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Original Investigation | Less Is More

Hospital Ward Antibiotic Prescribing and the Risks of Clostridium difficile Infection FREE

Kevin Brown, PhD1; Kim Valenta, PhD2; David Fisman, MD, MSc1; Andrew Simor, MD3; Nick Daneman, MD, MSc3
[+] Author Affiliations
1Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
2Department of Anthropology and School of the Environment, McGill University, Montreal, Quebec, Canada
3Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
JAMA Intern Med. 2015;175(4):626-633. doi:10.1001/jamainternmed.2014.8273.
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Published online

Importance  Only a portion of hospital-acquired Clostridium difficile infections can be traced back to source patients identified as having symptomatic disease. Antibiotic exposure is the main risk factor for C difficile infection for individual patients and is also associated with increased asymptomatic shedding. Contact with patients taking antibiotics within the same hospital ward may be a transmission risk factor for C difficile infection, but this hypothesis has never been tested.

Objectives  To obtain a complete portrait of inpatient risk that incorporates innate patient risk factors and transmission risk factors measured at the hospital ward level and to investigate ward-level rates of antibiotic use and C difficile infection risk.

Design, Setting, and Patients  A 46-month (June 1, 2010, through March 31, 2014) retrospective cohort study of inpatients 18 years or older in a large, acute care teaching hospital composed of 16 wards, including 5 intensive care units and 11 non–intensive care unit wards.

Exposures  Patient-level risk factors (eg, age, comorbidities, hospitalization history, antibiotic exposure) and ward-level risk factors (eg, antibiotic therapy per 100 patient-days, hand hygiene adherence, mean patient age) were identified from hospital databases.

Main Outcomes and Measures  Incidence of hospital-acquired C difficile infection as identified prospectively by hospital infection prevention and control staff.

Results  A total of 255 of 34 298 patients developed C difficile (incidence rate, 5.95 per 10 000 patient-days; 95% CI, 5.26-6.73). Ward-level antibiotic exposure varied from 21.7 to 56.4 days of therapy per 100 patient-days. Each 10% increase in ward-level antibiotic exposure was associated with a 2.1 per 10 000 (P < .001) increase in C difficile incidence. The association between C difficile incidence and ward antibiotic exposure was the same among patients with and without recent antibiotic exposure, and C difficile risk persisted after multilevel, multivariate adjustment for differences in patient-risk factors among wards (relative risk, 1.34 per 10% increase in days of therapy; 95% CI, 1.16-1.57).

Conclusions and Relevance  Among hospital inpatients, ward-level antibiotic prescribing is associated with a statistically significant and clinically relevant increase in C difficile risk that persists after adjustment for differences in patient-level antibiotic use and other patient- and ward-level risk factors. These data strongly support the use of antibiotic stewardship as a means of preventing C difficile infection.

Figures in this Article

Antibiotic exposure represents the principal risk factor for Clostridium difficile infection, and existing research estimates that inpatients taking antibiotics are, on average, 60% more likely to acquire the infection.1Prolonged antibiotic exposure and exposure to larger antibiotic doses are associated with increased C difficile infection risk,2 and some antibiotics (clindamycin, cephalosporins, and fluoroquinolones) are associated with a greater risk relative to other antibiotic classes.3,4 Risk may increase over time with increased prescribing of certain antimicrobials.5

Important gaps in knowledge remain with respect to the natural history of how C difficile bacteria are transmitted among hospitalized patients. Hospital environments are persistently contaminated with C difficile spores, and surfaces in rooms of infected patients are contaminated before, during, and after treatment for C difficile infection.6 Exposure to symptomatic patients with C difficile infection has been identified as an independent risk factor for transmission.7 However, exposure to spores from other symptomatic patients may not explain most new cases of C difficile infection acquired in hospitals.8 In a C difficile outbreak in a long-term care facility, almost half of the residents had asymptomatic colonization, and antibiotic exposure was the primary risk factor for asymptomatic colonization.9Although asymptomatically colonized individuals contribute less to environmental contamination at an individual level, asymptomatic carriers outnumber symptomatic patients by a ratio of 3:1 and as such could represent an important source of C difficile infection transmission.10

In the absence of reliable measures of patient colonization and environmental contamination, transmission risks can potentially be estimated as a function of aggregated measures of patient risk factors for colonization, such as mean ward- or hospital-level antibiotic prescribing.11 Multilevel models can be used to tease apart the effect of individual-level risk factors that affect patient susceptibility (direct effects) from group-level effects that affect transmission risks that are independent of individual-level effects (indirect effects).12 We sought to establish the effect of ward antibiotic-prescribing rate on ward C difficile infection incidence and whether the effects observed extended beyond the direct antibiotic effects on patients’ infection risk.

