British Journal of Clinical Pharmacology
DOI:10.1111/bcp.12255
Association between Clostridium difficile infection and antimicrobial usage in a large group of English hospitals Joao B. Pereira,1 Tracey M. Farragher,2 Mary P. Tully1 & Jonathan Cooke1,3
Correspondence Mr Joao Bissau Pereira PhD, Manchester Academic Health Science Centre and the University of Manchester, School of Pharmacy and Pharmaceutical Sciences, Manchester M13 9PT, UK. Tel.: 0044911782756 E-mail: [email protected] -----------------------------------------------------------------------
Keywords antimicrobial policy, defined daily doses, healthcare-associated infection, multilevel models, time series analysis -----------------------------------------------------------------------
Received 9 December 2012
Accepted 14 September 2013
1
Manchester Academic Health Science Centre and Manchester Pharmacy School, The University of Manchester, Stopford Building Oxford Road, Manchester M13 9PT, UK, 2Leeds Institute of Health Sciences, The University of Leeds, Charles Thackrah Building, 101 Clarendon Road, Leeds LS2 9LJ, UK and 3Infectious Diseases and Immunity Section, Division of Infectious Diseases, Department of Medicine, Imperial College, South Kensington Campus, London SW7 2AZ, UK
Accepted Article Published Online 15 October 2013
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • The number of Clostridium difficile infection (CDI) cases reported by National Health Service (NHS) trusts decreased between 2007 and 2008. • Antimicrobial use in English trusts changed between 2004 and 2009. • There are no studies investigating whether the reduction in the CDI cases reported by the NHS trusts is associated with the changes in antimicrobial use.
WHAT THIS STUDY ADDS • The reduction in the number of CDI cases reported by NHS hospitals is associated with concurrent changes in antimicrobial use. • The use of multilevel models to analyse data collected from the Health Protection Agency and IMS Health is a method that could be suitable for a national surveillance programme of antimicrobial use and its association with healthcare-associated infections (e.g. CDI).
AIMS This study aimed to determine the association between the reduction in the number of Clostridium difficile infection (CDI) cases reported by the English National Health Service (NHS) hospitals and concurrent antimicrobial use.
METHODS A retrospective ecological study for January 2005 to December 2008 was conducted using data from 26 of the 29 NHS trusts (i.e. a trust manages one or more hospitals) located in the North West Strategic Health Authority of England. Antimicrobial use data, for patients of all ages, were provided by IMS Health, and CDI case data for patients aged ≥65 years were provided by the Health Protection Agency. Antimicrobial use was converted into defined daily doses (DDDs). The overall association between antimicrobial use and CDI for the trusts was investigated using multilevel models.
RESULTS Our study shows a positive significant association between the CDI cases and the use of the following antimicrobials: ‘third-generation cephalosporins’ [11.62 CDI cases per 1000 DDDs; 95% confidence interval (CI), 5.92–17.31]; ‘fluoroquinolones’ (4.79 CDI cases per 1000 DDDs; 95% CI, 2.83–6.74); and ‘second-generation cephalosporins’ (4.25 CDI cases per 1000 DDDs; 95% CI, 1.66–6.83). The strength of this association was not significantly different (95% CI) among the antimicrobial groups.
CONCLUSIONS This study shows that the reduction in the number of CDI cases reported by the English NHS hospitals is associated with concurrent reductions in antimicrobial use. This means that the number of CDI cases over time decreased in a similar fashion to the usage of various antimicrobials.
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© 2013 The British Pharmacological Society
Clostridium difficile infection and antimicrobial usage
Introduction Clostridium difficile infection (CDI) is a healthcareassociated infection (HCAI) associated with high morbidity, mortality and healthcare expenditure. Patients with CDI have a 2.74-fold increase in the risk of dying during their hospital stay than all other hospitalized patients [1]. In England, a CDI patient has been reported to have an increased average length of hospital stay of 21 days, costing the National Health Service (NHS) approximately an extra £4000 [2]. Furthermore, according to the Office for National Statistics, for England and Wales in 2008, 2502 death certificates included CDI as an underlying cause of death [3]. This figure was 10 times larger than for other high-profile HCAIs, such as meticillin-resistant Staphylococcus aureus infection (i.e. 228 death certificates) [4]. For these reasons, and because the number of CDI cases between 1994 and 2004 had increased, this infection was included in the NHS mandatory reporting scheme for HCAIs, requiring NHS hospitals to report their CDI cases to the Health Protection Agency (HPA; now Public Health England, PHE) [5]. The relative risk of acquiring CDI through exposure to different antimicrobials is still unclear. Studies suggested that nearly all antimicrobials may precipitate CDI and that some antimicrobials may have a higher association with CDI than others [6–8]. However, the ability to generalize the results from such studies has been questioned due to the existence of serious threats to their validity [8]. There is no national surveillance system for medication use (i.e. prescribing, dispensing and administering) in English hospitals. However, more than 98% of these hospitals send their antimicrobial usage data (i.e. dispensing issue data) to IMS Health (IMS Health Ltd, London, England). This company may be a source of national hospital antimicrobial usage data that could potentially be used in pharmacoepidemiological studies. Clostridium difficile infection and meticillin-resistant S. aureus infection rates have been a target for performance in infection control in NHS hospitals for the past 5 years, and part of this control involved changes in disinfection policies and antimicrobial prescribing [9, 10]. According to the HPA, between 2007 and 2008 there was a 35% reduction in the number of CDI cases reported by NHS hospitals [10]. Furthermore, there was a dramatic change in antimicrobial use across English hospitals between 2004 and 2009 [11]. In fact, there was a reduction in usage of fluoroquinolones (>40%), second-generation cephalosporins (50%) and third-generation cephalosporins (22%), and an increase in usage of carbapenems (50%). It is not known, however, whether the reduction in the number of CDI cases was associated with these changes in antimicrobial use. The main objective of this paper was to test the hypothesis that there may be an association between the reduction in the number of CDI cases reported by the
English NHS hospitals and changes in antimicrobial use. A secondary objective was to investigate whether the usage of some antimicrobial groups was more strongly associated with CDI than others.
Methods Setting, study period and population At the time of the study, England was divided into 10 Strategic Health Authorities (SHAs) that oversaw all NHS activities, covering a population of more than 50 million. The hospitals located in each SHA are managed by trusts, and one trust can manage one or more hospitals. Data for all trusts in England were not available to us. Data for the North West (NW) SHA were selected for the following three reasons: it was the largest SHA geographically; it covered more than 7 million inhabitants with varying social backgrounds; and it included all categories of hospital trusts. Twenty-six trusts located in the NW SHA, managing 64 NHS hospitals, were included in the study sample. Their CDI cases and antimicrobial usage data for the quarters between 2005 and 2008 inclusive were analysed. Only three NW SHA trusts were excluded; two because no antimicrobial usage data were available through IMS Health and one because no CDI case data were available through the HPA because it was a paediatric trust.
Trust CDI data The HPA (the national agency that holds and publishes the CDI data for all NHS trusts) provided a database of trust CDI cases, which included the number of CDI cases for patients aged ≥65 years old reported by the 26 NW SHA trusts for 2005–2008 inclusive [5]. Only data for this age group were available for the time period included in this research [6, 12]. There were temporal differences in the HPA CDI reporting categories. To minimize these differences, for the quarters between January 2005 and March 2007 inclusive, all the ‘cases diagnosed in the acute trusts’ were included. For the quarters between April 2007 and December 2008, all the CDI reporting categories that referred to cases diagnosed in the trusts were included (i.e. ‘specimens taken up to 2 days after admission’, ‘specimens taken 3 days or more after admission’ and ‘specimens from non-admitted patients’).
Antimicrobial usage data The source of antimicrobial usage data was a database provided by IMS Health, which included the antimicrobial usage for patients of all ages for each of the 26 NW SHA trusts for 2005–2008 inclusive. This database included the monthly number of antimicrobial units (i.e. number of tablets, vials, syringes, etc) dispensed from the pharmacy to the wards and to hospital outpatients and the number Br J Clin Pharmacol
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of units of antimicrobials that were returned to the pharmacy. Duplicates had been removed. No primary care data were included in the database. Trust antimicrobial usage per quarter was quantified by converting the number of units of antimicrobials dispensed, minus the number of units returned, into defined daily doses (DDD). Then, the antimicrobials were classified and clustered into 23 drug groups (J01AA–J01XX plus A07AA) of the World Health Organization’s Anatomical Chemical Classification (ATC) system for analysis [13]. From these, all the antimicrobials that are nationally recommended [10] for first- or second-line treatment of CDI were excluded, i.e. oral vancomycin (A07AA), intravenous vancomycin (J01XA) and fusidic acid (J01XC). These antimicrobials were expected to be associated with CDI because they are used to treat it, rather than because they precipitate this infection. Data for the usage of metronidazole, also recommended for the treatment of CDI [10], were not included in the database provided by IMS Health and are therefore also excluded from analysis. Antimicrobials with zero usage for more than 50% of the observations in each trust were also excluded (i.e. J01BA, amphenicols; J01DF, monobactams; and J01FG, streptogramins) because there would not be enough data for to enable robust estimation of the parameters of the statistical models. The data provided by the HPA and IMS Health are classed as publically available; therefore, it was not necessary to obtain an ethical opinion from the National Research Ethics Service.
