scholarly journals Impact of Isolating Clostridium difficile Carriers on the Burden of Isolation Precautions: A Time Series Analysis

2017 ◽  
Vol 66 (9) ◽  
pp. 1377-1382 ◽  
Author(s):  
Yasi Xiao ◽  
Bianka Paquet-Bolduc ◽  
Christophe Garenc ◽  
Philippe Gervais ◽  
Sylvie Trottier ◽  
...  
Keyword(s):  
Time Series ◽  
Clostridium Difficile ◽  
Time Series Analysis ◽  
Series Analysis

Correlations between bed occupancy rates and Clostridium difficile infections: a time-series analysis

2010 ◽  
Vol 139 (3) ◽  
pp. 482-485 ◽  
Author(s):  
K. KAIER ◽  
D. LUFT ◽  
M. DETTENKOFER ◽  
M. KIST ◽  
U. FRANK
Keyword(s):  
Time Series ◽  
Clostridium Difficile ◽  
Time Series Analysis ◽  
Length Of Hospital Stay ◽  
Bed Occupancy ◽  
Series Analysis ◽  
General Wards ◽  
Occupancy Rates ◽  
The Impact ◽  
Bed Occupancy Rates

SUMMARYA time-series analysis was performed to identify the impact of bed occupancy rates and length of hospital stay on the incidence of Clostridium difficile infections (CDI). Between January 2003 and July 2008, a mean incidence of 0·5 CDI cases/1000 patient days was recorded. Application of a multivariate model (R2=0·50) showed that bed occupancy rates on general wards (P<0·01) and length of stay in intensive care units (ICUs) (P<0·01) influenced the incidence of CDI. Overcrowding on general wards and long periods in ICUs were identified as being positively associated with the incidence of CDI.

scholarly journals A Time-Series Analysis of Clostridium difficile and Its Seasonal Association with Influenza

2010 ◽  
Vol 31 (4) ◽  
pp. 382-387 ◽  
Author(s):  
Philip M. Polgreen ◽  
Ming Yang ◽  
Lucas C. Bohnett ◽  
Joseph E. Cavanaugh
Keyword(s):  
Time Series ◽  
Clostridium Difficile ◽  
Time Series Analysis ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Linear Trend ◽  
International Classification Of Diseases ◽  
Series Data ◽  
Series Analysis ◽  
Classification Of Diseases

Objective.To characterize the temporal progression of the monthly incidence of Clostridium difficile infections (CDIs) and to determine whether the incidence of CDI is related to the incidence of seasonal influenza.Design.A retrospective study of patients in the Nationwide Inpatient Sample during the period from 1998 through 2005.Methods.We identified all hospitalizations with a primary or secondary diagnosis of CDI with use of International Classification of Diseases, 9th Revision, Clinical Modification codes, and we did the same for influenza. The incidence of CDI was modeled as an autoregression about a linear trend. To investigate the association of CDI with influenza, we compared national and regional CDI and influenza series data and calculated cross-correlation functions with data that had been prewhitened (filtered to remove temporal patterns common to both series). To estimate the burden of seasonal CDI, we developed a proportional measure of seasonal CDI.Results.Time-series analysis of the monthly number of CDI cases reveals a distinct positive linear trend and a clear pattern of seasonal variation (R2 = 0.98). The cross-correlation functions indicate that influenza activity precedes CDI activity on both a national and regional basis. The average burden of seasonal (ie, winter) CDI is 23%.Conclusions.The epidemiologic characteristics of CDI follow a pattern that is seasonal and associated with influenza, which is likely due to antimicrobial use during influenza seasons. Approximately 23% of average monthly CDI during the peak 3 winter months could be eliminated if CDI remained at summer levels.

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