n=86) or moderate (n=40) illness severity.Conclusion. Characterizing the prevalence of pesticide-related hospitalizations among children and teenagers leads to a better understanding of the burden of pesticide exposures, including the type of pesticides used and the severity of potential health effects. This study found differences in the frequency of pesticide-related hospitalizations by sex, age, and intent (e.g., unintentional vs. intentional).

Injury is the leading cause of death among American youth, killing more 11-year-olds than all other causes combined. Children with symptoms of externalizing behavior disorders such as attention-deficit/hyperactivity disorder (ADHD) and conduct disorder (CD) may have increased risk. Our aims were to determine: (1) whether increasing symptoms of ADHD and CD associate positively with injuries among a community sample of fifth graders; and (2) whether symptoms of ADHD and CD have a multiplicative rather than additive association with injuries among the sample.

Agriculture has been documented to be one of the most hazardous work environments for both adults and children. Adolescents may be especially vulnerable to adverse health effects from agricultural exposures due to the rapid growth and development experienced during those years. Separating the occupational, economic, and social issues in this population is difficult. Weak regulatory protection, lack of compliance with existing regulations, and gaps in service provision characterize the working conditions of adolescent farmworkers. Although there is increasing research on the impact of work organization on mental and physical health in adult working populations, there is a scarcity of research focused on this concept in young workers—and it remains virtually unaddressed in young farmworkers.Work characteristics of the informal work sector, better delineated in international literature, should be considered when planning research or interventions in this at-risk population. Further, the population of adolescent farmworkers is diverse, and research strategies and interventions need to be targeted and tailored to the heterogeneous groups. This article addresses some of the nontraditional work factors associated with the less-than-formal work organization and environments in the farmworker adolescent population and how these factors may inform the planning of research and interventions. Specifically, mobility, cultural patterns and social networks, alternative sampling strategies, alternative delivery of health care and education, and involvement of a wide range of players in the work environment of adolescent farmworkers should all be considered when conducting research or planning programs for this population.

Adolescents are particularly vulnerable to the impacts of pesticide exposures, which can be assessed using surveys, environmental measurements, and biomonitoring. Biomonitoring of blood cholinesterase can be used to determine if an individual has been exposed to pesticides. A limitation of blood cholinesterase testing can be the use of a laboratory as well as time to receive results. In addition to laboratory tests, there are fingerstick cholinesterase (ChE) tests, which can eliminate the need for laboratory testing. Some populations, such as farmworkers, would benefit through fingerstick ChE tests. The objective of this pilot study is to determine the feasibility (eg, can the testing be used to assess ChE levels) of using fingerstick ChE testing in adolescent populations living along the Texas-Mexico border where adolescents who often engage in farm work live. A sub-objective was to explore differences in ChE levels by sex. The Model 400 Test-Mate ChE kit by EQM Research Inc (Cincinnati, OH) was used to assess for ChE inhibition in the participants, specifically acetylcholinesterase (AChE), which is 1 of the 2 ChE enzymes. During the postassessment, males had a mean AChE value of 3.75 U/mL (95% CI 3.51-3.98); whereas females had a mean AChE value of 2.86 U/mL (95% CI 2.64-3.08), which was statistically significant. Overall, the study supports the use of field ChE testing in adolescent populations with a small percentage (6.90%) refusing to complete ChE testing.

The article focuses on a study to investigate the prevalence and risk factors for hypertension, obesity, diabetes and back pain in farmworker adolescents. One-quarter to one-third of the study participants were referred for follow up care. The author notes that the ultimate goal of the research would be to use the findings in policy development to prevent chronic diseases and back pain.