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JAC Advance Access published online on April 9, 2008
Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkn150
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© The Author 2008. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Original research

Antibiotic use in 26 departments of internal medicine in 6 general hospitals in Israel: variability and contributing factors

Itamar Shalit*, Marcelo Low, Erez Levy, Michal Chowers, Oren Zimhony, Klaris Riesenberg, Jihad Bishara and Raul Raz

‘Clalit’ Health Services, Hospital Division, Tel Aviv, Israel

* Correspondence address. Infectious Diseases Unit, Schneider Children’s Medical Center of Israel, 14 Kaplan Street, Petach Tikva 49202, Israel. Tel: +972-3-925-3206; Fax: +972-3-924-7515; E-mail: itamar_s{at}clalit.org.il

Received 6 January 2008; returned 28 January 2008; revised 3 March 2008; accepted 9 March 2008


   Abstract
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 Abstract
Introduction
 Materials and methods
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 Discussion
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Objectives: Increased antibiotic consumption is associated with increased bacterial resistance worldwide. We aimed to analyse antibiotic consumption and potential contributory factors in internal medicine departments in Israel.

Methods: Data (2003–04) from 26 departments in 6 hospitals were retrieved. Defined daily doses (DDD)/100 bed-days were calculated for total antibiotic use and by antibiotic class. Patterns identified were correlated with 15 patients’ and departmental variables by univariate and multivariate analyses.

Results: Total antibiotic consumption differed by a factor of 2.3 (115 DDD/100 bed-days to 49.1 DDD/100 bed-days) between the highest and lowest consuming departments. Antibiotic classes differed by a factor of 22.8 for macrolides, a factor of 20 for piperacillin/tazobactam, a factor of 17 for carbapenems, a factor of 13.3 for quinolones, a factor of 9 for vancomycin, a factor of 6.8 for amoxicillin/clavulanate, a factor of 6.6 for aminoglycosides, a factor of 5.3 for penicillins and a factor of 2.8 for cephalosporins. Even among departments within hospitals, there was a difference of up to 1.5-fold for total use and antibiotic class differences ranged between 2.5- and 7.2-fold for third- and fourth-generation cephalosporins, despite similar Charlson scores and other patient variables. In the multivariate analysis, hospital affiliation and rate of 1 day hospitalization were the only significant variables predicting total antibiotic use, contributing 43% and 7.3%, respectively, to the variance. By antibiotic class, controlling for hospital affiliation, patients with neutropenia, lower respiratory tract infections and assisted ventilation were the most common significant contributors, ranging from 3.5% for quinolones to 7.7% for piperacillin/tazobactam.

Conclusions: Patterns of antibiotic use vary widely among internal medicine departments in Israel, which cannot be explained by objective parameters related either to patients or wards. Ongoing monitoring and guideline formulation are needed to regulate antibiotic prescription.

Key Words: antibiotic consumption , internal medicine departments , drug utilization


   Introduction
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Antibiotic resistance is increasing worldwide in both hospital and community environments.1 A direct relationship between rates of antibiotic use and the emergence of resistance has been reported in several studies.2–5 These findings were supported by trials showing a decrease in antibiotic resistance to agents whose use was intentionally reduced or restricted.6–8 To cope with the problem, international professional societies have established systems to monitor and compare antibiotic consumption. For example, the European Surveillance of Antimicrobial Consumption (ESAC) project performed a cross-national, retrospective comparison of antibiotic use in outpatient settings (26 countries)9 and hospitals (15 countries)10 from 1997 to 2002. A broad variation was detected in hospital consumption, with a 3-fold range among countries [1.3–3.9 defined daily doses (DDD)/1000 inhabitants/day]. However, most of the studies published so far investigated antibiotic use in hospitals in general, with a representative single measurement for each, without sub-analyses of their different departments. Furthermore, they were mainly descriptive and did not analyse potential variables that may have contributed to the observed variations.

The aim of the present study was to investigate patterns of antibiotic consumption in a large number of internal medicine wards exclusively, with an in-depth multivariate analysis of possible contributory factors. This design should shed more light on the patterns of antibiotic administration and facilitate the formulation of practice guidelines geared to internal medicine.