Statistical analysis Longitudinal analysis was used to investigate the overall association between the ‘number of CDI cases for patients aged 65 and above reported by the trusts’ and ‘antimicrobial usage for patients of all ages’ for the 26 trusts for 2005–2008 inclusive. Longitudinal analysis has been used in ecological studies to investigate the association between antimicrobial use and cases of infectious disease [14–18]. This is the statistical approach recommended in the ORION statement [19], because it is able to take into account that observations of one time point may be influenced by observations of previous time points [20]. Multilevel modelling is the most robust longitudinal analysis method, because it is able to take into account both the variability of observations within a trust over time (level 1) and between trusts (level 2) [21]. The data investigated in this work can be classified as count data (i.e. the number of times that the dose established as the DDD for an antimicrobial was used; counts of CDI cases). Longitudinal count data can be analysed using Poisson multilevel models. However, as these data included large numbers of counts (i.e. more than eight), they could be approximated to continuous normally distributed data and analysed using a linear multilevel model, as long as the residuals of the model followed a normal 898
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distribution [20, 21]. The advantage of using a standard linear multilevel model is that the resulting parameters are more generally understood than the equivalent Poisson model. Multilevel modelling is a method for analysing data from different groups, when a dependency among the observations in each group is expected [21]. For example, the association between the number of CDI cases and antimicrobial usage may vary from trust to trust, because this depends on trust differences, such as policies for disinfection, cleaning, antimicrobial use and isolation of CDI patients. Multilevel models can be used to account for differences between trusts in their association between the usage of each antimicrobial group and CDI cases, and so adjusting the overall associations for the trust differences. The association between CDI and antimicrobial usage was investigated by including in the final statistical model only those individual antimicrobial groups found to be associated with CDI when investigated in separate models (see online Appendix S1 for details). If a strong and significant correlation (i.e. Pearson correlation coefficient ≥0.8 and P < 0.05) between the usage of two antimicrobial groups was identified, an interaction parameter reflecting this was also included in the final model. The tenability of the linear multilevel model assumptions was investigated by testing the linearity of the outcome vs. predictor, normality and homoscedasticity of the residuals. No major violations of these were found (data not shown). The association between the CDI cases and the usage of an antimicrobial group over time was reported with 95% confidence intervals (CIs) to assess whether any association was statistically significant at the 5% level. The analysis was carried out using MLwiN version 2 (Centre for Multilevel Modelling home: Graduate School of Education, University of Bristol, Bristol, UK). The estimation of the parameters of the model was done using iterative generalized least squares.
Results The number of CDI cases reported by trusts decreased by 25.7% between 2005 and 2008. These ranged between 0 and 565 in 2005, 2 and 558 in 2006, 5 and 518 in 2007 and 2 and 484 in 2008. Although there was a 6.9% increase in ‘total’ antimicrobial usage, there was a decrease in the usage for ‘tetracyclines’ (6.9%), ‘second-generation cephalosporins’ (17.3%), ‘fluoroquinolones’ (25.1%) and ‘third-generation cephalosporins’ (35.8%; see Table 1). Total antimicrobial usage by trusts ranged between 32 266 and 711 493 DDD in 2005, between 29 359 and 740 989 DDD in 2006, between 27 552 and 771 089 DDD in 2007 and between 25 697 and 969 570 DDD in 2008. The antimicrobial groups whose individual usage was significantly associated with CDI, using separate multilevel models for each group, are presented in Table 2. There was
Clostridium difficile infection and antimicrobial usage
Table 1 Changes in antimicrobial usage between 2005 and 2008 for the 26 North West Strategic Health Authority trusts
Antimicrobial group (ATC code)
DDD (%)
(J01AA)a
Tetracyclines Penicillins with extended spectrum (J01CA)b β-Lactamase-sensitive penicillins (J01CE)c β-Lactamase-resistant penicillins (J01CF)d
−6.9 7.0 8.2 3.2
Combinations of penicillins, including β-lactamase inhibitors (J01CR)e First-generation cephalosporins (J01DB)f g
Second-generation cephalosporins (J01DC) Third-generation cephalosporins (J01DD)h
10.6 −17.3 −35.8 89.8 11.1
Combinations of sulfonamides and trimethoprim, including derivatives (J01EE)k Macrolides (J01FA)l
11.8
Lincosamides (J01FF)m Other aminoglycosides (J01GB)n
10.2 15.8
Other antibacterials (J01XX)q Total (J01)
Discussion
34.3
Carbapenems (J01DH)i Trimethoprim and derivatives (J01EA)j
Fluoroquinolones (J01MA)o Polymyxins (J01XB)p
microbial usage for one antimicrobial group is significantly different, or not, from that estimated for other antimicrobial groups. Where the 95% CIs overlapped, there was no significant difference between the strength of the associations.
18.5
−25.1 13.0 8.6 6.9
Abbreviations: ATC, Anatomical Chemical Classification; DDD, defined daily dose. aDemeclocycline, doxycycline, lymecycline, oxytetracycline, tetracycline, minocycline, tigecycline, chlortetracycline with tetracycline and demeclocycline; bampicillin, amoxicillin, pivmecillinam, temocillin; cbenzylpenicillin, phenoxymethylpenicillin; dflucloxacillin; eco-amoxiclav, ticarcillin with clavulanic acid, piperacillin with tazobactam, co-fluampicil; fcefalexin, cefadroxil, cefradine; g cefoxitin, cefuroxime, cefamandole, cefaclor; hcefotaxime, ceftazidime, ceftriaxone, cefixime, cefpodoxime; imeropenem, ertapenem, imipenem with cilastatin; jtrimethoprim; kco-trimoxazole; lerythromycin, erythromycin ethyl succinate, spiramycin, clarithromycin, azithromycin, telithromycin; mclindamycin; ntobramycin, gentamicin, neomycin, amikacin, netilmicin; oofloxacin, ciprofloxacin, norfloxacin, levofloxacin, moxifloxacin; pcolistin; qlinezolid, daptomycin.
