Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas

Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.

Medical Marijuana Dispensaries and Referrals for Child Maltreatment Investigations

The changing legal status of marijuana in the United States has increased access to the drug through medical marijuana dispensaries. Limited research exists that examines the effects of these dispensaries on social problems including child maltreatment. The current study examines how medical marijuana dispensaries may affect referrals for child abuse and neglect investigations. Data are analyzed from 2,342 Census tracts in Los Angeles County, California. Locations of medical marijuana dispensaries were obtained through Weedmaps.com . Using conditionally autoregressive models, local and spatially lagged dispensaries show a positive relationship to rates of referrals in the unadjusted models. However, when we adjust for alcohol outlet density and measures of social disorganization, this relationship is no longer significant. Although this study does not find a relationship between medical marijuana dispensaries and referrals for investigations of child maltreatment, it should not be considered a definitive finding of this relationship. The increasing number of states that are allowing marijuana to be used for medical and recreational purposes is resulting in more people using the drug and the effects on parenting are still unknown.

Opioid-related diagnoses and HIV, HCV and mental disorders: using Pennsylvania hospitalisation data to assess community-level relationships over space and time

BackgroundWe assessed the community-level spatiotemporal connexions between hospitalisations for common opioid comorbidities (HIV, hepatitis C (HCV) and mental disorders) and opioid-related hospitalisations in the current and previous year.MethodsWe used Bayesian hierarchical spatiotemporal Poisson regression with conditionally autoregressive spatial effects to assess counts of HCV-related, HIV-related and mental disorder–related hospitalisations at the ZIP code level from 2004 to 2014 in Pennsylvania. Models included rates of current-year and previous-year opioid-related hospitalisations as well as covariates measuring demographic and environmental characteristics.ResultsAfter adjusting for measures of demographic and environmental characteristics, current-year and previous-year opioid-related hospitalisations were associated with higher risk of HCV, HIV and mental disorders. The relative risks and 95% credible intervals for previous-year opioid-related hospitalisations were 1.092 (1.078 to 1.106) for HCV, 1.098 (1.068 to 1.126) for HIV and 1.020 (1.013 to 1.027) for mental disorders.ConclusionPrevious-year opioid-related hospitalisations are connected to common comorbid conditions such as HCV, HIV and mental disorders, illustrating some of the broader health-related impacts of the opioid epidemic. Public health interventions focused on the opioid epidemic must consider individual community needs and comorbid diagnoses.

The 50 American States in Space and Time: Applying Conditionally Autoregressive Models

Spatial conditionally autoregressive (CAR) models in a hierarchical Bayesian framework can be informative for understanding state politics, or any similar population of border-defined observations. This article explains how a hierarchical CAR model is specified and estimated and then uses Monte Carlo analyses to show when the CAR model offers efficiency gains. We apply this model to data structures common to state politics: A cross-sectional example replicates Erikson, Wright and McIver’s (1993) Statehouse Democracy model and a multilevel panel model example replicates Margalit’s (2013) study of social welfare policy preferences. The CAR model fits better in each case and some inferences differ from models that ignore geographic correlation.