scholarly journals Staffing Levels and COVID-19 Infections and Deaths in Korean Nursing Homes

2021 ◽  
pp. 152715442110560
Author(s):  
Jiyeon Lee ◽  
Juh Hyun Shin ◽  
Kyeong Hun Lee ◽  
Charlene A. Harrington ◽  
Sun Ok Jung
Keyword(s):  
Mortality Rate ◽  
Senior Citizen ◽  
Secondary Data ◽  
Large Data ◽  
High Risk Population ◽  
Term Care ◽  
Data Set ◽  
Staffing Levels ◽  
Welfare Department ◽  
Novel Coronavirus

The novel coronavirus disease 2019 (COVID-19) spread rapidly worldwide. Nursing home (NH) residents are the most vulnerable high-risk population to infection. Professional registered nurses’ (RNs’) infection control is irreplaceable. We used a secondary data analysis method using the government's senior citizen welfare department large data set about all NHs (N = 3,389) across Korea between January 20 and October 20, 2020. Bed size positively associated with the mortality rate (No. of COVID-19 resident deaths / No. of total residents) ( p  = .048). When the proportion of RNs to total nursing staff was higher, the infection rate was 0.626% lower ( p = .049), the mortality rate was 0.088% lower ( p = .076), the proportion of confirmed COVID-19 cases per resident out of the total number of NHs was 44.472% lower ( p = .041), and the proportion of confirmed COVID-19 deaths per resident out of the total number of NHs was 6.456% lower ( p = .055). This study highlighted nurse staffing criteria and suggests that increasing RNs in NHs will reduce infection and mortality rates during the COVID-19 pandemic. We strongly suggest NHs hire at least one RN per day to properly function, and a minimum of four RNs to provide a fully competent RN workforce in long-term care settings in Korean NHs.

scholarly journals Effects of Hurricane Katrina on Nursing Facility Resident Mortality, Hospitalization, and Functional Decline

2010 ◽  
Vol 4 (S1) ◽  
pp. S28-S32 ◽  
Author(s):  
David Dosa ◽  
Zhanlian Feng ◽  
Kathy Hyer ◽  
Lisa M. Brown ◽  
Kali Thomas ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Hurricane Katrina ◽  
Functional Decline ◽  
Secondary Data ◽  
Hospitalization Rate ◽  
Minimum Data Set ◽  
Nursing Facility ◽  
Data Set ◽  
Hospitalization Rates ◽  
Minimum Data

ABSTRACTBackground: The study was designed to examine the 30- and 90-day mortality and hospitalization rates among nursing facility (NF) residents in the affected areas of Louisiana and Mississippi following Hurricane Katrina and to assess the rate of significant posthurricane functional decline.Methods: A secondary data analysis was conducted using Medicare claims merged with NF resident data from the Minimum Data Set. Thirty- and 90-day mortality and hospitalization rates for long-stay (>90 days) residents residing in 141 at-risk NFs during Hurricane Katrina were compared to rates for residents residing at the same facilities during the same time period in prior nonhurricane years (2003 and 2004). Functional decline was assessed as a 4+ drop in function using a 28-point Minimum Data Set Activities of Daily Living Scale.Results: There were statistically significant differences (all P < .0001) in mortality, hospitalization, and functional decline among residents exposed to Hurricane Katrina. At 30 days, the mortality rate was 3.88% among the exposed cohort compared with 2.10% and 2.28% for residents in 2003 and 2004, respectively. The 90-day mortality rate was 9.27% compared with 6.71% and 6.31%, respectively. These mortality differences translated into an additional 148 deaths at 30 days and 230 deaths at 90 days. The 30-day hospitalization rate was 9.87% compared with 7.21% and 7.53%, respectively. The 90-day hospitalization rate was 20.39% compared with 18.61% and 17.82%, respectively. Finally, the rate of significant functional decline among survivors was 6.77% compared with 5.81% in 2003 and 5.10% in 2004.Conclusions: NF residents experienced a significant increase in mortality, hospitalization, and functional decline during Hurricane Katrina.(Disaster Med Public Health Preparedness. 2010;4:S28-S32)

The Polarity of Online Reviews: Prevalence, Drivers and Implications

2020 ◽  
Vol 57 (5) ◽  
pp. 853-877 ◽  
Author(s):  
Verena Schoenmueller ◽  
Oded Netzer ◽  
Florian Stahl
Keyword(s):  
Rating Scale ◽  
Secondary Data ◽  
Large Data ◽  
Online Reviews ◽  
Online Review ◽  
Data Set ◽  
Self Selection ◽  
Online Platforms ◽  
Cross Platform ◽  
High Degree

