Urban Planning & Public Policy

Primary care providers (PCPs) are increasingly called upon to screen for and treat depression. However, PCPs often lack the training to diagnose and treat depression. We designed an innovative 12-month evidence and mentorship-based primary care psychiatric training program entitled the University of California, Irvine (UCI) School of Medicine Train New Trainers Primary Care Psychiatry (TNT PCP) Fellowship and examined whether this training impacted clinician prescription rates for antidepressants. We retrieved information on 18,844 patients and 192 PCPs from a publicly insured health program in Southern California receiving care between 2017 and 2021. Of the 192 PCPs, 42 received TNT training and 150 did not. We considered a patient as exposed to the providers TNT treatment if they received care from a provider after the provider completed the 1-year fellowship. We utilized the number of antidepressant prescriptions per patient, per quarter-year as the dependent variable. Linear regression models controlled for provider characteristics and time trends. Robustness checks included clustering patients by provider identification. After PCPs completed TNT training, exposed patients received 0.154 more antidepressant prescriptions per quarter-year relative to expected levels (p < 0.01). Clustering of standard errors by provider characteristics reduced precision of the estimate (p < 0.10) but the direction and magnitude of the results were unchanged. Early results from the UCI TNT PCP Fellowship demonstrate enhanced antidepressant prescription behavior in PCPs who have undergone TNT training. A novel, and relatively low-cost, clinician training program holds the potential to empower PCPs to optimally deliver depression treatment.

The literatures on global commodity chains and global value chains rest on an unquestioned assumption: the continual expansion of globalization. The Trump Administration's trade wars challenged this foundational assumption and even today the new Biden regime also hints at the shift away from global supply chains. We find that the prior administration’s efforts caused continued disruption of long-established commodity chains in steel, aluminum, automobiles, and other manufactured products. Flows of raw materials, intermediate products and components, and finished goods now confront higher costs. Firms continue efforts to restructure commodity chains in ways that will require the disarticulation of some nodes and the creation of new nodes. We claim that these trade wars and breakdown of global commodity chains (GCCs) may in fact mark the start of the breakdown of the U.S.-led world order. This shift harkens the onset of a new era of economic and geopolitical conflict. A key question: has this disruption of old patterns and rise of new ones continued in the post-Trump era? Does the familiar pattern of globalization continue – or is competition, contestation and disarticulation leading to sectoral economic changes that drive larger patterns of economic ascent, dominance, and decline in the world economy?.

The literatures on global commodity chains and global value chains rest on an unquestioned assumption: the continual expansion of globalization. The Trump Administration's trade wars challenged this foundational assumption and even today the new Biden regime also hints at the shift away from global supply chains. We find that the prior administration’s efforts caused continued disruption of long-established commodity chains in steel, aluminum, automobiles, and other manufactured products. Flows of raw materials, intermediate products and components, and finished goods now confront higher costs. Firms continue efforts to restructure commodity chains in ways that will require the disarticulation of some nodes and the creation of new nodes. We claim that these trade wars and breakdown of global commodity chains (GCCs) may in fact mark the start of the breakdown of the U.S.-led world order. This shift harkens the onset of a new era of economic and geopolitical conflict. A key question: has this disruption of old patterns and rise of new ones continued in the post-Trump era? Does the familiar pattern of globalization continue – or is competition, contestation and disarticulation leading to sectoral economic changes that drive larger patterns of economic ascent, dominance, and decline in the world economy?.

Cities are making infrastructure investments to make travel by bicycle safer and more attractive. A challenge for promoting bicycling is effectively using data to support decision making and ensuring that data represent all communities. However, ecologists have been addressing a similar type of question for decades and have developed an approach to stratifying landscapes based on ecozones or areas with homogenous ecology. Our goal is to classify street and path segments and map streetscape categories by applying ecological classification methods to diverse spatial data on the built environment, communities, and bicycling. Piloted in Ottawa, Canada, we use GIS data on the built environment, socioeconomics and demographics of neighborhoods, and bicycling infrastructure, behavior, and safety, and apply a k-means clustering algorithm. Each street or path, an intuitive spatial unit that reflects lived experience in cities, is assigned a streetscape category: bicycling destination; wealthy neighborhoods; urbanized; lower income neighborhoods; and central residential streets. We demonstrate how streetscape categories can be used to prioritize monitoring (counts), safety, and infrastructure interventions. With growing availability of continuous spatial data on urban settings, it is an opportune time to consider how street and path classification approaches can help guide our data collection, analysis, and monitoring. While there is no one right answer to clustering, care must be taken when selecting appropriate input variables, the number of categories, and the correct spatial unit for output. The approach used here is designed for bicycling application, yet the methods are applicable to other forms of active transportation and micromobility.

Objectives: Longitudinal studies of the relationship between neighborhood change and changes in crime typically focus exclusively on the net level of change in key socio-demographic characteristics. Methods: We instead propose a demographic accounting strategy that captures the composition of neighborhood change: our measures capture which types of people are more likely to leave, stay, or enter the neighborhood. We use data for 3,325 tracts in the Southern California region over nearly two decades of 2000–2010 and 2010–2017 and construct flow measures based on race/ethnicity; the length of residence of owners and renters; the age structure. Results: These flow measures improve the predictive power of the models—implying important theoretical insights. Neighborhoods with higher percentages of middle-aged residents who recently entered the neighborhood exhibit larger increases in violent and property crime. The relative stability of those in the highest crime-prone ages (aged 15–29) is associated with the largest increases in violent and property crime. The greater loss of Black and Asian residents decreased crime while moderate outflows of Latinos increased crime. The mobility of long- and short-term renters was related to crime changes. Conclusions: This new technique will likely encourage further theoretical innovation for the neighborhoods and crime literature.

Environmental impact assessment (EIA) is a decision support tool that analyzes the environmental and social impacts of infrastructure projects. This paper focuses on the California Environmental Quality Act (CEQA), a law requiring EIA use in California, to examine where new infrastructure is proposed and whether EIA can shape infrastructure distribution and environmental justice through the review process. We analyze the temporal and spatial distribution of more than 7000 infrastructure projects and their environmental impacts as proposed under CEQA from 2011 to 2020. Using fixed-effects negative binomial regression to model the association between the number of initiated projects and existing socioeconomic and environmental conditions by census tract, and multinomial logistic regression to investigate determinants of a project's level of environmental review, we find an unequal distribution of infrastructure. We find that socio-economically vulnerable communities and those with greater burden of environmental pollution are less likely to be the site of newly proposed infrastructure, but that proposed projects tend to be beneficial, less-polluting infrastructure like parks or schools that could help redress past injustices. Moreover, projects proposed in vulnerable communities are less likely to receive the most stringent reviews or have their impacts mitigated. These findings suggest that CEQA interacts with distributive justice in contradictory ways. They also highlight the need to separately consider environmental amenities versus harms such that EIA processes do not stand as a barrier to constructing beneficial infrastructure in environmental justice communities.

Objective

Study design

Results

Conclusion