Electric Cars and Oil Prices

This paper studies the joint dynamics of oil prices and interest in electric cars, measured as the volume of Google searches for related phrases. Not surprisingly, I find that oil price shocks predict increases in Google searches for electric cars. Much more surprisingly, I also find that an increase in Google searches predicts declines in oil prices. The high level of public interest in electric cars between April and August of 2008 can explain approximately half of the decline in oil prices during the second half of 2008. These findings are significant because they show that oil markets respond to developments related to alternative technologies. I investigate several hypotheses explaining these results.Oil prices; crude oil; electric cars; electric vehicles; Google Trends; Google Insights;

Evaluation of interprofessional relational coordination and patients’ perception of care in outpatient oncology teams

This pilot study was designed to measure teamwork and the relationship of teamwork to patient perceptions of care among 63 members of 12 oncology teams at a Cancer Centre in the Midwest. Lack of teamwork in cancer care can result in serious clinical errors, fragmentation of care, and poor quality of care. Many oncology team members, highly skilled in clinical care, are not trained to work effectively as members of a care team. The research team administered the Relational Coordination survey to core oncology team members—medical oncologists, nurse coordinators, and clinical secretaries—to measure seven dimensions of team skills (four relating to communication [frequency, timeliness, accuracy, and problem solving] and three relating to relationship [shared goals, shared knowledge, and mutual respect]) averaged to create a Relational Coordination Index. The results indicated that among the team member roles, nurse coordinator relational coordination indices were the strongest and most positively correlated with patient perception of care. Statistically significant correlations were intra-nurse coordinator relational coordination indices and two patient perception of care factors (information and education and patient’s preferences). All other nurse coordinator intra-role as well as inter-role correlations were also positively correlated, although not statistically significant

Electric Cars and Oil Prices

This paper studies the joint dynamics of oil prices and interest in electric cars, measured as the volume of Google searches for related phrases. Not surprisingly, I find that oil price shocks predict increases in Google searches for electric cars. Much more surprisingly, I also find that an increase in Google searches predicts declines in oil prices. The high level of public interest in electric cars between April and August of 2008 can explain approximately half of the decline in oil prices during the second half of 2008. These findings are significant because they show that oil markets respond to developments related to alternative technologies. I investigate several hypotheses explaining these results

Electric Cars and Oil Prices

This paper studies the joint dynamics of oil prices and interest in electric cars, measured as the volume of Google searches for related phrases. Not surprisingly, I find that oil price shocks predict increases in Google searches for electric cars. Much more surprisingly, I also find that an increase in Google searches predicts declines in oil prices. The high level of public interest in electric cars between April and August of 2008 can explain approximately half of the decline in oil prices during the second half of 2008. These findings are significant because they show that oil markets respond to developments related to alternative technologies. I investigate several hypotheses explaining these results

Monopsony and Automation

We examine the impact of labor market power on firms' adoption of automation technologies. We develop a model that incorporates labor market power into the task-based theory of automation. We show that, due to higher marginal cost of labor, monopsonistic firms have stronger incentives to automate than wage-taking firms, which could amplify or mitigate the negative employment effects of automation. Using data from US commuting zones, our results show that commuting zones that are more exposed to industrial robots exhibit considerably larger reductions in both employment and wages when their labor markets demonstrate higher levels of concentration

Ultimate Ownership and Bank Competition

We use a uniquely extensive branch-level dataset on deposit account interest rates, maintenance fees, and fee thresholds, and document substantial time-series and cross-sectional variation in these prices. We then examine whether variation in bank concentration helps explain the variation in prices. The standard measure of concentration, the HHI, is not correlated with any of the outcome variables. We then construct a generalized HHI (GHHI) that captures both common ownership (the degree to which banks are commonly owned by the same investors) and cross-ownership (the extent to which banks own shares in each other). The GHHI is strongly correlated with all prices. We use the growth of index funds as an arguably exogenous source of cross-sectional variation of county-level common ownership growth to suggest a causal link from the GHHI to higher prices for banking products

