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We use New York City (NYC) taxi data to identify trips between mutual fund offices and local firm headquarters. NYC funds overweight the stocks of local firms they visit via taxi, and firm visits are associated with superior investment performance. Firm visits are elevated prior to earnings announcements, and mutual fund trades that are associated with firm taxi visits predict earnings surprises. The results are generally stronger when fund and firm executives share educational connections. Additional tests support the conclusion that funds’ local bias and investment performance are driven by portfolio managers’ efforts and ability to actively gather material information.
The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.
Publicly listed firms respond to capital supply conditions shaped by local investing preferences. Public firms headquartered in areas with higher proportions of senior citizens and women use more debt financing. These demographics are associated with conservative investing, leading to a higher and more stable local supply of debt capital. The demographics–leverage relation is more pronounced for firms that cannot easily tap public bond markets, which is the majority of public firms. Changes in firms’ financing activities around exogenous shocks to credit supplies, including interstate banking deregulation and the 2008–2009 financial crisis, support the local capital supply hypothesis.
It is unclear which pediatric disaster triage (PDT) strategy yields the best accuracy or best patient outcomes.
We conducted a cross-sectional analysis on a sample of emergency medical services providers from a prospective cohort study comparing the accuracy and triage outcomes for 2 PDT strategies (Smart and JumpSTART) and clinical decision-making (CDM) with no algorithm. Participants were divided into cohorts by triage strategy. We presented 10-victim, multi-modal disaster simulations. A Delphi method determined patients’ expected triage levels. We compared triage accuracy overall and for each triage level (RED/Immediate, YELLOW/Delayed, GREEN/Ambulatory, BLACK/Deceased).
There were 273 participants (71 JumpSTART, 122 Smart, and 81 CDM). There was no significant difference between Smart triage and CDM. When JumpSTART triage was used, there was greater accuracy than with either Smart (P<0.001; OR [odds ratio]: 2.03; interquartile range [IQR]: 1.30, 3.17) or CDM (P=0.02; OR: 1.76; IQR: 1.10, 2.82). JumpSTART outperformed Smart for RED patients (P=0.05; OR: 1.48; IQR: 1.01,2.17), and outperformed both Smart (P<0.001; OR: 3.22; IQR: 1.78,5.88) and CDM (P<0.001; OR: 2.86; IQR: 1.53,5.26) for YELLOW patients. Furthermore, JumpSTART outperformed CDM for BLACK patients (P=0.01; OR: 5.55; IQR: 1.47, 20.0).
Our simulation-based comparison suggested that JumpSTART triage outperforms both Smart and CDM. JumpSTART outperformed Smart for RED patients and CDM for BLACK patients. For YELLOW patients, JumpSTART yielded more accurate triage results than did Smart triage or CDM. (Disaster Med Public Health Preparedness. 2016;10:253–260)
Disasters are high-stakes, low-frequency events. Telemedicine may offer a useful adjunct for paramedics performing disaster triage. The objective of this study was to determine the feasibility of telemedicine in disaster triage, and to determine whether telemedicine has an effect on the accuracy of triage or the time needed to perform triage.
This is a feasibility study in which an intervention team of two paramedics used the mobile device Google Glass (Google Inc; Mountain View, California USA) to communicate with an off-site physician disaster expert. The paramedic team triaged simulated disaster victims at the triennial drill of a commercial airport. The simulated victims had preassigned expected triage levels. The physician had an audio-video interface with the paramedic team and was able to observe the victims remotely. A control team of two paramedics performed disaster triage in the usual fashion. Both teams used the SMART Triage System (TSG Associates LLP; Halifax, England), which assigns patients into Red, Yellow, Green, and Black triage categories. The paramedics were video recorded, and their time required to triage was logged. It was determined whether the intervention team and the control team varied regarding accuracy of triage. Finally, the amount of time the intervention team needed to triage patients when telemedicine was used was compared to when that team did not use telemedicine.
The two teams triaged the same 20 patients. There was no significant difference between the two groups in overall triage accuracy (85.7% for the intervention group vs 75.9% for the control group; P = .39). Two patients were triaged with telemedicine. For the intervention group, there was a significant difference in time to triage patients with telemedicine versus those without telemedicine (35.5 seconds; 95% CI, 72.5-143.5 vs 18.5 seconds; 95% CI, 13.4-23.6; P = .041).
There was no increase in triage accuracy when paramedics evaluating disaster victims used telemedicine, and telemedicine required more time than conventional triage. There are a number of obstacles to available technology that, if overcome, might improve the utility of telemedicine in disaster response.
CiceroMX, WalshB, SoladY, WhitfillT, PaesanoG, KimK, BaumCR, ConeDC. Do You See What I See? Insights from Using Google Glass for Disaster Telemedicine Triage. Prehosp Disaster Med. 2015;30(1):1-5.
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