Mortality and Risk Factors in Psychiatric Inpatient with Dementia: A 13-year Long-Term Data Analysis

2018 ◽  
Vol 08 (03) ◽  
Author(s):  
Ruey Chen ◽  
Wu Chien Chien ◽  
Hsin Chu ◽  
Huei Ling Chiu ◽  
Chi Hsiang Chung ◽  
...  
Keyword(s):  
Risk Factors ◽  
Data Analysis ◽  
Psychiatric Inpatient ◽  
Term Data

The Change in Patterns and Conditions of Algal Blooms Resulting from Construction of Weirs in the Youngsan River: Long-term Data Analysis

2015 ◽  
Vol 48 (4) ◽  
pp. 238-252 ◽  
Author(s):  
Yongsik Shin ◽  
Haengsun Yu ◽  
Hakyoung Lee ◽  
Dahye Lee ◽  
Gunwoo Park
Keyword(s):  
Data Analysis ◽  
Algal Blooms ◽  
Term Data

Nosocomial transmission clusters and risk factors inMoraxella catarrhalis

2008 ◽  
Vol 137 (4) ◽  
pp. 581-590 ◽  
Author(s):  
F. LEVY ◽  
S. C. LEMAN ◽  
F. A. SARUBBI ◽  
E. S. WALKER
Keyword(s):  
Risk Factors ◽  
Cluster Analysis ◽  
Significant Risk ◽  
Pathogen Transmission ◽  
Nosocomial Transmission ◽  
Respiratory Pathogen ◽  
Bacterial Antibiotic Resistance ◽  
Mean Size ◽  
Term Data

SUMMARYWe report an objective examination of nosocomial transmission events derived from long-term (10-year) data from a single medical centre. Cluster analysis, based on the temporal proximity of genetically identical isolates of the respiratory pathogenMoraxella catarrhalis, identified 40 transmission events involving 33 of the 52 genotypes represented by multiple isolates. There was no evidence of highly transmissible or outbreak-prone genotypes. Although most clusters were small (mean size 3·6 isolates) and of short duration (median duration 25 days), clustering accounted for 38·7% of all isolates. Significant risk factors for clustering were multi-bed wards, and winter and spring season, but bacterial antibiotic resistance, manifested as the ability to produce a β-lactamase was not a risk factor. The use of cluster analysis to identify transmission events and its application to long-term data demonstrate an approach to pathogen transmission that should find wide application beyond hospital populations.

scholarly journals Prospective evaluation of early local complications of stoma and their risk factors

2020 ◽  
Vol 7 (9) ◽  
pp. 3024
Author(s):  
Koustav Jana ◽  
Pritin Bera ◽  
Arunima Mukhopadhyay ◽  
Ujjwal Bhattacharya
Keyword(s):  
Risk Factors ◽  
Operative Time ◽  
Bowel Perforation ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Local Complications ◽  
Stomal Prolapse ◽  
Common Etiology ◽  
Term Data

Background: Bowel stoma namely ileostomy and colostomy are frequently performed procedure and often associated with complications. Aim of this study was to assess early local complications and association with any risk factors.Methods: A total of 99 patients undergoing ileostomy or colostomy were analysed prospectively over a period 18 months. Comorbidity, preoperative clinical data, operative time, local complications within 12 weeks and hospital stay were documented. Univariate and multivariate analysis were used to find out any association.Results: Sixty-four ileostomies and 35 colostomies were analysed in our study. Median age was 55 years in both groups. Nontraumatic bowel perforation (42%) and colorectal malignancy (48.6%) were most common etiology for ileostomy and colostomy formation respectively.  64% of ileostomy and 68% of colostomy had one or more complications. Skin excoriation (31%) was most common complication in ileostomy group and it was stomal prolapse (17%) in colostomy group. Poor stoma care was prevalent in both groups. We found diabetes to have significant association with skin excoriation (p=0.002).Conclusions: Diabetes was identified as significant risk factor for skin excoriation in ileostomy group. Further long term data and larger population are needed for better evaluation of stomal complication and their risk factors.

scholarly journals Evaluation of extreme wave probability on the basis of long-term data analysis

2018 ◽  
Author(s):  
Kirill Bulgakov ◽  
Vadim Kuzmin ◽  
Shilov Dmitry
Keyword(s):  
Data Analysis ◽  
Wave Height ◽  
Significant Wave Height ◽  
Extreme Wave ◽  
Method Of Calculation ◽  
Significant Wave ◽  
Wave Heights ◽  
Term Data

Abstract. A method of calculation of wave height probability based on the significant wave height probability is described. An application of the method on the basis of long-term data analysis is presented. Examples of averaged annual and seasonal fields of extreme wave heights obtained by the above method are given.

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