scholarly journals Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Amornrat Luenam ◽  
Nattapong Puttanapong
Keyword(s):  
Environmental Factors ◽  
Regression Models ◽  
Prediction Models ◽  
Random Effect ◽  
Spatial Association ◽  
Remotely Sensed ◽  
Fixed Effect ◽  
Panel Regression ◽  
Normalised Difference Vegetation Index ◽  
Panel Regression Models

This study statistically identified the association of remotely sensed environmental factors, such as Land Surface Temperature (LST), Night Time Light (NTL), rainfall, the Normalised Difference Vegetation Index (NDVI) and elevation with the incidence of leptospirosis in Thailand based on the nationwide 7,495 confirmed cases reported during 2013–2015. This work also established prediction models based on empirical findings. Panel regression models with random-effect and fixed-effect specifications were used to investigate the association between the remotely sensed environmental factors and the leptospirosis incidence. The Local Indicators of Spatial Association (LISA) statistics were also applied to detect the spatial patterns of leptospirosis and similar results were found (the R2 values of the random-effect and fixed-effect models were 0.3686 and 0.3684, respectively). The outcome thus indicates that remotely sensed environmental factors possess statistically significant contribution in predicting this disease. The highest association in 3 years was observed in LST (random- effect coefficient = -9.787, p<0.001; fixed-effect coefficient = -10.340, p = 0.005) followed by rainfall (random-effect coefficient = 1.353, p <0.001; fixed-effect coefficient = 1.347, p <0.001) and NTL density (random-effect coefficient = -0.569, p = 0.004; fixed-effect coefficient = -0.564, p = 0.001). All results obtained from the bivariate LISA statistics indicated the localised associations between remotely sensed environmental factors and the incidence of leptospirosis. Particularly, LISA’s results showed that the border provinces in the northeast, the northern and the southern regions displayed clusters of high leptospirosis incidence. All obtained outcomes thus show that remotely sensed environmental factors can be applied to panel regression models for incidence prediction, and these indicators can also identify the spatial concentration of leptospirosis in Thailand.

scholarly journals Bank’s Stability through Risk Factors Growth and Capital Ratio: Evidence from Kuwait

2018 ◽  
Vol 8 (10) ◽  
pp. 361-376
Author(s):  
Ahmed Nahar Al Hussaini
Keyword(s):  
Risk Factors ◽  
Economic Growth ◽  
Regression Models ◽  
Critical Role ◽  
Random Effect ◽  
Panel Regression ◽  
Total Expenditure ◽  
Time Period ◽  
The Stability ◽  
Panel Regression Models

The objective of present study is to examine the effect of risk factors, and capital ratio along business and economic growth in stability of the banks in the case of Kuwait. For this purpose, 10 banks are selected for time period of 2010 to 2016, with the annual observations. Panel regression models like fixed and random effect are applied to check the significance of selected predictors on outcome factors of the study. The results of the study explain that there exists a significant impact of risk factors like liquidity and credit on the stability of the selected banks in the region of Kuwait. In addition, operational efficiency in the form of total expenditure as percent of total assets are also playing critical role. This study is contributing in existing literature from the context of stability and risk with the provision of some useful results.

scholarly journals Relationships between Renewable Energy and the Prevalence of Morbidity in the Countries of the European Union: A Panel Regression Approach

2021 ◽  
Vol 18 (12) ◽  
pp. 6548
Author(s):  
Robert Stefko ◽  
Beata Gavurova ◽  
Miroslav Kelemen ◽  
Martin Rigelsky ◽  
Viera Ivankova
Keyword(s):  
European Union ◽  
Renewable Energy ◽  
Statistical Methods ◽  
Regression Models ◽  
Kidney Diseases ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Panel Regression ◽  
The European Union ◽  
Panel Regression Models

The main objective of the presented study was to examine the associations between the use of renewable energy sources in selected sectors (transport, electricity, heating, and cooling) and the prevalence of selected groups of diseases in the European Union, with an emphasis on the application of statistical methods considering the structure of data. The analyses included data on 27 countries of the European Union from 2010 to 2019 published in the Eurostat database and the Global Burden of Disease Study. Panel regression models (pooling model, fixed (within) effects model, random effects model) were primarily used in analytical procedures, in which a panel variable was represented by countries. In most cases, positive and significant associations between the use of renewable energy sources and the prevalence of diseases were confirmed. The results of panel regression models could be generally interpreted as meaning that renewable energy sources are associated with the prevalence of diseases such as cardiovascular diseases, diabetes and kidney diseases, digestive diseases, musculoskeletal disorders, neoplasms, sense organ diseases, and skin and subcutaneous diseases at a significance level (α) of 0.05 and lower. These findings could be explained by the awareness of the health problem and the response in the form of preference for renewable energy sources. Regarding statistical methods used for country data or for data with a specific structure, it is recommended to use the methods that take this structure into account. The absence of these methods could lead to misleading conclusions.