a positive and significant (at 5% level) association between the CDI cases and the use of (presented as per ATC code): ‘penicillins with extended spectrum’, ‘β-lactamasesensitive penicillins’, ‘first-generation cephalosporins’, ‘second-generation cephalosporins’, ‘third-generation cephalosporins’ and ‘fluoroquinolones’. For example, with each decrease in the use of 1000 DDD of ‘third-generation cephalosporins’ in patients of all ages, the number of CDI cases in those aged ≥65 years old decreased on average by 5.2 patients (95% CI, 4.5–5.9). A strong and significant correlation between the usage of two of the above antimicrobial groups was identified only for the combination ‘third-generation cephalosporins’ and ‘fluoroquinolones’ (Pearson’s correlation coefficient 0.85; P = 0.014). The results of the final model for investigating the overall association between CDI and antimicrobial usage for the trusts are presented in Table 3. There was a positive and significant association between the CDI cases and the use of (presented from highest to lowest association): ‘third-generation cephalosporins’, ‘fluoroquinolones’ and ‘second-generation cephalosporins’. The 95% CIs presented in Table 3 can be used to establish whether the association between CDI and anti-
This study shows an association between CDI cases, reported for patients aged ≥65 years old, and antimicrobial usage, by patients of all ages, in the NW SHA trusts. According to our final model, there is a positive significant association between the number of CDI cases and the usage of ‘third-generation cephalosporins’, ‘fluoroquinolones’ and of ‘second-generation cephalosporins’. The strength of this association was not significantly different among these antimicrobial groups. The first potential limitation of this work is that the results were at the level of trusts (i.e. groups of patients) and may not be generalizable to individual patients [15, 22]. However, as will be discussed below, the results were similar to those obtained in previous studies using data on attributes of individual patients [14, 18, 23–34]. Another potential limitation is that the association between CDI and antimicrobial usage may have been underestimated for three reasons. Firstly, there were temporal differences in the reporting of CDI cases to the HPA. The measures taken to minimize this limitation were presented in the Methods section (i.e. all ‘cases diagnosed in the acute trusts’ for the study time period were included). Secondly, the antimicrobial usage data referred to patients of all ages, whereas the CDI case data referred only to those aged ≥65 years old. Consequently, not all antimicrobial usage explained variability in the number of CDI cases, and this may have weakened the association between CDI and antimicrobial usage. Finally, the antimicrobial usage data also included a fraction of usage by hospital outpatients, and the CDI case data also included a fraction of community-acquired cases. It was not possible to investigate how large these fractions were. All the antimicrobial usage data provided by IMS Health were aggregated and included both inpatient and outpatient usage. The CDI case data were also aggregated by trust without distinguishing hospital-acquired from community-acquired cases, because the HPA only started distinguishing these from April 2007. Our model did not include variables for disinfection and cleaning policies of the trusts, and these may also have contributed to decreases in CDI. Consequently, it was not possible to estimate the association between these policies and CDI. However, multilevel models allow us to take the trust differences into account [21]. In this way, the results of our model are adjusted for the overall differences among the trusts (e.g. disinfection, cleaning, isolation of Br J Clin Pharmacol
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Table 2 Overall association between number of CDI cases for patients aged ≥65 years old and antimicrobial usage for patients of all ages in the 36 North West Strategic Health Authority trusts for the quarters between 2005 and 2008 inclusive, using separate multilevel models for each antimicrobial group individually
Model
Mean number of CDI cases per 1000 DDDs of antimicrobial use (95% CI)
Antimicrobial group (ATC code)
1 2
Tetracyclines (J01AA)a Penicillins with extended spectrum (J01CA)b
3 4
β-Lactamase-sensitive penicillins (J01CE)c β-Lactamase-resistant penicillins (J01CF)d
5 6
Combinations of penicillins, including β-lactamase inhibitors (J01CR)e First-generation cephalosporins (J01DB)f
0.4 (−0.3 to 1) 9.6 (4.1–15)*
7 8
Second-generation cephalosporins (J01DC)g Third-generation cephalosporins (J01DD)h
8.6 (3.6–13.6)* 5.2 (4.5–5.9)*
1.1 (−0.5–2.7) 1.4 (0.5–2.3)* 6.7 (1.3–12)* −0.6 (−2.7 to 1.5)
−6.6 (−13.7 to 0.5) −1.3 (−5 to 2.5)
9 10
Carbapenems (J01DH)i Trimethoprim and derivatives (J01EA)j
11 12
Combinations of sulfonamides and trimethoprim, including derivatives (J01EE)k Macrolides (J01FA)l
13 14
Lincosamides (J01FF)m Other aminoglycosides (J01GB)n
−2 (−8.8 to 4.8) −4.8 (−11.4 to 1.7)
15 16
Fluoroquinolones (J01MA)o Polymyxins (J01XB)p
4.2 (2.8–5.5)* 23.3 (−34.1 to 80.8)
17
Other antibacterials (J01XX)q
16.3 (−5.7 to 38.2)
0.6 (−3.9 to 5.2) 1.2 (−0.3 to 2.7)
Abbreviations: CDI, Clostridium difficile infection; CI, confidence interval; DDD, defined daily dose. aDemeclocycline, doxycycline, lymecycline, oxytetracycline, tetracycline, minocycline, tigecycline, chlortetracycline with tetracycline and demeclocycline; bampicillin, amoxicillin, pivmecillinam, temocillin; cbenzylpenicillin, phenoxymethylpenicillin; d flucloxacillin; eco-amoxiclav, ticarcillin with clavulanic acid, piperacillin with tazobactam, co-fluampicil; fcefalexin, cefadroxil, cefradine; gcefoxitin, cefuroxime, cefamandole, cefaclor; hcefotaxime, ceftazidime, ceftriaxone, cefixime, cefpodoxime; imeropenem, ertapenem, imipenem with cilastatin; jtrimethoprim; kco-trimoxazole; lerythromycin, erythromycin ethyl succinate, spiramycin, clarithromycin, azithromycin, telithromycin; mclindamycin; ntobramycin, gentamicin, neomycin, amikacin, netilmicin; oofloxacin, ciprofloxacin, norfloxacin, levofloxacin, moxifloxacin; pcolistin; qlinezolid, daptomycin. *Significant at the 5% level.
Table 3 Overall association between number of CDI cases for patients aged ≥65 years old and antimicrobial usage for patients of all ages in the 36 North West Strategic Health Authority trusts for the quarters between 2005 and 2008 inclusive, including all antimicrobial groups in a single multilevel model (final model)
Antimicrobial group (ATC code)
Mean number of CDI cases per 1000 DDDs of antimicrobial use (95% CI)
Penicillins with extended spectrum (J01CA)a β-Lactamase-sensitive penicillins (J01CE)b
−0.05 (−0.95 to 0.85) 0.41 (−2.09 to 2.9)
First-generation cephalosporins (J01DB)c Second-generation cephalosporins (J01DC)d
0.11 (−3.74 to 3.97) 4.25 (1.66–6.83)*
Third-generation cephalosporins (J01DD)e Fluoroquinolones (J01MA)f
11.62 (5.92–17.31)* 4.79 (2.83–6.74)*
The results were adjusted for the strong and significant correlation (Pearson’s correlation coefficient ≥0.8; P < 0.05) between the usage of third-generation cephalosporins and of fluoroquinolones. Abbreviations: CDI, Clostridium difficile infection; CI, confidence interval; DDD, defined daily dose. aAmpicillin, amoxicillin, pivmecillinam, temocillin; bbenzylpenicillin, phenoxymethylpenicillin; ccefalexin, cefadroxil, cefradine; dcefoxitin, cefuroxime, cefamandole, cefaclor; ecefotaxime, ceftazidime, ceftriaxone, cefixime, cefpodoxime; fofloxacin, ciprofloxacin, norfloxacin, levofloxacin, moxifloxacin. *Significant at the 5% level.
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CDI patients and different staff) despite not having included such variables explicitly. The results of this study cannot be used to establish a causal relationship between the changes in the number of CDI cases and in the usage of certain antimicrobials shown in Table 3. Our study only shows that the number of CDI cases over time decreased in a similar fashion to the usage of certain antimicrobials. However, as we found a constant association between the number of CDI cases and antimicrobial usage across the time frame investigated, we can be confident that this is a consistent association. Our study allows hypothesizing that the decrease in the usage of the above antimicrobial groups may have contributed to the decrease in the number of CDI cases. Antimicrobials included in these groups have previously been associated with CDI in studies using both patient-level [23–25, 27–31, 35] and hospital-level data [14, 18, 32–34]. Those studies suggested that this association was a consequence of antimicrobial use being a risk factor for CDI. Consequently, a reduction in the usage of these antimicrobial groups had been presented by the NHS as a strategy for reducing the number of CDI cases [10]. According to the present study, the strength of the association between CDI and antimicrobial usage is not
Clostridium difficile infection and antimicrobial usage
significantly different among antimicrobial groups. It is possible that our study was not sufficiently powered to identify such differences (i.e. it did not include a large enough number of observations). More research is still needed to investigate this. Studies have linked the use of other antimicrobials, such as clindamycin (a lincosamide) and carbapenems, with cases of CDI [24, 36–38]. However, an association between these antimicrobial groups and CDI was not identified in the present study. A possible explanation for this may be that, for the trusts and time period included in the present research, the usage of these antimicrobials was not high enough for an association with CDI to be identified at trust level. For example, despite the nearly twofold increase in the usage of carbapenems (see Table 1), this represented only 1.0% of ‘total’ antimicrobial usage. Another reason that may explain the differences between the results of this research and those of previously published studies is an unknown but potential variability in the local prevalence of C. difficile ribotypes. For example, a study by Sundram et al. [23] suggested that the usage of ciprofloxacin for more than 7 days was associated with CDI by C. difficile ribotype 027 rather than ribotype 106. Therefore, the results of studies about the association between CDI and antimicrobial usage may be different, depending on the local prevalence of the various ribotypes of C. difficile. Studies in individual hospitals have suggested that disinfection control policies, without considerable changes in antimicrobial usage, may be sufficient to reduce CDI numbers [39, 40]. However, a study by van der Kooi et al. [34] showed that the aggregated usage of secondgeneration cephalosporins and macrolides was still associated with CDI, even after adjusting for disinfection control policies. Furthermore, it has also been suggested that changes in the incidence of CDI occur in the same direction as changes in the usage of antimicrobials associated with this infection (i.e. cephalosporins and clindamycin) [7]. Therefore, the relative weight of different measures to reduce the incidence of CDI is still not clear. Consequently, interventions to control CDI should be multifaceted and include changes in both antimicrobial usage and disinfection policies. A study by van der Kooi [34] used an autoregressive multilevel model to investigate the association between CDI and antimicrobial usage, using quarterly observations. Their model investigated this association with a time lag of one quarter, because they argued that antimicrobial usage at hospital level was associated with CDI after an interval of 2–3 months. We did not include a time lag in our analysis, however, because we considered that quarterly units of time were too large to assess time lag sensitively, as that represents a 4 month interval between the first and third months of two consecutive quarters. Four main strengths can be presented for this research. Firstly, a large-scale pharmacoepidemiological study was
carried out using data from organizations that already have a system in place to collect it directly from the trusts, which was less time-consuming for the researchers. Secondly, the fact that it was an ecological study allowed us to take into account not only the direct effects of antimicrobial usage in individual patients, but also the effects of antimicrobial usage in a population [41, 42]. Thirdly, this is the first study to evaluate the results of a nationwide intervention to promote prudent antimicrobial use in order to control CDI. Finally, as presented in the statistical analysis subsection, multilevel models allow us to take the overall trust differences into account (e.g. different disinfection policies and different staff) without explicitly measuring them [21]. Despite the limitations presented above, the similarities between the results of the present research and those of other studies [14, 18, 23–34] suggest that multilevel modelling to analyse HPA and IMS Health data can be used to investigate the association between CDI, or other problem organisms (e.g. meticillin-resistant S. aureus, glycopeptide-resistant enterococcal, meticillin-sensitive S. aureus and Escherichia coli), and antimicrobial usage. Furthermore, these databases might allow validation of hospital antimicrobial usage indicators as part of a wider antimicrobial stewardship programme. In future research, it would be important to investigate to what extent the association between CDI and antimicrobial usage at trust level can be generalized to patient level. This could be done in a similar fashion to that presented by Muller et al. [15], by analysing patient- and hospital-level data using a multilevel model. In conclusion, the reduction in the number of CDI cases reported by the NHS trusts is significantly associated with changes in antimicrobial usage. We did not find a statistically significant difference in the strength of this association among antimicrobial groups whose usage is associated with CDI.