   Materials and methods
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Data collection and analysis

Clalit Health Services is the largest health maintenance organization (HMO) in Israel, covering 55% of all Israeli citizens. It owns and operates 14 general and specialty hospitals, accounting for 35% of the entire hospital-bed capacity in the country. The data for each Clalit-run hospital and its departments are accessible through the HMO’s central computerized system. For the present study, the Clalit system was searched for all data on antibiotic use in 26 internal medicine departments of 6 of its general hospitals in 2003–04. The findings were validated for accuracy and consistency by two independent data managers. The hospitals included are located across the country; all are university-affiliated secondary (n = 4) or tertiary (n = 2) facilities. Each of their internal medicine departments has a permanent medical and nursing staff, and all are engaged in teaching medical students. Only those internal medicine departments without subspecialty beds (i.e. oncology unit) were included in the study. It should be noted that >85% of admissions to internal medicine departments in Israel are emergency admissions and only a minority elective. The assignment of patients admitted from the emergency department to the internal medicine departments is by rotation, i.e. quasi-randomized.

Antibiotic consumption was recorded as DDD/100 occupied bed-days, according to the consensus definition of the World Health Organization (2003).11 We analysed all systemic antibiotics included in the Anatomic Therapeutic Chemical (ATC) classification J0112 by total use and by antibiotic class, as follows: penicillins with an extended-spectrum, such as ampicillin and amoxicillin (J01CA); penicillins combined with β-lactamase inhibitors (J01CR); ureidopenicillins combined with β-lactamase inhibitors (J01CR05); cephalosporins (divided into four generations) (J01D); fluoroquinolones (J01M); glycopeptides (J01XA); aminoglycosides (J01GB); macrolides (J01FA); and carbapenems (J01DH).

A subgroup analysis was performed for internal medicine departments within the same hospital, which presumably have similar patient variables and antibiotic resistance patterns.

Contributory factors

Fifteen patient- and department-related variables were analysed for their potential influence on antibiotic consumption:

  1. Patient variables: age (average; and percentage of patients aged <54, 55–74 and >75 years); percentage of males; percentage of patients on assisted ventilation; percentage of patients with cellulitis, lower respiratory tract infection (LRTI) or urinary tract infection (UTI); percentage of patients with neutropenia and Charlson co-morbidity score. The Charlson Index13 contains 19 categories of co-morbidity, which are primarily defined using ICD-9-CM diagnoses codes. Each category has an associated weight (ranging from 1 to 6), which is based on the adjusted risk of 1 year mortality. The total score ranges from 0 to 37. The overall co-morbidity score reflects the cumulative increased likelihood of 1 year mortality; the higher the score, the more severe the burden of co-morbidity.
  2. Department variables: occupancy rate; rate of 1 day hospitalization; average length of stay; rate of re-hospitalization within 1 week of discharge.

Statistical analysis

Statistical analyses were performed using SPSS software, version 13.0. Pearson's correlation test was used for univariate analysis. The Kruskal–Wallis test was used to measure the difference between hospitals’ antibiotic consumption. All variables found to be significant on univariate analysis were entered into the generalized linear models (GLM) analysis. This procedure provides regression analysis and analysis of variance for departmental consumption of antibiotics by patient and department variables as covariates, controlling for hospital group. The percentage contribution to the variance was calculated using Partial Eta Square as well as regression coefficient (B), and its 95% confidence interval where determined. Linear regression model was used to find out the percent contribution to variance (adjusted R square) in the antibiotic classes consumption.


   Results
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Antibiotic consumption patterns

By hospital. In 2003–04, the total antibiotic consumption in the internal medicine departments of the six hospitals included in our study ranged, by hospital, from 98.5 to 54.7 DDD/100 bed-days (1.8-fold difference; P < 0.007) (Figure 1). The relative contribution of the different classes of antibiotic drugs to the total antibiotic consumption is illustrated in Figure 2. The most commonly administered agents were cephalosporins and penicillins plus β-lactamase inhibitors (cumulative use for all hospitals, 26% and 25%, respectively). Penicillins plus β-lactamase inhibitors were administered less often than cephalosporins in three hospitals and more often in two.