Ethics Statement

Study approval was obtained from the Research Ethics Board of Sunnybrook Health Sciences Centre. The board waived the need for patient consent because there was no contact with patients and patient anonymity was assured.

Study Design and Participants

A retrospective cohort study design was used to assess the association of individual- and ward-level risk factors with the incidence of C difficile infection among patients admitted to Sunnybrook Hospital, a large, acute care teaching hospital located in Toronto, Ontario, Canada. The source cohort consisted of all patients older than 18 years without a previous C difficile infection diagnosis who were hospitalized in an acute care ward at Sunnybrook Hospital from June 1, 2010, through March 31, 2014. We excluded patients in the hospital’s psychiatry, obstetrics, neonatal, and long-term care wards given a low expected event rate of C difficile infection.

Case Definition

Patientsinfected with C difficile were identified by the Infection Prevention and Control Department via active surveillance during the study period. A C difficile infection case was defined as any hospitalized patient with laboratory confirmation of a positive toxin assay result together with diarrhea or visualization of pseudomembranes on sigmoidoscopy, colonoscopy, or histopathologic analysis.13,14 For the purposes of case identification, diarrhea was defined as 3 or more loose or watery bowel movements in a 24-hour period, which was unusual or different for the patient, and with no other recognized cause. When a patient developed a C difficile infection, the remaining hospitalized days were excluded from the at-risk patient-days. Toxin assays at the hospital have been performed by polymerase chain reaction (BD GeneOhm Cdiff; Becton, Dickinson and Company) since September 2009, which includes the entire study period.

For C difficile infection case admissions, event time was the number of days from hospital admission to symptom onset or positive toxin assay result for rare cases (<1%) in which symptom onset was missing. For noncase admissions, censoring time was the number of days from hospital entry until discharge, study termination, or death. In addition to excluding hospitalized days after C difficile infection, the first 2 days of each hospital admission were also excluded because patients are not at risk of nosocomial infection at the beginning of a hospital stay.

Antimicrobial Exposure Assessment

Patient antibiotic exposures were drawn from pharmacy dispensing records. We examined records for receipt of any antibiotic in the prior 10 days but excluded exposure to metronidazole, oral vancomycin hydrochloride, or fidaxomicin because these may be treatments for C difficile infection.15 Antibiotic receipt was classified according to the Anatomical Therapeutic Chemical (ATC) Classification System, 17th edition.16As per previous work,4 we classified individual patients according to whether they had received a high-risk antibiotic (defined as receipt of cephalosporins or carbapenems, fluoroquinolones, or clindamycin and other lincosamides; ATC codes: J01D, J01M, and J01FF), had received a medium-risk antibiotic but not a high-risk antibiotic (defined as penicillins, sulfonamides and trimethoprim, macrolides and streptogramins, or aminoglycosides; ATC codes: J01C, J01E, J01FA, J01FG, and J01G), or had received no antibiotics or a low-risk antibiotic only (defined as receipt of tetracyclines; ATC code: J01A).

Patient Risk Factors

Patient age, sex, admission unit (classified as medical, surgical, or oncologic), and number of previous admissions were retrieved from hospital administrative records. Any patient receiving insulin or an antidiabetic medication (ATC code: A10) at any point during any hospitalization was considered diabetic. We also examined the use of antacids (ATC code: A02), chemotherapeutic agents (ATC code: L01), and feeding tubes (gastric, nasogastric, or jejunostomy). To account for the time delay between transient pharmaceutical exposures and C difficile infection risk, we measured receipt in any of the previous 10 days rather than receipt on a given day.

Hospital Ward Risk Factors

Using hospital bed assignment information, we identified the ward occupants for each inpatient day; when a patient was located in multiple wards on a given day, we considered that patient to be an occupant of the ward on which he or she was located at noon. We calculated ward-level risk factors that represented mean characteristics of the ward patient population during the 46-month study period. The following 5 ward-level measures were retrieved from the hospital information system: age (mean age), antibiotic use in days of therapy (DOTs) per 100 patient-days, antacid use (DOTs per 100 patient-days), chemotherapeutic agent use (DOTs per 100 patient-days), and feeding tube use (tube in situ per 100 patient-days). Within each ward, observer nurses measured hand hygiene adherence at specific hand hygiene moments (before entering patient room, after leaving patient room, before aseptic procedure, and after body fluid contact) on a quarterly basis through the study period, as per provincial guidelines.17 Adherence was pooled across periods and hand hygiene moments and was reported as a percentage of total hand hygiene opportunities.