Competing Interests All authors have completed the Unified Competing Interest form at http://www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: JBP, MPT and JC had support from The University of Manchester and from the University Hospital of South Manchester NHS Foundation Trust for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work. This work was undertaken with funding from the Department of Pharmacy, University Hospital of South Manchester NHS Foundation Trust and Manchester Pharmacy School, University of Manchester. The authors would like to thank the Chief Pharmacists in the North West of England for allowing Br J Clin Pharmacol
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data to be used, Peter Stephens of IMS Health, London and David Lloyd of the NHS Information Centre, Leeds for help and assistance in using the Hospital Antibiotic Audit database, and Miranda Murray of the Health Protection Agency for data on the use of Clostridium difficile data reported from local hospitals.
REFERENCES 1 Wenisch JM, Schmid D, Tucek G, Kuo HW, Allerberger F, Michl V, Tesik P, Laferl H, Wenisch C. A prospective cohort study on hospital mortality due to Clostridium difficile infection. Infection 2012; 40: 479–84. 2 Wilcox MH, Cunniffe JG, Trundle C, Redpath C. Financial burden of hospital-acquired Clostridium difficile infection. J Hosp Infect 1996; 34: 23–30. 3 Office for National Statistics [Internet]. Deaths involving Clostridium difficile: England and Wales 2008. 2009. Available at http://www.statistics.gov.uk/hub/index.html (last accessed 10 February 2010). 4 Office for National Statistics [Internet]. Deaths involving MRSA: England and Wales 2008. 2009. Available at http://www.statistics.gov.uk/hub/index.html (last accessed 10 February 2010). 5 Health Protection Agency [Internet]. Health Protection Agency. 2010. Available at http://www.hpa.org.uk/ (last accessed 18 March 2010). 6 Bignardi GE. Risk factors for Clostridium difficile infection. J Hosp Infect 1998; 40: 1–15. 7 Davey P, Brown E, Fenelon L, Finch R, Gould I, Holmes A, Ramsay C, Taylor E, Wiffen P, Wilcox M. Systematic review of antimicrobial drug prescribing in hospitals. Emerg Infect Dis 2006; 12: 211–6. 8 Thomas C, Stevenson M, Riley TV. Antibiotics and hospital-acquired Clostridium difficile-associated diarrhoea: a systematic review. J Antimicrob Chemother 2003; 51: 1339–50. 9 Stone SP, Fuller C, Savage J, Cookson B, Hayward A, Cooper B, Duckworth G, Michie S, Murray M, Jeanes A, Roberts J, Teare L, Charlett A. Evaluation of the national Cleanyourhands campaign to reduce Staphylococcus aureus bacteraemia and Clostridium difficile infection in hospitals in England and Wales by improved hand hygiene: four year, prospective, ecological, interrupted time series study. BMJ 2012; 344: e3005. 10 Department of Health & Health Protection Agency. Clostridium difficile Infection: how to deal with the problem. London, 2008. Available at http://www.hpa.org.uk/ web/HPAwebFile/HPAweb_C/1232006607827 (last accessed 18 March 2010). 11 Ashiru-Oredope D, Sharland M, Charani E, McNulty C, Cooke J, Group AAS. Improving the quality of antibiotic prescribing in the NHS by developing a new Antimicrobial Stewardship 902
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Programme: start Smart-Then Focus. J Antimicrob Chemother 2012; 67: (Suppl. 1): i51–63. 12 Djuretic T, Wall PG, Brazier JS. Clostridium dificile: an update on its epidemiology and role in hospital outbreaks in England and Wales. J Hosp Infect 1999; 41: 213–8. 13 WHO Collaborating Centre for Drug Statistics Methodology. Guidelines for ATC classification and DDD assignment 2009. Oslo, 2008. 14 Vernaz N, Hill K, Leggeat S, Nathwani D, Philips G, Bonnabry P, Davey P. Temporal effects of antibiotic use and Clostridium difficile infections. J Antimicrob Chemother 2009; 63: 1272–5. 15 Muller A, Mauny F, Talon D, Donnan PT, Harbarth S, Bertrand X. Effect of individual- and group-level antibiotic exposure on MRSA isolation: a multilevel analysis. J Antimicrob Chemother 2006; 58: 878–81. 16 Monnet DL, Lopez-Lozano JM, Campillos P, Burgos A, Yague A, Gonzalo N. Making sense of antimicrobial use and resistance surveillance data: application of ARIMA and transfer function models. Clin Microbiol Infect 2001; 7: (Suppl. 5): 29–36. 17 Lopez-Lozano JM, Monnet DL, Yague A, Burgos A, Gonzalo N, Campillos P, Saez M. Modelling and forecasting antimicrobial resistance and its dynamic relationship to antimicrobial use: a time series analysis. Int J Antimicrob Agents 2000; 14: 21–31. 18 Vernaz N, Sax H, Pittet D, Bonnabry P, Schrenzel J, Harbarth S. Temporal effects of antibiotic use and hand rub consumption on the incidence of MRSA and Clostridium difficile. J Antimicrob Chemother 2008; 62: 601–7. 19 Stone SP, Cooper BS, Kibbler CC, Cookson BD, Roberts JA, Medley GF, Duckworth G, Lai R, Ebrahim S, Brown EM, Wiffen PJ, Davey PG. The ORION statement: guidelines for transparent reporting of Outbreak Reports and Intervention studies of nosocomial infection. J Antimicrob Chemother 2007; 59: 833–40. 20 Katz MH. Multivariable Analiysis – A Practical Guide for Clinitians, 2nd edn. Cambridge: Cambridge University Press, 2006. 21 Snijders TAB, Bosker RJ. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. London: Sage Publications, 2004. 22 Harbarth S, Harris AD, Carmeli Y, Samore MH. Parallel analysis of individual and aggregated data on antibiotic exposure and resistance in gram-negative bacilli. Clin Infect Dis 2001; 33: 1462–8. 23 Sundram F, Guyot A, Carboo I, Green S, Lilaonitkul M, Scourfield A. Clostridium difficile ribotypes 027 and 106: clinical outcomes and risk factors. J Hosp Infect 2009; 72: 111–8. 24 Baxter R, Ray GT, Fireman BH. Case-control study of antibiotic use and subsequent Clostridium difficile-associated diarrhea in hospitalized patients. Infect Control Hosp Epidemiol 2008; 29: 44–50. 25 Debast SB, Vaessen N, Choudry A, Wiegers-Ligtvoet EA, van den Berg RJ, Kuijper EJ, Wiegers-Ligtvoet EAJ. Successful
