Importance
Clostridium difficile infection (CDI) is a major cause of health care–associated infection worldwide, and new preventive strategies are urgently needed. Current control measures do not target asymptomatic carriers, despite evidence that they can contaminate the hospital environment and health care workers’ hands and potentially transmit C difficile to other patients.
Objective
To investigate the effect of detecting and isolating C difficile asymptomatic carriers at hospital admission on the incidence of health care–associated CDI (HA-CDI).
Design, Setting, and Participants
We performed a controlled quasi-experimental study between November 19, 2013, and March 7, 2015, in a Canadian acute care facility. Admission screening was conducted by detecting the tcdB gene by polymerase chain reaction on a rectal swab. Carriers were placed under contact isolation precautions during their hospitalization.
Main Outcomes and Measures
Changes in HA-CDI incidence level and trend during the intervention period (17 periods of 4 weeks each) were compared with the preintervention control period (120 periods of 4 weeks each) by segmented regression analysis and autoregressive integrated moving average (ARIMA) modeling. Concomitant changes in the aggregated HA-CDI incidence at other institutions in Québec City, Québec (n = 6) and the province of Québec (n = 94) were also examined.
Results
Overall, 7599 of 8218 (92.5%) eligible patients were screened, among whom 368 (4.8%) were identified as C difficile carriers. During the intervention, 38 patients (3.0 per 10 000 patient-days) developed an HA-CDI compared with 416 patients (6.9 per 10 000 patient-days) during the preintervention control period (P < .001). There was no immediate change in the level of HA-CDIs on implementation (P = .92), but there was a significant decrease in trend over time of 7% per 4-week period (rate ratio, 0.93; 95% CI, 0.87-0.99 per period; P = .02). ARIMA modeling also detected a significant effect of the intervention, represented by a gradual progressive decrease in the HA-CDI time series by an overall magnitude of 7.2 HA-CDIs per 10 000 patient-days. We estimated that the intervention had prevented 63 of the 101 (62.4%) expected cases. By contrast, no significant decrease in HA-CDI rates occurred in the control groups.
Conclusions and Relevance
Detecting and isolating C difficile carriers was associated with a significant decrease in the incidence of HA-CDI. If confirmed in subsequent studies, this strategy could help prevent HA-CDI.
Clostridium difficile infection (CDI) can cause symptoms ranging from mild diarrhea to life-threatening toxic megacolon and has become the leading cause of health care–associated infection in the United States.1-3 Approximately half a million cases occur each year in the United States, causing 29 000 deaths and generating $4.8 billion in excess medical costs.2 An increase in CDI incidence occurred in the early 2000s owing to the emergence of the North American pulsed-field gel electrophoresis (PFGE) type 1 (NAP1) strain.4-7 Infection control measures were intensified in response to the epidemic, with partial success.8
Current infection control recommendations focus mainly on patients with CDI.9,10 However, C difficile asymptomatic carriers may also have a role in spore dissemination11 for several reasons: they outnumber patients with CDI,12-14 they can contaminate the environment and caregivers’ hands,12,15 and they are not detected and thus are not placed under isolation precautions.16 Still, present guidelines do not recommend screening and isolating asymptomatic carriers, mainly because, to our knowledge, there has been no published study to evaluate the effectiveness of this strategy and because there is a shortage of private rooms in many hospitals.9,10
The Québec Heart and Lung Institute (QHLI) is a 354-bed Canadian tertiary institution in Québec City, Québec. Despite significant efforts to control CDI, the institution has remained endemic for CDI, with health care–associated CDI (HA-CDI) rates frequently exceeding the government-imposed target of 9.0 per 10 000 patient-days. Many cases of HA-CDI were thought to be attributable to cross-contamination from asymptomatic carriers.12,14,17 Consequently, the executive committee of the QHLI endorsed a policy to detect and isolate asymptomatic carriers in October 2013. The present study reports the effect of this strategy on the incidence of HA-CDI.
