Prediction of Death Rate Using Regression Analysis

2018 ◽  
pp. 735-745
Author(s):  
Satya Ranjan Dash ◽  
Rekha Sahu
Keyword(s):  
Regression Analysis ◽  
Death Rate

Chinese Provinces Safety Production Situation Analysis and Prediction

2014 ◽  
Vol 644-650 ◽  
pp. 5953-5957
Author(s):  
Yong He ◽  
Liang Liang Wang ◽  
Shao Bing Qin
Keyword(s):  
Regression Analysis ◽  
Death Rate ◽  
Production Control ◽  
Production Level ◽  
Situation Analysis ◽  
Per Capita Gdp ◽  
Safety Production ◽  
Per Capita ◽  
The Way ◽  
Production Situation

To study the condition of safety production situation and the control index, this paper analyze some Chinese provinces death rate per hundred million GDP and per capita GDP by the way of Regression analysis, at the same time, defining the coefficient that can reflect some of Chinese provinces safety production level. Predict the level of safety production situation by the theory of safety forecast, and giving a way to make safety production control index to provide the basis for some provinces’ safety production regulation in the coming year.

scholarly journals Applicability of Paediatric Index of Mortality 2 Score to Predict Outcome in Children Admitted to Paediatric Intensive Care Unit

2018 ◽  
Vol 38 (3) ◽  
pp. 149-152
Author(s):  
Shrikiran Aroor ◽  
Sandeep Kumar ◽  
Pushpa Kini ◽  
Suneel Mundkur
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Intensive Care ◽  
Logistic Regression Analysis ◽  
Paediatric Intensive Care Unit ◽  
Death Rate ◽  
Paediatric Intensive Care ◽  
P Value ◽  
The Mean

Introduction: Research on critically ill children admitted to the intensive care unit has shown the usefulness of Paediatric Index of Mortality 2 (PIM2) score at admission to predict outcome. This study was conducted to estimate PIM2 score in children admitted to Paediatric Intensive Care Unit and its correlation with clinical outcome. Methods: This prospective observational study was conducted in children of age group one month to 18 years admitted to the paediatric intensive care unit of a tertiary care hospital. Data including demographics, diagnostic categories, duration of hospital stay, predicted death rate (PDR) measured by PIM2 score was compared between survivors and non survivors. Logistic regression analysis was performed to arrive at a risk adjusted relationship between the different predictor variables and the probability of death. Results: Consecutive 130 children admitted to PICU during the study period were enrolled. The mean PDR (%) of the total study population was 22.4 ± 10.60. The mean PDR in survivors was 12.4 ± 7.80 while the PDR in non survivors was 44.2 ± 12.62 (p value < 0.001). Children with PDR < 1% had a mortality rate of 2.4% when compared to 71.4% in children with PDR > 5% (p value < 0.001). PDR by PIM2 score and the presence of hypo-albuminemia remained significant even after adjusting for age in multivariate logistic regression analysis. Conclusion: PDR measured by PIM2 score differentiated well between survivors and non survivors in PICU. The predicted death rate was less than the observed death rate. PIM2 score is a useful tool to assess the severity of illness and predict outcome.

The Analysis of Three Natural Experiments Relating To Fire Death Rate

1983 ◽  
Vol 1 (1) ◽  
pp. 33-53
Author(s):  
Jerry Banks ◽  
Douglas C. Montgomery
Keyword(s):  
Regression Analysis ◽  
Death Rate ◽  
Statistical Procedure ◽  
Natural Experiments ◽  
Fire Death ◽  
The Relationship ◽  
Analyze Data

This article relates the incidence of fire deaths to a number of regressor variables that may have predictive or explanatory value. The data for these analyses arise in the setting of "natural experiments." That is, we analyze data as it arises in nature. Regression analysis is a statistical procedure for modeling and investigating the relationship between variables in this setting.