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combat of an outbreak due to Clostridium difficile PCR ribotype 027 and recognition of specific risk factors. Clin Microbiol Infect 2009; 15: 427–34. 26 Minson Q, Mok S. Relationship between antibiotic exposure and subsequent Clostridium difficile-associated diarrhea. Hosp Pharm 2007; 42: 430–4. 27 Muto CA, Pokrywka M, Shutt K, Mendelsohn AB, Nouri K, Posey K, Roberts T, Croyle K, Krystofiak S, Patel-Brown S, Pasculle AW, Paterson DL, Saul M, Harrison LH, Muto CA, Pokrywka M, Shutt K, Mendelsohn AB, Nouri K, Posey K, Roberts T, Croyle K, Krystofiak S, Patel-Brown S, Pasculle AW, Paterson DL, Saul M, Harrison LH. A large outbreak of Clostridium difficile-associated disease with an unexpected proportion of deaths and colectomies at a teaching hospital following increased fluoroquinolone use. Infect Control Hosp Epidemiol 2005; 26: 273–80. 28 Kazakova SV, Ware K, Baughman B, Bilukha O, Paradis A, Sears S, Thompson A, Jensen B, Wiggs L, Bessette J, Martin J, Clukey J, Gensheimer K, Killgore G, McDonald LC. A hospital outbreak of diarrhea due to an emerging epidemic strain of Clostridium difficile. Arch Intern Med 2006; 166: 2518–24. 29 Modena S, Bearelly D, Swartz K, Friedenberg FK. Clostridium difficile among hospitalized patients receiving antibiotics: a case-control study. Infect Control Hosp Epidemiol 2005; 26: 685–90. 30 Changela U, Cannon JP, Aneziokoro C, Shah PS, Thottapurathu L, Lentino J. Risk factors and mortality associated with Clostridium difficile-associated diarrhoea at a VA hospital. Int J Antimicrob Agents 2004; 24: 562–6. 31 McCusker ME, Harris AD, Perencevich E, Roghmann MC, Harris AD, Perencevich E, Roghmann M-C. Fluoroquinolone use and Clostridium difficile-associated diarrhea. Emerg Infect Dis 2003; 9: 730–3. 32 Aldeyab MA, Harbarth S, Vernaz N, Kearney MP, Scott MG, Funston C, Savage K, Kelly D, Aldiab MA, McElnay JC. Quasiexperimental study of the effects of antibiotic use, gastric acid-suppressive agents, and infection control practices on the incidence of Clostridium difficile-associated diarrhea in hospitalized patients. Antimicrob Agents Chemother 2009; 53: 2082–8. 33 Kaier K, Hagist C, Frank U, Conrad A, Meyer E. Two time-series analyses of the impact of antibiotic consumption and alcohol-based hand disinfection on the incidences of nosocomial methicillin-resistant Staphylococcus aureus infection and Clostridium difficile infection. Infect Control Hosp Epidemiol 2009; 30: 346–53. 34 van der Kooi TI, Koningstein M, Lindemans A, Notermans DW, Kuijper E, van den Berg R, Boshuizen H, Filius PMG, van
den Hof S. Antibiotic use and other risk factors at hospital level for outbreaks with Clostridium difficile PCR ribotype 027. J Med Microbiol 2008; 57: 709–16. 35 Loo VG, Poirier L, Miller MA, Oughton M, Libman MD, Michaud S, Bourgault AM, Nguyen T, Frenette C, Kelly M, Vibien A, Brassard P, Fenn S, Dewar K, Hudson TJ, Horn R, Rene P, Monczak Y, Dascal A. A predominantly clonal multi-institutional outbreak of Clostridium difficile – associated diarrhea with high morbidity and mortality. N Engl J Med 2005; 353: 2442–9. 36 Hawkey PM, Livermore DM. Carbapenem antibiotics for serious infections. BMJ 2012; 344: e3236. 37 Hensgens MPM, Goorhuis A, Dekkers OM, Kuijper EJ. Time interval of increased risk for Clostridium difficile infection after exposure to antibiotics. J Antimicrob Chemother 2012; 67: 742–8. 38 Chang VT, Nelson K. The role of physical proximity in nosocomial diarrhea. Clin Infect Dis 2000; 31: 717–22. 39 Salgado CD, Mauldin PD, Fogle PJ, Bosso JA. Analysis of an outbreak of Clostridium difficile infection controlled with enhanced infection control measures. Am J Infect Control 2009; 37: 458–64. 40 Weiss K, Boisvert A, Chagnon M, Duchesne C, Habash S, Lepage Y, Letourneau J, Raty J, Savoie M. Multipronged intervention strategy to control an outbreak of Clostridium difficile infection (CDI) and its impact on the rates of CDI from 2002 to 2007. Infect Control Hosp Epidemiol 2009; 30: 156–62. 41 Lipsitch M, Samore MH. Antimicrobial use and antimicrobial resistance: a population perspective. Emerg Infect Dis 2002; 8: 347–54. 42 Lipsitch M, Samore MH. Erratum: antimicrobial use and antimicrobial resistance: a population perspective (Emerging Infectious Disease 8:4). Emerg Infect Dis 2002; 8: 540.
Supporting Information Additional Supporting Information may be found in the online version of this article at the publisher’s website: Appendix S1 Steps for the development of the final multilevel model used to investigate the association between CDI cases and antimicrobial usage
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DOI:10.1111/bcp.12255
Association between Clostridium difficile infection and antimicrobial usage in a large group of English hospitals Joao B. Pereira,1 Tracey M. Farragher,2 Mary P. Tully1 & Jonathan Cooke1,3
Correspondence Mr Joao Bissau Pereira PhD, Manchester Academic Health Science Centre and the University of Manchester, School of Pharmacy and Pharmaceutical Sciences, Manchester M13 9PT, UK. Tel.: 0044911782756 E-mail: [email protected] -----------------------------------------------------------------------
Keywords antimicrobial policy, defined daily doses, healthcare-associated infection, multilevel models, time series analysis -----------------------------------------------------------------------
Received 9 December 2012
Accepted 14 September 2013
1
Manchester Academic Health Science Centre and Manchester Pharmacy School, The University of Manchester, Stopford Building Oxford Road, Manchester M13 9PT, UK, 2Leeds Institute of Health Sciences, The University of Leeds, Charles Thackrah Building, 101 Clarendon Road, Leeds LS2 9LJ, UK and 3Infectious Diseases and Immunity Section, Division of Infectious Diseases, Department of Medicine, Imperial College, South Kensington Campus, London SW7 2AZ, UK
Accepted Article Published Online 15 October 2013
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • The number of Clostridium difficile infection (CDI) cases reported by National Health Service (NHS) trusts decreased between 2007 and 2008. • Antimicrobial use in English trusts changed between 2004 and 2009. • There are no studies investigating whether the reduction in the CDI cases reported by the NHS trusts is associated with the changes in antimicrobial use.
WHAT THIS STUDY ADDS • The reduction in the number of CDI cases reported by NHS hospitals is associated with concurrent changes in antimicrobial use. • The use of multilevel models to analyse data collected from the Health Protection Agency and IMS Health is a method that could be suitable for a national surveillance programme of antimicrobial use and its association with healthcare-associated infections (e.g. CDI).
AIMS This study aimed to determine the association between the reduction in the number of Clostridium difficile infection (CDI) cases reported by the English National Health Service (NHS) hospitals and concurrent antimicrobial use.
METHODS A retrospective ecological study for January 2005 to December 2008 was conducted using data from 26 of the 29 NHS trusts (i.e. a trust manages one or more hospitals) located in the North West Strategic Health Authority of England. Antimicrobial use data, for patients of all ages, were provided by IMS Health, and CDI case data for patients aged ≥65 years were provided by the Health Protection Agency. Antimicrobial use was converted into defined daily doses (DDDs). The overall association between antimicrobial use and CDI for the trusts was investigated using multilevel models.
RESULTS Our study shows a positive significant association between the CDI cases and the use of the following antimicrobials: ‘third-generation cephalosporins’ [11.62 CDI cases per 1000 DDDs; 95% confidence interval (CI), 5.92–17.31]; ‘fluoroquinolones’ (4.79 CDI cases per 1000 DDDs; 95% CI, 2.83–6.74); and ‘second-generation cephalosporins’ (4.25 CDI cases per 1000 DDDs; 95% CI, 1.66–6.83). The strength of this association was not significantly different (95% CI) among the antimicrobial groups.
