scholarly journals A multivariate analysis of CalEnviroScreen: comparing environmental and socioeconomic stressors versus chronic disease

2017 ◽  
Author(s):  
Ben K. Greenfield ◽  
Jayant Rajan ◽  
Thomas E. McKone
Keyword(s):  
Principal Component Analysis ◽  
Environmental Protection Agency ◽  
Disease Burden ◽  
Multiple Stressors ◽  
Health Hazard ◽  
Principal Component ◽  
Component Analysis ◽  
Environmental Indicators ◽  
Spatial Autoregression ◽  
Study Results

AbstractBackgroundThe health-risk assessment paradigm is shifting from single stressor evaluation towards cumulative assessments of multiple stressors. Recent efforts to develop broad-scale public health hazard datasets provide an opportunity to develop and evaluate multiple exposure hazards in combination.MethodsWe performed a multivariate study of the spatial relationship between 12 indicators of environmental hazard, 5 indicators of socioeconomic hardship, and 3 health outcomes. Indicators were obtained from CalEnviroScreen (version 3.0), a publicly available environmental justice screening tool developed by the State of California Environmental Protection Agency. The indicators were compared to the total rate of hospitalization for 14 ICD-9 disease categories (a measure of disease burden) at the zip code tabulation area population level. We performed principal component analysis to visualize and reduce the CalEnviroScreen data and spatial autoregression to evaluate associations with disease burden.ResultsCalEnviroScreen was strongly associated with the first principal component (PC) from a principal component analysis (PCA) of all 20 variables (Spearman ρ = 0.95). In a PCA of the 12 environmental variables, two PC axes explained 43% of variance, with the first axis indicating industrial activity and air pollution, and the second associated with ground-level ozone, drinking water contamination and PM2.5. Mass of pesticides used in agriculture was poorly or negatively correlated with all other environmental indicators, and with the CalEnviroScreen calculation method, suggesting a limited ability of the method to capture agricultural exposures. In a PCA of the 5 socioeconomic variables, the first PC explained 66% of variance, representing overall socioeconomic hardship. In simultaneous autoregressive models, the first environmental and socioeconomic PCs were both significantly associated with the disease burden measure, but more model variation was explained by the socioeconomic PCs.ConclusionsThis study supports the use of CalEnviroScreen for its intended purpose of screening California regions for areas with high environmental exposure and population vulnerability. Study results further suggest a hypothesis that, compared to environmental pollutant exposure, socioeconomic status has greater impact on overall burden of disease.

scholarly journals Adoption of E-commerce in Bangladesh: Probit Regression and Principal Component Analysis Approach

2021 ◽  
pp. 1-13
Keyword(s):  
Principal Component Analysis ◽  
Risk Perception ◽  
Market Orientation ◽  
Principal Component ◽  
Component Analysis ◽  
Ease Of Use ◽  
Accurate Information ◽  
Probit Regression ◽  
Study Results ◽  
Number Of Customers

Web based services have turned into an integral part of peoples’ life. Ease of use and several advantages lead to the popularity of adopting electronic commerce (e-commerce). Though a big number of customers visit and register on the commerce sites daily, the selling rate is low for lack of trust and loyalty. Trust is the main connector of companies and customers. Loyal customers are the important assets for the companies. This study aims at identifying the factors that are associated with the trust and risk perception of the customers on e-commerce. A primary dataset is collected on this occasion. Likert scale type questionnaire is involved in the questionnaire and data reliability is checked through Cronbach's alpha. Probit regression and principal component analysis are used as statistical analysis. The study finds the variables age, internet using purpose, market orientation, and technology as significantly related to trustworthiness of the customers on e-commerce, while the variables experienced using the internet, internet using purpose, and market orientation are potential determinants of risk perception. The study results suggest the e-commerce companies emphasize technologies for keeping accurate information, performing the outmost of the customers’ benefit, and collecting customers’ information. In addition, they should be conscious of the customers’ opinion and pleasant experience, along with maintaining the mentioned delivery time.

scholarly journals Principal Component Analysis and Factor Analysis: differences and similarities in Nutritional Epidemiology application

2019 ◽  
Vol 22 ◽  
Author(s):  
Roberta de Oliveira Santos ◽  
Bartira Mendes Gorgulho ◽  
Michelle Alessandra de Castro ◽  
Regina Mara Fisberg ◽  
Dirce Maria Marchioni ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Statistical Methods ◽  
Principal Component ◽  
Component Analysis ◽  
Food Group ◽  
Correlated Data ◽  
Nutritional Epidemiology ◽  
Study Results ◽  
Nutritional Studies