Box Section Ref IDKey Points
Question Could the identification and isolation of Clostridium difficile carriers on hospital admission help decrease the incidence of health care–associated C difficile infections?
Findings In this quasi-experimental study that included 7599 patients over 15 months, the identification and isolation of C difficile carriers was followed by a significant progressive decrease in the incidence of hospital-associated C difficile infections from 6.9 per 10 000 patient-days before the intervention to 3.0 per 10 000 patient-days during the intervention.
Meaning The identification and isolation of C difficile asymptomatic carriers could help decrease the incidence of C difficile infections in acute care hospitals.
We used a quasi-experimental design and conducted a time series analysis to evaluate the effect of the intervention.18 The study population included all patients admitted between August 22, 2004, and March 7, 2015. Incidence rates were computed as the number of HA-CDI cases divided by the number of patient-days per 4-week period, as recommended by the mandatory Québec CDI surveillance program.19-21
Ethical and Methodological Considerations
The institutional research ethics committee of the QHLI approved the extraction of data for analysis. Only aggregated, anonymized data collected as per Accreditation Canada requirements were extracted. This report follows the Outbreak Reports and Intervention Studies of Nosocomial Infection (ORION) statement for the reporting of nosocomial infection intervention studies.18
Clostridium difficile infection was defined as (1) diarrhea (≥3 unformed or liquid stools in <24 hours) lasting at least 24 hours without any other known etiology, combined with a positive assay for toxigenic C difficile; (2) visualization of pseudomembranes by colonoscopy; or (3) a histopathologic diagnosis.22 A case was categorized as health care-associated if symptoms appeared at least 72 hours after admission and up to 4 weeks after discharge. Cases in which the patient had consulted an ambulatory clinic in the previous 4 weeks were considered ambulatory care associated. Cases in which the patient developed symptoms within 72 hours of admission, but with no history of a hospitalization or ambulatory care visit in the previous 4 weeks, were considered community acquired. Asymptomatic patients with a positive C difficile admission screening test result were defined as asymptomatic carriers. Surveillance was conducted prospectively by infection control practitioners.
CDI Infection Control Measures
Patients with CDI were placed under contact isolation precautions (eBox in the Supplement).9 Patients with diarrhea were isolated empirically, pending confirmation of the diagnosis. Since April 2011, isolation precautions are maintained until discharge.23 Before this date, precautions were lifted 72 hours after resolution of symptoms.
Starting November 19, 2013, patients admitted through the emergency department were screened for C difficile carriage. Short-stay patients (<24 hours) were excluded because they were often discharged before the screening results were available. Patients admitted directly to the wards were also excluded because of logistical constraints. Identified carriers were placed under infection control measures resembling those for CDI but tailored to have minimal effect on bed management and work flow (eBox in the Supplement). For example, health care workers were not required to wear isolation gowns, and carriers could share a room with noncarriers, with the privacy curtains drawn. There were no other planned changes in infection control measures during the intervention. No weekly or discharge screenings of hospitalized patients were performed.
C difficile Assays to Diagnose CDI
Before October 2009, stool samples were tested using an algorithm that included glutamate dehydrogenase antigen and toxin A and B detection by enzyme immunoassay and cell culture cytotoxicity assay, as described previously.24 In October 2009, a one-step polymerase chain reaction (PCR) assay targeting the tcdB gene (BD GeneOhm Cdiff Assay; BD Diagnostics) was introduced.24,25 Only unformed or watery stools were tested. Nurses were required to submit samples from patients with new-onset diarrhea, and a medical order was not required.
Screening was conducted by rectal sampling with a sterile swab (Liquid Stuart Aerobic Transport Media; Copan Italia). Swabs were tested for the presence of tcdB by PCR (BD GeneOhm Cdiff Assay) once daily, 7 days a week. The results were available within 24 hours and documented in the patients’ medical records.