CONCLUSIONS This study shows that the reduction in the number of CDI cases reported by the English NHS hospitals is associated with concurrent reductions in antimicrobial use. This means that the number of CDI cases over time decreased in a similar fashion to the usage of various antimicrobials.
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Clostridium difficile infection and antimicrobial usage
Introduction Clostridium difficile infection (CDI) is a healthcareassociated infection (HCAI) associated with high morbidity, mortality and healthcare expenditure. Patients with CDI have a 2.74-fold increase in the risk of dying during their hospital stay than all other hospitalized patients [1]. In England, a CDI patient has been reported to have an increased average length of hospital stay of 21 days, costing the National Health Service (NHS) approximately an extra £4000 [2]. Furthermore, according to the Office for National Statistics, for England and Wales in 2008, 2502 death certificates included CDI as an underlying cause of death [3]. This figure was 10 times larger than for other high-profile HCAIs, such as meticillin-resistant Staphylococcus aureus infection (i.e. 228 death certificates) [4]. For these reasons, and because the number of CDI cases between 1994 and 2004 had increased, this infection was included in the NHS mandatory reporting scheme for HCAIs, requiring NHS hospitals to report their CDI cases to the Health Protection Agency (HPA; now Public Health England, PHE) [5]. The relative risk of acquiring CDI through exposure to different antimicrobials is still unclear. Studies suggested that nearly all antimicrobials may precipitate CDI and that some antimicrobials may have a higher association with CDI than others [6–8]. However, the ability to generalize the results from such studies has been questioned due to the existence of serious threats to their validity [8]. There is no national surveillance system for medication use (i.e. prescribing, dispensing and administering) in English hospitals. However, more than 98% of these hospitals send their antimicrobial usage data (i.e. dispensing issue data) to IMS Health (IMS Health Ltd, London, England). This company may be a source of national hospital antimicrobial usage data that could potentially be used in pharmacoepidemiological studies. Clostridium difficile infection and meticillin-resistant S. aureus infection rates have been a target for performance in infection control in NHS hospitals for the past 5 years, and part of this control involved changes in disinfection policies and antimicrobial prescribing [9, 10]. According to the HPA, between 2007 and 2008 there was a 35% reduction in the number of CDI cases reported by NHS hospitals [10]. Furthermore, there was a dramatic change in antimicrobial use across English hospitals between 2004 and 2009 [11]. In fact, there was a reduction in usage of fluoroquinolones (>40%), second-generation cephalosporins (50%) and third-generation cephalosporins (22%), and an increase in usage of carbapenems (50%). It is not known, however, whether the reduction in the number of CDI cases was associated with these changes in antimicrobial use. The main objective of this paper was to test the hypothesis that there may be an association between the reduction in the number of CDI cases reported by the
English NHS hospitals and changes in antimicrobial use. A secondary objective was to investigate whether the usage of some antimicrobial groups was more strongly associated with CDI than others.
Methods Setting, study period and population At the time of the study, England was divided into 10 Strategic Health Authorities (SHAs) that oversaw all NHS activities, covering a population of more than 50 million. The hospitals located in each SHA are managed by trusts, and one trust can manage one or more hospitals. Data for all trusts in England were not available to us. Data for the North West (NW) SHA were selected for the following three reasons: it was the largest SHA geographically; it covered more than 7 million inhabitants with varying social backgrounds; and it included all categories of hospital trusts. Twenty-six trusts located in the NW SHA, managing 64 NHS hospitals, were included in the study sample. Their CDI cases and antimicrobial usage data for the quarters between 2005 and 2008 inclusive were analysed. Only three NW SHA trusts were excluded; two because no antimicrobial usage data were available through IMS Health and one because no CDI case data were available through the HPA because it was a paediatric trust.
Trust CDI data The HPA (the national agency that holds and publishes the CDI data for all NHS trusts) provided a database of trust CDI cases, which included the number of CDI cases for patients aged ≥65 years old reported by the 26 NW SHA trusts for 2005–2008 inclusive [5]. Only data for this age group were available for the time period included in this research [6, 12]. There were temporal differences in the HPA CDI reporting categories. To minimize these differences, for the quarters between January 2005 and March 2007 inclusive, all the ‘cases diagnosed in the acute trusts’ were included. For the quarters between April 2007 and December 2008, all the CDI reporting categories that referred to cases diagnosed in the trusts were included (i.e. ‘specimens taken up to 2 days after admission’, ‘specimens taken 3 days or more after admission’ and ‘specimens from non-admitted patients’).
Antimicrobial usage data The source of antimicrobial usage data was a database provided by IMS Health, which included the antimicrobial usage for patients of all ages for each of the 26 NW SHA trusts for 2005–2008 inclusive. This database included the monthly number of antimicrobial units (i.e. number of tablets, vials, syringes, etc) dispensed from the pharmacy to the wards and to hospital outpatients and the number Br J Clin Pharmacol
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of units of antimicrobials that were returned to the pharmacy. Duplicates had been removed. No primary care data were included in the database. Trust antimicrobial usage per quarter was quantified by converting the number of units of antimicrobials dispensed, minus the number of units returned, into defined daily doses (DDD). Then, the antimicrobials were classified and clustered into 23 drug groups (J01AA–J01XX plus A07AA) of the World Health Organization’s Anatomical Chemical Classification (ATC) system for analysis [13]. From these, all the antimicrobials that are nationally recommended [10] for first- or second-line treatment of CDI were excluded, i.e. oral vancomycin (A07AA), intravenous vancomycin (J01XA) and fusidic acid (J01XC). These antimicrobials were expected to be associated with CDI because they are used to treat it, rather than because they precipitate this infection. Data for the usage of metronidazole, also recommended for the treatment of CDI [10], were not included in the database provided by IMS Health and are therefore also excluded from analysis. Antimicrobials with zero usage for more than 50% of the observations in each trust were also excluded (i.e. J01BA, amphenicols; J01DF, monobactams; and J01FG, streptogramins) because there would not be enough data for to enable robust estimation of the parameters of the statistical models. The data provided by the HPA and IMS Health are classed as publically available; therefore, it was not necessary to obtain an ethical opinion from the National Research Ethics Service.
Statistical analysis Longitudinal analysis was used to investigate the overall association between the ‘number of CDI cases for patients aged 65 and above reported by the trusts’ and ‘antimicrobial usage for patients of all ages’ for the 26 trusts for 2005–2008 inclusive. Longitudinal analysis has been used in ecological studies to investigate the association between antimicrobial use and cases of infectious disease [14–18]. This is the statistical approach recommended in the ORION statement [19], because it is able to take into account that observations of one time point may be influenced by observations of previous time points [20]. Multilevel modelling is the most robust longitudinal analysis method, because it is able to take into account both the variability of observations within a trust over time (level 1) and between trusts (level 2) [21]. The data investigated in this work can be classified as count data (i.e. the number of times that the dose established as the DDD for an antimicrobial was used; counts of CDI cases). Longitudinal count data can be analysed using Poisson multilevel models. However, as these data included large numbers of counts (i.e. more than eight), they could be approximated to continuous normally distributed data and analysed using a linear multilevel model, as long as the residuals of the model followed a normal 898
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distribution [20, 21]. The advantage of using a standard linear multilevel model is that the resulting parameters are more generally understood than the equivalent Poisson model. Multilevel modelling is a method for analysing data from different groups, when a dependency among the observations in each group is expected [21]. For example, the association between the number of CDI cases and antimicrobial usage may vary from trust to trust, because this depends on trust differences, such as policies for disinfection, cleaning, antimicrobial use and isolation of CDI patients. Multilevel models can be used to account for differences between trusts in their association between the usage of each antimicrobial group and CDI cases, and so adjusting the overall associations for the trust differences. The association between CDI and antimicrobial usage was investigated by including in the final statistical model only those individual antimicrobial groups found to be associated with CDI when investigated in separate models (see online Appendix S1 for details). If a strong and significant correlation (i.e. Pearson correlation coefficient ≥0.8 and P < 0.05) between the usage of two antimicrobial groups was identified, an interaction parameter reflecting this was also included in the final model. The tenability of the linear multilevel model assumptions was investigated by testing the linearity of the outcome vs. predictor, normality and homoscedasticity of the residuals. No major violations of these were found (data not shown). The association between the CDI cases and the usage of an antimicrobial group over time was reported with 95% confidence intervals (CIs) to assess whether any association was statistically significant at the 5% level. The analysis was carried out using MLwiN version 2 (Centre for Multilevel Modelling home: Graduate School of Education, University of Bristol, Bristol, UK). The estimation of the parameters of the model was done using iterative generalized least squares.
