Association between Pseudomonas aeruginosa positive water samples and healthcare-associated cases: nine-year study at one university hospital.

OBJECTIVE: To study the association between the results of water samples and Pseudomonas aeruginosa healthcare-associated cases in a French university hospital. METHODS: Generalized Estimating Equations were used on complete case and imputed datasets. The spatial unit was the building and the time unit was the quarter. RESULTS: For the period 2004-2013, 2932 water samples were studied; 17% were positive for P. aeruginosa. A higher incidence of P. aeruginosa cases was associated with a higher proportion of positive water samples (P=0.056 in complete case analysis and P=0.031 with the imputed dataset). The association was no longer observed when haematology and intensive care units were excluded, but was significant in analyses of data concerning intensive care units alone (P<0.001). CONCLUSION: This study suggests that water outlet contamination in hospitals can lead to an increase in healthcare-associated P. aeruginosa cases in wards dealing with susceptible patients, but does not play a significant role in other wards.

[Comparing results of methicillin-resistant Staphylococcus aureus (MRSA) surveillance using the French DRG-based information system (PMSI)]. / État des lieux et évaluation de la surveillance des Staphylococcus aureus résistants à la méticilline (SARM) : PMSI versus surveillance Raisin.

BACKGROUND: The surveillance of methicillin-resistant Staphylococcus aureus (MRSA) is a national priority. The rate of MRSA infections is one of six indicators tracked by the Department of Health. Since 2002, the French institute for public health surveillance (InVS) has monitored MRSA infections to estimate incidence density. Today, the use of the French administrative database (PMSI) could facilitate this surveillance. The aim of this study was to compare MRSA incidence density computed at a national level using PMSI databases with the results from the InVS taken as the reference. METHODS: PMSI databases for the years 2006 to 2009 were used. The reference results were those published by the InVS from 2006 to 2009. MRSA density defined as the number of MRSA infections recorded per year over 1000 hospital stays was computed. It was then compared with the MRSA incidence density measured by InVS. The time course of MRSA incidence in the PMSI records was modeled using a Poisson regression. RESULTS: The incidence density measured by the InVS was higher than the MRSA density computed using the PMSI, but this difference appeared to decrease over time. The PMSI density/InVS MRSA incidence density ratio was 0.8% in 2006 and about 9.2% in 2009. We observed inverted trends with a growing trend in MRSA density identified by the PMSI. Furthermore, the year of study was significantly associated with incidence density (P=0.01). CONCLUSION: Using PMSI data as an additional source of information in the hospital MRSA surveillance process makes it possible to detect and analyze patient repeats at the regional and national levels with linkage facilities. Estimation of incidence density for hospitals not participating to this surveillance system will be the next step.

Classification of hospital pathways in the management of cancer: application to lung cancer in the region of burgundy.

CONTEXT: The evaluation of national cancer plans is an important aspect of their implementation. For this evaluation, the principal actors in the field (doctors, nurses, etc.) as well as decision-makers must have access to information that is reliable, synthetic and easy to interpret, and which reflects the implementation process in the field. We propose here a methodology to make this type of information available in the context of reducing inequalities with regard to access to healthcare for patients with lung cancer in the region of Burgundy. METHODS: We used the national medico-administrative DRG-type database, which gathers together all hospital stays. By using this database, it was possible to identify and reconstruct the care management history of these patients. That is, by linking together all attended hospitals, sorted chronologically. Eligible patients were at least 18 years old, whatever the gender and had undergone surgery for their lung cancer. They had to be residents of Burgundy at the time of the first operation between 2006 and 2008. Patient's pathway was defined as the sequence of all attended hospitals (hospital stays) during the year of follow up linked together using an anonymised patient identifier. We then constructed a pathway typology of pathway using an unsupervised clustering method, and conducted a spatial analysis of this typology. RESULTS: Between 2006 and 2008, we selected 495 patients in the 4 administrative departments of the Burgundy region. They accounted for a total of 3821 stays during the year of follow-up. There were 393 men (79%) and the mean age was 64 (95% confidence interval: 63-65) years. We reconstructed 94 pathways (about five per patient). Here, neighbourhood's cares accounted for 41% of them, while 44% included a surgical intervention outside the region of Burgundy. We constructed a pathway typology with five classes. Spatial analysis showed that the vast majority of initial surgeries took place in the major regional centres. CONCLUSION: The construction of a pathway typology leads to better understanding of the reasoning that lies behind the movements of patients. It opens the way for analysis of the collaboration between the different healthcares establishments attended, which should bring to light associations that need to be developed.