Strain Typing of CDI Cases
The Québec Public Health Laboratory conducts a provincewide surveillance of strains isolated from patients with HA-CDI. Pulsed-field gel electrophoresis typing is performed on 10 to 20 consecutive strains per hospital every year or every other year, as described previously.20 Briefly, genomic DNA is digested with SmaI restriction enzyme, and a PFGE system (CHEF-DR III PFGE Systems; Bio-Rad) is used for electrophoresis.26 The relatedness of the various isolates is determined according to the criteria by Tenover et al27 and using a software program (BioNumerics, version 6.5; Applied Maths NV) by the unweighted pair group method with arithmetic mean (UPGMA) clustering methodology using the Dice coefficient with both position tolerance and optimization of 1.1%.
Change in the HA-CDI incidence rate per 10 000 patient-days after implementation of the intervention at the QHLI was selected as the main outcome. Changes in the HA-CDI incidence rates from other institutions that participated in CDI surveillance during the same period were used as controls. Two non–mutually exclusive control groups were created: (1) other institutions in the Québec City area (n = 6) and (2) all other institutions in the province of Québec (n = 94). Changes in the proportion of HA-CDI cases with complications and in strain types causing CDI constituted secondary outcomes.
First, aggregated data were analyzed for the entire study period. The preintervention period was divided into an epidemic period (38 periods of 4 weeks each) and a postepidemic period (82 periods of 4 weeks each) to take into account the NAP1 epidemic.21 The rates and proportions during the intervention period were compared with the postepidemic period by χ2 test and Fisher exact test, with normal approximation and square root transformation, as appropriate.
In a second step, we used 2 different methods to estimate the effect of the intervention on the HA-CDI incidence time series at the QHLI and to estimate concomitant trends among the control groups. Segmented regression of interrupted time series data was used to assess the effect of the intervention on HA-CDI incidence, both immediately (change in level) and over time (change in trend) by creating indicator variables, as described elsewhere.28 A trend and periodic seasonal terms were applied to the entire study period (August 2004 to March 2015), in addition to indicator variables (see the Supplementary Statistical Analysis Methods in the Supplement for details). Also, an autoregressive integrated moving average (ARIMA) model using the Box-Jenkins approach was fitted for the HA-CDI incidence time series by using the standard approach of identification, estimation, and checking.29,30 An intervention analysis was performed to assess the effect of the intervention on the HA-CDI incidence time series (see the Supplementary Statistical Analysis Methods in the Supplement for details).29 The introduction of the C difficile PCR assay to diagnose CDI was taken into account in both statistical methods.
Finally, a separate ARIMA model built for the preintervention period was used to forecast the HA-CDI time series during the intervention period. The number of HA-CDI cases prevented by the intervention was estimated by calculating the difference between the predicted number and the observed number of cases.
To assess the robustness of the results, a sensitivity analysis was performed that excluded the 2004 to 2007 epidemic period. To analyze the effect of the intervention on C difficile strains, the strains were grouped into NAP1 and non-NAP1 strains. Changes in the proportion of NAP1 strains during the intervention were assessed by Fisher exact test.
To account for potential confounders, changes in levels and trends in antimicrobial and proton pump inhibitor (PPI) use in defined daily dose31 per 1000 patient-days during the intervention period were compared with the preceding 2 years using segmented regression analysis. Hand hygiene compliance rates before vs during the intervention were also compared by χ2 test.
Statistical software (SAS, version 9.2; SAS Institute Inc) was used for all analyses. P < .05 was considered statistically significant.
Before the intervention, 722 cases of HA-CDI were diagnosed (Table 1). Preintervention HA-CDI incidence rates per 4-week period at the QHLI ranged from 1.3 to 28.6 per 10 000 patient-days, for an average of 8.2 per 10 000 patient-days (Figure 1). Incidence rates during the epidemic and postepidemic periods preceding the intervention were 11.1 and 6.9 per 10 000 patient-days, respectively.
