scholarly journals Emergency activations for chest pain and ventricular arrhythmias related to regional COVID-19 across the US

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sidney Aung ◽  
Eric Vittinghoff ◽  
Gregory Nah ◽  
Anthony Lin ◽  
Sean Joyce ◽  
...  
Keyword(s):  
Chest Pain ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Myocardial Infarctions ◽  
Healthcare Facilities ◽  
Binomial Regression ◽  
The Us ◽  
National Emergency ◽  
Us Census

AbstractEvidence that patients may avoid healthcare facilities for fear of COVID-19 infection has heightened the concern that true rates of myocardial infarctions have been under-ascertained and left untreated. We analyzed data from the National Emergency Medical Services Information System (NEMSIS) and incident COVID-19 infections across the United States (US) between January 1, 2020 and April 30, 2020. Grouping events by US Census Division, multivariable adjusted negative binomial regression models were utilized to estimate the relationship between COVID-19 and EMS cardiovascular activations. After multivariable adjustment, increasing COVID-19 rates were associated with less activations for chest pain and non-ST-elevation myocardial infarctions. Simultaneously, increasing COVID-19 rates were associated with more activations for cardiac arrests, ventricular fibrillation, and ventricular tachycardia. Although direct effects of COVID-19 infections may explain these discordant observations, these findings may also arise from patients delaying or avoiding care for myocardial infarction, leading to potentially lethal consequences.

scholarly journals ECONOMIC IMPACT OF HOSPITALIZATIONS IN US ADULTS WITH SARCOPENIA

2019 ◽  
pp. 1-7
Author(s):  
S. Goates ◽  
K. Du ◽  
M.B. Arensberg ◽  
T. Gaillard ◽  
J. Guralnik ◽  
...  
Keyword(s):  
Black Women ◽  
Economic Burden ◽  
Negative Binomial ◽  
Functional Limitations ◽  
Negative Binomial Regression ◽  
Health And Nutrition ◽  
Binomial Regression ◽  
The Us ◽  
The Cost ◽  
Us Census

ackground: Sarcopenia is characterized by progressive loss of muscle mass with corresponding decline in strength and/or physical function. The economic burden of sarcopenia-associated disability is considerable in the US. Objective: To estimate the cost of hospitalizations in US adults with sarcopenia categorized by age, sex, and race/ethnicity. Design, setting and participants: A retrospective, prevalence based, economic burden study, consisting of 4011 adults aged ≥40 years with and without sarcopenia. Methods: Data on prevalence of low lean mass, functional limitations, and hospitalizations were obtained from the National Health and Nutrition Examination Survey (1999-2004); cost of hospitalizations was obtained from the Healthcare Cost and Utilization Project – National Inpatient Sample (2014), and population estimates were obtained from the US Census (2014). Probability and cost of hospitalizations were estimated by multiple logistic regression and negative binomial regression models, respectively. Results: The total estimated cost of hospitalizations in individuals with sarcopenia was USD $40.4 billion with an average per person cost of USD $260. Within this category, average per person cost was highest for Hispanic women (USD $548) and lowest for Non-Hispanic Black women (USD $25); average per person cost was higher for older adults (≥65 years) (USD $375) than younger adults (40-64 years) (USD $204) with sarcopenia. The total cost of hospitalizations in individuals with sarcopenia (≥65 years) was USD $19.12 billion. Individuals with sarcopenia had greater odds of hospitalization (OR, 1.95; p<.001) compared to those without and had an annual marginal increase in cost of USD $2315.7 per person compared to individuals without sarcopenia. Conclusion: Sarcopenia places considerable economic burden on the US healthcare system. The ethnic disparity and economic burden associated with sarcopenia warrant further investigation.

Property Crime Clearance in Small Jurisdictions

2018 ◽  
Vol 43 (4) ◽  
pp. 477-493 ◽  
Author(s):  
Jeffrey J. Roth
Keyword(s):  
Negative Binomial ◽  
Property Crime ◽  
Negative Binomial Regression ◽  
The United States ◽  
Police Departments ◽  
Clearance Rates ◽  
Residential Instability ◽  
Large Cities ◽  
Binomial Regression ◽  
Crime Clearance

Research on the factors that influence crime clearance rates has primarily studied violent crimes in large cities. However, property crimes are among the most commonly occurring and least frequently cleared offenses, and the majority of police departments in the United States serve small jurisdictions. Thus, this study undertook an examination of the predictors of clearance rates for burglary, larceny, and vehicle theft in a sample of agencies serving populations of 50,000 people or fewer. Independent variables included both policing factors (e.g., workload, funding, broken windows arrests) and social disorganization indicators (e.g., residential instability, poverty). Negative binomial regression analyses revealed variation in the significance of the predictors across the three crimes. Additionally, many predictors found to be influential in prior work were insignificant in this study, which suggests differences in the nature of crime clearance between large cities and smaller jurisdictions and a need for further research in this area.

scholarly journals Application of Discrete Regression Models for Analyzing K-8 Students Nonchronic Absenteeism in the United States

