scholarly journals Are Smaller Emergency Departments More Prone to Volume Variability?

2021 ◽  
Vol 22 (4) ◽  
pp. 878-881
Author(s):  
Sara Nourazari ◽  
Jonathan Harding ◽  
Samuel Davis ◽  
Ori Litvak ◽  
Stephen Traub ◽  
...  
Keyword(s):  
Emergency Departments ◽  
Regression Models ◽  
Pearson Correlation ◽  
High Volume ◽  
Linear Regression Models ◽  
Strong Negative Correlation ◽  
Lower Volume ◽  
Pearson Correlation Test ◽  
Patient Arrival ◽  
Computational Simplicity

Introduction: Daily patient volume in emergency departments (ED) varies considerably between days and sites. Although studies have attempted to define “high-volume” days, no standard definition exists. Furthermore, it is not clear whether the frequency of high-volume days, by any definition, is related to the size of an ED. We aimed to determine the correlation between ED size and the frequency of high-volume days for various volume thresholds, and to develop a measure to identify high-volume days. Methods: We queried retrospective patient arrival data including 1,682,374 patient visits from 32 EDs in 12 states between July 1, 2018–June 30, 2019 and developed linear regression models to determine the correlation between ED size and volume variability. In addition, we performed a regression analysis and applied the Pearson correlation test to investigate the significance of median daily volumes with respect to the percent of days that crossed four volume thresholds ranging from 5–20% (in 5% increments) greater than each site’s median daily volume. Results: We found a strong negative correlation between ED median daily volume and volume variability (R2 = 81.0%; P < 0.0001). In addition, the four regression models for the percent of days exceeding specified thresholds greater than their daily median volumes had R2 values of 49.4%, 61.2%, 70.0%, and 71.8%, respectively, all with P < 0.0001. Conclusion: We sought to determine whether smaller EDs experience high-volume days more frequently than larger EDs. We found that high-volume days, when defined as days with a count of arrivals at or above certain median-based thresholds, are significantly more likely to occur in lower-volume EDs than in higher-volume EDs. To the extent that EDs allocate resources and plan to staff based on median volumes, these results suggest that smaller EDs are more likely to experience unpredictable, volume-based staffing challenges and operational costs. Given the lack of a standard measure to define a high-volume day in an ED, we recommend 10% above the median daily volume as a metric, for its relevance, generalizability across a broad range of EDs, and computational simplicity.

Study to relate mini-spun yarn tenacity with cotton fiber strength

2019 ◽  
Vol 89 (21-22) ◽  
pp. 4491-4501 ◽  
Author(s):  
Yongliang Liu ◽  
B Todd Campbell ◽  
Christopher Delhom
Keyword(s):  
Cotton Fiber ◽  
Fiber Strength ◽  
Pearson Correlation ◽  
High Volume ◽  
Short Fiber ◽  
Ratio Method ◽  
Linear Regression Models ◽  
Fiber Properties ◽  
Yarn Tenacity ◽  
Cotton Cultivar

There has been great interest in assessing yarn tenacity directly from available cotton fiber property data acquired by various means, including high-volume instrumentation (HVI). The HVI test is a primary and routine measurement providing fiber properties to cotton researchers. Knowledge about yarn tenacity within a cotton cultivar or between cultivars could be useful with regard to understanding the selection of cotton cultivars. This study examined the effect of cotton growth location, crop year, and cultivar on three relationships (fiber strength versus fiber micronaire, yarn tenacity versus fiber micronaire, and fiber strength versus yarn tenacity), and found great variations in the Pearson correlation and the gradients of respective regression lines. Instead of developing linear regression models from HVI fiber properties to predict yarn tenacity, this study applied a simple ratio method (i.e. normalized fiber strength or yarn tenacity against five HVI fiber properties) to relate fiber strength with yarn tenacity. The short fiber index was found to have a greater effect on the correlation between modified yarn tenacity and modified fiber strength than micronaire, yellowness, upper-half mean length, or uniformity index. This result implied the feasibility of utilizing HVI fiber short fiber index and strength data, as a semiquantitative and fast approach, to compare yarn tenacity performance within a cotton cultivar or between cultivars.

scholarly journals Model development for the estimation of urban air temperature based on surface temperature and NDVI