The intervention was implemented on November 19, 2013. Over the following 17 periods of 4 weeks each, 7599 of 8218 (92.5%) eligible patients were screened. Of these patients, 368 (4.8%) were identified as asymptomatic carriers. Thirty-eight cases of HA-CDI were diagnosed, for an incidence of 3.0 per 10 000 patient-days (P < .001 compared with the postepidemic period). There was a significant decrease in the proportion of 4-week periods above target compared with the control period (0% vs 24.4%, P = .02). There was no significant change in the incidence of community-acquired and ambulatory care–associated CDI or in the proportion of cases with complications (P > .05 for all).
Segmented Regression Analysis
Before the intervention, HA-CDI incidence was slightly but significantly decreasing at the QHLI by 0.4% per 4-week period (rate ratio [RR], 0.996; P < .001), similar to other institutions in Québec City (RR, 0.996; P < .001). However, rates were stable in the province as a whole (P > .05) (Table 2 and Figure 2). The intervention had no immediate effect on the HA-CDI level at the QHLI (P = .92) but led to a significant decrease in trend by 7% per 4-week period (RR, 0.93 per period; P = .02). By contrast, an immediate increase in HA-CDI levels was observed in the Québec City control group at the time of intervention implementation (RR, 1.44; P = .04). During the intervention period, no significant changes in HA-CDI trends were observed in the control groups.
Intervention analysis detected no immediate effect of the intervention on HA-CDI incidence. However, a significant effect, represented by a gradual progressive decrease in the HA-CDI time series by an overall magnitude of 7.2 HA-CDIs per 10 000 patient-days, was detected by the end of the study period. The effect of the intervention was represented by a gradual progressive decrease in HA-CDI incidence, taking the form ω0 / (1 − δ), where ω0 = −0.0001044 (SE = 0.0001424) (P = .46) and δ = 0.86394 (SE = 0.30038) (P = .004). Most of the decrease (approximately 6.0 HA-CDIs per 10 000 patient-days) occurred during the first half of the intervention. Using forecast modeling, the intervention was estimated to have prevented 63 of the 101 (62.4%) expected cases of HA-CDI (Figure 1). By contrast, no significant immediate or gradual change in HA-CDI incidence was detected among the control groups.
The results of the sensitivity analyses that excluded the epidemic period were similar to those of the primary analyses. There was no significant change in level, but a significant change in trend (RR, 0.94; 95% CI, 0.91-0.98; P = .048) was detected during the intervention by the segmented regression. Also, a significant gradual decrease in rates was detected by ARIMA modeling (ω0 = −0.0000676 [SE = 0.0000935] [P = .47] and δ = 0.90693 [SE = 0.26453] [P < .001]).
Four hundred sixty-six strains were typed from 6 institutions in Québec City between 2005 and 2014 (eFigure 1 in the Supplement). The proportion of HA-CDI due to NAP1 at the QHLI was higher than that in other hospitals in 2005, indicating that the institution was the first in the region to be affected by the epidemic. The proportion of HA-CDI due to NAP1 at the QHLI was comparable to the proportion found in other hospitals between 2007 and 2013 (P > .05). However, a significant decrease in the proportion of NAP1 from 59.2% (45 of 76) before the intervention to 20.0% (2 of 10) during the intervention was observed (P = .049). No concomitant decrease was observed at the other institutions in Québec City (80% NAP1 in 2014).
A carbapenemase-producing Enterobacteriaceae outbreak occurred on 2 wards between December 2014 and January 2015. Contained single-ward outbreaks of influenza and viral gastroenteritis occurred in December 2014 and January 2015, respectively. An increase in hand hygiene compliance was seen during the intervention, from 36.6% (943 of 2580 opportunities) in 2013 to 49.7% (1465 of 2945 opportunities) in 2014 (P < .001).