Statistical Analysis
Patient Risk Factors

To estimate the effect of individual risk factors on C difficile infection risk, we developed a Poisson regression model that aimed to predict the time elapsed from hospital admission to the occurrence of a first C difficile infection. Our data were structured in counting process format with one record for each patient-day. The crude incidence rate ratio was assessed in a Poisson regression for each of the 12 individual-level risk factors.

Hospital Ward Risk Factors

Using the ward-level risk factors above in addition to ward-level C difficile infection incidence, we developed 5 bivariate inverse-variance–weighted linear mixed-effects regression models to estimate the effect of each ward-level factor on C difficile infection incidence, which were fitted using the Hartung-Knapp-Sidik-Jonkman method.18 We also considered the best-fitting, 2-covariate, ward-level model by comparing model Akaike Information Criterion for all 15 two-covariate models. As a sensitivity analysis, we examined the association between ward-level antibiotic use and C difficile infection incidence among the 11 non–intensive care unit (ICU) wards separately, excluding the 5 ICU wards.

To clearly distinguish patient-level and ward-level antibiotic effects, we measured the association between ward-level antibiotic prescribing and C difficile risk separately in patients with and without direct recent antibiotic exposure. We tested whether there was a difference in association of ward-level antibiotic use and C difficile risk between the 2 groups using the Δ method.

Multilevel Model

To assess the independent effect of individual exposures and aggregate ward-level antibiotic exposure, we developed a multilevel Poisson regression model with random intercepts corresponding to wards. The multilevel model included 8 individual-level risk factors: time since admission (modeled as a spline with a knot at 5 days for first admission and readmission separately), patient age (per 10-year increase), sex, diabetes mellitus, and individual exposure to antibiotics, gastric acid inhibitors, chemotherapeutic agents, and presence of a feeding tube. The number of adjustment factors was restricted to ensure at least 10 events per covariate,19 and the selection of covariates was based on established associations with C difficile infection risk.2,20

Analyses were conducted using R statistical software, version 3.0.2 (R Foundation for Statistical Computing); the glm, rma, and glmer functions were used for the unadjusted, bivariate mixed-effects and the multivariate mixed-effects statistical models, respectively.

Inpatient Cohort

We identified 34 298 patients who had an acute care hospital stay that exceeded 2 days at Sunnybrook Hospital from June 1, 2010, through March 31, 2014. These patients spent 428 588 patient-days in the 16 study wards during the 46-month study period. The median age of the cohort was 68.4 years (interquartile range, 54.3-81.0 years), whereas 9718 (28.3%) of the 34 298 patients had additional admissions. Patients received antibiotics in 21 239 (45.5%) of 46 661 admissions and had feeding tubes in 4765 (10.2%) of 46 661 admissions.

Patients Developing C difficile Infection

We identified 255 patients developing a new-onset C difficile infection during the 46-month study period (incidence rate, 5.95 per 10 000 patient-days; 95% CI, 5.26-6.73). Cases were distributed across 3 types of admitting services, with 111 among patients admitted via surgery services, 110 admitted via medicine services, and 34 admitted via oncology services.

Individual Patient Characteristics and the Risk of Infection

The incidence rates for patients with and without individual-level risk factors are given in Table 1. The individual-level risk factors associated with C difficile infection were age, readmission, direct exposure to antibiotics, and use of a feeding tube. Each 10-year increase in age was associated with a 1.07-fold increase in C difficile infection risk (95% CI, 1.00-1.17). Having a previous admission was associated with a 1.42-fold increase in risk (95% CI, 1.10-1.82).

Table Graphic Jump LocationTable 1.  Individual-Level Risk Factors and Clostridium difficile Infection Incidence
Ward Characteristics and C difficile Infection Incidence

The 16 study wards included 2 level II ICUs (patients requiring detailed observation) and 3 level III ICUs (patients requiring advanced respiratory support), 2 cardiology wards, 4 internal medicine wards, 4 surgery wards, and 1 oncology ward. Ward-level characteristics are given in Table 2. The rate of antibiotic use in wards varied from 21.7 DOTs per 100 patient-days in ward 6 to 56.4 DOTs per 100 patient-days in ICU 3 (median, 30.6 DOTs per 100 patient-days; interquartile range, 26.6-36.9 DOTs). Mean antibiotic use in the 5 ICUs was 47.2 DOTs per 100 patient-days compared with 30.9 DOTs per 100 patient-days in non-ICU wards (P < .001).

