International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2013
Seasonal humidity may influence Pseudomonas aeruginosa hospital-acquired infection rates.
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Ramos Guilherme P, Rocha Jaime L, Tuon Felipe F
Аннотация
OBJECTIVE: The objective of this study was to determine the association of seasonal climatic conditions with the incidence of Pseudomonas aeruginosa infections.
METHODS: A retrospective study was carried out to evaluate all infections caused by P. aeruginosa in a 660-bed tertiary-care hospital in Brazil over a period of 5 years. To assess seasonal patterns, monthly temperature, relative humidity, and precipitation averages were obtained. Correlations of seasonal variations with infection rates (IR) were determined by Pearson correlation coefficient. Linear regression was used to determine trends, and multivariable linear regression was performed using a Poisson distribution.
RESULTS: A total of 844 cases of P. aeruginosa infection were identified for 1 058 501 patient-days during 1826 days (overall IR 7.97/10 000 patient-days). The mean temperature was 18.2±2.8°C, relative humidity was 80.3±3.6%, and precipitation was 104.7±64.38mm. The Pearson correlation was significant between urinary tract infection and temperature (R=0.29; p=0.021) and precipitation (R=0.27; p=0.036). A correlation was also significant between hospital-associated pneumonia and precipitation (R=0.29; p=0.022) and relative humidity (R=0.31; p=0.013). Relative humidity was associated with a higher IR of other infections caused by P. aeruginosa, but it was not possible to build a predictive model when multiple linear regression and Poisson regression were tested.
CONCLUSION: Climatic conditions are another factor that may interfere with the IR of Pseudomonas aeruginosa.
METHODS: A retrospective study was carried out to evaluate all infections caused by P. aeruginosa in a 660-bed tertiary-care hospital in Brazil over a period of 5 years. To assess seasonal patterns, monthly temperature, relative humidity, and precipitation averages were obtained. Correlations of seasonal variations with infection rates (IR) were determined by Pearson correlation coefficient. Linear regression was used to determine trends, and multivariable linear regression was performed using a Poisson distribution.
RESULTS: A total of 844 cases of P. aeruginosa infection were identified for 1 058 501 patient-days during 1826 days (overall IR 7.97/10 000 patient-days). The mean temperature was 18.2±2.8°C, relative humidity was 80.3±3.6%, and precipitation was 104.7±64.38mm. The Pearson correlation was significant between urinary tract infection and temperature (R=0.29; p=0.021) and precipitation (R=0.27; p=0.036). A correlation was also significant between hospital-associated pneumonia and precipitation (R=0.29; p=0.022) and relative humidity (R=0.31; p=0.013). Relative humidity was associated with a higher IR of other infections caused by P. aeruginosa, but it was not possible to build a predictive model when multiple linear regression and Poisson regression were tested.
CONCLUSION: Climatic conditions are another factor that may interfere with the IR of Pseudomonas aeruginosa.