Departments of 1 Medical Microbiology and Infectious Diseases and 2 Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Dr Molewaterplein 40, 3015 GD Rotterdam; 3 Department of Pharmacy, Apotheek Haagse Ziekenhuizen, The Hague; 4 Department of Clinical Pharmacy and Toxicology, Maasland Ziekenhuis, Sittard; 5 Department of Clinical Pharmacy, University Medical Center Nijmegen and Nijmegen University Center for Infectious Diseases, The Netherlands
Received 13 July 2004; returned 8 September 2004; revised 20 December 2004; accepted 8 February 2005
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Abstract |
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Patients and methods: Trends in antibiotic use in acute care Dutch hospitals between 19972001 were studied. Antibiotic use was expressed in DDD per 100 patient days and in DDD per 100 admissions.
Results: From 1997 to 2001, total systemic antibiotic use significantly increased from 47.2 to 54.7 DDD per 100 patient days, whereas expressed in DDD per 100 admissions it remained constant. Some individual antibiotics increases in DDD per 100 patient days were not accompanied by increases in DDD per 100 admissions and vice versa. The mean number of total DDD per hospital decreased (not significantly) between 1997 and 2001. The mean number of patient days, admissions and length of stay decreased significantly.
Conclusions: Knowledge of variation in resource indicators and additional expression of the data in DDD per 100 admissions is imperative for a meaningful understanding of observed trends in antibiotic use expressed in DDD per 100 patient days. Further research is needed to determine the correlation between different measures of antibiotic use and the level of antibiotic resistance.
Keywords: antibiotic usage , defined daily doses , selection density , selection pressure
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Introduction |
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The World Health Organization (WHO) Collaborating Centre for Drug Statistics and Methodology recommends using the number of defined daily doses (DDD) per 100 patient days to quantify antibiotic use.4 The DDD is a technical unit of measurement and corresponds to the assumed average maintenance dose per day, for the main indication of the drug, in adults. The number of DDD per 100 patient days has been used as a proxy for the selection density and is an indicator for the selection pressure exerted by antibiotic use in the hospital setting. However, this measure does not fully describe the actual selection density, since it does not provide information on the number and proportion of patients actually exposed to antibiotics.
Over the last decade several national surveillance systems on antibiotic use and/or resistance have been set up.58 Critical assessment of the units of measurement used to quantify antibiotic use and discussions about the interpretation of these units are, however, rarely presented in the scientific literature.911 Most of the surveillance systems use the number of DDD per 100 patient days to compare consumption rates over time and between hospitals, geographical regions and countries. In our view, conclusions drawn from these surveillance systems should be interpreted with care. The number of DDD per 100 patient days does not fully address the selection density and is sensitive to changes in hospital resource indicators over time. Additional information is required to facilitate interpretation. The number of DDD per 100 admissions could be a valuable additional unit of measurement. The aim of this study is to investigate the importance of units of measurement in presenting antibiotic use data with regards to antibiotic resistance risks. We therefore compared and analysed trends in the use of antibiotics in Dutch hospitals between 1997 and 2001 expressed in both DDD per 100 patient days and in DDD per 100 admissions.
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Patients and methods |
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Data on the use of antibiotics in acute care Dutch hospitals between 19972001 were collected by means of a questionnaire distributed to Dutch hospital pharmacies by the Working Party on Antibiotic Policy (SWAB) (for source data see NethMap 2003 on-line at www.swab.nl). Pharmacies were requested to report on the annual consumption of antibiotics for systemic use, as defined by group J01 of the Anatomical Therapeutic Chemical (ATC) Classification system for the classification of drugs. Outpatient use and dispensing of antibiotics to nursing homes were excluded. For each hospital the annual number of admissions and days spent in the hospital (bed days) were recorded. The number of bed days was calculated by multiplying the number of admissions with the average length of stay or the number of beds multiplied by the average occupancy rate; the choice between these methods was dependent on the preference of the individual hospital administrations.
Analysis
The ATC/DDD classification from the WHO, version 2002, was used to calculate the number of DDD of the various antibiotics.4 The number of patient days was obtained by subtracting the number of admissions from the number of bed days, as the number of bed days overestimates actual treatment days by including both the day of admission and the day of discharge. For the period 19972001 an overall pooled mean (i.e. weighted mean) was calculated for each year by aggregating data on antibiotic use, patient days and admissions from all hospitals. The use of antibiotics was expressed in DDD per 100 patient days and in DDD per 100 admissions. Trends in antibiotic use and hospital resource indicators were studied by a mixed model for repeated measurements with the hospitals as cofactor. P values < 5% were considered statistically significant. All statistical analyses were performed using SAS 8.2 (SAS Institute, Cary, NC, USA).
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Results |
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The use of penicillins in combination with ß-lactamase inhibitors, co-amoxiclav and piperacillintazobactam, increased significantly when expressed in DDD per 100 patient days. However, this increase was observed for piperacillintazobactam (P=0.003) when only admissions were used as the criterion (data not shown).
The use of lincosamides and fluoroquinolones expressed in both DDD per 100 patient days and DDD per 100 admissions increased significantly. This increased use was due to significant increases in the use of clindamycin (P < 0.001) and ciprofloxacin (P < 0.001), respectively (data not shown).