Table Graphic Jump LocationTable 2.  Variation in Ward-Level Characteristics and Clostridium difficile Infection Incidence

At the ward level, antibiotic use was the strongest predictor of C difficile infection incidence (Figure 1). Each 10% increase in ward-level antibiotic use was associated with an increased incidence of C difficile infection of 2.1 per 10 000 patient-days (slope = 2.1, P < .001, R2 = 0.50). The largest negative outlier in the association was ICU 5, which was the hospital burn ICU. Rate of ward-level feeding tube exposure was marginally associated with C difficile infection (slope = 0.59, P = .10, R2 = 0.11). Other ward-level factors, including hand hygiene adherence, mean inpatient age, and rates of antacid use and chemotherapeutic agent use, were not significantly associated with C difficile infection incidence. The addition of any of the other 4 ward-level factors to the model with ward-level antibiotic use did not alter the association between ward-level antibiotic use and C difficile infection incidence (data not shown). When we examined the association between ward-level antibiotic use and C difficile infection incidence among the 11 non-ICU wards, the association remained statistically significant (slope = 4.1, P = .03, R2 = 0.41).

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Figure 1.
Association of Ward-Level Exposures With Ward Clostridium difficile Infection (CDI) Incidence

A, Antibiotic use; B, hand hygiene; C, antacid use; and D, feeding tube use. Each symbol represents a hospital ward. The size of the symbols is proportional to the amount of follow-up time on each ward. DOTs indicates days of therapy.

Graphic Jump Location

We separately measured the association between C difficile infection incidence and ward antibiotic exposure rate among patients recently exposed and those not recently exposed to antibiotics (Figure 2). Each 10% increase in ward-level antibiotic use was associated with a 1.8 per 10 000 increase (slope = 1.8, P = .005, R2 = 0.50) in the incidence of C difficile infection among patients without direct recent exposure and a 1.6 per 10 000 increase (slope = 1.6, P = .05, R2 = 0.14) among patients with direct recent exposure to antibiotics. The effect of ward-level antibiotic exposure on C difficile infection incidence did not differ significantly between patients directly using or not directly using antibiotics (P = .16).

Place holder to copy figure label and caption
Figure 2.
Ward Clostridium difficile Infection (CDI) Incidence and Antibiotic Use Across Hospital Wards and Among Patients With and Without Direct Antibiotic Exposure

Each pair of numbered symbols represents the incidence of C difficile infection among the subset of patients who received antibiotics (diamonds) and those who did not (circles) within a given ward. For correspondence of ward identifiers, see Table 2. DOTs indicates days of therapy.

Graphic Jump Location
Ward-Level Antibiotic Use and C difficile Infection Incidence: Multilevel Model

After adjustment for patient characteristics, the ward-level antibiotic exposure remained associated with C difficile infection risk (Table 3). Each 10% increase in ward antibiotic exposure rate was associated with a 1.34-fold increase in C difficile infection risk (95% CI, 1.16-1.57).

Table Graphic Jump LocationTable 3.  Patient- and Ward-Level Risk Factors for Clostridium difficile Infection From a Multilevel Model

In this 46-month cohort study of C difficile infection risk, we found that ward-level antibiotic exposure is the main risk factor for infection. The effect of antibiotic prescribing reaches beyond individual-level antibiotic use, such that all patients, irrespective of whether they receive antibiotics directly, are at higher risk of C difficile infection in high antibiotic-prescribing wards. Ward-level C difficile infection risk was not confounded by other ward-level aggregate patient characteristics, including antacid use, chemotherapy, feeding tube presence, age, or crowding, or by individual-level patient comorbidities or antibiotic exposures.

This is the first study, to our knowledge, to consider ward-level antibiotic exposure as a risk factor for C difficile infection. In a previous multilevel study considering individual- and hospital-level risk factors, Pakyz et al11 found that hospital-level antibiotic exposure rates were not a significant predictor of hospital-level C difficile infection incidence. This finding suggests that hospital-level antimicrobial use may not differ meaningfully across centers or that factors that were not considered, such as infection control practices or C difficile diagnostic testing rate,21 may have confounded an underlying association.

We hypothesize that the marked effects of ward-level antibiotic exposure rate are likely explained by an increase in the number of patients colonized with, and shedding, C difficile in wards with high rates of antibiotic use. This high prevalence of antibiotic use would increase environmental contamination and the incidence of C difficile infection. This mechanism is supported by research indicating that antibiotic exposure is the principal risk factor for C difficile colonization10 and that approximately half of C difficile strains among C difficile infection cases in hospitals cannot be genetically linked to previously identified symptomatic patients.8 The hospital burn center was the only outlier, with lower-than-expected C difficile infection incidence given its high ward-level antibiotic use. The burn center is unique in that it had a low nurse-patient staffing ratio, single-bed rooms, and a younger patient population, which is consistent with findings from a prior study.22 Our multilevel statistical model revealed that younger age and patient pharmaceutical exposures did not completely account for the lower-than-expected incidence in the burn ICU, suggesting that other patient or ward characteristics may have been be at play.

