Workforce Initiatives to Advance Health Equity and Diverse Representation.

The persistence of health inequity and the need for workforce diverse representation within child and adolescent psychiatry require systemic solutions. There are recommendations and strategies particularly for the training programs with "all of the above" approach to tackle these complex systemic issues. One of the ways is to think through existing and innovative training pipelines by making them less leaky, enhancing quality, expanding the type and size, and connecting them to reach children and adolescents in need.

Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique.

Street crime is a common social problem that threatens the security of people's lives and property. Understanding the influencing mechanisms of street crime is an essential precondition for formulating crime prevention strategies. Widespread concern has contributed to the development of streetscape environment features as they can significantly affect the occurrence of street crime. Emerging street view images are a low-cost and highly accessible data source. On the other hand, machine-learning models such as XGBoost (eXtreme Gradient Boosting) usually have higher fitting accuracies than those of linear regression models. Therefore, they are popular for modeling the relationships between crime and related impact factors. However, due to the "black box" characteristic, researchers are unable to understand how each variable contributes to the occurrence of crime. Existing research mainly focuses on the independent impacts of streetscape environment features on street crime, but not on the interaction effects between these features and the community socioeconomic conditions and their local variations. In order to address the above limitations, this study first combines street view images, an objective detection network, and a semantic segmentation network to extract a systematic measurement of the streetscape environment. Then, controlling for socioeconomic factors, we adopted the XGBoost model to fit the relationships between streetscape environment features and street crime at the street segment level. Moreover, we used the SHAP (Shapley additive explanation) framework, a post-hoc machine-learning explainer, to explain the results of the XGBoost model. The results demonstrate that, from a global perspective, the number of people on the street, extracted from street view images, has the most significant impact on street property crime among all the street view variables. The local interpretability of the SHAP explainer demonstrates that a particular variable has different effects on street crime at different street segments. The nonlinear associations between streetscape environment features and street crime, as well as the interaction effects of different streetscape environment features are discussed. The positive effect of the number of pedestrians on street crime increases with the length of the street segment and the number of crime generators. The combination of street view images and interpretable machine-learning techniques is helpful in better accurately understanding the complex relationships between the streetscape environment and street crime. Furthermore, the readily comprehensible results can offer a reference for formulating crime prevention strategies.

Assessment spatio-temporal coupling coordination relationship between mountain rural ecosystem health and urbanization in Chongqing municipality, China.

Coercive and constraints mechanism analysis between rural ecosystem health (REH) and urbanization is required to comprehend spatio-temporal coupling coordination relationship and promote rural ecosystem health development under the pressure of urbanization, especially those with dramatic changes in Chinese countryside. This paper, taking Chongqing municipality as the case study, analyzed the temporal and spatial characteristics of the coupling coordination between urbanization and REH of 38 districts and counties from 2010 to 2018 with support of coordination coupling degree (CCD) model, entropy method, and GIS spatial methods. The main conclusions were as follows: (1) the EUS (34.84%) and RSceS (35.64%) separately made the highest share contribution to urbanization and REH, the proportion of non-agricultural population in total population and consumption of farmland chemical fertilizer intensity were the main two indicators. (2) The study has confirmed the existence of an obvious differentiated U-shaped curves between urbanization and REH, a dynamic coupling coordination-X was found to exist, which conformed to a linear continuous growth curve, and has transitioned to "high quality coupling coordination" stage from "severe disorder" stage from 2000 to 2018; (3) the spatial differentiation characteristics of coupling coordination in 2018 have shown a significant regional differences pattern, and the coupling coordination in more developed areas was generally higher than most developed areas and less developed areas, namely western Chongqing > metropolitan areas > northeastern Chongqing and southeastern Chongqing. This report could provide policy implications for Chinese decision makers to formulate sustainable measures to balance urbanization development and REH protection.

Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States.

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study's findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.

Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA.

The U.S. has merely 4% of the world population, but contains 25% of the world's COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

Assessment of the pivot shift using inertial sensors.

The pivot shift test is an important clinical tool used to assess the stability of the knee following an injury to the anterior cruciate ligament (ACL). Previous studies have shown that significant variability exists in the performance and interpretation of this manoeuvre. Accordingly, a variety of techniques aimed at standardizing and quantifying the pivot shift test have been developed. In recent years, inertial sensors have been used to measure the kinematics of the pivot shift. The goal of this study is to present a review of the literature and discuss the principles of inertial sensors and their use in quantifying the pivot shift test.