| Objective:We analyzed the correlation between the qualified rate of routine environmental health monitoring and the hospital infection rate in a third-grade general hospital in Yan'an City from 2011 to 2017,which provided a theoretical basis for the development,prevention and control of nosocomial infections.The grey GM?1,1?prediction model,exponential smoothing model,exponential smoothing model and grey GM?1,1?prediction model combined method were used to explore the development trend of hospital infection rate and find the optimal model for predicting hospital infection rate.Methods:We selected a third-grade hospital in Yan'an City from January 1,2011 to December 31,2017 for routine environmental monitoring:air,medical staff hands,surface of objects,and disinfectant in use as environmental health monitoring targets.At the same time,patients who were hospitalized for more than 48 hours from January 1,2011 to December 31,2017 were selected as hospital infection subjects.Data were collected by retrospective analysis,and statistical analysis of hospital infection rate and environmental health monitoring passing rate were performed using chi-square test.The exponential smoothing model,grey GM?1,1?prediction model,exponential smoothing model and grey GM?1,1?prediction model combined method were used to fit the hospital infection rate in 2011-2016,respectively.Three methods were used to predict the hospital infection rate in 2017,and the MAPE was used to evaluate the model prediction effect.Data collection and collation were performed using Excel 2010,and the exponential smoothing model was fitted using SPSS 24.0statistical software.Grey GM?1,1?model was modeled with the seventh edition of grey modeling software,P<0.05 was statistically significant.Results:1.Th ere was si gnifi cant differences i n t he qu ali fi ed rat e o f environmental sanitation monitoring in different years from 2011 to 2017(c2=22.333,P=0.001).The difference in the qualified rate of air monitoring in different years from 2011 to 2017 was statistically significant(c2=14.627,P=0.023).The difference in the qualified rate of object surface monitoring in different years from 2011 to 2017 was statistically significant(c2=17.135,P=0.009).There was no significant difference in the qualified rate of hand monitoring of medical staff in different years from 2011 to 2017(c2=5.517,P=0.479).There was no significant difference in the qualified rate of disinfectant in use in different years from 2011 to 2017(c2=1.890,P=0.930).2.The hospital infection rate in different years from 2011 to 2017 was statistically significant(c2=54.898,P<0.001).The proportion of infections in various parts of hospital infections in 2011-2017,the number of lower respiratory tract infection was the highest?composition ratio was 49.61%?.The lower respiratory tract infection rate in different years from 2011 to 2017 was statistically significant(c2=45.501,P<0.001).There were statistically significant differences in infections between different clinical departments between 2011 and 2017(c2=3899.248,P<0.001).There was a statistically significant difference in hospital infection rate among the different departments of internal medicine(c2=67.141,P<0.001).There was a statistically significant difference in the infection rate among the main different departments of surgery in 2011-2017(c2=2392.467,P<0.001).The incidence of total infection of ventilator-associated pneumonia in the intensive care unit in 2013-2017was 7.67,and the incidence of total infection of urinary catheter related urinary tract in the intensive care unit was 1.21in 2013-2017,and the incidence of total infection of catheter-related bloodstream in intensive care unit in 2013-2017 was2.42.The total antibiotic use rate of discharged patients in 2016-2017 was statistically significant,and there was a significant difference in antibiotic use rate in different departments(c2=17049.001,P<0.001);the difference in antibiotic use rate of discharged patients in different years between 2016 and 2017 was statistically significant(c2=90.856,P<0.001).3.The correlation between the air monitoring passing rate and the hospital infection rate in 2011-2017 was statistically significant?r=-0.768,P=0.044?.The correlation between the surface of objects monitoring passing rate and the hospital infection rate in 2011-2017 was not statistically significant?r=-0.359,P=0.430?;there was no statistically significant difference between the medical staff hand-monitoring passing rate and the hospital infection rate in 2011-2017?r=-0.612,P=0.144?;there was no statistically significant difference in the correlation between the disinfectant in use monitoring passing rate and the hospital infection rate in 2011-2017?r=-0.612,P=0.144?.The correlation between the rate of grade A healing of the sterile incision and the hospital infection rate in2011-2017 was statistically significant?r=-0.929,P=0.003?.The correlation between the rate of grade A healing of sterile incision and the rate of air monitoring in2011-2017 was statistically significant?r=0.867,P=0.012?.The correlation between the environmental monitoring passing rate and the incidence of ventilator-associated pneumonia infection in 2013-2017 was not statistically significant?P=0.450?,the correlation between the environmental monitoring passing rate and the incidence of catheter-related urinary tract infection in 2013-2017 was statistically significant?P=0.041?,and the environmental monitoring passing rate and the incidence of catheter-related bloodstream infection was not statistically significant?P=0.858?. 4.2011-2016 hospital infection rate established model,exponential smoothing model MAPE was 9.432%,grey GM?1,1?prediction model MAPE was 11.585%,exponential smoothing model and grey GM?1,1?prediction model combined method MAPE was 8.77%.In 2017,the hospital infection rate was evaluated by model,the exponential smoothing model MAPE was 13.95%,the grey GM?1,1?prediction model MAPE was 32.56%,the exponential smoothing model and grey GM?1,1?prediction model combined method MAPE was 27.91%.Conclusion:1.The correlation test between the qualified rate of hospital environmental health monitoring and the rate of hospital infection,indicating that improving the quality of environmental health was an important method for controlling hospital infection.Increasing the environmental sanitation pass rate,especially the air monitoring pass rate,and strictly implementing antibiotic use standards were conducive to reducing the hospital infection rate.2.The exponential smoothing model was a relatively optimal model for predicting the hospital infection rate.The short-term prediction effect was relatively best,and the prediction accuracy was relatively high,which could make early warning for the hospital infection rate. |
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