The independent association of ward antibiotic exposure with C difficile infection risk most likely reflects the nonindependence of communicable disease cases. Communicable diseases differ from other classes of disease because a case is also a risk factor.23 In the context of C difficile infection, this statement means that an increase in disease-related force of infection could occur via antibiotic exposure in individuals who never themselves become symptomatic cases.24 Such indirect effects are well recognized with communicable disease control interventions, and indeed this effect may be conceptualized as an inverse of herd immunity seen with vaccines.25 Analogously, beneficial herd effects would logically be seen in wards with reduced antibiotic prescribing, as was observed in our study. A previous meta-analysis26 of antimicrobial stewardship interventions lends credibility to this explanation because these interventions have produced substantial reductions in C difficile infection incidence with only small reductions in antibiotic prescribing.

As such, the principal clinical implication of this study is that aggregate ward-level antibiotic use should be subject to surveillance by infection control and stewardship personnel. Hospital antimicrobial stewardship programs consistently achieve substantial reductions (22%-36%) in overall antibiotic use,27 and such interventions reduce C difficile infection incidence by 50%.26 Because almost all antibiotics are associated with increased C difficile infection risk,4 antimicrobial stewardship initiatives aiming to reduce infection incidence should aim to reduce overall antimicrobial exposure in addition to reducing use of specific high-risk agents. Furthermore, our results suggest that hand hygiene with soap and water should be considered before and after caring for patients using antibiotics, especially in ICU wards with high levels of antibiotic use.

Like any observational study, ours was subject to a number of limitations, including confounding by unmeasured patient characteristics, such as comorbidities, and outcome ascertainment bias related to potential systematic differences in physicians’ vigilance for detecting milder cases of infection. This was a single-hospital study, and the overall number of wards at our study hospital was small (n = 16). Furthermore, our study was subject to limitations because of incomplete follow-up information on patients after hospital discharge. We considered patients who were discharged as censored, but patient censoring may not have been independent of the study outcome.28

Our 46-month study of inpatient C difficile infection risk across 16 wards of a large tertiary care hospital found a strong association between ward antibiotic prescribing and C difficile infection incidence that affected patients with and without recent antibiotic exposure. Future studies of C difficile infection etiology should seek to quantify patient, ward, and airborne contamination with C difficile spores to more clearly describe the mechanisms that link ward-level antimicrobial use and infection incidence. These findings strongly support the further funding and development of hospital antibiotic stewardship programs.

Accepted for Publication: November 8, 2014.

Corresponding Author: Nick Daneman, MD, MSc, Division of Infectious Diseases and Clinical Epidemiology, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada (nick.daneman@sunnybrook.ca).

Published Online: February 23, 2015. doi:10.1001/jamainternmed.2014.8273.

Author Contributions: Drs Brown and Daneman had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Brown, Valenta, Fisman, Daneman.

Acquisition, analysis, or interpretation of the data: Brown, Simor, Daneman.

Drafting of the manuscript: Brown.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Brown.

Obtained funding: Brown, Fisman.

Administrative, technical, or material support: Brown, Valenta, Simor, Daneman.

Study supervision: Fisman, Daneman.

Conflict of Interest Disclosures: Dr Simor reported receiving honoraria for speaking on behalf of Optimer Pharmaceuticals Inc Canada (now Cubist Pharmaceuticals). No other disclosures were reported.

Funding/Support: This study was supported by a doctoral research award from the Canadian Institutes for Health Research (Dr Brown).

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Additional Contributions: Marion Elligsen, BScPhm, developed the infection prevention and control database and aided in antimicrobial coding. Dariusz Pajak, BASc, CPHI(C), provided important information on hospital ward configuration.

