scholarly journals Proximate and Underlying Deforestation Causes in a Tropical Basin through Specialized Consultation and Spatial Logistic Regression Modeling

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 186
Author(s):  
Wenseslao Plata-Rocha ◽  
Sergio Alberto Monjardin-Armenta ◽  
Carlos Eduardo Pacheco-Angulo ◽  
Jesus Gabriel Rangel-Peraza ◽  
Cuauhtemoc Franco-Ochoa ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Statistical Significance ◽  
Regression Modeling ◽  
Point Of View ◽  
Natural Protected Areas ◽  
Logistic Regression Modeling ◽  
The North ◽  
Wood Extraction ◽  
Underlying Causes ◽  
Vegetation Maps

The present study focuses on identifying and describing the possible proximate and underlying causes of deforestation and its factors using the combination of two techniques: (1) specialized consultation and (2) spatial logistic regression modeling. These techniques were implemented to characterize the deforestation process qualitatively and quantitatively, and then to graphically represent the deforestation process from a temporal and spatial point of view. The study area is the North Pacific Basin, Mexico, from 2002 to 2014. The map difference technique was used to obtain deforestation using the land-use and vegetation maps. A survey was carried out to identify the possible proximate and underlying causes of deforestation, with the aid of 44 specialized government officials, researchers, and people who live in the surrounding deforested areas. The results indicated total deforestation of 3938.77 km2 in the study area. The most important proximate deforestation causes were agricultural expansion (53.42%), infrastructure extension (20.21%), and wood extraction (16.17%), and the most important underlying causes were demographic factors (34.85%), economics factors (29.26%), and policy and institutional factors (22.59%). Based on the spatial logistic regression model, the factors with the highest statistical significance were forestry productivity, the slope, the altitude, the distance from population centers with fewer than 2500 inhabitants, the distance from farming areas, and the distance from natural protected areas.

Antibiotic Use in Lateral Skull Base Surgery: A Survey of the North American Skull Base Society

2021 ◽  
Author(s):  
Kevin L. Li ◽  
Christina H. Fang ◽  
Vivian S. Hawn ◽  
Vijay Agarwal ◽  
Varun R. Kshretty ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Skull Base ◽  
North American ◽  
Skull Base Surgery ◽  
Antibiotic Use ◽  
Regression Modeling ◽  
Bivariate Analysis ◽  
Logistic Regression Modeling ◽  
The North ◽  
Lateral Skull Base

Abstract Objectives Antibiotic use in lateral skull base surgery (LSBS) has not been thoroughly investigated in the literature. There is wide variability in antibiotic use and insufficient data to guide management. This study aims to describe the factors and patterns influencing antibiotic use in LSBS among the membership of the North American Skull Base Society (NASBS). Design An online-based survey was designed and distributed to the membership of the NASBS. Data was analyzed using bivariate analysis and logistic regression modeling. Setting Online-based questionnaire. Participants NASBS membership. Main Outcome Measures Use of intraoperative antibiotics and use of postoperative antibiotics. Results The survey response rate was 26% (208 respondents). Of the 208 total respondents, 143 (69%) respondents performed LSBS. Most respondents are neurosurgeons (69%) with the remaining being otolaryngologists (31%). The majority of respondents (79%) are fellowship-trained in skull base surgery. Academic or government physicians make up 69% of respondents and 31% are in private practice with or without academic affiliations. Bivariate analysis showed that practice setting significantly influenced intraoperative antibiotic use (p = 0.01). Geographic location significantly affected postoperative antibiotic use (p = 0.01). Postoperative antibiotic duration was significantly affected by presence of chronic otitis media, cerebrospinal fluid leak, and surgeon training (p = 0.02, p = 0.01, and p = 0.006, respectively). Logistic regression modeling showed that the motivation to reduce infection significantly impacted postoperative antibiotic use (p = 0.03). Conclusion This study demonstrates significant variations in intraoperative and postoperative antibiotic use in LSBS among the NASBS membership. Appropriate guidelines for optimal perioperative antibiotic use patterns should be determined with randomized studies in the future.

Logistic Regression Modeling of Cropping Systems To Predict Fumonisin Contamination in Maize

2008 ◽  
Vol 56 (21) ◽  
pp. 10433-10438 ◽  
Author(s):  
Paola Battilani ◽  
Amedeo Pietri ◽  
Carlo Barbano ◽  
Andrea Scandolara ◽  
Terenzio Bertuzzi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cropping Systems ◽  
Regression Modeling ◽  
Logistic Regression Modeling

Logistic Regression Modeling for Evaluation of Factors Affecting Trauma Outcome in a Level I Trauma Center in Shiraz

2016 ◽  
Vol 19 (1) ◽  
Author(s):  
Mahnaz Yadollahi ◽  
Mehrdad Anvar ◽  
Haleh Ghaem ◽  
Shahram Bolandparvaz ◽  
Shahram Paydar ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Trauma Center ◽  
Regression Modeling ◽  
Factors Affecting ◽  
Logistic Regression Modeling ◽  
Trauma Outcome ◽  
Level I
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