ABSTRACT: Introduction: Statistical methods such as Principal Component Analysis (PCA) and Factor Analysis (FA) are increasingly popular in Nutritional Epidemiology studies. However, misunderstandings regarding the choice and application of these methods have been observed. Objectives: This study aims to compare and present the main differences and similarities between FA and PCA, focusing on their applicability to nutritional studies. Methods: PCA and FA were applied on a matrix of 34 variables expressing the mean food intake of 1,102 individuals from a population-based study. Results: Two factors were extracted and, together, they explained 57.66% of the common variance of food group variables, while five components were extracted, explaining 26.25% of the total variance of food group variables. Among the main differences of these two methods are: normality assumption, matrices of variance-covariance/correlation and its explained variance, factorial scores, and associated error. The similarities are: both analyses are used for data reduction, the sample size usually needs to be big, correlated data, and they are based on matrices of variance-covariance. Conclusion: PCA and FA should not be treated as equal statistical methods, given that the theoretical rationale and assumptions for using these methods as well as the interpretation of results are different.

scholarly journals Visualization of classified data with kernel principal component analysis

2018 ◽  
Author(s):  
Toni Bakhtiar
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Feature Space ◽  
Original Data ◽  
Component Analysis ◽  
Kernel Functions ◽  
Kernel Principal Component Analysis ◽  
Gaussian Kernel ◽  
Kernel Pca ◽  
Study Results

Kernel Principal Component Analysis (Kernel PCA) is a generalization of the ordinary PCA which allows mapping the original data into a high-dimensional feature space. The mapping is expected to address the issues of nonlinearity among variables and separation among classes in the original data space. The key problem in the use of kernel PCA is the parameter estimation used in kernel functions that so far has not had quite obvious guidance, where the parameter selection mainly depends on the objectivity of the research. This study exploited the use of Gaussian kernel function and focused on the ability of kernel PCA in visualizing the separation of the classified data. Assessments were undertaken based on misclassification obtained by Fisher Discriminant Linear Analysis of the first two principal components. This study results suggest for the visualization of kernel PCA by selecting the parameter in the interval between the closest and the furthest distances among the objects of original data is better than that of ordinary PCA.

A Lifecycle Sustainability Assessment of CO2 Emissions, Energy Consumption and Social Aspects of Methylic and Ethylic Biodiesel Using Principal Component Analysis

2019 ◽  
Vol 965 ◽  
pp. 1-12
Author(s):  
Stefano Ferrari Interlenghi ◽  
José Luiz de Medeiros ◽  
Ofélia de Queiroz Fernandes Araújo
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Energy Loss ◽  
Principal Component ◽  
Component Analysis ◽  
Environmental Indicators ◽  
Biodiesel Production ◽  
Social Aspects ◽  
Carbon Intensity ◽  
Production Chain

The possibility of using renewable feedstocks for biodiesel production and reducing gas emissions makes it an attractive large-scale substitute to traditional fossil diesel. Although renewability is one of the main driving forces in biodiesel use, traditional production routes employ methanol as the transesterification agent, a chemical generated from fossil carbon. Aiming at further improving biodiesel’s sustainable performance, the replacement of methanol by ethanol has been proposed. Use of the ethylic production route could further reduce CO2 emissions, energy consumption and generate more jobs. The objective of this study is to unveil whether substituting methanol for ethanol does indeed result in a less carbon and energy intensive production chain while also increasing job generation and decreasing social strife. To assess production chain performance a lifecycle approach was used composed by: (i) Data assemblage from literature to represent the ethylic/methylic biodiesel systems; (ii) Construction of quantitative indicators to compare material and energetic flows; and (iii) Principal Component Analysis (PCA) for data interpretation and relevance ranking of calculated social/environmental indicators. Focus was given to CO2 emissions, energy consumption and social aspects of sustainability. Results show that use of ethanol does indeed reduce CO2 emissions, due to extra agricultural carbon sinks in the production chain but increases energy consumption and energy loss. Methanol also resulted in a chain with higher average wages, more jobs generated and less forced labor cases but with a higher accident rate and a high salary disparity. PCA showed that carbon intensity is one of the most important environmental metrics while energy consumption was considered secondary, but the high correlation between these aspects highly impact chain sustainability. PCA also greatly differentiated agricultural and industrial links of respective production chains, with industrial links being governed by CO2 emissions and process safety and agricultural links by water consumption, land use and energy loss. A distinct tradeoff was seen between environmental and social considerations of sustainability and between carbon intensity and energy consumption reductions. As a result, substitution is only justified in scenarios in which CO2 emissions outweigh energy intensity and social aspects.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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