In this research, the authors investigate the prevalence, robustness, and possible reasons underlying the polarity of online review distributions, with the majority of the reviews at the positive end of the rating scale, a few reviews in the midrange, and some reviews at the negative end of the scale. Compiling a large data set of online reviews—over 280 million reviews from 25 major online platforms—the authors find that most reviews on most platforms exhibit a high degree of polarity, but the platforms vary in the degree of polarity on the basis of how selective customers are in reviewing products on the platform. Using cross-platform and multimethod analyses, including secondary data, experiments, and survey data, the authors empirically confirm polarity self-selection, described as the higher tendency of consumers with extreme evaluations to provide a review as an important driver of the polarity of review distributions. In addition, they describe and demonstrate that polarity self-selection and the polarity of the review distribution reduce the informativeness of online reviews.

scholarly journals Indo-Pak Comparison and Forecasting of Covid-19 Epidemic: A Secondary Data Analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 184-188
Author(s):  
Osama Ajaz ◽  
Muhammad Irfan ◽  
Ayesha Siddiqa ◽  
Muhammad Amjad
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Current Trend ◽  
Secondary Data ◽  
World Health ◽  
Data Set ◽  
Related Case ◽  
The World ◽  
Novel Coronavirus ◽  
Health Organization

Background: The world has historically faced and recovered from many pandemics. The most recent global pandemic that the whole world is facing is Novel Coronavirus – Covid-19. The objective of current study is to compare and forecast COVID-19 trends for Pakistan and India. Methods: The data set for this research is obtained from the World Health Organization (WHO) online repository (https://covid19.who.int/). The time period we have considered since the first corona related case and death were observed in both countries. This research paper analyzes corona related cases and deaths in Pakistan and India till 28th February 2021, a total of 578,797 cases in Pakistan and 11,096,731 cases in India has been confirmed including 128,37 and 1,570,51 deaths respectively. The Auto-Regressive Integrated Moving Average (ARIMA) model is used to forecast the variables cumulative cases and deaths. It is simple to use and more predictive than any other regression model. Results: Based on the current trend, the forecast graph reveals that the number of cumulative corona cases could reach 999,767 in Pakistan and 16,481,122 in India up to 31st December 2021. Conclusion: This research found that corona related cumulative cases and deaths are on the rise in both countries. The pandemic situation in India is worse than in Pakistan nevertheless both countries are at high risk. There is a sudden increasing pattern in the number of corona related cases in both countries. Both governments must impose effective policies to control this pandemic.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Pengaruh Reputasi Underwriter, Umur Perusahaan, dan Persentase Penawaran Saham 53 kepada Publik terhadap Underpricing

2016 ◽  
Vol 8 (1) ◽  
pp. 53-74
Author(s):  
Maria Jeanne ◽  
Chermian Eforis
Keyword(s):  
Initial Public Offering ◽  
Stock Price ◽  
Stock Exchange ◽  
Secondary Data ◽  
Secondary Market ◽  
Financial Statement ◽  
Data Set ◽  
Multiple Regression Method ◽  
Underwriter Reputation ◽  
Public Offering

The objective of this research is to obtain empirical evidence about the effect of underwriter reputation, company age, and the percentage of share’s offering to public toward underpricing. Underpricing is a phenomenon in which the current stock price initial public offering (IPO) was lower than the closing price of shares in the secondary market during the first day. Sample in this research was selected by using purposive sampling method and the secondary data used in this research was analyzed by using multiple regression method. The samples in this research were 72 companies conducting initial public offering (IPO) at the Indonesian Stock Exchange in the period January 2010 - December 2014; perform initial offering of shares; suffered underpricing; has a complete data set forth in the company's prospectus, IDX monthly statistics, financial statement and stock price site (e-bursa); and use Rupiah currency. Results of this research were (1) underwriter reputation significantly effect on underpricing; (2) company age do not effect on underpricing; and (3) the percentage of share’s offering to public do not effect on undepricing. Keywords: company age, the percentage of share’s offering to public, underpricing, underwriter reputation.

Territorial Distribution of Alcohol and Drug Addictions Mortality Concerning Regional Disparities in the Slovak Republic from Year 1996 to Year 2015

2020 ◽  
Keyword(s):  
Mortality Rate ◽  
Slovak Republic ◽  
Regional Disparities ◽  
Point Of View ◽  
Extreme Position ◽  
Data Set ◽  
Female Sex ◽  
Male Sex ◽  
The Individual ◽  
High Level