2021 ◽  
Vol 10 (4) ◽  
pp. 127
Author(s):  
Khairul Islam ◽  
Tanweer J. Shapla
Keyword(s):  
United States ◽  
Regression Models ◽  
Negative Binomial ◽  
Descriptive Analysis ◽  
Negative Binomial Regression ◽  
The United States ◽  
Chronic Absenteeism ◽  
Factors Affecting ◽  
National Crisis ◽  
Binomial Regression

Absenteeism is a national crisis in the United States, and must be addressed adequately at the early stages or at its onset, to prevent consequential disaster and burden due to absenteeism. A pervasive and persuasive nonchronic absenteeism results in chronic absenteeism, and causes severe damage to students&rsquo; life, schools and societies. While a good number of articles address various issues relating to chronic absenteeism, no evidence of research exists investigating nonchronic absenteeism. The aim of this article is to investigate factors affecting nonchronic absenteeism in K-8 students in the United States by applying discrete regression models. Initially, we investigate K-8 students nonchronic absenteeism discrepancies due to socio-demographic and parental involvement factors via descriptive analysis and then employ Poisson and negative binomial regression models for exploring significant factors of K-8 nonchronic absenteeism. The findings of this study will be of great use to stakeholders in developing appropriate incentive measures for reducing nonchronic absenteeism early and thereby reducing chronic absenteeism.

Identification of Critical Intersection Angle through Crash Modification Functions

2019 ◽  
Vol 2673 (2) ◽  
pp. 531-543 ◽  
Author(s):  
Wesley Kumfer ◽  
David Harkey ◽  
Bo Lan ◽  
Raghavan Srinivasan ◽  
Daniel Carter ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Skew Angle ◽  
Intersection Angle ◽  
Crash Data ◽  
Highway Safety Manual ◽  
Intersection Safety ◽  
Binomial Regression ◽  
Safety Effect

A significant portion of both fatal and total crashes occurs at intersections in the United States. Skew angle may be a significant contributor to these crashes. This paper examines the effects of intersection angle on intersection safety performance. With seven years of crash data from Minnesota and five years of crash data from Ohio, random forest regression data mining and negative binomial regression models were developed to estimate crash modification functions at three-leg and four-leg stop-controlled intersections with two-lane and multilane major legs. Where possible, the results were compared between the two states and used to develop average crash modification function curves. This study shows that over half of the intersection types experience the highest number of predicted crashes when the intersection angle between roadway legs is between 50 degrees and 65 degrees. These results have practical implications for engineers and safety professionals. First, the crash modification function curves supplement and revise the guidance for intersection angle in the Highway Safety Manual and Policy on Geometric Design of Highways and Streets. Second, the functions offer new guidance to agencies planning intersection improvements. Third, the crash modification functions can be used to determine the safety effect of changes in intersection angle.

Normative uncertainty and middle-status innovation in the US daily newspaper industry

2019 ◽  
Vol 18 (3) ◽  
pp. 377-406
Author(s):  
Bo Kyung Kim
Keyword(s):  
Digital Media ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Daily Newspaper ◽  
Newspaper Industry ◽  
Adoption And Implementation ◽  
Binomial Regression ◽  
The Us ◽  
Normative Uncertainty ◽  
Middle Status

This article focuses on normatively uncertain innovations and asks when and how organizational status affects the adoption and implementation of these innovations. I argue that middle-status organizations perceive normative uncertainty more as an opportunity for gain, whereas high- and low-status organizations perceive it more as a threat of loss and competition. Consequently, middle-status organizations are likely to be the first to adopt normatively uncertain innovations and to implement them in a way that emphasizes normative uncertainty. Event history and negative binomial regression analyses of the US newspaper industry from 1993 to 2007, the period after the emergence of digital media that was normatively uncertain, support my theoretical arguments. Middle-status newspapers launched their websites faster than other newspapers did. In addition, they emphasized interactivity on the web, which could potentially change the identity of newspapers from sole news producers to facilitators.

Longitudinal Analysis of Light Rail and Streetcar Safety in the United States

2020 ◽  
Vol 2674 (9) ◽  
pp. 83-95
Author(s):  
Abubakr Ziedan ◽  
Candace Brakewood
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Descriptive Statistics ◽  
Light Rail ◽  
Right Of Way ◽  
Binomial Regression ◽  
Using Data ◽  
American Cities

Many American cities have launched or expanded light rail or streetcar services recently, which has resulted in a 61% increase in light rail and streetcar revenue miles nationwide during the period 2006–2016. Moreover, light rail and streetcars exhibit higher fatality rates per passenger mile traveled compared with other transit modes. In light of these trends, this study explores light rail and streetcar collisions, injuries, and fatalities using data obtained from the National Transit Database. This study applies a two-part methodology. In the first part, descriptive statistics are calculated for light rail and streetcar collisions, injuries, and fatalities, and a comparative analysis of light rail and streetcars is performed. In the second part, multilevel negative binomial regression models are used to analyze light rail and streetcar collisions and injuries. Three key findings have emerged from this study. First, the results generally align with findings from prior studies that show the majority of light rail and streetcar collisions occur in mixed right-of-way or near at-grade crossings. Second, this analysis revealed an issue predominantly at stations: 42% of light rail injuries were people waiting or leaving. Third, suicide was the leading cause of light rail fatalities, which represents 28% of all light rail fatalities. The implications of this study are important for cities that currently operate these modes or are planning to introduce new light rail or streetcar service to improve safety.