After implementation of the intervention, there was a significant decrease in the use of anti–C difficile antibiotics (ie, oral or intravenous metronidazole and oral vancomycin), followed by a significant decrease in trend (RR, 0.97; P < .001) (Table 3 and eFigure 2 in the Supplement). By contrast, there was a small but significant immediate increase in global use of other antimicrobials, followed by a small increase in trend (RR, 1.004; P < .001). Proton pump inhibitor use decreased on implementation of the intervention but was followed by a slight increase in trend (RR, 1.005; P < .001).
Clostridium difficile infection is a serious problem, and new preventive strategies are urgently needed. In our study, detecting and isolating asymptomatic carriers was associated with a significant decrease in the HA-CDI incidence rate. To our knowledge, this study is the first to assess the benefit of such an intervention. In total, 121 patients needed to be screened and 6 asymptomatic carriers needed to be isolated to prevent one HA-CDI. The intervention may be effective not only by preventing direct patient-to-patient transmission but also by limiting contamination of the hospital environment.12,15 The gradual rather than immediate decrease in HA-CDI incidence on implementation of the intervention may be due to the presence of spores lingering in the environment, combined with their capacity to survive for extended periods.1 By contrast, no decrease in trend was observed in the control groups, suggesting that the decrease is not due to other regional or provincial phenomena. In fact, an increase in HA-CDI was observed in both control groups in November 2013 (ie, on implementation of the intervention at the QHLI) owing to seasonality.19
The decrease in the proportion of CDI due to the NAP1 strain is surprising and should be considered exploratory. The fact that the decrease occurred only at the QHLI suggests that it is not a coincidence but rather a consequence of the intervention. This observation was reported previously in an intervention to control an outbreak in the United Kingdom, where improving infection control measures had a greater effect on NAP1 than on other strains.32 Other results also suggest that C difficile strains may differ in their epidemiology and transmissibility.17,33
The study has limitations. The intervention was conducted in a single center. Further investigations (ultimately cluster randomized trials) will be required to confirm our findings. Still, our study was essential to justify the realization of these more costly studies. To date, no commercial test is approved in Canada or the United States to detect asymptomatic carriers, and the optimal detection method is unknown. Compliance with isolation precautions was not assessed, and it was impossible to mask patients, caregivers, and investigators to the intervention. Despite standardized definitions, knowledge of carrier status could have influenced the ascertainment of HA-CDI cases. However, the gradual decrease in HA-CDI over 15 months argues in favor of a true change in C difficile hospital ecology rather than a change in case ascertainment. Although global antimicrobial and PPI consumption remained stable during the intervention, improvements in the appropriateness of antimicrobial use could not be investigated and may have positively affected HA-CDI incidence. Applicability of our findings to settings with a lower proportion of NAP1 is uncertain. Institutional hand hygiene compliance increased during the intervention. Direct observation tends to overestimate compliance; hence, the magnitude of the increase may have been overestimated as well. Many hospitals have reported similar increases, but, to our knowledge, none have ever reported such an important concomitant reduction in CDI.34 Also, because 93% of hand hygiene at the QHLI is performed with an alcohol-based solution to which C difficile spores are resistant, it is unlikely that such an improvement could have greatly interrupted cross-transmission.34
It is possible that knowledge of carriage status may have influenced antibiotic management among asymptomatic carriers and hence influenced the potential development of CDI. Patient-level data were not available for analysis, and the proportion of asymptomatic carriers who progressed to CDI is unknown.35 Nevertheless, because the ultimate goal is to prevent HA-CDI, this possibility would still support our main finding, which is that identifying and isolating asymptomatic carriers is associated with a decrease in HA-CDI incidence.
The study has numerous strengths. The study period spans a decade. Prevalence of carriage on admission was similar to that in other investigations, suggesting that our screening strategy was adequate to identify carriers.17 Intrahospital and interhospital comparisons were conducted to account for potential confounders.24,36 The effect of the intervention was measured using different statistical approaches that yielded similar results, indicative of their robustness. Although the primary outcome was unmasked, the significant decrease in anti-CDI antibiotic use suggests that the decrease in HA-CDI is real.