Figure 1
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  		Figure 1. Total antibiotic consumption by drug classes (DDD/100 occupied bed-days) in internal medicine departments of six hospitals. Differences between hospitals in total DDD/100 bed-days were statistically significant ({chi}2 = 15.98, P < 0.007). AB, antibiotics. A colour version of this figure is available as Supplementary data at JAC Online (http://jac.oxfordjournals.org/).

 


Figure 2
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  		Figure 2. Relative contribution of antibiotic classes to total consumption (DDD/100 occupied bed-days) in internal medicine departments of six hospitals. AB, antibiotics. A colour version of this figure is available as Supplementary data at JAC Online (http://jac.oxfordjournals.org/).

 
By department. The findings for the 26 internal medicine departments are summarized in Table 1 and illustrated in Figure 3. Total antibiotic consumption ranged from 49.1 to 115 DDD/100 bed-days (2.3-fold difference). Within each hospital, total consumption differed between departments by 1.2-fold (in hospital 3) to 1.5-fold (in hospital 2) (Figure 3).


Figure 3
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  		Figure 3. Total antibiotic consumption by drug classes (DDD/100 occupied bed-days) in 26 internal medicine departments (IMDs). AB, antibiotics. A colour version of this figure is available as Supplementary data at JAC Online (http://jac.oxfordjournals.org/).

 


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  		Table 1. Antibiotic use (DDD/100 bed-days) in 26 internal medicine departments, 2003–04

 
Analysis by antibiotic class yielded the highest inter-departmental variation in use for fourth-generation cephalosporins (44-fold), followed by carbapenems (17-fold). Fourth-generation cephalosporins accounted for 2% of the cumulative antibiotic consumption in all hospitals and carbapenems for 1%. The lowest, albeit still wide, variation was noted for penicillins with extended-spectrum (5.3-fold), which accounted for 9% of the total antibiotic use. Of interest was the high rate of variation in the use of macrolides (22.8-fold) and ureidopenicillins plus β-lactamase inhibitors (20.0-fold), which accounted, respectively, for 8% and 2% of the total antibiotic use.

On similar analysis of within-hospital variations, the difference was smaller, but still reached 7.2-fold for fourth-generation cephalosporins, 5.6-fold for carbapenems, 3.9-fold for ureidopenicillins plus β-lactamase inhibitors, 3.5-fold for fluoroquinolones, 2.5-fold for third-generation cephalosporins and 1.5-fold for total antibiotic consumption.

Variables influencing antibiotic use

The findings for the effect of the department- and patient-related variables on antibiotic consumption in the 26 departments of internal medicine are shown in Table 2. Overall, these departments had 180 999 admissions accounting for 750 577 hospitalization days during 2003–04. The departments differed markedly in percentage of patients aged over 75 years (28.7% to 45.9%) and rate of 1 day hospitalization (7.9% to 28.6%), as well as in occupancy rate, percentage of patients with neutropenia and percentage of patients on assisted ventilation. Less variation was noted for Charlson score, which reflects, to an extent, the disease severity score of the patients (scores of 3.6 ± 0.2).


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  		Table 2. Departmental, clinical and patient variables in 26 internal medicine departments, 2003–04

 
Table 3 shows the findings on univariate analysis of the correlation of the objective factors with antibiotic consumption. Positive correlations were found between high total DDD/100 bed-days and percentage of patients aged 55–74 years and rate of 1 day hospitalization; a negative correlation was found between high total DDD/100 bed-days and Charlson score. By class of drug, none of the variables analysed correlated with consumption of penicillins plus β-lactamase inhibitors, despite the wide range in their use among departments (6.8-fold). Patient age, Charlson score and rate of 1 day hospitalizations were significantly associated with the rate of consumption of penicillins with extended-spectrum and aminoglycosides whereas for cephalosporins, the most significant associations were found with length of stay, rate of 1 day hospitalization and percentage of patients with cellulitis.


