scholarly journals The Association of Coal-Fired Power Plants and the Mortality of COVID-19

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Alexander Irwin ◽  
Robert Hodgson
Keyword(s):  
Air Pollution ◽  
Regression Analysis ◽  
Mortality Rate ◽  
Poisson Regression ◽  
Power Plants ◽  
Energy Information Administration ◽  
Chi Square ◽  
Poisson Regression Analysis ◽  
Confounding Variables ◽  
Chi Square Test

With the outbreak and spread of severe acute respiratory syndrome coronavirus 2 (COVID-19), the world was ushered into a global pandemic. One of the most alarming aspects of the disease is the high mortality rate. Previous research has shown that long term exposure to air pollution has been correlated with the mortality rate of respiratory diseases. The focus of this study was to determine if there was any association between the mortality of COVID-19 and one of the primary producers of air pollution, coal-fired power plants. Using data from the US Energy Information Administration, John Hopkins University, and census department, a chi-square test and a poisson regression analysis were conducted to determine if living in proximity to coal-fired power plants had any effects on the mortality of COVID-19. The chi-square test results showed that there was no statistical significance as the variables showed independence. These results illustrate that there is no association between coal-fired power plants and the mortality rate of COVID-19. To expand on the results of the chi-square test, a poisson regression analysis was performed to account for the presence of confounding variables. This analysis showed similar results to the chi-square test, but due to issues with outliers in the data causing overdispersion, the model was unable to be accurately conducted, making all results inconclusive. With the inconclusive results of the poisson regression analysis, the conclusions drawn from the chi-square test were not able to be generalized as they were not verified in the presence of confounding variables. 

scholarly journals Factors associated with breakfasting in users of a public health service

2013 ◽  
Vol 26 (2) ◽  
pp. 195-203 ◽  
Author(s):  
Patrícia Pinheiro de Freitas ◽  
Raquel de Deus Mendonça ◽  
Aline Cristine Souza Lopes
Keyword(s):  
Public Health ◽  
Health Promotion ◽  
Regression Analysis ◽  
Poisson Regression ◽  
Dietary Habits ◽  
Statistical Treatment ◽  
Cross Sectional Study ◽  
Chi Square ◽  
Cross Sectional ◽  
Poisson Regression Analysis

OBJECTIVE: This study identified the sociodemographic, lifestyle, dietary and anthropometric factors of users of a public health promotion service who have breakfast regularly. METHODS: This cross-sectional study included all users aged 20 years or more who joined the service between March 2007 and December 2010. Their socioeconomic and anthropometric data, dietary habits and health status were investigated. Statistical treatment included the Chi-square, Mann-Whitney, Fisher's exact and Student's t tests and Poisson regression analysis (p<0.05). RESULTS: Most of the participants (87.1%, n=528) breakfasted often, especially those aged 48.5 years or more (p=0.041). Poisson regression analysis showed association between breakfasting often and not smoking (PR=1.45, 95%CI: 1.10-1.91), having a greater number of daily meals (PR=1.15, 95%CI: 1.06-1.25), appropriate intake of deep-fried foods (PR=1.12, 95%CI: 1.01-1.25), lower fat intake (PR=0.78, 95%CI: 0.68-0.89) and smaller prevalence of excess weight (PR=0.85, 95%CI: 0.78-0.92). CONCLUSION: The positive relationship found between breakfasting often and not smoking, appropriate food and nutrient intakes and a healthier body weight shows the need of emphasizing this meal in health services as a simple and doable health promotion strategy that helps to prevent and control chronic diseases.

scholarly journals PEMODELAN ANGKA KEMATIAN KECELAKAAN LALU LINTAS DI KOTA DENPASAR

2021 ◽  
Vol 10 (2) ◽  
pp. 111
Author(s):  
NI LUH WIWIN YUNIARTI ◽  
I GUSTI AYU MADE SRINADI ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Traffic Congestion ◽  
Poisson Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Poisson Regression Model ◽  
Chi Square ◽  
Poisson Regression Analysis ◽  
Accident Data

Denpasar City is one of the most crowded areas on the island of Bali, this is due to the fast population growth rate. This fast population can cause problems, one of the problem is in the transportation sector. The increase in the volume of transportation can cause traffic congestion which can lead to a high number of traffic accidents, this can lead to death due to traffic accidents in Denpasar City. To determine the factors that influence traffic accident mortality, researchers used Poisson regression analysis. Based on data on traffic accidents in Denpasar City in 2018, the deviance value is smaller than the chi square value. Therefore Poisson regression analysis is sufficient to model traffic accident data in Denpasar City. The Poisson regression model obtained from this research is. Based on the Poisson regression model obtained, the independent variable that contributes significantly and has a high effect on the number of people who die in traffic accidents is the driver factor.

