Time Series Analysis of Health Care-Associated Urinary Tract Infections

• Main Authors: Ruei-Fang Wang, 王瑞芳
• Other Authors: Hsiu-Hsi Chen
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/94356538685896905365

碩士 === 臺灣大學 === 流行病學研究所 === 98 === Background The time-series longitudinal incidence data on healthcare-associated infection (HAI) can reveal its trend, seasonal, and cyclical components. As far as the urinary tract system, the most common type of healthcare-associated infection, is concerned modeling trend and seasonal components is therefore of great interest. However, the time-series data are often challenged by autocorrelations between successive time measurements. Objectives This study aimed to utilize a combination of time series models to analyze the long-term healthcare-associated infection time series data, to elucidate the trend, seasonal, cyclical patterns, making allowance for autocorrelations, and to forecast future events and incidences of each major urinary system HAI pathogens. Material and Methods Those who were diagnosed as healthcare-associated infection during the period of January, 1994 and June, 2009 in Shin-Kong Wu Ho-Su memorial hospital in Taipei, Taiwan were included for analysis. This study focused on the major pathogens of healthcare-associated urinary tract infections, such as yeast, Escherichia coli, Pseudomonas aeruginosa, and Enterococcus species. Information used for the study includes (1) patients background; (2) infection data: admission and discharged date, ward of getting infection; (3) pathogen classification: species, sampling sites, antibiotics sensitivity tests; (4) invasive procedures: urinary catheter usage, parenteral nutrition via central line, central venous catheter usage, and surgery; (5) whole hospital admission patients number and person-day of the admitted patients. The infection events per month and the incidences time series of different pathogens were analyzed using decomposition methods, Durbin-Watson statistics, and Box-Jenkins model. We also projected the outcome of the coming year by using these models. Results There were 6519 infection events during the inclusion period of 15.5 years in the Shin-Kong Wu Ho-Su medical center. The first four ranks of urinary tract healthcare-associated infections in order were yeast, Escherichia coli, Pseudomonas aeruginosa, and Enterococcus species. The Gram-negative bacteria were the most common pathogens, accounting for 61.7%(4024/6519) of all urinary system HAIs。 Time series analysis of decomposition methods revealed the remarkable trend and seasonal patterns in these four major pathogens. The time trend of infection incidence and events of Pseudomonas aeruginosa has been decreasing, whereas those of the other three major pathogens have been increasing with time. Box-Jenkins models of both yeast and Escherichia coli had first-order auto-regression, and those of Enterococcus species had second-order and fourth-order auto-regression (time lag, 2 and 4 months). The time series of Pseudomonas aeruginosa are shown in stochastic process. Conclusion We observed that the trend, seasonal, cyclic patterns, and autocorrelations were different with respect to different infectious pathogens associated with healthcare-associated urinary tract infections by using time series analysis. The historical patterns of the events and the incidences patterns are very useful for model construction and forecasting of such time-series data.