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  		Table 3. Pearson correlation (univariate analysis) between use of antibiotic classes (DDD/100 bed-days) and departmental, clinical and patient variables

 
On multivariate analysis (Table 4), controlling for hospital affiliation, we found that the only variable independently correlated with total antibiotic consumption was the percentage of patients with 1 day hospitalization. However, this variable contributed only 7.4% to the observed variance. By antibiotic class, only three patient-related variables correlated with consumption. These variables affected only 3 of the 13 antibiotic classes and contributed only a marginal percentage (3.5–7.7%) to the observed variance. When we analysed the variance in total antibiotic consumption by hospital clusters, we found that belonging to a given hospital was an independent variable for total DDD/100 bed-days, contributing 43% to the observed variance.


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  		Table 4. Analysis of covariance of total antibiotics and antibiotic class consumption (DDD/100 bed-days) and demographic, clinical and patient variables (controlling for hospitala)

 
The findings on univariate analysis of the inter-relationship between the use of selected classes of antibiotics within each department of internal medicine are presented in Figure 4; the multivariate analysis of the inter-relationship by antibiotic classes is presented in Table 5. In most cases, there was a positive correlation between high use of a certain class of antibiotics and increased use of additional classes. For example, departments characterized by a high consumption of glycopeptides also showed a high consumption of carbapenems, fluoroquinolones and macrolides. Likewise, increased use of ureidopenicillins with β-lactamase inhibitors was significantly correlated with increased use of carbapenems. Only in very few cases was higher use of one class of agents correlated with lower use of other classes (Figure 4).


Figure 4
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  		Figure 4. Univariate analysis of inter-correlation between consumption of various antibiotic classes and correlation with total antibiotic consumption.

 


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  		Table 5. Multivariate analysis of inter-correlation between consumption of various antibiotic classes and correlation with total antibiotic consumption

 

   Discussion
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
Funding
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 References
 
Our study was designed to analyse the variability of antibiotic consumption in a group of internal medicine departments and to correlate the findings with a broad range of potential factors that may affect patterns of use.

Our main finding was the large variation in antibiotic consumption among the 26 departments of internal medicine of the 6 hospitals in Israel over a 2 year period. The difference between the highest and lowest total antibiotic users by department reached a factor of 2.3. The range was considerably wider when classes of antibiotics were analysed separately, reaching a factor of 22.8 for macrolides and 20.0 for ureidopenicillins with β-lactamase inhibitors. More importantly, even when we compared the pattern of use between internal medicine wards within the same hospital, a large variation was noted, despite their highly similar patient population, Charlson score, and antibiotic resistance pattern: 5.6-fold for carbapenems, 3.9-fold for ureidopenicillins plus β-lactamase inhibitors and 3.5-fold for fluoroquinolones. The antibiotics most often consumed in the internal medicine departments were cephalosporins and penicillins plus β-lactamase inhibitors, which together accounted for more than 50% of the total antibiotic consumption (DDD/100 bed-days). These were followed by the fluoroquinolones, penicillins with extended-spectrum and macrolides.

Previous studies have reported a similarly wide variability in antibiotic consumption among countries. In a study of hospital antibiotic consumption from 1997 to 2002 in 15 European countries, Vander Stichele et al.10 reported that overall use varied by a factor of 3 between the highest and lowest consumers, with penicillins and cephalosporins accounting for more than 50% of the total consumption in 14/15 countries. This is in line with our findings. In 8/15 countries, the macrolides and quinolones were the next most frequent antibiotics used. MacKenzie et al.14 analysed antibiotic consumption in 139 hospitals across 30 European countries in 2001. Median total antibiotic use was 49.6 DDD/100 bed-days, with an interquartile range of 37.1– 65.4 DDD/100 bed-days (min=5, max=121). Similar to our study, penicillins, cephalosporins and quinolones were the most frequently used.

