F Ansari1, H Molana, H Goossens, P Davey. 1. Division of Clinical and Population Sciences and Education, University of Dundee, Dundee, UK.
Abstract
OBJECTIVES: Our objective was to develop and test standardized methods for collection and statistical analysis of longitudinal data on hospital antibacterial use from different countries. METHODS: We collected data on monthly supply of antibiotics from pharmacies in one hospital from each of 18 European countries. We applied a standardized method to classify drugs, measure use in defined daily doses and compare the effect of using occupied bed-days (OBDs) or admissions as denominators for longitudinal analysis. RESULTS: Antibiotic use increased in 14 (78%) hospitals and decreased in 4 hospitals. For 16 (89%) hospitals, adjustment of antibiotic use with OBDs resulted in larger changes over time than adjustment with admissions. Inclusion of all hospital clinical activity variables (admissions, length of stay and OBDs) in multivariate time series analysis identified distinct hospital groups. Nine (50%) hospitals had statistically significant changes in antibiotic use (six increasing and three decreasing) that were not explained (n = 3) or only partially explained (n = 6) by change in clinical activity. Three (17%) hospitals had no significant change in antibiotic use. In the remaining six hospitals, apparent changes in antibiotic use were largely explained by changes in clinical activity. CONCLUSIONS: This is the first study to use a standardized method for data collection and longitudinal analysis of antibiotic use in different hospitals. These data suggest that determination of changes in antibiotic exposure of hospital patients over a period of time is unreliable if only one clinical activity variable (such as OBDs) is used as the denominator. We recommend inclusion of admissions, OBDs and length of stay in statistical, time series analysis of antibiotic use. This model is also relevant to longitudinal analysis of infections in hospitals.
OBJECTIVES: Our objective was to develop and test standardized methods for collection and statistical analysis of longitudinal data on hospital antibacterial use from different countries. METHODS: We collected data on monthly supply of antibiotics from pharmacies in one hospital from each of 18 European countries. We applied a standardized method to classify drugs, measure use in defined daily doses and compare the effect of using occupied bed-days (OBDs) or admissions as denominators for longitudinal analysis. RESULTS: Antibiotic use increased in 14 (78%) hospitals and decreased in 4 hospitals. For 16 (89%) hospitals, adjustment of antibiotic use with OBDs resulted in larger changes over time than adjustment with admissions. Inclusion of all hospital clinical activity variables (admissions, length of stay and OBDs) in multivariate time series analysis identified distinct hospital groups. Nine (50%) hospitals had statistically significant changes in antibiotic use (six increasing and three decreasing) that were not explained (n = 3) or only partially explained (n = 6) by change in clinical activity. Three (17%) hospitals had no significant change in antibiotic use. In the remaining six hospitals, apparent changes in antibiotic use were largely explained by changes in clinical activity. CONCLUSIONS: This is the first study to use a standardized method for data collection and longitudinal analysis of antibiotic use in different hospitals. These data suggest that determination of changes in antibiotic exposure of hospital patients over a period of time is unreliable if only one clinical activity variable (such as OBDs) is used as the denominator. We recommend inclusion of admissions, OBDs and length of stay in statistical, time series analysis of antibiotic use. This model is also relevant to longitudinal analysis of infections in hospitals.
Authors: Johannes P Borde; Klaus Kaier; Michaela Steib-Bauert; Werner Vach; Annette Geibel-Zehender; Hansjörg Busch; Hartmut Bertz; Martin Hug; Katja de With; Winfried V Kern Journal: BMC Infect Dis Date: 2014-04-15 Impact factor: 3.090