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The New York Stock Exchange - University of Wisconsin ...dquint/econ690/visuals lecture 4.pdf ·...
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ORIGINAL ARTICLE INTRODUCTION The incidence and severity of healthcare-associated Clostridium difficile infections (HA-CDI) have been increasing since the emergence and the epidemic spread of the invasive strain BI/ NAP1/027 (Khanna & Pardi, 2010; Khanna et al, 2013; Barbut & Petit, 2001; Freeman et al., 2010). Concern is also growing that Clostridium difficile (C. difficile), historically considered a healthcare-associated infection, is increasingly a cause of diarrhea in the community, causing community-associated Clostridium difficile infections (CA-CDI) (Khanna & Pardi, 2010; Khanna et al., 2012). Although many studies have explored Epidemiology of patients hospitalized with Clostridium difficile infection: A comparative analysis of community-associated and healthcare-associated Clostridium difficile infections Maryam Salaripour, MSc, MPH, PhD; 1 Jennie Johnstone, MD, PhD, FRCPC; 2,3,5 Michael Gardam, MSc, MD, CM, MSc, FRCPC 1,4,5 1 School of Health Policy and Management, York University, Toronto, ON 2 Public Health Ontario, Toronto, ON 3 St. Joseph’s Health Centre, Toronto, ON 4 Humber River Hospital, Toronto, ON 5 Department of Medicine, University of Toronto, ON Corresponding author: Maryam Salaripour, MSC, MPH, PhD, School of Health Policy and Management, York University, Room 409 HNES Building, 4700 Keele Street, Toronto, ON M3J 1P3 Email: [email protected]; [email protected] Alternate correspondence to: Dr. Michael Gardam, MSc, MD, CM, MSc, FRCPC, Chief of Staff, Humber River Hospital, 1235 Wilson Avenue, Toronto, ON M3M 0B2 Email: [email protected] the increasing burden of HA-CDI, more research is required to fully understand the epidemiology of patients hospitalized with CA-CDI (Levy et al., 2015; Dumyati et al., 2012). In the summer of 2011, the Niagara Health System (NHS) in Ontario experienced an unusual increase in hospitalized HA-CDI and CA-CDI cases, combined with multiple HA-CDI outbreaks that were reported to the local public health department. To this end, this paper describes the clinical characteristics and the epidemiology of patients admitted to NHS hospitals with CA-CDI and compares them to the epidemiology of patients admitted with HA-CDI during the same period. ABSTRACT Objectives: To compare the epidemiology of hospitalized patients with community-acquired Clostridium difficile infections (CA-CDI) and those with healthcare-associated Clostridium difficile infections (HA-CDI). Design: A retrospective case series analysis was conducted. Setting: Niagara Health System, a multi-site hospital amalgamation in the Niagara Region, Ontario, Canada. Participants: Hospitalized patients with confirmed CA-CDI and HA-CDI between September 2011 and December 2013. Methods: Patients with Clostridium difficile infections (CDI) were identified through surveillance and laboratory testing, then stratified in two groups: CA-CDIs and HA-CDIs. Data were obtained from the Infection Prevention and Control (IPAC) surveillance database and the Decision Support database. Nonparametric descriptive statistics were applied to compare the characteristics of patients with CA-CDI and HA-CDI. Results: Of 628 hospitalized patients identified with CDI, 315 (50.2%) had CA-CDI and 313 (49.8%) had HA-CDI. Compared to patients with HA-CDI, patients with CA-CDI were younger (median age 72 years, interquartile range [IQR] 26, versus 77 years, IQR 18; p<.001), had less exposure to antibiotics (52% versus 83%, p<.001), and used fewer proton pump inhibitors (PPI) (30% versus 52%, p<.001). Gender proportions were similarly distributed between the two groups (58% of CA-CDI and 55% of HA-CDI were female, p=.38). There were differences in the proportion of comorbidities between CA-CDI and HA-CDI as follows: presence of an inflammatory bowel disease (18% of CA-CDI versus 40% of HA-CDI, p<.001) and surgery in the past three months (13% of CA-CDI versus 23% of HA-CDI; p<.001). Conclusion: CA-CDI must be considered as a potential diagnosis in patients admitted to hospital with diarrhea, even in the absence of conventional CDI risk factors. KEYWORDS: Epidemiology; Clostridium difficile; infections; community-acquired Acknowledgements: We would like to thank the staff of the Decision Support and Infection Prevention and Control departments at Niagara Health System. Funding: None. Conflicts of interest: All authors report no conflicts of interest relevant to this article. Canadian Journal of Infection Control | Summer 2018 | Volume 33 | Issue 2 | 96-101 96
Transcript of The New York Stock Exchange - University of Wisconsin ...dquint/econ690/visuals lecture 4.pdf ·...