The importance of monitoring antibiotic consumption stems from its observed association with rising bacterial resistance. This is exemplified by the outpatient study of Goossens et al.,9 encompassing 26 European countries from 1997 to 2002. The authors reported a statistically significant correlation between use of erythromycin and increased macrolide resistance of S. pneumoniae and S. pyogenes. Similarly, the increased use of co-trimoxazole and quinolones was associated with a significant increase in the resistance of Escherichia coli. In hospital settings, Monnet et al.15 found a correlation of consumption of macrolides, third-generation cephalosporins and fluoroquinolone with an increased rate of isolation of methicillin-resistant S. aureus (MRSA). Similarly, Weber et al.16 demonstrated that increased exposure to levofloxacin or ciprofloxacin were significant independent risk factors for isolation of MRSA. In another 5 year retrospective study in a Swiss tertiary-care hospital, Loeffler et al.17 found a statistically significant positive correlation between antibiotic consumption and resistance of E. coli to piperacillin, of P. aeruginosa to piperacillin, cephalosporins and aminoglycosides and of K. pneumoniae to cephalosporins.

Of even greater consequence were the reported changes in antibiotic resistance following interventional programmes designed to reduce the non-judicious use of antimicrobials. In a prospective study, Bantar et al.18 introduced several interventional steps to optimize the quality of antimicrobial prescription in an adult hospital in Argentina. A significant correlation was found between decreased carbapenem prescription and carbapenem resistance in P. aeruginosa, and between decreased vancomycin prescription and methicillin resistance in S. aureus. Cook et al.7 instituted a hospital-wide programme to decrease prescription of ciprofloxacin and showed a similar, although moderate, effect on MRSA isolation rates.

The growing resistance of hospital pathogens has significantly hampered the adequate treatment of healthcare-related infection. Certain resistant pathogens have also been associated with increased attributable mortality. Lee et al.19 reported a 34.8% rate of nosocomial sepsis-related death due to multidrug-resistant A. baumannii compared with 13.0% for non-multidrug-resistant A. baumannii, with an attributable mortality of 21.8%. Similar findings were reported by Grupper et al.20 in a prospective comparative study of nosocomial A. baumannii bacteraemia. Schwaber et al.21 compared inpatients with bacteraemia caused by extended-spectrum β-lactamase (ESBL)- or non-ESBL-producing Enterobacteriaceae and found that death due to infection was 2.3 times higher (P < 0.03) in the EBSL bacteraemic group. Taken together, these findings indicate that more interventions to regulate antibiotic prescription are acutely needed in most countries.

Ours is the first study to perform an in-depth analysis of possible contributory factors to antibiotic consumption within a homogeneous group of internal medicine departments. Both patient-related (age, Charlson score, complex intervention, underlying and infectious conditions) and department-related (length of stay, occupancy rate and re-hospitalization rate) factors were investigated. Although several were found to be significant on univariate analysis, only one, percentage of patients with 1 day hospitalization, proved to be a significant independent predictor of total antibiotic consumption (DDD/100 bed-days). However, it contributed only 7.4% to the variance. On the other hand, we found, not surprisingly, that hospital clustering had a significant effect on total antibiotic use in the internal medicine departments and contributed 43% to the observed variance. This finding indicates an inherent role of the hospital and its variables in relationship to antibiotic consumption. However, this is unlikely to be due to the level of bacterial resistance or number of infections. Antibiotic resistance patterns do not influence the total antibiotic consumption because they apply to total consumption of any kind of antibiotic, regardless of its spectrum of activity. In addition, we quantified and analysed the most common infections in internal medicine departments (LRTIs, UTIs and cellulitis) and none were found to be correlated with total antibiotic consumption. A more likely explanation for the effect of hospital clustering on total antibiotic consumption is the role of hospital infectious disease consultants, existence and implementation of practice guidelines, antibiotic monitoring and approval systems and other forms of antibiotic stewardship programmes. Thus, in practice, none of our potential factors could explain the large variation observed, indicating that the diversity of antibiotic consumption is most likely not related to objective, justifiable patients’ conditions. Moreover, our correlational analysis rarely revealed a link between an increase in the administration of one class of antibiotics and a parallel decrease in another class. Rather, the opposite was true: departments characterized by a high utilization of one class of broad-spectrum antibiotics tended to show high utilization of other classes of broad-spectrum antibiotics as well.