Owens  RC  Jr, Donskey  CJ, Gaynes  RP, Loo  VG, Muto  CA.  Antimicrobial-associated risk factors for Clostridium difficile infection. Clin Infect Dis. 2008;46(suppl 1):S19-S31.
PubMed   |  Link to Article
Stevens  V, Dumyati  G, Fine  LS, Fisher  SG, van Wijngaarden  E.  Cumulative antibiotic exposures over time and the risk of Clostridium difficile infection. Clin Infect Dis. 2011;53(1):42-48.
PubMed   |  Link to Article
Slimings  C, Riley  TV.  Antibiotics and hospital-acquired Clostridium difficile infection: update of systematic review and meta-analysis. J Antimicrob Chemother. 2014;69(4):881-891.
PubMed   |  Link to Article
Brown  KA, Khanafer  N, Daneman  N, Fisman  DN.  Meta-analysis of antibiotics and the risk of community-associated Clostridium difficile infection. Antimicrob Agents Chemother. 2013;57(5):2326-2332.
PubMed   |  Link to Article
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(1):44-50.
PubMed   |  Link to Article
Sethi  AK, Al-Nassir  WN, Nerandzic  MM, Bobulsky  GS, Donskey  CJ.  Persistence of skin contamination and environmental shedding of Clostridium difficile during and after treatment of C. difficile infection. Infect Control Hosp Epidemiol. 2010;31(1):21-27.
PubMed   |  Link to Article
Dubberke  ER, Reske  KA, Olsen  MA,  et al.  Evaluation of Clostridium difficile–associated disease pressure as a risk factor for C difficile–associated disease. Arch Intern Med. 2007;167(10):1092-1097.
PubMed   |  Link to Article
Eyre  DW, Cule  ML, Wilson  DJ,  et al.  Diverse sources of C. difficile infection identified on whole-genome sequencing. N Engl J Med. 2013;369(13):1195-1205.
PubMed   |  Link to Article
Riggs  MM, Sethi  AK, Zabarsky  TF, Eckstein  EC, Jump  RLP, Donskey  CJ.  Asymptomatic carriers are a potential source for transmission of epidemic and nonepidemic Clostridium difficile strains among long-term care facility residents. Clin Infect Dis. 2007;45(8):992-998.
PubMed   |  Link to Article
Guerrero  DM, Becker  JC, Eckstein  EC,  et al.  Asymptomatic carriage of toxigenic Clostridium difficile by hospitalized patients. J Hosp Infect. 2013;85(2):155-158.
PubMed   |  Link to Article
Pakyz  AL, Jawahar  R, Wang  Q, Harpe  SE.  Medication risk factors associated with healthcare-associated Clostridium difficile infection: a multilevel model case-control study among 64 US academic medical centres. J Antimicrob Chemother. 2014;69(4):1127-1131.
PubMed   |  Link to Article
Diez Roux  AV, Aiello  AE.  Multilevel analysis of infectious diseases. J Infect Dis. 2005;191(suppl 1):S25-S33.
PubMed   |  Link to Article
Provincial Infectious Diseases Advisory Committee. Best Practices Document for the Management of Clostridium difficile in All Health Care Settings Protecting Patients and Staff. Toronto, ON: Ministry of Health and Long-Term Care; 2007.
McDonald  LC, Coignard  B, Dubberke  E, Song  X, Horan  T, Kutty  PK; Ad Hoc Clostridium difficile Surveillance Working Group.  Recommendations for surveillance of Clostridium difficile–associated disease. Infect Control Hosp Epidemiol. 2007;28(2):140-145.
PubMed   |  Link to Article
Zar  FA, Bakkanagari  SR, Moorthi  KMLST, Davis  MB.  A comparison of vancomycin and metronidazole for the treatment of Clostridium difficile–associated diarrhea, stratified by disease severity. Clin Infect Dis. 2007;45(3):302-307.
PubMed   |  Link to Article
WHO Collaborating Centre for Drug Statistics Methodology, Norwegian Institute of Public Health. Guidelines for ATC classification and DDD assignment 2014. Oslo, Norway: WHO Collaborating Centre for Drug Statistics Methodology, Norwegian Institute of Public Health; 2013. http://www.whocc.no/atc_ddd_publications/guidelines/. Accessed June 5, 2014.
Sax  H, Allegranzi  B, Chraïti  M-N, Boyce  J, Larson  E, Pittet  D.  The World Health Organization hand hygiene observation method. Am J Infect Control. 2009;37(10):827-834.
PubMed   |  Link to Article
IntHout  J, Ioannidis  JP, Borm  GF.  The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol. 2014;14(1):25.
PubMed   |  Link to Article
Peduzzi  P, Concato  J, Feinstein  AR, Holford  TR.  Importance of events per independent variable in proportional hazards regression analysis, II: accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503-1510.
PubMed   |  Link to Article
Bignardi  GE.  Risk factors for Clostridium difficile infection. J Hosp Infect. 1998;40(1):1-15.
PubMed   |  Link to Article
Brown  KA, Fisman  DN, Daneman  N.  Hospital Clostridium difficile infection testing rates: is “don’t ask, don’t tell” at play? Infect Control Hosp Epidemiol. 2014;35(7):911-912.
PubMed   |  Link to Article
Crabtree  SJ, Robertson  JL, Chung  KK,  et al.  Clostridium difficile infections in patients with severe burns. Burns. 2011;37(1):42-48.
PubMed   |  Link to Article
Giesecke  J. Modern Infectious Disease Epidemiology. 2nd ed. New York, NY: Oxford University Press; 2002.
Vynnycky  E. An Introduction to Infectious Disease Modelling. New York, NY: Oxford University Press; 2010.
Halloran  ME, Longini  IM  Jr, Struchiner  CJ.  Design and interpretation of vaccine field studies. Epidemiol Rev. 1999;21(1):73-88.
PubMed   |  Link to Article
Feazel  LM, Malhotra  A, Perencevich  EN, Kaboli  P, Diekema  DJ, Schweizer  ML.  Effect of antibiotic stewardship programmes on Clostridium difficile incidence: a systematic review and meta-analysis. J Antimicrob Chemother. 2014;69(7):1748-1754.
PubMed   |  Link to Article
Dellit  TH, Owens  RC, McGowan  JE  Jr,  et al; Infectious Diseases Society of America; Society for Healthcare Epidemiology of America.  Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin Infect Dis. 2007;44(2):159-177.
PubMed   |  Link to Article
Schumacher  M, Allignol  A, Beyersmann  J, Binder  N, Wolkewitz  M.  Hospital-acquired infections: appropriate statistical treatment is urgently needed! Int J Epidemiol. 2013;42(5):1502-1508.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Association of Ward-Level Exposures With Ward Clostridium difficile Infection (CDI) Incidence