Predicting the death rate around the world due to Covid-19 using regression analysis

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Regression Analysis ◽  
Death Rate ◽  
Total Population ◽  
Reproductive Rate ◽  
The World ◽  
Near Future ◽  
Death Cases

Nowadays, COVID-19 is considered to be the biggest disaster that the world is facing. It has created a lot of destruction in the whole world. Due to this COVID-19, analysis has been done to predict the death rate and infected rate from the total population. To perform the analysis on COVID-19, regression analysis has been implemented by applying the differential equation and ordinary differential equation (ODE) on the parameters. The parameters taken for analysis are the number of susceptible individuals, the number of Infected Individuals, and the number of Recovered Individuals. This work will predict the total cases, death cases, and infected cases in the near future based on different reproductive rate values. This work has shown the comparison based on 4 different productive rates i.e. 2.45, 2.55, 2.65, and 2.75. The analysis is done on two different datasets; the first dataset is related to China, and the second dataset is associated with the world's data. The work has predicted that by 2020-08-12: 59,450,123 new cases and 432,499,003 total cases and 10,928,383 deaths.

scholarly journals Determinants of COVID-19 Death Rate in Europe: Empirical Analysis

2021 ◽  
Vol 16 (1) ◽  
pp. 17-28
Author(s):  
Serhii Kozlovskyi ◽  
◽  
Daria Bilenko ◽  
Oleksandr Dluhopolskyi ◽  
Serhii Vitvitskyi ◽  
...  
Keyword(s):  
Economic Development ◽  
Regression Analysis ◽  
Death Rate ◽  
Linear Regression Analysis ◽  
Developed Countries ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Mortality Statistics ◽  
The World ◽  
The Impact

At the end of 2019, the new virus called Coronavirus disease (COVID-19) spread widely from China all over the world (including Europe). Most countries in Europe at the beginning of 2020 have been quarantined. The aim of the work is to develop the system dynamics model for assessing the impact of the different factors on the COVID-19 death rate in Europe. There were tested three hypotheses about factors of reducing the COVID-19 death rate with the help of linear regression analysis. The density of the population of European countries doesn’t affect the COVID-19 death rate. Also, COVID-19 death rate does not drastically affect mortality statistics. But the level of country’s economic development is a factor of COVID-19 death rate because in high developed countries the pandemic death rate is lower, regardless of the mechanisms of the spread of the disease and its impact on human health.

scholarly journals Evaluation of APACHE II system among intensive care patients at a teaching hospital

2003 ◽  
Vol 121 (2) ◽  
pp. 53-57 ◽  
Author(s):  
Paulo Antonio Chiavone ◽  
Yvoty Alves dos Santos Sens
Keyword(s):  
Intensive Care Unit ◽  
Regression Analysis ◽  
Intensive Care ◽  
Linear Regression ◽  
Death Rate ◽  
Elective Surgery ◽  
Linear Regression Analysis ◽  
Apache Ii ◽  
Surgical Patients ◽  
Apache Ii Score

CONTEXT: The high-complexity features of intensive care unit services and the clinical situation of patients themselves render correct prognosis fundamentally important not only for patients, their families and physicians, but also for hospital administrators, fund-providers and controllers. Prognostic indices have been developed for estimating hospital mortality rates for hospitalized patients, based on demographic, physiological and clinical data. OBJECTIVE: The APACHE II system was applied within an intensive care unit to evaluate its ability to predict patient outcome; to compare illness severity with outcomes for clinical and surgical patients; and to compare the recorded result with the predicted death rate. DESIGN: Diagnostic test. SETTING: Clinical and surgical intensive care unit in a tertiary-care teaching hospital. PARTICIPANTS: The study involved 521 consecutive patients admitted to the intensive care unit from July 1998 to June 1999. MAIN MEASUREMENTS: APACHE II score, in-hospital mortality, receiver operating characteristic curve, decision matrices and linear regression analysis. RESULTS: The patients' mean age was 50 ± 19 years and the APACHE II score was 16.7 ± 7.3. There were 166 clinical patients (32%), 173 (33%) post-elective surgery patients (33%), and 182 post-emergency surgery patients (35%), thus producing statistically similar proportions. The APACHE II scores for clinical patients (18.5 ± 7.8) were similar to those for non-elective surgery patients (18.6 ± 6.5) and both were greater than for elective surgery patients (13.0 ± 6.3) (p < 0.05). The higher this score was, the higher the mortality rate was (p < 0.05). The predicted death rate was 25.6% and the recorded death rate was 35.5%. Through the use of receiver operating curve analysis, good discrimination was found (area under the curve = 0.80). From the 2 x 2 decision matrix, 72.2% of patients were correctly classified (sensitivity = 35.1%; specificity = 92.6%). Linear regression analysis was equivalent to r² = 0.92. CONCLUSIONS: APACHE II was useful for stratifying these patients. The illness severity and death rate among clinical patients were higher than those recorded for surgical patients. Despite the stratification ability of the APACHE II system, it lacked accuracy in predicting death rates. The recorded death rate was higher than the predicted rate.