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

This article is made available for unrestricted research re-use and secondary analysis in any form or be any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Background Predicting the risk of in-hospital mortality on admission is challenging but essential for risk stratification of patient outcomes and designing an appropriate plan-of-care, especially among transferred patients. Objective Develop a model that uses administrative and clinical data within 24 h of transfer to predict 30-day in-hospital mortality at an Academic Health Center (AHC). Design Retrospective cohort study. We used 30 putative variables in a multiple logistic regression model in the full data set (n = 10,389) to identify 20 candidate variables obtained from the electronic medical record (EMR) within 24 h of admission that were associated with 30-day in-hospital mortality (p < 0.05). These 20 variables were tested using multiple logistic regression and area under the curve (AUC)–receiver operating characteristics (ROC) analysis to identify an optimal risk threshold score in a randomly split derivation sample (n = 5194) which was then examined in the validation sample (n = 5195). Participants Ten thousand three hundred eighty-nine patients greater than 18 years transferred to the Indiana University (IU)–Adult Academic Health Center (AHC) between 1/1/2016 and 12/31/2017. Main Measures Sensitivity, specificity, positive predictive value, C-statistic, and risk threshold score of the model. Key Results The final model was strongly discriminative (C-statistic = 0.90) and had a good fit (Hosmer-Lemeshow goodness-of-fit test [X2 (8) =6.26, p = 0.62]). The positive predictive value for 30-day in-hospital death was 68%; AUC-ROC was 0.90 (95% confidence interval 0.89–0.92, p < 0.0001). We identified a risk threshold score of −2.19 that had a maximum sensitivity (79.87%) and specificity (85.24%) in the derivation and validation sample (sensitivity: 75.00%, specificity: 85.71%). In the validation sample, 34.40% (354/1029) of the patients above this threshold died compared to only 2.83% (118/4166) deaths below this threshold. Conclusion This model can use EMR and administrative data within 24 h of transfer to predict the risk of 30-day in-hospital mortality with reasonable accuracy among seriously ill transferred patients

Noxious effects of riot control agents on the ocular surface: Pathogenic mechanisms and management

Riot Control Agents (RCAs) are chemical compounds used by law enforcement agencies to quell violent demonstrations as an alternative to lethal force and as part of police/military training. They are also known as tear gases because of the hallmark ocular irritation and lacrimation they cause. The most common RCAs include oleoresin capsicum (contained in Mace and pepper spray), chlorobenzylidene malononitrile, dibenzoxazepine, and chloroacetophenone (previously the main content of Mace); some of which have been in use for decades. Their immediate incapacitating effects are mediated through polymodal afferent fibers innervating the corneal surface, inducing the release of peptides that cause neurogenic inflammation. Although previously thought to have only transient effects on exposed patients more severe complications such as corneal stromal opacities, corneal neovascularization, neurotrophic keratopathy, conjunctival necrosis, and pseudopterygium can occur. Concerningly, the lack of research and specific therapies restrict the current management to decontamination and symptom-tailored support. This manuscript will provide an overview of the toxic mechanisms of RCAs, their clinical manifestations, and current therapy after exposure to tear gases

Developing the Agile Implementation Playbook for Integrating Evidence-Based Health Care Services into Clinical Practice

Problem: Despite the more than $32 billion the National Institutes of Health has invested annually, evidence-based health care services are not reliably implemented, sustained, or distributed in health care delivery organizations, resulting in suboptimal care and patient harm. New organizational approaches and frameworks that reflect the complex nature of health care systems are needed to achieve this goal. Approach: To guide the implementation of evidence-based health care services at their institution, the authors used a number of behavioral theories and frameworks to develop the Agile Implementation (AI) Playbook, which was finalized in 2015. The AI Playbook leverages these theories in an integrated approach to selecting an evidence-based health care service to meet a specific opportunity, rapidly implementing the service, evaluating its fidelity and impact, and sustaining and scaling up the service across health care delivery organizations. The AI Playbook includes an interconnected eight-step cycle: (1) identify opportunities; (2) identify evidence-based health care services; (3) develop evaluation and termination plans; (4) assemble a team to develop a minimally viable service; (5) perform implementation sprints; (6) monitor implementation performance; (7) monitor whole system performance; and (8) develop a minimally standardized operating procedure. Outcomes: The AI Playbook has helped to improve care and clinical outcomes for intensive care unit survivors and is being used to train clinicians and scientists in AI to be quality improvement advisors. Next Steps: The authors plan to continue disseminating the details of the AI Playbook and illustrating how health care delivery organizations can successfully leverage it