A, Antibiotic use; B, hand hygiene; C, antacid use; and D, feeding tube use. Each symbol represents a hospital ward. The size of the symbols is proportional to the amount of follow-up time on each ward. DOTs indicates days of therapy.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Ward Clostridium difficile Infection (CDI) Incidence and Antibiotic Use Across Hospital Wards and Among Patients With and Without Direct Antibiotic Exposure

Each pair of numbered symbols represents the incidence of C difficile infection among the subset of patients who received antibiotics (diamonds) and those who did not (circles) within a given ward. For correspondence of ward identifiers, see Table 2. DOTs indicates days of therapy.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Individual-Level Risk Factors and Clostridium difficile Infection Incidence
Table Graphic Jump LocationTable 2.  Variation in Ward-Level Characteristics and Clostridium difficile Infection Incidence
Table Graphic Jump LocationTable 3.  Patient- and Ward-Level Risk Factors for Clostridium difficile Infection From a Multilevel Model

References

Owens  RC  Jr, Donskey  CJ, Gaynes  RP, Loo  VG, Muto  CA.  Antimicrobial-associated risk factors for Clostridium difficile infection. Clin Infect Dis. 2008;46(suppl 1):S19-S31.
PubMed   |  Link to Article
Stevens  V, Dumyati  G, Fine  LS, Fisher  SG, van Wijngaarden  E.  Cumulative antibiotic exposures over time and the risk of Clostridium difficile infection. Clin Infect Dis. 2011;53(1):42-48.
PubMed   |  Link to Article
Slimings  C, Riley  TV.  Antibiotics and hospital-acquired Clostridium difficile infection: update of systematic review and meta-analysis. J Antimicrob Chemother. 2014;69(4):881-891.
PubMed   |  Link to Article
Brown  KA, Khanafer  N, Daneman  N, Fisman  DN.  Meta-analysis of antibiotics and the risk of community-associated Clostridium difficile infection. Antimicrob Agents Chemother. 2013;57(5):2326-2332.
PubMed   |  Link to Article
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(1):44-50.
PubMed   |  Link to Article
Sethi  AK, Al-Nassir  WN, Nerandzic  MM, Bobulsky  GS, Donskey  CJ.  Persistence of skin contamination and environmental shedding of Clostridium difficile during and after treatment of C. difficile infection. Infect Control Hosp Epidemiol. 2010;31(1):21-27.
PubMed   |  Link to Article
Dubberke  ER, Reske  KA, Olsen  MA,  et al.  Evaluation of Clostridium difficile–associated disease pressure as a risk factor for C difficile–associated disease. Arch Intern Med. 2007;167(10):1092-1097.
PubMed   |  Link to Article
Eyre  DW, Cule  ML, Wilson  DJ,  et al.  Diverse sources of C. difficile infection identified on whole-genome sequencing. N Engl J Med. 2013;369(13):1195-1205.
PubMed   |  Link to Article
Riggs  MM, Sethi  AK, Zabarsky  TF, Eckstein  EC, Jump  RLP, Donskey  CJ.  Asymptomatic carriers are a potential source for transmission of epidemic and nonepidemic Clostridium difficile strains among long-term care facility residents. Clin Infect Dis. 2007;45(8):992-998.
PubMed   |  Link to Article
Guerrero  DM, Becker  JC, Eckstein  EC,  et al.  Asymptomatic carriage of toxigenic Clostridium difficile by hospitalized patients. J Hosp Infect. 2013;85(2):155-158.
PubMed   |  Link to Article
Pakyz  AL, Jawahar  R, Wang  Q, Harpe  SE.  Medication risk factors associated with healthcare-associated Clostridium difficile infection: a multilevel model case-control study among 64 US academic medical centres. J Antimicrob Chemother. 2014;69(4):1127-1131.
PubMed   |  Link to Article
