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This study contributes to efforts towards creating livable communities, by identifying and proposing countermeasures for factors that are associated with residential area crashes. Three risky driving pre-crash actions: driving under the influence of alcohol or drug, aggressive driving, and speeding were considered. The study used residential area crash data from Alabama, USA and developed a latent class multinomial logit model to identify factors associated with the pre-crash actions. The results showed that male drivers had 0.44% and 2.09% higher chances to be at fault in driving under the influence of alcohol or drug and aggressive driving crashes, respectively, while female drivers were found to have a 1.82% higher likelihood to be at fault in speeding-related crashes but less likely to be at fault in aggressive driving or driving under the influence of alcohol or drug-related crashes in residential areas. Speeding and driving under the influence of were found to be the leading contributing factors in rural area residential crashes whereas aggressive driving was more pronounced in urban areas. These findings provide a basis for the implementation of appropriate crash countermeasures to reduce residential area crashes, as part of efforts geared towards promoting community safety and livability across the state.

This study was carried out to examine the factors associated with motorcycle crash severity in Alabama, under different manner of crash and causal scenarios using mixed logit modeling. Three crash mechanisms were considered in this study: single-vehicle motorcycle crash with motorcyclist at fault, multi-vehicle collision between a motorcycle and another vehicle with motorcyclist being at fault, and motorcyclist not at fault in a collision between a motorcycle and another vehicle. The model estimation results showed that crashes that happened in rural areas were more likely to be severe, irrespective of the causal unit or manner of collision. The results also show that fatigue among motorcyclists was associated with severe injury, whereas driver fatigue was linked to no injury outcome. Further, it was found that risky behaviors such as speeding, driving/riding under the influence of alcohol or drugs, driving/riding with invalid license were significantly associated with severe injury outcome. Developing the injuryseverity models based on the segmented crash data has helped to reveal some similarities and differences in crash outcomes based on the crash mechanism and the at-fault road user. It is expected that these findings would provide a datadriven evidence to improve motorcycle safety in the state. [ABSTRACT FROM AUTHOR]

The present study utilised the social determinants of health (SDH) framework to see whether indicators of the framework have an impact on anxiety and depression of people living in rural Black Belt communities in Alabama. Data from a convenient sample of 159 African-Americans aged 18 or older were from two sites in rural Alabama. The levels of anxiety and depression were measured by the Patient Health Questionnaire-9 (PHQ9). Multiple linear regression model was used to examine the association between SDH and anxiety and depression level of participants. The mean PHQ9 score of participants was 5.57 out of twenty-seven. Four SDH were significantly related to PHQ9 levels amongst participants: participants with higher food insecurity scores, higher transportation needs and higher threats to interpersonal safety tended to have higher scores in PHQ9. Moreover, health literacy levels were negatively associated with PHQ9 scores amongst participants. Our study highlights understanding SDH specifically for residents in rural communities that are socially and culturally isolated is important for developing preventive approaches that enhance access to mental health treatments. A comprehensive public health policy that incorporates our study findings is needed for the rural areas of the USA. [ABSTRACT FROM AUTHOR]

A higher Body Mass Index (BMI) is often associated with higher risks for health-related issues (e.g., arthritis, cancer). Lifestyle behaviors (e.g., healthy diet) are believed to be important factors closely related to people's BMI and health. Among the behaviors, active travel modes (e.g., walking and bicycling) may be an important factor correlated with BMI. Such travel behaviors can somehow be affected by community design (e.g., walkability) and environmental features (e.g., air quality). These factors exhibit complex relationships with a person's health as they may either directly or indirectly correlate with health through travel behaviors. To this end, this study employed a pathway analysis approach to unveil potential health benefits relating to community living environments and travel behaviors to BMI and health. Specifically, this study integrated national-level health and travel behavior datasets and developed rigorous multi-level hierarchical models to explore and explain how the health pathways are related to community living environments, travel behaviors, and BMI. The modeling results showed that limited walking facilities and unhealthy air quality could significantly associate with people's reduced willingness to engage in active travel modes, which correlated with subsequent higher obesity risk, and a higher risk of poor health. More importantly, if walking or bicycling facilities were not fully utilized, health benefits gained from these facilities were found to be substantially reduced. Other significant health-related behaviors (e.g., sleep/exercise time, fruit consumption, smoking) are also discussed in detail. [ABSTRACT FROM AUTHOR]