A potential limitation of our study is the lack of precise data on antibiotic resistance patterns in each department. However, as mentioned above, antibiotic resistance patterns do not influence the total antibiotic consumption, as they apply to total consumption of any kind of antibiotic, regardless of its spectrum of activity. Therefore, the large variation in total consumption observed here cannot be attributed to a variation in resistance pattern. Furthermore, we compared both the total antibiotic consumption and the consumption by antibiotic classes among departments within the same hospital. In Israel, most patients are admitted to internal medicine wards from the emergency room, and patient allocation is based on a randomization table or similar systems to ensure a balanced case load and case mix. In addition, the general intensive care unit, one of the epicentres of the development of antibiotic resistance, is shared by all the internal medicine departments within the hospital, with routine bilateral movement of patients. Thus, in a given hospital, we would not expect to see different patterns of antibiotic resistance among its internal medicine departments. Nevertheless, a wide variation in antibiotic consumption was found, which is likely not explained by differences in resistance patterns.

It should also be noted that our study is based on ecological data and not individual patient data and thus subject to ecological fallacy. For example, using one kind of antibiotic will be associated with less use of other kinds of antibiotics in an individual patient, whereas in one department, high use of one kind of antibiotic is more bound to be linked to high use of other antibiotics (total antibiotic use) as was indeed found in our study. Thus, analysing individual patient data and additional factors such as number of physicians and nurses and their level of training, and the scope of involvement of infectious diseases specialists is probably warranted to further explore the differences in antibiotic prescribing.

Together, our findings indicate that antibiotic prescription in departments of internal medicine is probably affected by non-objective, non-patient-related factors, such as personal preferences, local routines and differential patterns of risk-aversion. Similar findings were reported in a qualitative study of antimicrobial prescribing by non-consultant hospital doctors in Ireland.22 Although non-objective factors are difficult to assess, they may be amenable to guidance, education and continued and comparative monitoring. Such antimicrobial stewardship programmes have been implemented in several healthcare systems23 and some of their educational and restriction policies led to more rational and appropriate antibiotic prescriptions and reduced antimicrobial resistance.

Our findings should prompt other groups to actively monitor patterns of antibiotic use in various departments and disciplines. We trust that they will also encourage the formulation of clinical practice guidelines geared to internal medicine departments to promote the judicious use of antibiotics for the treatment of commonly encountered infectious conditions.


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 Abstract
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No specific funding was received.


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None to declare.


   Supplementary data
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Colour versions of Figures 1, 2 and 3 are available as Supplementary data at JAC Online (http://jac.oxfordjournals.org/).


   Acknowledgements
 
This work was presented as a poster at the Forty-sixth Interscience Conference on Antimicrobial Agents and Chemotherapy, San Francisco, CA, USA, 2006.


   References
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1 . Goldstein FW. Combating resistance in a challenging, changing environment. Clin Microbiol Infect (2007) 13(Suppl 2):2–6.[CrossRef][Web of Science][Medline]

2 . MacDougall C, Powell JP, Johnson CK, et al. Hospital and community fluoroquinolone use and resistance in Staphylococcus aureus and Escherichia coli in 17 US hospitals. Clin Infect Dis (2005) 41:435–40.[CrossRef][Web of Science][Medline]

3 . Frank U, Kleissle EM, Daschner FD, et al. Multicentre study of antimicrobial resistance and antibiotic consumption among 6,780 patients with bloodstream infections. Eur J Clin Microbiol Infect Dis (2006) 25:815–7.[CrossRef][Web of Science][Medline]

4 . Riedel S, Beekmann SE, Heilmann KP, et al. Antimicrobial use in Europe and antimicrobial resistance in Streptococcus pneumoniae. Eur J Clin Microbiol Infect Dis (2007) 26:485–90.[CrossRef][Web of Science][Medline]

5 . Malhotra-Kumar S, Lammens C, Coenan S, et al. Impact of azithromycin and clarithromycin therapy on pharyngeal carriage of macrolide-resistant streptococci among healthy volunteers: a randomised, double-blind, placebo-controlled study. Lancet (2007) 369:482–90.[CrossRef][Web of Science][Medline]