2020 ◽  
Vol 54 (3) ◽  
pp. 29-40 ◽  
Author(s):  
Y Guo ◽  
Tamás Gál ◽  
Guohang Tian ◽  
János Unger
Keyword(s):  
Linear Regression ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Regression Models ◽  
Pearson Correlation ◽  
Model Development ◽  
Urban Air ◽  
Landsat 8 ◽  
Linear Regression Models

Predictive models for urban air temperature (Tair) were developed by using urban land surface temperature (LST) retrieved from Landsat-8 and MODIS data, NDVI retrieved from Landsat-8 data and Tair measured by 24 climatological stations in Szeged. The investigation focused on summer period (June−September) during 2016−2019 in Szeged. The relationship between Tair and LST was analyzed by calculating Pearson correlation coefficient, root-mean-square error and mean-absolute error using the data of 2017−2019, then unary (LST) and binary (LST and NDVI) linear regression models were developed for estimating Tair. The data in 2016 were used to validate the accuracy of the models. Correlation analysis indicated that there were strong correlations during the nighttime and relatively weaker ones during the daytime. The errors between Tair and LSTMODIS-Night was the smallest, followed by LSTMODIS-Day and LSTLandsat-8 respectively. The validation results showed that all models could perform well, especially during nighttime with an error of less than 1.5o. However, the addition of NDVI into the linear regression models did not significantly improve the accuracy of the models, and even had a negative effect. Finally, the influencing factors and temporal and spatial variability of the correlation between Tair and LST were analyzed. LSTLandsat-8 had a larger original error with Tair, but the regression model based on Landsat-8 had a stronger ability to reduce errors.

scholarly journals Impact of hospital volume on in-hospital mortality and 30-day cardiac readmission of hospitalized patients with heart faliure. A population based study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J.L Bonilla Palomas ◽  
M.P Anguita-Sanchez ◽  
F.J Elola ◽  
J.L Bernal ◽  
C Fernandez-Perez ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Regression Models ◽  
Hospital Volume ◽  
Clustering Algorithm ◽  
Pearson Correlation ◽  
High Volume ◽  
Hospitalized Patients ◽  
Data Set ◽  
High Volume Hospital ◽  
Low Volume

Abstract Background Heart failure (HF) is a major health care problem. Epidemiological data from hospitalized patients are scarce and the association between hospital volume and patient outcomes is largely unknown. Purpose The aim of this study was to analyze the relationship between hospital volume and outcomes (in-hospital mortality and 30-day cardiac readmission). Methods We conducted an observational study of patients discharged with the principal diagnosis of HF from The National Health System' acute hospitals during 2015. The source of the data was the Minimum Basic Data Set of the Ministry of Health, Consumer and Social Welfare. We calculated risk-standardized mortality rates (RSMR) at the index episode and risk-standardized cardiac diseases readmissions rates (RSRR) within 30 days after discharge by using a risk adjustment multilevel logistic regression models developed by the Medicare and Medicaid Services. Information on the number of HF discharges at each hospital in 2015 was analysed to classify centres into 2 categories (high- and low-volume hospitals). To discriminate between high- and low-volume centers, a K-means clustering algorithm was used. The association between volume and RSMR or RSRR was tested with the Pearson correlation coefficient and linear regression models. Results A total of 117 233 episodes of HF were selected during 2015. The mean age was 80±10 years and 46% were women. The crude in-hospital mortality rate was 12.1% and 30-day cardiac readmission rate was 18%. The cut-off point was set at 517 HF discharges per hospital during 2015. High volume hospitals had a statistically lower RSMR (10.3±2.8 vs 11.3±3.6; p&lt;0.001) and higher RSRR (10.7±1.9 vs 9.2±1.6; p&lt;0.001) than low volume hospitals. Low-volume hospitals showed higher dispersion of outcomes than high-volume, both for RSMR and RSRR (Figure). Conclusions We found that patients hospitalized for HF in 2105 had lower in-hospital mortality if they were admitted to a high-volume hospital. We have also found that high-volume hospitals had higher 30-day cardiac readmission rates. Funding Acknowledgement Type of funding source: None

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jaffer Okiring ◽  
Adrienne Epstein ◽  
Jane F. Namuganga ◽  
Victor Kamya ◽  
Asadu Sserwanga ◽  
...  
Keyword(s):  
Regression Models ◽  
Malaria Incidence ◽  
Temporal Changes ◽  
Linear Regression Models ◽  
High Burden ◽  
Exponential Regression ◽  
Malaria Morbidity ◽  
Laboratory Test Results ◽  
Combining Data ◽  
Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

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