Antibiotic Use in Short-Stay Settings: The Role of the Number of Admissions and Average Length of Stay in Data Interpretation. ATB-RAISIN Network. Supplementary Report, 2011 Data
The ATB-Raisin surveillance network collects and analyzes antibiotic consumption data in terms of defined daily doses (DDD) per 1,000 patient-days (PD). In 2011, the number of short-stay admissions (SA) was collected to study, on the one hand, the links between average length of stay (ALOS) and antibiotic consumption during short-stay care and, on the other hand, the impact of expressing consumption in ADD/100 SA on the interpretation of these consumption rates. Antibiotic consumption during short-stay admissions in the 210 healthcare facilities (HFs) that provided data varied significantly according to the ADL in general hospitals and medical-surgical centers but not in follow-up care facilities or local hospitals: 634 DDJ/1,000 bed-days in the 51 CHs with an average length of stay (ALS) of 6 days and 597 DDJ/1,000 bed-days in the 42 CHs with an ALS >6 days (or 285 and 425 DDJ/100 AD, respectively); 445 DDD/1,000 bed-days in the 32 medical centers with an average length of stay (ALS) of 4 days and 463 DDD/1,000 bed-days in the 43 medical centers with an ALS >4 days (or 130 and 247 DDD/100 AD, respectively). The greater differences between groups when consumption was expressed per 100 AD reflected the link between DMS and the number of admissions. Significant differences also existed for certain antibiotics: piperacillin/tazobactam for hospitals and MCOs; C3G, carbapenems, and fluoroquinolones for MCOs. The ranking of healthcare facilities did not vary when consumption was expressed per 1,000 patient-days or per 100 AD. Thus, DMS in short-stay settings allows for the definition of homogeneous groups of healthcare facilities, within which comparisons are more relevant. In the context of reducing DMS, monitoring antibiotic consumption over time relative to both units of activity measurement—as is done in the Netherlands—should allow for a better interpretation of trends, since the per 100 AD measure is more sensitive to variations in DMS.
Author(s): Uranga J, Pefau M, Dumartin C, Réseau d'alerte d'investigation et de surveillance des infections nosocomiales (RAISIN
Publishing year: 2014
Pages: 20 p.
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