BACKGROUND: This paper deals with territorial distribution of the alcohol and drug addictions mortality at a level of the districts of the Slovak Republic. AIM: The aim of the paper is to explore the relations within the administrative territorial division of the Slovak Republic, that is, between the individual districts and hence, to reveal possibly hidden relation in alcohol and drug mortality. METHODS: The analysis is divided and executed into the two fragments – one belongs to the female sex, the other one belongs to the male sex. The standardised mortality rate is computed according to a sequence of the mathematical relations. The Euclidean distance is employed to compute the similarity within each pair of a whole data set. The cluster analysis examines is performed. The clusters are created by means of the mutual distances of the districts. The data is collected from the database of the Statistical Office of the Slovak Republic for all the districts of the Slovak Republic. The covered time span begins in the year 1996 and ends in the year 2015. RESULTS: The most substantial point is that the Slovak Republic possesses the regional disparities in a field of mortality expressed by the standardised mortality rate computed particularly for the diagnoses assigned to the alcohol and drug addictions at a considerably high level. However, the female sex and the male sex have the different outcome. The Bratislava III District keeps absolutely the most extreme position. It forms an own cluster for the both sexes too. The Topoľčany District bears a similar extreme position from a point of view of the male sex. All the Bratislava districts keep their mutual notable dissimilarity. Contrariwise, evaluation of a development of the regional disparities among the districts looks like notably heterogeneously. CONCLUSIONS: There are considerable regional discrepancies throughout the districts of the Slovak Republic. Hence, it is necessary to create a common platform how to proceed with the solution of this issue.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

Immiserizing Growth

2019 ◽  
Keyword(s):  
Political Economy ◽  
Secondary Data ◽  
Household Survey ◽  
Data Set ◽  
Republic Of China ◽  
The Poor ◽  
Poverty Measures ◽  
Entry Points ◽  
Country Analysis ◽  
Cross Country

Immiserizing Growth occurs when growth fails to benefit, or harms, those at the bottom. It is not a new concept, appearing such figures as Malthus, Ricardo and Marx. It is also not empirically insignificant, occurring in between 10% and 35% of cases, depending on the data set and the growth and poverty measures used. In spite of this, it has not received its due attention in the academic literature, dominated by the prevailing narrative that ‘growth is good for the poor’. The chapters in this volume aim to arrive at a better understanding of when, why and how growth fails the poor. They combine discussion of mechanisms of Immiserizing Growth with empirical data on trends in growth, poverty and related welfare indicators. In terms of mechanisms, politics and political economy are chosen as useful entry points to explain IG episodes. The disciplinary focus is diverse, drawing on economics, political economy, applied social anthropology, and development studies. A number of methodological approaches are represented including statistical analysis of household survey and cross-country data, detailed ethnographic work and case study analysis drawing on secondary data. Geographical coverage is wide including Bolivia, the Dominican Republic, Ecuador, India, Indonesia, Mexico, Nigeria, the People’s Republic of China, Singapore, and South Korea, in addition to cross-country analysis. As the first book-length treatment of Immiserizing Growth in the literature, we believe that this volume constitutes an important step in redirecting attention to this issue.

scholarly journals Vulnerable Older Adults in Disasters: Effects of Hurricane Irma on Nursing Homes and Assisted Living

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 733-734
Author(s):  
Lindsay Peterson ◽  
David Dosa ◽  
Patricia D’Antonio
Keyword(s):  
Older Adults ◽  
Assisted Living ◽  
Gulf Coast ◽  
Secondary Data ◽  
Morbidity And Mortality ◽  
Term Care ◽  
The Face ◽  
Qualitative Methodologies ◽  
Hurricane Irma

Abstract Preparedness of residents in long-term care (LTC) in the face of hurricane emergencies is a contested and largely unanswered question. Our prior work involving the U.S. Gulf Coast hurricanes of 2005-08 showed that exposure to various storms on nursing home (NH) residents resulted in significantly more deaths than reported by health care officials. This work also highlighted that evacuation of NH residents, compared to sheltering in place, was independently associated with morbidity and mortality. Hurricane Irma struck Florida on Sept. 10, 2017, prompting the evacuation of thousands of NH and assisted living community (ALC) residents. This symposium will discuss the effects of Hurricane Irma on vulnerable older adults residing in NHs and ALCs using mixed quantitative and qualitative methodologies. The first presentation will discuss morbidity and mortality of NH residents exposed to Hurricane Irma and will stratify by long stay/short stay status and hospice enrollment. The second presentation will discuss improvements and continued barriers to NH preparedness based on interviews with 30 administrators following Hurricane Irma. Using a novel methodology to identify residents of ALCs using secondary data sources, the third presentation will document AL resident morbidity and mortality risk following Hurricane Irma. The final presentation will highlight results of interviews with 70 stakeholders from small and large ALCs concerning the hurricane experiences of residents, including those with dementia. This symposium offers a multi-faceted view of a disaster’s effects on LTC residents across Florida, including novel data from the NH environment and lesser-examined ALCs.

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