Hate Groups and Muslim Population Changes in the Fifty States: Does the Presence of Muslims Encourage Hate Group Formation?

2018 ◽  
Vol 25 (2) ◽  
pp. 317-332
Author(s):  
Daniel Hummel
Keyword(s):  
Negative Binomial ◽  
Group Formation ◽  
Negative Binomial Regression ◽  
The United States ◽  
Future Research ◽  
Group Status ◽  
Hate Groups ◽  
Muslim Population ◽  
Binomial Regression ◽  
Positive Correlations

The size of the Muslim population in the United States is growing, while the number of hate groups also continues to increase across the states. Based on the social dominance theory and group threat theory, there may be a link between these two dynamics as social dominators become concerned about their group status, i.e. white Christian males. This potential relationship is explored in this article. Although there were significant positive correlations between the number of hate groups and the size of the Muslim population, a panelled negative binomial regression with a number of relevant control variables found that there was not a significant relationship between these variables at the 0.05 probability level. The article further explores these findings and future research in this area.

Self-Reported Inmate Conduct

2016 ◽  
Vol 97 (1) ◽  
pp. 79-93
Author(s):  
Lisa Degiorgio Worthy
Keyword(s):  
Negative Binomial ◽  
Violent Behavior ◽  
Area Under The Curve ◽  
Negative Binomial Regression ◽  
The United States ◽  
Female Inmates ◽  
Operating Characteristics ◽  
Dynamic Factors ◽  
Binomial Regression ◽  
Improved Model

This article applies Walters’s criteria of dynamic factors in prison assessment to assess the predictive capabilities of the Prison Inmate Inventory (PII) in a sample of offenders from a southeastern state in the United States. Nested negative binomial regression was used, as well as ROC/AUC (receiver operating characteristics/area under the curve) to evaluate accuracy. The results show that beyond the effects of demographic and static variables, dynamic factors incrementally improved model fit and estimated counts of female inmates, non-White inmates, and inmates reporting more disciplinary actions and violent behavior. All PII scales demonstrated accuracy above .50; however, only two were within the range considered acceptable for criminal justice instruments. Practical implications have been discussed.

Homeland Violence and Diaspora Insecurity: An Analysis of Israel and American Jewry

2019 ◽  
Vol 13 (1) ◽  
pp. 1-27
Author(s):  
Ayal K. Feinberg
Keyword(s):  
United States ◽  
Fixed Effects ◽  
Hate Crime ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Hate Crimes ◽  
Federal Bureau Of Investigation ◽  
American Jewry ◽  
Binomial Regression

AbstractJews and Jewish institutions have suffered the majority of reported religion-motivated hate crimes in the United States for nearly two decades. According to the Federal Bureau of Investigation (FBI), in 2014 the 609 reported anti-Semitic incidents made up 59% of all religious bias hate crimes alone. Rates of reported anti-Semitic hate crimes vary considerably over the course of a year. Yet, little scholarly attention has been given to what factors cause reported anti-Semitic hate crimes to fluctuate so substantially in the United States. This paper hypothesizes that violent Israeli military engagements are critical in explaining weekly surges of reported anti-Semitic hate crimes. Utilizing FBI hate crime data from 2001 to 2014 and fixed effects negative binomial regression models, consistent findings underscore that violent Israeli military engagements significantly increase the likelihood of a state reporting anti-Semitic hate crime. Most dramatically, their occurrence increases the likelihood of reported hate crime intimidating individuals or characterized as violent by nearly 35%. This paper underscores that homeland perpetrated violence can directly impact the security of diaspora communities.

EMPIRICAL ANALYSIS OF TECHNOLOGY TRANSFER IN KOREAN UNIVERSITIES

2016 ◽  
Vol 20 (08) ◽  
pp. 1640018
Author(s):  
JUNGHEE HAN ◽  
JUNGHO KIM
Keyword(s):  
Technology Transfer ◽  
Research Productivity ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Cooperative Research ◽  
Initial Stage ◽  
Binomial Regression ◽  
The Government ◽  
The University

This paper is to elucidate the determinant of technology transfer in universities in Korea. To achieve this goal, the paper relies on a unique dataset where university data were sourced from IUCF, and data were gathered from a number of sources. We apply a negative binomial regression analysis to estimate models testing 12 research hypotheses for 135 universities in Korea. Our findings show that the characteristics of research funds, research productivity and the presence of incubators within universities are positively related with financial rewards in terms of technology transfer. Unlike in the United States, research funds sponsored by the government are not related to technology transfer. Patents, technology transfer experts, firms located adjacent to the university, incubators within the university, and research productivity are positively related to technology transfer. The empirical findings in this study indicate that in order to boost technology transfer, universities should undertake cooperative research, especially with private firms, and should also use incubators at the initial stage of the technology transfer mechanism.

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