scholarly journals Critical Care Medical Centers May Play an Important Role in Reducing the Risk of COVID-19 Death in Japan

2020 ◽  
Vol 2 (11) ◽  
pp. 2147-2150
Author(s):  
Yohei Ishikawa ◽  
Toru Hifumi ◽  
Mitsuyoshi Urashima
Keyword(s):  
Critical Care ◽  
Regression Models ◽  
Primary Outcome ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Positive Ratio ◽  
Medical Centers ◽  
Number Of Patients ◽  
Polymerase Chain ◽  
Positive Results

AbstractMarked differences in COVID-19 mortalities have been observed among 47 prefectures in Japan. Here, we explored associations between COVID-19 mortalities and medical and public health capacities in individual prefectures. The following data by prefecture were abstracted from open resources provided by the Ministry of Health, Labour and Welfare in Japan as of May 24, 2020: total number of COVID-19 deaths; polymerase chain reaction (PCR)-positive ratio (i.e., number of patients with PCR-positive results/number of patients aiming diagnosis of COVID-19 or individuals in close contacted with COVID-19 patients); number of call centers, outpatient centers, and hospital beds specifically for patients diagnosed with COVID-19; and others. The primary outcome was COVID-19 mortality per million population. Multiple and simple linear regression models were applied. Two variables were significantly associated with COVID-19 mortality: PCR-positive ratio (P < 0.001) and number of critical care medical centers per million population (P = 0.001). PCR-positive ratio was positively associated with COVID-19 mortality (aR-sqr = 0.522). Low PCR-positive ratio, especially ≤ 4%, was associated with low COVID-19 mortality. Critical care medical centers may also play an important role in reducing the risk of COVID-19 death.

On Consistency of Estimators in Simple Linear Regression Models

2002 ◽  
Vol 53 (3-4) ◽  
pp. 261-264 ◽  
Author(s):  
Anindya Roy ◽  
Thomas I. Seidman
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Regression Models ◽  
Sufficient Condition ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Least Squares Estimators

We derive a property of real sequences which can be used to provide a natural sufficient condition for the consistency of the least squares estimators of slope and intercept for a simple linear regression models.

scholarly journals Regression models for productivity prediction in cactus pear cv. Gigante

2020 ◽  
Vol 24 (11) ◽  
pp. 721-727
Author(s):  
Bruno V. C. Guimarães ◽  
Sérgio L. R. Donato ◽  
Ignacio Aspiazú ◽  
Alcinei M. Azevedo ◽  
Abner J. de Carvalho
Keyword(s):  
Linear Regression ◽  
Multiple Regression ◽  
Regression Models ◽  
Yield Prediction ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Ct Number ◽  
Cactus Pear ◽  
Quadratic Interaction ◽  
Multiple Regression Models

ABSTRACT The understanding of plant behavior and its reflexes on yield is essential for rural planning; thus, the biomathematical models are promising in the yield prediction of cactus pear cv. Gigante. This study aimed to adjust, through simple and multiple regression analysis, models for predicting the yield of cactus pear cv. Gigante. The study, using homogeneous treatments, was developed at the Instituto Federal Baiano, Campus of Guanambi, Bahia, Brazil. Data were collected in an area consisting of 384 basic units (plants), in which the yield, defined as a dependent variable, and the predictor variables: plant height (PH), cladode length (CL), cladode width (CW), and cladode thickness (CT), number of cladodes (NC), cladode area (CA), and total cladode area (TCA) were evaluated. Simple linear regression models, multiple regression models only with simple effects for the explanatory variables, and the multiple regression models considering the simple and quadratic effects, and all its possible interactions were adjusted. From this last model, a reduced model was obtained by discarding the less relevant effects, using the Stepwise methodology. The use of the vegetative traits, TCA, NC, CA, CL, CT, and CW, through the adoption of multiple linear regression, quadratic interaction or just the variable TCA by the use of simple linear regression, allows the yield prediction of cactus pear, with adjusted R² of 0.82, 0.76, and 0.74, respectively.

scholarly journals Trends in radiotherapy inpatient admissions in Germany: a population-based study over a 10-year period

2021 ◽  
Author(s):  
Daniel Medenwald ◽  
Rainer Fietkau ◽  
Gunther Klautke ◽  
Susan Langer ◽  
Florian Würschmidt ◽  
...  
Keyword(s):  
Regression Models ◽  
Data Centers ◽  
Temporal Trends ◽  
Population Based ◽  
Main Diagnosis ◽  
Constant Number ◽  
Linear Regression Models ◽  
Population Based Study ◽  
Number Of Patients ◽  
Related Group

Abstract Objective With the increasing complexity of oncological therapy, the number of inpatient admissions to radiotherapy and non-radiotherapy departments might have changed. In this study, we aim to quantify the number of inpatient cases and the number of radiotherapy fractions delivered under inpatient conditions in radiotherapy and non-radiotherapy departments. Methods The analysis is founded on data of all hospitalized cases in Germany based on Diagnosis-Related Group Statistics (G-DRG Statistics, delivered by the Research Data Centers of the Federal Statistical Office). The dataset includes information on the main diagnosis of cases (rather than patients) and the performed procedures during hospitalization based on claims of reimbursement. We used linear regression models to analyze temporal trends. The considered data encompass the period from 2008 to 2017. Results Overall, the number of patients treated with radiotherapy as inpatients remained constant between 2008 (N = 90,952) and 2017 (N = 88,998). Starting in January 2008, 48.9% of 4000 monthly cases received their treatment solely in a radiation oncology department. This figure decreased to 43.7% of 2971 monthly cases in October 2017. We found a stepwise decrease between December 2011 and January 2012 amounting to 4.3%. Fractions received in radiotherapy departments decreased slightly by 29.3 (95% CI: 14.0–44.5) fractions per month. The number of days hospitalized in radiotherapy departments decreased by 83.4 (95% CI: 59.7, 107.0) days per month, starting from a total of 64,842 days in January 2008 to 41,254 days in 2017. Days per case decreased from 16.2 in January 2008 to 13.9 days in October 2017. Conclusion Our data give evidence to the notion that radiotherapy remains a discipline with an important inpatient component. Respecting reimbursement measures and despite older patients with more comorbidities, radiotherapy institutions could sustain a constant number of cases with limited temporal shifts.

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