Diez Roux  AV, Aiello  AE.  Multilevel analysis of infectious diseases. J Infect Dis. 2005;191(suppl 1):S25-S33.
PubMed   |  Link to Article
Provincial Infectious Diseases Advisory Committee. Best Practices Document for the Management of Clostridium difficile in All Health Care Settings Protecting Patients and Staff. Toronto, ON: Ministry of Health and Long-Term Care; 2007.
McDonald  LC, Coignard  B, Dubberke  E, Song  X, Horan  T, Kutty  PK; Ad Hoc Clostridium difficile Surveillance Working Group.  Recommendations for surveillance of Clostridium difficile–associated disease. Infect Control Hosp Epidemiol. 2007;28(2):140-145.
PubMed   |  Link to Article
Zar  FA, Bakkanagari  SR, Moorthi  KMLST, Davis  MB.  A comparison of vancomycin and metronidazole for the treatment of Clostridium difficile–associated diarrhea, stratified by disease severity. Clin Infect Dis. 2007;45(3):302-307.
PubMed   |  Link to Article
WHO Collaborating Centre for Drug Statistics Methodology, Norwegian Institute of Public Health. Guidelines for ATC classification and DDD assignment 2014. Oslo, Norway: WHO Collaborating Centre for Drug Statistics Methodology, Norwegian Institute of Public Health; 2013. http://www.whocc.no/atc_ddd_publications/guidelines/. Accessed June 5, 2014.
Sax  H, Allegranzi  B, Chraïti  M-N, Boyce  J, Larson  E, Pittet  D.  The World Health Organization hand hygiene observation method. Am J Infect Control. 2009;37(10):827-834.
PubMed   |  Link to Article
IntHout  J, Ioannidis  JP, Borm  GF.  The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol. 2014;14(1):25.
PubMed   |  Link to Article
Peduzzi  P, Concato  J, Feinstein  AR, Holford  TR.  Importance of events per independent variable in proportional hazards regression analysis, II: accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503-1510.
PubMed   |  Link to Article
Bignardi  GE.  Risk factors for Clostridium difficile infection. J Hosp Infect. 1998;40(1):1-15.
PubMed   |  Link to Article
Brown  KA, Fisman  DN, Daneman  N.  Hospital Clostridium difficile infection testing rates: is “don’t ask, don’t tell” at play? Infect Control Hosp Epidemiol. 2014;35(7):911-912.
PubMed   |  Link to Article
Crabtree  SJ, Robertson  JL, Chung  KK,  et al.  Clostridium difficile infections in patients with severe burns. Burns. 2011;37(1):42-48.
PubMed   |  Link to Article
Giesecke  J. Modern Infectious Disease Epidemiology. 2nd ed. New York, NY: Oxford University Press; 2002.
Vynnycky  E. An Introduction to Infectious Disease Modelling. New York, NY: Oxford University Press; 2010.
Halloran  ME, Longini  IM  Jr, Struchiner  CJ.  Design and interpretation of vaccine field studies. Epidemiol Rev. 1999;21(1):73-88.
PubMed   |  Link to Article
Feazel  LM, Malhotra  A, Perencevich  EN, Kaboli  P, Diekema  DJ, Schweizer  ML.  Effect of antibiotic stewardship programmes on Clostridium difficile incidence: a systematic review and meta-analysis. J Antimicrob Chemother. 2014;69(7):1748-1754.
PubMed   |  Link to Article
Dellit  TH, Owens  RC, McGowan  JE  Jr,  et al; Infectious Diseases Society of America; Society for Healthcare Epidemiology of America.  Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin Infect Dis. 2007;44(2):159-177.
PubMed   |  Link to Article
Schumacher  M, Allignol  A, Beyersmann  J, Binder  N, Wolkewitz  M.  Hospital-acquired infections: appropriate statistical treatment is urgently needed! Int J Epidemiol. 2013;42(5):1502-1508.
PubMed   |  Link to Article

Correspondence

CME


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