The cost-benefit of this strategy is unknown, but preliminary estimates suggest that the intervention may be cost-effective.37 The intervention cost US $130 000 over 17 periods and prevented approximately 63 cases. Because each case costs US $3427 to $9960, the savings in averted CDI (US $216 000 to $627 000) are greater than the costs of the intervention.38
The effect of our findings is significant in many ways. The magnitude of the decrease in HA-CDI incidence is substantial. To put our results into perspective, the QHLI now has the lowest HA-CDI incidence rates among 22 academic institutions in the province of Québec. The intervention is simple and could be easily implemented in other institutions. If confirmed in subsequent studies, isolating asymptomatic carriers could potentially prevent thousands of cases of HA-CDI every year in North America.
Implementing a protocol to identify and isolate C difficile asymptomatic carriers was associated with a significant decrease in the incidence of HA-CDI. Additional studies are warranted to further investigate this promising strategy.
Accepted for Publication: January 17, 2016.
Corresponding Author: Yves Longtin, MD, Infection Prevention and Control Unit, Jewish General Hospital Sir Mortimer B. Davis, 3755 Côte-Sainte-Catherine Rd, Room E-0057, Montreal, QC H3T 1E2, Canada (yves.longtin@mcgill.ca).
Published Online: April 25, 2016. doi:10.1001/jamainternmed.2016.0177.
Author Contributions: Dr Y. Longtin had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Y. Longtin, Paquet-Bolduc, J. Longtin, Ben-David, Loo.
Acquisition, analysis, or interpretation of data: Y. Longtin, Paquet-Bolduc, Gilca, Garenc, Fortin, J. Longtin, Trottier, Gervais, Roussy, Lévesque, Cloutier, Loo.
Drafting of the manuscript: Y. Longtin.
Critical revision of the manuscript for important intellectual content: Gilca, Trottier, Gervais, Roussy, Lévesque, Ben-David, Cloutier, Loo.
Statistical analysis: Gilca, Garenc, Fortin.
Administrative, technical, or material support: Paquet-Bolduc, Trottier.
Study supervision: Y. Longtin, Paquet-Bolduc.
Conflict of Interest Disclosures: Dr Y. Longtin reported being a coapplicant on patent PCT/CA2015/050020 held by The Royal Institution for the Advancement of Learning/McGill University on methods, reagents, and kits for the assessment of bacterial infections. Dr Gilca reported receiving research grants from Sanofi Pasteur. Dr Loo reported serving on the advisory boards of Merck and Otsuka Pharmaceuticals Inc and reported receiving speaker fees from Merck. No other disclosures were reported.
Funding/Support: This work was supported by the Québec Heart and Lung Institute. Surveillance activities conducted by the Québec National Institute of Public Health and the Québec Public Health Laboratory are funded by the Québec Ministry of Health and Social Services. Dr Y. Longtin’s research is funded by the Québec Foundation for Health Research.
Role of the Funder/Sponsor: The sponsors had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.
Additional Contributions: Michel Delamarre, MSc (Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale), Isabelle Perreault, MSc (Québec Heart and Lung Institute [QHLI]), Nathalie Thibault, MSc (QHLI), Marie-Claude Beaudoin, MD (QHLI), Alain Paradis, MD (Centre Hospitalier Universitaire de Québec), and Pascal Martineau, TM (QHLI) provided logistical support throughout the study, and Manale Ouakki, PhD (Institut National de Santé Publique du Québec) provided expert statistical support. None of these contributors were compensated for their contribution. Rosemary Sudan (Geneva, Switzerland) provided remunerated expert editorial support.
2.Lessa
FC, Mu
Y, Bamberg
WM,
et al. Burden of
Clostridium difficile infection in the United States.
N Engl J Med. 2015;372(9):825-834.
