hospital antibiotic consumption
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2020 ◽  
Vol 64 (11) ◽  
Author(s):  
Deanna J. Buehrle ◽  
Brooke K. Decker ◽  
Marilyn M. Wagener ◽  
Amesh Adalja ◽  
Nina Singh ◽  
...  

ABSTRACT There are scant data on the impact of coronavirus disease 2019 (COVID-19) on hospital antibiotic consumption, and no data from outside epicenters. At our nonepicenter hospital, antibiotic days of therapy (DOT) and bed days of care (BDOC) were reduced by 151.5/month and 285/month, respectively, for March to June 2020 compared to 2018–2019 (P = 0.001 and P < 0.001). DOT per 1,000 BDOC was increased (8.1/month; P = 0.001). COVID-19 will impact antibiotic consumption, stewardship, and resistance in ways that will likely differ temporally and by region.


2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Florence Stordeur ◽  
Katiuska Miliani ◽  
Ludivine Lacavé ◽  
Anne-Marie Rogues ◽  
Catherine Dumartin ◽  
...  

Abstract Background Antibiotic use (ABU) surveillance in healthcare facilities (HCFs) is essential to guide stewardship. Two methods are recommended: antibiotic consumption (ABC), expressed as the number of DDD/1000 patient-days; and prevalence of antibiotic prescription (ABP) measured through point prevalence surveys. However, no evidence is provided about whether they lead to similar conclusions. Objectives To compare ABC and ABP regarding HCF ranking and their ability to identify outliers. Methods The comparison was made using 2012 national databases from the antibiotic surveillance network and prevalence study. HCF rankings according to each method were compared with Spearman’s correlation coefficient. Analyses included the ABU from entire HCFs as well as according to type, clinical ward and by antibiotic class and specific molecule. Results A total of 1076 HCFs were included. HCF rankings were strongly correlated in the whole cohort. The correlation was stronger for HCFs with a higher number of beds or with a low or moderate proportion of acute care beds. ABU correlation between ABC or ABP was globally moderate or weak in specific wards. Furthermore, the two methods did not identify the same outliers, whichever HCF characteristics were analysed. Correlation between HCF ranking varied according to the antibiotic class. Conclusions Both methods ranked HCFs similarly overall according to ABC or ABP; however, major differences were observed in ranking of clinical wards, antibiotic classes and detection of outliers. ABC and ABP are two markers of ABU that could be used as two complementary approaches to identify targets for improvement.


2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Bongyoung Kim ◽  
Hyeonjun Hwang ◽  
Jieun Kim ◽  
Myoung-jae Lee ◽  
Hyunjoo Pai

2017 ◽  
Vol 72 (10) ◽  
pp. 2931-2937 ◽  
Author(s):  
Gesche Först ◽  
Katja de With ◽  
Nadine Weber ◽  
Johannes Borde ◽  
Christiane Querbach ◽  
...  

Abstract Background The WHO/ATC (Anatomical Therapeutic Chemical) index DDD (WHO-DDD) is commonly used for drug consumption measurement. Discrepancies between WHO-DDD and actual prescribed daily doses (PDD) in hospitals have prompted alternative dose definitions adapted to doses recommended in hospital practice guidelines [recommended daily doses (RDD)]. Methods In order to validate RDD we performed modified point prevalence surveys in 24 acute care hospitals and recorded 20620 PDD of antibiotics given to 4226 adult patients on the day of the survey and the 6 preceding days. We calculated RDD and WHO-DDD and compared them with PDD. Results The rate of RDD corresponding to PDD was higher than the corresponding rate for WHO-DDD (pooled data, 55% versus 30%) and the differences were similar across the hospital sample, but varied according to drug/drug class, route of administration, indication and renal function. RDD underestimated actual consumption by 14% overall, while WHO-DDD overestimated total antibacterial consumption by 28% (pooled data; median values RDD −10% versus WHO-DDD +32%). The deviations of estimated from actual drug use volumes were largest for β-lactams (RDD −11% versus WHO-DDD +49%), in particular for penicillins (−11% versus +64%), if WHO-DDD were used. Conclusions Hospital antibiotic consumption surveillance systems using current WHO-DDD should address the uneven discrepancies between actual prescribing and consumption estimates according to drug class that may lead to misclassification in benchmark analyses. We recommend using validated RDD as a supplementary measure to the WHO-DDD for detailed analyses.


2016 ◽  
Vol 21 (32) ◽  
Author(s):  
Ajay Oza ◽  
Fionnuala Donohue ◽  
Howard Johnson ◽  
Robert Cunney

As antibiotic consumption rates between hospitals can vary depending on the characteristics of the patients treated, risk-adjustment that compensates for the patient-based variation is required to assess the impact of any stewardship measures. The aim of this study was to investigate the usefulness of patient-based administrative data variables for adjusting aggregate hospital antibiotic consumption rates. Data on total inpatient antibiotics and six broad subclasses were sourced from 34 acute hospitals from 2006 to 2014. Aggregate annual patient administration data were divided into explanatory variables, including major diagnostic categories, for each hospital. Multivariable regression models were used to identify factors affecting antibiotic consumption. Coefficient of variation of the root mean squared errors (CV-RMSE) for the total antibiotic usage model was very good (11%), however, the value for two of the models was poor (> 30%). The overall inpatient antibiotic consumption increased from 82.5 defined daily doses (DDD)/100 bed-days used in 2006 to 89.2 DDD/100 bed-days used in 2014; the increase was not significant after risk-adjustment. During the same period, consumption of carbapenems increased significantly, while usage of fluoroquinolones decreased. In conclusion, patient-based administrative data variables are useful for adjusting hospital antibiotic consumption rates, although additional variables should also be employed.


2011 ◽  
Vol 79 (2) ◽  
pp. 166-171 ◽  
Author(s):  
C. Plüss-Suard ◽  
A. Pannatier ◽  
A. Kronenberg ◽  
K. Mühlemann ◽  
G. Zanetti

Infection ◽  
2009 ◽  
Vol 37 (4) ◽  
pp. 349-352 ◽  
Author(s):  
K. de With ◽  
H. Bestehorn ◽  
M. Steib-Bauert ◽  
W. V. Kern

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