Results The number of CDI cases reported by trusts decreased by 25.7% between 2005 and 2008. These ranged between 0 and 565 in 2005, 2 and 558 in 2006, 5 and 518 in 2007 and 2 and 484 in 2008. Although there was a 6.9% increase in ‘total’ antimicrobial usage, there was a decrease in the usage for ‘tetracyclines’ (6.9%), ‘second-generation cephalosporins’ (17.3%), ‘fluoroquinolones’ (25.1%) and ‘third-generation cephalosporins’ (35.8%; see Table 1). Total antimicrobial usage by trusts ranged between 32 266 and 711 493 DDD in 2005, between 29 359 and 740 989 DDD in 2006, between 27 552 and 771 089 DDD in 2007 and between 25 697 and 969 570 DDD in 2008. The antimicrobial groups whose individual usage was significantly associated with CDI, using separate multilevel models for each group, are presented in Table 2. There was
Clostridium difficile infection and antimicrobial usage
Table 1 Changes in antimicrobial usage between 2005 and 2008 for the 26 North West Strategic Health Authority trusts
Antimicrobial group (ATC code)
DDD (%)
(J01AA)a
Tetracyclines Penicillins with extended spectrum (J01CA)b β-Lactamase-sensitive penicillins (J01CE)c β-Lactamase-resistant penicillins (J01CF)d
−6.9 7.0 8.2 3.2
Combinations of penicillins, including β-lactamase inhibitors (J01CR)e First-generation cephalosporins (J01DB)f g
Second-generation cephalosporins (J01DC) Third-generation cephalosporins (J01DD)h
10.6 −17.3 −35.8 89.8 11.1
Combinations of sulfonamides and trimethoprim, including derivatives (J01EE)k Macrolides (J01FA)l
11.8
Lincosamides (J01FF)m Other aminoglycosides (J01GB)n
10.2 15.8
Other antibacterials (J01XX)q Total (J01)
Discussion
34.3
Carbapenems (J01DH)i Trimethoprim and derivatives (J01EA)j
Fluoroquinolones (J01MA)o Polymyxins (J01XB)p
microbial usage for one antimicrobial group is significantly different, or not, from that estimated for other antimicrobial groups. Where the 95% CIs overlapped, there was no significant difference between the strength of the associations.
18.5
−25.1 13.0 8.6 6.9
Abbreviations: ATC, Anatomical Chemical Classification; DDD, defined daily dose. aDemeclocycline, doxycycline, lymecycline, oxytetracycline, tetracycline, minocycline, tigecycline, chlortetracycline with tetracycline and demeclocycline; bampicillin, amoxicillin, pivmecillinam, temocillin; cbenzylpenicillin, phenoxymethylpenicillin; dflucloxacillin; eco-amoxiclav, ticarcillin with clavulanic acid, piperacillin with tazobactam, co-fluampicil; fcefalexin, cefadroxil, cefradine; g cefoxitin, cefuroxime, cefamandole, cefaclor; hcefotaxime, ceftazidime, ceftriaxone, cefixime, cefpodoxime; imeropenem, ertapenem, imipenem with cilastatin; jtrimethoprim; kco-trimoxazole; lerythromycin, erythromycin ethyl succinate, spiramycin, clarithromycin, azithromycin, telithromycin; mclindamycin; ntobramycin, gentamicin, neomycin, amikacin, netilmicin; oofloxacin, ciprofloxacin, norfloxacin, levofloxacin, moxifloxacin; pcolistin; qlinezolid, daptomycin.
a positive and significant (at 5% level) association between the CDI cases and the use of (presented as per ATC code): ‘penicillins with extended spectrum’, ‘β-lactamasesensitive penicillins’, ‘first-generation cephalosporins’, ‘second-generation cephalosporins’, ‘third-generation cephalosporins’ and ‘fluoroquinolones’. For example, with each decrease in the use of 1000 DDD of ‘third-generation cephalosporins’ in patients of all ages, the number of CDI cases in those aged ≥65 years old decreased on average by 5.2 patients (95% CI, 4.5–5.9). A strong and significant correlation between the usage of two of the above antimicrobial groups was identified only for the combination ‘third-generation cephalosporins’ and ‘fluoroquinolones’ (Pearson’s correlation coefficient 0.85; P = 0.014). The results of the final model for investigating the overall association between CDI and antimicrobial usage for the trusts are presented in Table 3. There was a positive and significant association between the CDI cases and the use of (presented from highest to lowest association): ‘third-generation cephalosporins’, ‘fluoroquinolones’ and ‘second-generation cephalosporins’. The 95% CIs presented in Table 3 can be used to establish whether the association between CDI and anti-
This study shows an association between CDI cases, reported for patients aged ≥65 years old, and antimicrobial usage, by patients of all ages, in the NW SHA trusts. According to our final model, there is a positive significant association between the number of CDI cases and the usage of ‘third-generation cephalosporins’, ‘fluoroquinolones’ and of ‘second-generation cephalosporins’. The strength of this association was not significantly different among these antimicrobial groups. The first potential limitation of this work is that the results were at the level of trusts (i.e. groups of patients) and may not be generalizable to individual patients [15, 22]. However, as will be discussed below, the results were similar to those obtained in previous studies using data on attributes of individual patients [14, 18, 23–34]. Another potential limitation is that the association between CDI and antimicrobial usage may have been underestimated for three reasons. Firstly, there were temporal differences in the reporting of CDI cases to the HPA. The measures taken to minimize this limitation were presented in the Methods section (i.e. all ‘cases diagnosed in the acute trusts’ for the study time period were included). Secondly, the antimicrobial usage data referred to patients of all ages, whereas the CDI case data referred only to those aged ≥65 years old. Consequently, not all antimicrobial usage explained variability in the number of CDI cases, and this may have weakened the association between CDI and antimicrobial usage. Finally, the antimicrobial usage data also included a fraction of usage by hospital outpatients, and the CDI case data also included a fraction of community-acquired cases. It was not possible to investigate how large these fractions were. All the antimicrobial usage data provided by IMS Health were aggregated and included both inpatient and outpatient usage. The CDI case data were also aggregated by trust without distinguishing hospital-acquired from community-acquired cases, because the HPA only started distinguishing these from April 2007. Our model did not include variables for disinfection and cleaning policies of the trusts, and these may also have contributed to decreases in CDI. Consequently, it was not possible to estimate the association between these policies and CDI. However, multilevel models allow us to take the trust differences into account [21]. In this way, the results of our model are adjusted for the overall differences among the trusts (e.g. disinfection, cleaning, isolation of Br J Clin Pharmacol
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Table 2 Overall association between number of CDI cases for patients aged ≥65 years old and antimicrobial usage for patients of all ages in the 36 North West Strategic Health Authority trusts for the quarters between 2005 and 2008 inclusive, using separate multilevel models for each antimicrobial group individually
Model
Mean number of CDI cases per 1000 DDDs of antimicrobial use (95% CI)
Antimicrobial group (ATC code)
1 2
Tetracyclines (J01AA)a Penicillins with extended spectrum (J01CA)b
3 4
β-Lactamase-sensitive penicillins (J01CE)c β-Lactamase-resistant penicillins (J01CF)d
5 6
Combinations of penicillins, including β-lactamase inhibitors (J01CR)e First-generation cephalosporins (J01DB)f
0.4 (−0.3 to 1) 9.6 (4.1–15)*
7 8
Second-generation cephalosporins (J01DC)g Third-generation cephalosporins (J01DD)h
8.6 (3.6–13.6)* 5.2 (4.5–5.9)*
1.1 (−0.5–2.7) 1.4 (0.5–2.3)* 6.7 (1.3–12)* −0.6 (−2.7 to 1.5)
−6.6 (−13.7 to 0.5) −1.3 (−5 to 2.5)
9 10
Carbapenems (J01DH)i Trimethoprim and derivatives (J01EA)j
11 12
Combinations of sulfonamides and trimethoprim, including derivatives (J01EE)k Macrolides (J01FA)l
13 14
Lincosamides (J01FF)m Other aminoglycosides (J01GB)n
−2 (−8.8 to 4.8) −4.8 (−11.4 to 1.7)
15 16
Fluoroquinolones (J01MA)o Polymyxins (J01XB)p
4.2 (2.8–5.5)* 23.3 (−34.1 to 80.8)
17
Other antibacterials (J01XX)q
16.3 (−5.7 to 38.2)
0.6 (−3.9 to 5.2) 1.2 (−0.3 to 2.7)
Abbreviations: CDI, Clostridium difficile infection; CI, confidence interval; DDD, defined daily dose. aDemeclocycline, doxycycline, lymecycline, oxytetracycline, tetracycline, minocycline, tigecycline, chlortetracycline with tetracycline and demeclocycline; bampicillin, amoxicillin, pivmecillinam, temocillin; cbenzylpenicillin, phenoxymethylpenicillin; d flucloxacillin; eco-amoxiclav, ticarcillin with clavulanic acid, piperacillin with tazobactam, co-fluampicil; fcefalexin, cefadroxil, cefradine; gcefoxitin, cefuroxime, cefamandole, cefaclor; hcefotaxime, ceftazidime, ceftriaxone, cefixime, cefpodoxime; imeropenem, ertapenem, imipenem with cilastatin; jtrimethoprim; kco-trimoxazole; lerythromycin, erythromycin ethyl succinate, spiramycin, clarithromycin, azithromycin, telithromycin; mclindamycin; ntobramycin, gentamicin, neomycin, amikacin, netilmicin; oofloxacin, ciprofloxacin, norfloxacin, levofloxacin, moxifloxacin; pcolistin; qlinezolid, daptomycin. *Significant at the 5% level.