PubMedGoogle ScholarCrossref 4.Pépin
J, Valiquette
L, Alary
ME,
et al.
Clostridium difficile–associated diarrhea in a region of Quebec from 1991 to 2003: a changing pattern of disease severity.
CMAJ. 2004;171(5):466-472.
PubMedGoogle ScholarCrossref 5.McDonald
LC, Killgore
GE, Thompson
A,
et al. An epidemic, toxin gene–variant strain of
Clostridium difficile. N Engl J Med. 2005;353(23):2433-2441.
PubMedGoogle ScholarCrossref 6.Warny
M, Pepin
J, Fang
A,
et al. Toxin production by an emerging strain of
Clostridium difficile associated with outbreaks of severe disease in North America and Europe.
Lancet. 2005;366(9491):1079-1084.
PubMedGoogle ScholarCrossref 7.Loo
VG, Poirier
L, Miller
MA,
et al. A predominantly clonal multi-institutional outbreak of
Clostridium difficile–associated diarrhea with high morbidity and mortality.
N Engl J Med. 2005;353(23):2442-2449.
PubMedGoogle ScholarCrossref 9.Dubberke
ER, Carling
P, Carrico
R,
et al. Strategies to prevent
Clostridium difficile infections in acute care hospitals: 2014 update.
Infect Control Hosp Epidemiol. 2014;35(suppl 2):S48-S65.
PubMedGoogle Scholar 10.Vonberg
RP, Kuijper
EJ, Wilcox
MH,
et al; European
C difficile–Infection Control Group; European Centre for Disease Prevention and Control (ECDC). Infection control measures to limit the spread of
Clostridium difficile. Clin Microbiol Infect. 2008;14(suppl 5):2-20.
PubMedGoogle ScholarCrossref 11.Lanzas
C, Dubberke
ER, Lu
Z, Reske
KA, Gröhn
YT. Epidemiological model for
Clostridium difficile transmission in healthcare settings.
Infect Control Hosp Epidemiol. 2011;32(6):553-561.
PubMedGoogle ScholarCrossref 12.Riggs
MM, Sethi
AK, Zabarsky
TF, Eckstein
EC, Jump
RL, 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.
PubMedGoogle ScholarCrossref 13.Johnson
S, Clabots
CR, Linn
FV, Olson
MM, Peterson
LR, Gerding
DN. Nosocomial
Clostridium difficile colonisation and disease.
Lancet. 1990;336(8707):97-100.
PubMedGoogle ScholarCrossref 14.Galdys
AL, Curry
SR, Harrison
LH. Asymptomatic
Clostridium difficile colonization as a reservoir for
Clostridium difficile infection.
Expert Rev Anti Infect Ther. 2014;12(8):967-980.
PubMedGoogle ScholarCrossref 15.Bobulsky
GS, Al-Nassir
WN, Riggs
MM, Sethi
AK, Donskey
CJ.
Clostridium difficile skin contamination in patients with
C. difficile–associated disease.
Clin Infect Dis. 2008;46(3):447-450.
PubMedGoogle ScholarCrossref 16.Lanzas
C, Dubberke
ER. Effectiveness of screening hospital admissions to detect asymptomatic carriers of
Clostridium difficile: a modeling evaluation.
Infect Control Hosp Epidemiol. 2014;35(8):1043-1050.
PubMedGoogle ScholarCrossref 17.Loo
VG, Bourgault
AM, Poirier
L,
et al. Host and pathogen factors for
Clostridium difficile infection and colonization.
N Engl J Med. 2011;365(18):1693-1703.
PubMedGoogle ScholarCrossref 18.Stone
SP, Cooper
BS, Kibbler
CC,
et al. The ORION statement: guidelines for transparent reporting of outbreak reports and intervention studies of nosocomial infection.
Lancet Infect Dis. 2007;7(4):282-288.