Between 1997 and 2001 changes in hospital resource indicators were observed. The mean number of patient days per hospital decreased significantly from 142 339 to 108 128 (24%; P < 0.001) and the mean number of admissions significantly decreased from 17 405 to 15 677 (10%; P=0.02). The mean length of stay decreased significantly from 8.0 to 6.9 days (14%; P < 0.001).
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Discussion |
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In the present study, data on antibiotic use in Dutch hospitals between 1997 and 2001 were expressed using two different units of measurement, DDD per 100 patient days and DDD per 100 admissions. From our data it is evident that trends over time in DDD per 100 patient days did not always correlate with trends in DDD per 100 admissions. Differences in trends between the two units of measurement seem to be the result of changes in resource indicators over time. We measured a 24% decrease in the mean number of patient days per hospital. The mean number of admissions also decreased, but to a lesser extent (10%). The mean length of stay decreased by 14%. The mean number of total DDD of antibiotics used also decreased (12%). Taking these findings together we can easily understand the differences found when total use was expressed in DDD per 100 patient days (+16%) and in DDD per 100 admissions (2%). Small discrepancies seem to be the result of the use of pooled and geometric means.
Without further information, an increase in DDD per 100 patient days might be interpreted as an actual increased use per patient. However, the number of DDD per 100 admissions remained constant. From our data we can only conclude that on average patients used the same number of DDD and were admitted to the hospital for a shorter period of time. This resulted in an intensification of antibiotic therapy per patient day.
An increase in the number of DDD per 100 patient days is often interpreted as worrisome with regards to the potential for antibiotic resistance development. However, in the Dutch situation, a constant use per patient combined with a significant decrease in the number of admissions are indicative for a lowering of the selection pressure exerted by antibiotic use over the years. Moreover, an intensification of antibiotic therapy per patient day suggests a shortening of duration of antibiotic treatment. Short duration of therapy may lead to less selection of resistant microorganisms.12
It appears that the number of DDD per 100 patient days can only be used as a reliable and robust monitor of the selection density over time or between geographical areas when relevant hospital resource indicators remain constant. Furthermore, neither unit of measurement fully represents the selection density. Neither DDD per 100 patient days nor DDD per 100 admissions indicates the number of patients exposed or the proportion of patients on antibiotics. It is arguable that the selection density does not best represent selection pressure or predict resistance development in a given geographical setting. For example, the number of exposed individual commensal microflora might best express selection pressure. However, there is a lack of studies to determine the correlation between different measures of antibiotic use and the level of antibiotic resistance.
In conclusion, the data presented in this article showed that to understand trends in antibiotic use over time or between hospitals or countries, data should not only be presented in DDD per 100 patient days. Knowledge of variation in resource indicators and additional expression of the data in DDD per 100 admissions are imperative for a meaningful understanding of observed trends in antibiotic use expressed in DDD per 100 patient days. Further research is needed to determine the correlation between different measures of antibiotic use and the level of antibiotic resistance.
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Footnotes |
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Acknowledgements |
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References |
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2 . Rubin, M. A. & Samore, M. H. (2002). Antimicrobial use and resistance. Current Infectious Disease Reports 4, 4917.[Medline]
3 . Levy, S. B. (2001). Antibiotic resistance: Consequences of inaction. Clinical Infectious Diseases 33, Suppl.3, S1249.[CrossRef][ISI][Medline]
4 . WHO Collaborating Centre for Drug Statistics Methodology (Norway) (2002). Guidelines For ATC Classification and DDDs Assignment. WHO Collaborating Centre, Oslo, Norway.
5 . SWAB (2004). NethMap 2004 Consumption of Antibiotic Agents and Antibiotic Resistance among Medically Important Bacteria in The Netherlands. [On-line.] http://www.swab.nl (15 December 2004, date last accessed).
6 . NORM/NORM-VET 2002 (2002). Consumption of Antimicrobial Agents and Occurrence of Resistance in Norway. Tromsø/Oslo 2003. ISSN 15022307. University Hospital of North Norway, Tromsø, Norway.
7 . SWEDRES 2003 (2003). A Report on Swedish Antibiotic Utilisation and Resistance in Human Medicine. ISSN 14003473. Swedish Strategic Programme for the Rational Use of Antimicrobial Agents (STRAMA) and the Swedish Institute for Infectious Disease Control, Solna, Sweden.
8 . DANMAP 2003 (2003). Use of Antimicrobial Agents and Occurrence of Antimicrobial Resistance in Bacteria from Food Animals, Foods and Humans in Denmark. ISSN 16002032. Danish Veterinary Institute, Copenhagen, Denmark.
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Monnet, D. L., Mölstad, S. & Cars, O. (2004). Defined daily doses of antibiotics reflect antibiotic prescriptions in ambulatory care. Journal of Antimicrobial Chemotherapy 53, 110911.
10 . Mandy, B., Koutny, E., Cornette, C. et al. (2004). Methodological validation of monitoring indicators of antibiotic use in hospitals. Pharmacy World and Science 26, 905.[CrossRef]
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Curtis, C., Marriott, J. & Langley, C. (2004). Development of a prescribing indicator for objective quantification of antibiotic usage in secondary care. Journal of Antimicrobial Chemotherapy 54, 52933.
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Schrag, S. J., Pena, C., Fernandez, J. et al. (2001). Effect of short-course, high-dose amoxicillin therapy on resistant pneumococcal carriage: a randomized trial. Journal of the American Medical Association 286, 4956.
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