scholarly journals BRAZIL IS PROJECTED TO BE THE NEXT GLOBAL COVID-19 PANDEMIC EPICENTER

2020 ◽  
Author(s):  
Pedro de Lemos Menezes ◽  
David M. Garner ◽  
Vitor E. Valenti
Keyword(s):  
United States ◽  
Regression Analysis ◽  
United States Of America ◽  
Death Rate ◽  
The United States ◽  
Economic Systems ◽  
Death Rates ◽  
Link Type ◽  
Exponential Increase ◽  
Public Strategies

ABSTRACTCoronavirus disease 2019 (COVID-19) is a disease triggered by SARS-CoV-2 infection, which is related in the most recent pandemic situation, significantly affecting health and economic systems. In this study we assessed the death rate associated to COVID-19 in Brazil and the United States of America (USA) to estimate the probability of Brazil becoming the next pandemic epicenter. We equated data between Brazil and USA obtained through the Worldometer website (www.worldometer.info). Epidemic curves from Brazil and USA were associated and regression analysis was undertaken to predict the Brazilian death rate regarding COVID-19 in June. In view of data from April 9th 2020, death rates in Brazil follow a similar exponential increase to USA (r=0.999; p<0.001), estimating 64,310 deaths by June 9th 2020. In brief, our results demonstrated that Brazil follows an analogous progression of COVID-19 deaths cases when compared to USA, signifying that Brazil could be the next global epicenter of COVID-19. We highlight public strategies to decrease the COVID-19 outbreak.

scholarly journals Regional Association of Disability and SARS-CoV-2 Infection in 369 Counties of the United States

2020 ◽  
Author(s):  
Oluwaseyi Olulana ◽  
Vida Abedi ◽  
Venkatesh Avula ◽  
Durgesh Chaudhary ◽  
Ayesha Khan ◽  
...  
Keyword(s):  
United States ◽  
Regression Analysis ◽  
Death Rate ◽  
The United States ◽  
Lower Percentage ◽  
P Value ◽  
Median Income ◽  
Bivariate Regression ◽  
Using Data ◽  
Related Mortality

AbstractBackgroundThere have been outbreaks of SARS-CoV-2 in long term care facilities and recent reports of disproportionate death rates among the vulnerable population. The goal of this study was to better understand the impact of SARS-CoV-2 infection on the non-institutionalized disabled population in the United States using data from the most affected states as of April 9th, 2020.MethodsThis was an ecological study of county-level factors associated with the infection and mortality rate of SARS-CoV-2 in the non-institutionalized disabled population. We analyzed data from 369 counties from the most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts) in the United States using data available by April 9th, 2020. The variables include changes in mobility reported by Google, race/ethnicity, median income, education level, health insurance, and disability information from the United States Census Bureau. Bivariate regression analysis adjusted for state and median income was used to analyze the association between death rate and infection rate.ResultsThe independent sample t-test of two groups (group 1: Death rate≥3.4% [median] and group 2: Death rate < 3.4%) indicates that counties with a higher total population, a lower percentage of Black males and females, higher median income, higher education, and lower percentage of disabled population have a lower rate (< 3.4%) of SARS-CoV-2 related mortality (all p-values<4.3E-02). The results of the bivariate regression when controlled for median income and state show counties with a higher White disabled population (est: 0.19, 95% CI: 0.01-0.37; p-value:3.7E-02), and higher population with independent living difficulty (est: 0.15, 95% CI: −0.01-0.30; p-value: 6.0E-02) have a higher rate of SARS-CoV-2 related mortality. Also, the regression analysis indicates that counties with higher White disabled population (est: - 0.22, 95% CI: −0.43-(-0.02); p-value: 3.3E-02), higher population with hearing disability (est: −0.26, 95% CI: - 0.42- (-0.11); p-value:1.2E-03), and higher population with disability in the 18-34 years age group (est: −0.25, 95% CI: −0.41-(-0.09); p-value:2.4E-03) show a lower rate of SARS-CoV-2 infection.ConclusionOur results indicate that while counties with a higher percentage of non-institutionalized disabled population, especially White disabled population, show a lower infection rate, they have a higher rate of SARS-CoV-2 related mortality.

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