PubMedGoogle ScholarCrossref 20.Hubert
B, Loo
VG, Bourgault
AM,
et al. A portrait of the geographic dissemination of the
Clostridium difficile North American pulsed-field type 1 strain and the epidemiology of
C. difficile–associated disease in Québec.
Clin Infect Dis. 2007;44(2):238-244.
PubMedGoogle ScholarCrossref 21.Gilca
R, Hubert
B, Fortin
E, Gaulin
C, Dionne
M. Epidemiological patterns and hospital characteristics associated with increased incidence of
Clostridium difficile infection in Quebec, Canada, 1998-2006.
Infect Control Hosp Epidemiol. 2010;31(9):939-947.
PubMedGoogle ScholarCrossref 23.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.
PubMedGoogle ScholarCrossref 24.Longtin
Y, Trottier
S, Brochu
G,
et al. Impact of the type of diagnostic assay on
Clostridium difficile infection and complication rates in a mandatory reporting program.
Clin Infect Dis. 2013;56(1):67-73.
PubMedGoogle ScholarCrossref 25.Bélanger
SD, Boissinot
M, Clairoux
N, Picard
FJ, Bergeron
MG. Rapid detection of
Clostridium difficile in feces by real-time PCR.
J Clin Microbiol. 2003;41(2):730-734.
PubMedGoogle ScholarCrossref 26.Miller
M, Gravel
D, Mulvey
M,
et al. Health care
–associated
Clostridium difficile infection in Canada: patient age and infecting strain type are highly predictive of severe outcome and mortality.
Clin Infect Dis. 2010;50(2):194-201.
PubMedGoogle ScholarCrossref 27.Tenover
FC, Arbeit
RD, Goering
RV,
et al. Interpreting chromosomal DNA restriction patterns produced by pulsed-field gel electrophoresis: criteria for bacterial strain typing.
J Clin Microbiol. 1995;33(9):2233-2239.
PubMedGoogle Scholar 28.Wagner
AK, Soumerai
SB, Zhang
F, Ross-Degnan
D. Segmented regression analysis of interrupted time series studies in medication use research.
J Clin Pharm Ther. 2002;27(4):299-309.
PubMedGoogle ScholarCrossref 29.Helfenstein
U. The use of transfer function models, intervention analysis and related time series methods in epidemiology.
Int J Epidemiol. 1991;20(3):808-815.
PubMedGoogle ScholarCrossref 32.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.
PubMedGoogle ScholarCrossref 33.Archbald-Pannone
LR, Boone
JH, Carman
RJ, Lyerly
DM, Guerrant
RL.
Clostridium difficile ribotype 027 is most prevalent among inpatients admitted from long-term care facilities.
J Hosp Infect. 2014;88(4):218-221.
PubMedGoogle ScholarCrossref 35.Zacharioudakis
IM, Zervou
FN, Pliakos
EE, Ziakas
PD, Mylonakis
E. Colonization with toxinogenic
C. difficile upon hospital admission, and risk of infection: a systematic review and meta-analysis.
Am J Gastroenterol. 2015;110(3):381-390.
PubMedGoogle ScholarCrossref 36.Gilca
R, Fortin
E, Frenette
C, Longtin
Y, Gourdeau
M. Seasonal variations in
Clostridium difficile infections are associated with influenza and respiratory syncytial virus activity independently of antibiotic prescriptions: a time series analysis in Quebec, Canada.
Antimicrob Agents Chemother. 2012;56(2):639-646.
PubMedGoogle ScholarCrossref 37.Bartsch
SM, Curry
SR, Harrison
LH, Lee
BY. The potential economic value of screening hospital admissions for
Clostridium difficile. Eur J Clin Microbiol Infect Dis. 2012;31(11):3163-3171.
PubMedGoogle ScholarCrossref 38.Kwon
JH, Olsen
MA, Dubberke
ER. The morbidity, mortality, and costs associated with
Clostridium difficile infection. Infect Dis Clin North Am. 2015;29(1):123-134.
PubMedGoogle ScholarCrossref