Abstract 005: A Comparison of Methods for Identifying Hospital Performance Outliers in Cardiac Surgery

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Xiaoting Wu ◽  
Min Zhang ◽  
Richard L Prager ◽  
Donald S Likosky
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Random Effect ◽  
Hospital Performance ◽  
Fixed Effect ◽  
Logistic Regression Models ◽  
Indirect Standardization ◽  
Statistical Approaches ◽  
Random Effect Models ◽  
Direct Standardization

Introduction: A number of statistical approaches have been advocated and implemented to estimate adjusted hospital outcomes for public reporting or reimbursement. Nonetheless, the ability of these methods to identify hospital performance outliers in support of quality improvement has not yet been fully investigated. Methods: We leveraged data from patients undergoing coronary artery bypass grafting surgery between 2012-2015 at 33 hospitals participating in a statewide quality collaborative. We applied 5 different statistical approaches (1: indirect standardization with standard logistic regression models, 2: indirect standardization with fixed effect models, 3: indirect standardization with random effect models, 4: direct standardization with fixed effect models, 5: direct standardization with random effect models) to estimate hospital post-operative pneumonia rates adjusting for patients’ risk. Unlike the standard logistic regression models, both fixed effect and random effect models accounted for hospital effect. We applied each method to each year, and subsequently compared methods in their ability to identify hospital performance outliers. Results: Pneumonia rates ranged from 0 % to 24 %. The standard logistic regression models for 2013-2015 had c-statistics of 0.73-0.75, fixed effect models had c-statistics of 0.81-0.83, and random effect models had c-statistics of 0.80-0.83. Each method differed in its ability to identify performance outliers (Figure 1). In direct standardization, random effect models stabilized the hospital rates by moving the estimated rates toward the average rate, fixed effect models produced larger standard errors of hospital effect (particularly for hospitals with low case volumes). In indirect standardization, the three models showed high agreement on their derived observed: expected ratio (intraclass correlation =0.95). Indirect standardization with fixed effect or random effect models, identified similar hospital performance outliers in each year. Conclusion: The five statistical approaches varied in their ability to identify performance outliers. Given its higher sensitivity to outlier hospitals, indirect standardization methods with fixed or random effect models, may be best suited to support quality improvement activities.

scholarly journals Does ESG Disclosure Create Value to Firms? The Malaysian Case

2018 ◽  
pp. 515-521
Keyword(s):  
Social Norms ◽  
Regression Models ◽  
Financial Data ◽  
Panel Regression ◽  
Tobin’S Q ◽  
Publicly Traded ◽  
Tobin's Q ◽  
Publicly Traded Firms ◽  
Governance Disclosure ◽  
Panel Regression Models

We examine whether ESG (Environmental, Social and Governance) disclosure creates value to Malaysian firms. Based on the dataset of 37 Malaysian publicly traded firms, our results obtained from various panel regression models show that the overall ESG disclosure score and its environmental and governance pillars are positively associated with Tobin’s Q. This implies that Malaysian firms which act in accordance to social norms will be rewarded by the market. The outcomes of this research highlight the importance of non-financial data disclosure in Malaysian market.

Influence of tree cover and urban area on streamflow in the United States using multiple statistical attribution techniques

2021 ◽  
Author(s):  
Bailey Anderson ◽  
Louise Slater ◽  
Simon Dadson ◽  
Annalise Blum
Keyword(s):  
Land Cover ◽  
Urban Area ◽  
Regression Models ◽  
The United States ◽  
Additive Models ◽  
Tree Cover ◽  
Panel Regression ◽  
Distributional Regression ◽  
Panel Regression Models ◽  
Modelling Approaches

&lt;p&gt;There is still limited quantitative understanding of the effects of tree cover and urbanisation on streamflow at large scales, making it difficult to generalize these relationships. We use the globally consistent European Space Agency (ESA) Climate Change Initiative (CCI) Global Land Cover dataset to estimate the relationships between streamflow, calculated as high (Q0.99), median (Q0.50), and low (Q0.01) flow quantiles, and urbanization or tree cover changes in 2865 catchments between the years 1992 through 2018. We apply three statistical modelling approaches and examine the consistencies and inconsistencies between them. First, we use distributional regression models -- generalized additive models for location, scale, and shape (GAMLSS) -- at each site and assess goodness-of-fit. Model fits suggested a strong association between land cover, especially urban area, and low and median flows at sites with statistically significant trends in streamflow. We then examine the sign of the distributional regression model coefficients to determine whether the inclusion of a land cover variable in the regression models results in a relative increase or decrease in flow, regardless of the overall direction of trends in streamflow. Finally, we use fixed effects panel regression models to estimate the average effect across all sites. Panel regression results suggested that a 1% increase in urban area corresponds to between a &lt; 1% and 2.1% increase in streamflow for all quantiles. Results for the tree cover panel regression models were not significant. We highlight the value of statistical approaches for large-sample attribution of hydrological change, while cautioning that considerable variability exists across catchments and modelling approaches.&lt;/p&gt;

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