The New York Stock Exchange
Example of a limit order book (“depth chart”)
Source: https://medium.com/on-banking/high-frequency-trading-on-the-coinbase-exchange-f804c80f507b
Next four pages: source is Budish, Cramton and Shim (2015), The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response, Quarterly Journal of Economics 130.4 1550 QUARTERLY JOURNAL OF ECONOMICS
FIGURE I ES and SPY Time Series at Human-Scale and High-Frequency Time Horizons
This figure illustrates the time series of the E-mini S&P 500 future (ES) and SPDR S&P 500 ETF (SPY) bid-ask midpoints over the course of a trading day (August 9, 2011) at different time resolutions: the full day (a), an hour (b), a minute (c), and 250 milliseconds (d). SPY prices are multiplied by 10 to reflect that SPY tracks 1 the S&P 500 Index. Note that there is a difference in levels between the two financial instruments due to differences in cost-of-carry, divi- dend exposure, and ETF tracking error; for details see Section V.B. For details regarding the data, see Section IV.
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THE HIGH-FREQUENCY TRADING ARMS RACE 1551
FIGURE I
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FIGURE IV Duration of ES & SPY Arbitrage Opportunities over Time: 2005–2011 Panel A shows the median duration of ES-SPY arbitrage opportunities for
each day in our data. Panel B plots arbitrage duration against the proportion of opportunities lasting at least that duration, for each year in our data. We drop opportunities that last fewer than 4 milliseconds, the speed-of-light travel time between New York and Chicago. Prior to November 24, 2008, we drop oppor- tunities that last fewer than 9 milliseconds, the maximum combined effect of the speed-of-light travel time and the rounding of CME data to centiseconds. See Section V.B for details regarding the arbitrage. See Section IV for details regarding the data.
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FIGURE V Profitability of ES & SPY Arbitrage Opportunities over Time: 2005–2011 Panel A shows the median profitability of
ES-SPY arbitrage opportunities, per unit traded, for each day in our data. Panel B plots the kernel density of the profitability of arbitrage opportunities, per unit traded, for each year in our data. See Section V.B for details regarding the ES-SPY arbitrage. See Section IV for details regarding the data.
Next thee pages: source is Shkililko and Sokolov, Every Cloud has a Silver Lining: Fast Trading, Microwave Connectivity and Trading Costs
Figure 1. Microwave network paths The figure maps tower locations of three microwave networks (blue, yellow and purple icons) obtained from the Federal Communications Commission. There are more than three microwave networks between Chicago and New York during our sample period; however, we plot only three to avoid clutter. The remaining networks follow very similar paths. The red markers indicate locations of the CME’s data center in Aurora, IL (marker A); the NYSE data center in Mahwah, NJ (marker M); Nasdaq data center in Carteret, NJ (marker C); BATS data center in Weehawken, NJ (marker W); and Direct Edge data center in Secaucus, NJ (marker S).
Figure 3. A typical weather front As a weather front moves over the microwave paths, it disrupts data transmission forcing trading firms to fall back on the fiber-optic cable.