6 . Seppala H, Klaukka T, Vuopio-Varkila J, et al. The effect of changes in the consumption of macrolide antibiotics on erythromycin resistance in group A streptococci in Finland. Finnish Study Group for Antimicrobial Resistance. N Engl J Med (1997) 337:441–6.[Abstract/Free Full Text]

7 . Cook PP, Catrou P, Gooch M, et al. Effect of reduction in ciprofloxacin use on prevalence of methicillin-resistant Staphylococcus aureus rates within individual units of a tertiary care hospital. J Hosp Infect (2006) 64:348–51.[CrossRef][Web of Science][Medline]

8 . Zillich AJ, Sutherland JM, Wilson SJ, et al. Antimicrobial use control measures to prevent and control antimicrobial resistance in US hospitals. Infect Control Hosp Epidemiol (2006) 27:1088–95.[CrossRef][Web of Science][Medline]

9 . Goossens H, Ferech M, Vander Stichele R, et al. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet (2005) 365:579–87.[Web of Science][Medline]

10 . Vander Stichele RH, Elseviers MM, Ferech M, et al. Hospital consumption of antibiotics in 15 European countries: results of the ESAC Retrospective Data Collection (1997–2002). J Antimicrob Chemother (2006) 58:159–67.[Abstract/Free Full Text]

11 . WHO Collaborating Centre for Drug Statistics and Methodology. Guidelines for ATC Classification and DDD Assignment (2003) Oslo, Norway: WHO.

12 . WHO Collaborating Centre for Drug Statistics Methodology. ATC/DDD Index (2005) http://www.whocc.no/atcddd/.

13 . Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis (1987) 40:373–83.[CrossRef][Web of Science][Medline]

14 . MacKenzie FM, Bruce J, Van Looveren M, et al. Antimicrobial susceptibility testing in European hospitals: report from the ARPAC study. Clin Microbiol Infect (2006) 12:1185–92.[CrossRef][Web of Science][Medline]

15 . Monnet DL, MacKenzie FM, López-Lozano JM, et al. Antimicrobial drug use and methicillin-resistant Staphylococcus aureus, Aberdeen, 1996–2000. Emerg Infect Dis (2004) 10:1432–41.[Web of Science][Medline]

16 . Weber SG, Gold HS, Hooper DC, et al. Fluoroquinolones and the risk for methicillin-resistant Staphylococcus aureus in hospitalized patients. Emerg Infect Dis (2003) 9:1415–22.[Web of Science][Medline]

17 . Loeffler JM, Garbino J, Lew D, et al. Antibiotic consumption, bacterial resistance and their correlation in a Swiss university hospital and its adult intensive care units. Scand J Infect Dis (2003) 35:843–50.[CrossRef][Web of Science][Medline]

18 . Bantar C, Sartori B, Vesco E, et al. A hospitalwide intervention program to optimize the quality of antibiotic use: impact on prescribing practice, antibiotic consumption, cost savings, and bacterial resistance. Clin Infect Dis (2003) 37:180–6.[CrossRef][Web of Science][Medline]

19 . Lee NY, Lee HC, Ko NY, et al. Clinical and economic impact of multidrug resistance in nosocomial Acinetobacter baumannii bacteremia. Infect Control Hosp Epidemiol (2007) 28:713–9.[CrossRef][Web of Science][Medline]

20 . Grupper M, Sprecher H, Mashiach T, et al. Attributable mortality of nosocomial Acinetobacter bacteremia. Infect Control Hosp Epidemiol (2007) 28:293–8.[CrossRef][Web of Science][Medline]

21 . Schwaber MJ, Navon-Venezia S, Kaye KS, et al. Clinical and economic impact of bacteremia with extended-spectrum-β-lactae-producing Enterobacteriaceae. Antimicrob Agents and Chemother (2006) 50:1257–62.[Abstract/Free Full Text]

22 . De Souza V, MacFarlane A, Murphy AW, et al. A qualitative study of factors influencing antimicrobial prescribing by non-consultant hospital doctors. J Antimicrob Chemother (2006) 58:840–3.[Abstract/Free Full Text]

23 . MacDougall C, Polk RE. Antimicrobial stewardship programs in health care systems. Clin Microbiol Rev (2005) 18:638–56.[Abstract/Free Full Text]


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