Quantitative analysis of the impact of craft worker availability on construction project safety performance

2016 ◽  
Vol 16 (3) ◽  
pp. 307-322 ◽  
Author(s):  
Hossein Karimi ◽  
Timothy R.B. Taylor ◽  
Paul M. Goodrum ◽  
Cidambi Srinivasan
Keyword(s):  
Regression Analysis ◽  
Poisson Regression ◽  
Construction Projects ◽  
Construction Project ◽  
Safety Performance ◽  
Content Type ◽  
Poisson Regression Analysis ◽  
Incident Cases ◽  
The Impact ◽  
Craft Worker

Purpose This paper aims to quantify the impact of craft worker shortage on construction project safety performance. Design/methodology/approach A database of 50 North American construction projects completed between 2001 and 2014 was compiled by taking information from a research project survey and the Construction Industry Institute Benchmarking and Metrics Database. The t-test and Mann-Whitney test were used to determine whether there was a significant difference in construction project safety performance on projects with craft worker recruiting difficulty. Poisson regression analysis was then used to examine the relationship between craft worker recruiting difficulty and Occupational Safety and Health Administration Total Number of Recordable Incident Cases per 200,000 Actual Direct Work Hours (TRIR) on construction projects. Findings The result showed that the TRIR distribution of a group of projects that reported craft worker recruiting difficulty tended to be higher than the TRIR distribution of a group of projects with no craft worker recruiting difficulty (p-value = 0.004). Moreover, the average TRIR of the projects that reported craft worker recruiting difficulty was more than two times the average TRIR of projects that experienced no craft recruiting difficulty (p-value = 0.035). Furthermore, the Poisson regression analysis demonstrated that there was a positive exponential relationship between craft worker recruiting difficulty and TRIR in construction projects (p-value = 0.004). Research limitations/implications The projects used to construct the database are heavily weighted towards industrial construction. Practical implications There have been significant long-term gains in construction safety within the USA. However, if recent craft shortages continue, the quantitative analyses presented herein indicate a strong possibility that more safety incidents will occur unless the shortages are reversed. Innovative construction means and methods should be developed and adopted to work in a safe manner with a less qualified workforce. Originality/value The Poisson regression model is the first model that quantifiably links project craft worker availability to construction project safety performance.

Changes in tobacco depictions after implementation of tobacco-free film and TV rules in Bollywood films in India: a trend analysis

2021 ◽  
pp. tobaccocontrol-2021-056629
Author(s):  
Gaurang P Nazar ◽  
Monika Arora ◽  
Nitika Sharma ◽  
Surbhi Shrivastava ◽  
Tina Rawal ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Trend Analysis ◽  
Poisson Regression ◽  
Poisson Regression Analysis ◽  
Free Film ◽  
Rule Compliance ◽  
Increasing Trend ◽  
Before And After ◽  
Year 2013

BackgroundIndia’s tobacco-free film and TV rules were implemented from 2012. To assess the effect of the rules, we studied tobacco depictions in top-grossing Bollywood films released between 2006 and 2017 and rule compliance after 2012.MethodsTobacco incidents and brand appearances were coded in 240 top-grossing Bollywood films (2006–2017) using the Breathe California method. Trends in number of tobacco incidents per film per year were studied before and after implementation of the rules using Poisson regression analysis. Compliance with rules over the years was studied using Pearson product-moment correlations.ResultsForty-five films were U-rated (all ages), 162 were UA-rated (below age 12 years must be adult-accompanied), and 33 were A-rated (age 18+ years only). Before implementation of the rules, the number of tobacco incidents per film was increasing by a factor of 1.1/year (95% CI 1.0 to 1.2, p=0.002). However, beginning year 2013, the number of incidents per film started falling significantly by a factor of 0.7/year (95% CI 0.6 to 0.9; p=0.012) compared with the previous increasing trend. The percentage of youth-rated (U and UA) films with any tobacco incidents also declined from a peak of 76% in 2012 to 35% in 2017. The percentage of films complying with the rules (audio-visual disclaimers, health spots, static warnings) did not change significantly from 2012 to 2017.ConclusionIndia’s 2012 rules were followed by a reduction in tobacco depictions in Bollywood films. Enhanced monitoring of compliance is needed to ensure the continued effectiveness of the rules.

scholarly journals Clinical and Demographic Predictors of Mortality due to COVID-19 Pandemic: A Retrospective Study

2021 ◽  
Vol 11 (5) ◽  
pp. 74-81
Author(s):  
Ayushi Rajkumar Jain ◽  
Doss Prakash ◽  
Sheetal Swamy
Keyword(s):  
Risk Factors ◽  
Mortality Rate ◽  
Early Stage ◽  
Major Complication ◽  
Respiratory Arrest ◽  
Chi Square ◽  
High Risk Factors ◽  
Demographic Predictors ◽  
Chi Square Test ◽  
The Common

The alarming statistics of COVID-19 surges up in 2021 throwing an enormous burden on the healthcare system across the world. According to WHO reports on 14th April 2021, globally 136,996,364 confirmed COVID-19 cases are reported across the continents, including 2,951,832 deaths. The state of Maharashtra reported the maximum number of cases of India including high mortality rate. This study was conducted to identify and describe the relation of different predictors (Age, gender, duration of hospital stay, presence of co morbidities) of mortality among the COVID-19 deceased patients by retrospectively analyzing the medical case records of 121 patients from a dedicated COVID hospital at Aurangabad from July 2020 to December 2020. Chi-square test was performed to assess the association between causes of death with different cluster of variables and their significance. This study helps us to identify risk factors that show association between various predictors and mortality rate in COVID-19 patients. Out of 121 deaths, 96 (79%) were male, 61 (49.6%) were in age group between 60-79 years, ARDS was one of the major complication in the deceased patients accounting 29.8% and cardio respiratory arrest was the common cause of death among the deceased patients with 85%. It was also observed that mortality rate was very higher in the initial five days of hospitalization with critical care support. Our result findings provide clinical inferences for physicians to identify high-risk factors with COVID-19 at a very early stage. Key words: COVID-19, Mortality rate, Demographic predictors, Co-morbidities, Cardio respiratory arrest.

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