Table 3 Overall association between number of CDI cases for patients aged ≥65 years old and antimicrobial usage for patients of all ages in the 36 North West Strategic Health Authority trusts for the quarters between 2005 and 2008 inclusive, including all antimicrobial groups in a single multilevel model (final model)
Antimicrobial group (ATC code)
Mean number of CDI cases per 1000 DDDs of antimicrobial use (95% CI)
Penicillins with extended spectrum (J01CA)a β-Lactamase-sensitive penicillins (J01CE)b
−0.05 (−0.95 to 0.85) 0.41 (−2.09 to 2.9)
First-generation cephalosporins (J01DB)c Second-generation cephalosporins (J01DC)d
0.11 (−3.74 to 3.97) 4.25 (1.66–6.83)*
Third-generation cephalosporins (J01DD)e Fluoroquinolones (J01MA)f
11.62 (5.92–17.31)* 4.79 (2.83–6.74)*
The results were adjusted for the strong and significant correlation (Pearson’s correlation coefficient ≥0.8; P < 0.05) between the usage of third-generation cephalosporins and of fluoroquinolones. Abbreviations: CDI, Clostridium difficile infection; CI, confidence interval; DDD, defined daily dose. aAmpicillin, amoxicillin, pivmecillinam, temocillin; bbenzylpenicillin, phenoxymethylpenicillin; ccefalexin, cefadroxil, cefradine; dcefoxitin, cefuroxime, cefamandole, cefaclor; ecefotaxime, ceftazidime, ceftriaxone, cefixime, cefpodoxime; fofloxacin, ciprofloxacin, norfloxacin, levofloxacin, moxifloxacin. *Significant at the 5% level.
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CDI patients and different staff) despite not having included such variables explicitly. The results of this study cannot be used to establish a causal relationship between the changes in the number of CDI cases and in the usage of certain antimicrobials shown in Table 3. Our study only shows that the number of CDI cases over time decreased in a similar fashion to the usage of certain antimicrobials. However, as we found a constant association between the number of CDI cases and antimicrobial usage across the time frame investigated, we can be confident that this is a consistent association. Our study allows hypothesizing that the decrease in the usage of the above antimicrobial groups may have contributed to the decrease in the number of CDI cases. Antimicrobials included in these groups have previously been associated with CDI in studies using both patient-level [23–25, 27–31, 35] and hospital-level data [14, 18, 32–34]. Those studies suggested that this association was a consequence of antimicrobial use being a risk factor for CDI. Consequently, a reduction in the usage of these antimicrobial groups had been presented by the NHS as a strategy for reducing the number of CDI cases [10]. According to the present study, the strength of the association between CDI and antimicrobial usage is not
Clostridium difficile infection and antimicrobial usage
significantly different among antimicrobial groups. It is possible that our study was not sufficiently powered to identify such differences (i.e. it did not include a large enough number of observations). More research is still needed to investigate this. Studies have linked the use of other antimicrobials, such as clindamycin (a lincosamide) and carbapenems, with cases of CDI [24, 36–38]. However, an association between these antimicrobial groups and CDI was not identified in the present study. A possible explanation for this may be that, for the trusts and time period included in the present research, the usage of these antimicrobials was not high enough for an association with CDI to be identified at trust level. For example, despite the nearly twofold increase in the usage of carbapenems (see Table 1), this represented only 1.0% of ‘total’ antimicrobial usage. Another reason that may explain the differences between the results of this research and those of previously published studies is an unknown but potential variability in the local prevalence of C. difficile ribotypes. For example, a study by Sundram et al. [23] suggested that the usage of ciprofloxacin for more than 7 days was associated with CDI by C. difficile ribotype 027 rather than ribotype 106. Therefore, the results of studies about the association between CDI and antimicrobial usage may be different, depending on the local prevalence of the various ribotypes of C. difficile. Studies in individual hospitals have suggested that disinfection control policies, without considerable changes in antimicrobial usage, may be sufficient to reduce CDI numbers [39, 40]. However, a study by van der Kooi et al. [34] showed that the aggregated usage of secondgeneration cephalosporins and macrolides was still associated with CDI, even after adjusting for disinfection control policies. Furthermore, it has also been suggested that changes in the incidence of CDI occur in the same direction as changes in the usage of antimicrobials associated with this infection (i.e. cephalosporins and clindamycin) [7]. Therefore, the relative weight of different measures to reduce the incidence of CDI is still not clear. Consequently, interventions to control CDI should be multifaceted and include changes in both antimicrobial usage and disinfection policies. A study by van der Kooi [34] used an autoregressive multilevel model to investigate the association between CDI and antimicrobial usage, using quarterly observations. Their model investigated this association with a time lag of one quarter, because they argued that antimicrobial usage at hospital level was associated with CDI after an interval of 2–3 months. We did not include a time lag in our analysis, however, because we considered that quarterly units of time were too large to assess time lag sensitively, as that represents a 4 month interval between the first and third months of two consecutive quarters. Four main strengths can be presented for this research. Firstly, a large-scale pharmacoepidemiological study was
carried out using data from organizations that already have a system in place to collect it directly from the trusts, which was less time-consuming for the researchers. Secondly, the fact that it was an ecological study allowed us to take into account not only the direct effects of antimicrobial usage in individual patients, but also the effects of antimicrobial usage in a population [41, 42]. Thirdly, this is the first study to evaluate the results of a nationwide intervention to promote prudent antimicrobial use in order to control CDI. Finally, as presented in the statistical analysis subsection, multilevel models allow us to take the overall trust differences into account (e.g. different disinfection policies and different staff) without explicitly measuring them [21]. Despite the limitations presented above, the similarities between the results of the present research and those of other studies [14, 18, 23–34] suggest that multilevel modelling to analyse HPA and IMS Health data can be used to investigate the association between CDI, or other problem organisms (e.g. meticillin-resistant S. aureus, glycopeptide-resistant enterococcal, meticillin-sensitive S. aureus and Escherichia coli), and antimicrobial usage. Furthermore, these databases might allow validation of hospital antimicrobial usage indicators as part of a wider antimicrobial stewardship programme. In future research, it would be important to investigate to what extent the association between CDI and antimicrobial usage at trust level can be generalized to patient level. This could be done in a similar fashion to that presented by Muller et al. [15], by analysing patient- and hospital-level data using a multilevel model. In conclusion, the reduction in the number of CDI cases reported by the NHS trusts is significantly associated with changes in antimicrobial usage. We did not find a statistically significant difference in the strength of this association among antimicrobial groups whose usage is associated with CDI.
Competing Interests All authors have completed the Unified Competing Interest form at http://www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: JBP, MPT and JC had support from The University of Manchester and from the University Hospital of South Manchester NHS Foundation Trust for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work. This work was undertaken with funding from the Department of Pharmacy, University Hospital of South Manchester NHS Foundation Trust and Manchester Pharmacy School, University of Manchester. The authors would like to thank the Chief Pharmacists in the North West of England for allowing Br J Clin Pharmacol
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data to be used, Peter Stephens of IMS Health, London and David Lloyd of the NHS Information Centre, Leeds for help and assistance in using the Hospital Antibiotic Audit database, and Miranda Murray of the Health Protection Agency for data on the use of Clostridium difficile data reported from local hospitals.
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Supporting Information Additional Supporting Information may be found in the online version of this article at the publisher’s website: Appendix S1 Steps for the development of the final multilevel model used to investigate the association between CDI cases and antimicrobial usage
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