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Results of an audit study conducted during the 2016 election cycle demonstrate that bias toward Latinos observed during the 2012 election has persisted. In addition to replicating previous results, we show that Arab/Muslim Americans face an even greater barrier to communicating with local election officials, but we find no evidence of bias toward blacks. An innovation of our design allows us to measure whether e-mails were opened by recipients, which we argue provides a direct test of implicit discrimination. We find evidence of implicit bias toward Arab/Muslim senders only.
Introduction: Opioid overdoses (OODs) have become a public health emergency, yet little is known about their long-term outcomes following an OD. We determined the one-year all-cause mortality and associated risk factors in a cohort of patients treated in an urban emergency department (ED) for an OOD. Methods: We reviewed records of all patients who visited St. Paul's Hospital ED from January 2013 to August 2017 and had a discharge diagnosis of OOD or had received naloxone in the ED as per pharmacy records. Patients with a suspected OOD were identified on structured chart review. A patient's first visit for an OOD during the study period was used as the index visit, with subsequent visits excluded. The primary outcome was mortality during the year after the index visit. Mortality was assessed by linking patient electronic medical records with Vital Statistics data. Deaths that occurred in the ED on the index visit were excluded. Patients admitted to hospital following ED treatment were included in this study. We described patient characteristics, calculated mortality rates, and used Cox regression to identify risk factors. Results: A total of 2239 patients visited the ED for an OOD during the study period, with a median patient age of 37 years (IQR 29, 49). Males comprised 73% of patients, while 28% had no fixed address, and 21% received take-home naloxone at the index visit. In total, 137 patients (6.1%) died within 1 year of the index visit. Eighty-one deaths (3.6%) occurred within 6 months, including 24 deaths (1.1%) that occurred within 1 month. The highest mortality rate occurred in 2017, with 8.0% of patients entering the cohort that year dying within 1 year. Gender did not significantly impact mortality risk. A Cox regression analysis controlled for gender, housing status, and whether take-home naloxone was provided at the index visit indicated that advancing age (adjusted hazards ratio [AHR] 1.03; 95%CI: 1.01-1.04 for each year increase in age) and the index visit calendar year (AHR 1.30; 95%CI: 1.10-1.54 for each yearly increase in the study period) were significant factors for mortality within 1 year. Conclusion: The mortality rate following an opioid OD treated in the ED is high, with over 6% of patients in our study dying within 1 year. The rising mortality risk with increasing calendar year may reflect the growing harms of fentanyl-related OODs. Patients visiting the ED for an OOD should be considered high risk and offered preventative treatment and referrals prior to discharge.
A major challenge facing archaeologists is communicating our research to the
public. Thankfully, new computational tools have enabled the testing and
visualization of complex ideas in an easily packageable format. In this article
we illustrate not only how agent-based modeling provides a platform for
communicating complex ideas, but also how these game-like computer models can be
explored and manipulated by members of the public therefore increasing their
engagement in archaeological explanations. We suggest that these new digital
tools serve as an excellent aid for education on the importance of
archaeological sites and artifacts. To illustrate the above we walk the reader
through a step-by-step pipeline of how to run an ABM model as an experiment and
how to export it into a form ready to be sent to SHPO and THPO offices in tandem
with reports. Ultimately, we hope that this work will help demystify the
computational archaeology process and lead to more fluency in using agent-based
modeling in research and outreach.
Formal models of past human societies informed by archaeological research have a high potential for shaping some of the most topical current debates. Agent-based models, which emphasize how actions by individuals combine to produce global patterns, provide a convenient framework for developing quantitative models of historical social processes. However, being derived from computer science, the method remains largely specialized in archaeology. In this paper and the associated tutorial, we provide a jargon-free introduction to the technique, its potential and limits as well as its diverse applications in archaeology and beyond. We discuss the epistemological rationale of using computational modeling and simulation, classify types of models, and give an overview of the main concepts behind agent-based modeling.
Archaeologists are using spatial data in increasingly sophisticated analyses and invoking more explicit considerations of space in their interpretations. Geographic information systems (GIS) have become standard technology for professional archaeologists in the collection and management of spatial data. Many calls have been made to develop and adapt digital geospatial technologies for interpretation and understanding past social dynamics, but this has been limited to some extent by the static nature of map-oriented GIS approaches. Here, we illustrate how coupling GIS with agent-based modeling (ABM) can assist with more dynamic explorations of past uses of space and geospatial phenomena.
OBJECTIVES/SPECIFIC AIMS: The Duke Multidisciplinary Education and Research in Translational Science (MERITS) program was introduced with the goal of providing education and resources to faculty and trainees who are involved in translational research. However, the definition of what translational science is and entails can be widely variable, even within a single institution or department, which creates difficulties in appropriate dissemination of information regarding translational resources and assistance. This objective of this study was thus to obtain baseline information and views of translational science from a pilot population of Duke faculty. Based on data collected in a previous focus group, we expected to observe a lack of consensus regarding the definition and inclusion principles of translational science. METHODS/STUDY POPULATION: A digital survey was distributed to Duke Department of Surgery faculty regarding translational science, including opinions on definition, impacts, experienced barriers, known resources, and future training preferences. RESULTS/ANTICIPATED RESULTS: Ninety-five total responses were obtained, with 79.3% of respondents identifying their work as translational. There was no consensus on the precise definition of translational science, although the majority of respondents reported similar essential elements including multidisciplinary science and transitioning between investigative stages. Respondents noted that translational science increased their job satisfaction and recognition in their field, but also stated that they had experienced barriers to translational science. These barriers were primarily funding (56.4%) or lack of training (38.2%) related. DISCUSSION/SIGNIFICANCE OF IMPACT: The results of our pilot survey suggest that the MERITS program should focus on training investigators on the resources available for translational investigations and expound upon how it fits into and enhances their current and future research endeavors.
Until recently, researchers who wanted to examine the determinants of state respect for most specific negative rights (i.e., physical integrity and empowerment rights) needed to rely on data from the CIRI or the Political Terror Scale (PTS). The new Varieties of Democracy (V-Dem) dataset offers scholars a potential alternative to the individual human rights variables from CIRI. We analyze a set of key Cingranelli–Richards (CIRI) Human Rights Data Project and V-Dem negative rights indicators, finding unusual and unexpectedly large patterns of disagreement between the two sets. First, we discuss the new V-Dem dataset by comparing it to the disaggregated CIRI indicators, discussing the history of each project, and describing its empirical domain. Second, we identify a set of disaggregated human rights measures that are similar across the two datasets and discuss each project’s measurement approach. Third, we examine how these measures compare to each other empirically, showing that they diverge considerably across both time and space. These findings point to several important directions for future work, such as how conceptual approaches and measurement strategies affect rights scores. For the time being, our findings suggest that researchers should think carefully about using the measures as substitutes.