Key Points español 中文 (chinese) Question
What is the incidence of hospital-onset Clostridium difficile infection (CDI) and its associated length of stay?
Findings
This systematic review and meta-analysis of 13 studies using patient-days as the denominator found that the incidence of hospital-onset CDI was 8.3 cases per 10 000 patient-days. Among propensity score–matched studies of the length of stay, the mean difference in length of stay between patients with and those without CDI varied from 3.0 to 21.6 days.
Meaning
Pooled estimates from currently available literature suggest that CDI is associated with a large burden on the US health care system.
Importance
An understanding of the incidence and outcomes of Clostridium difficile infection (CDI) in the United States can inform investments in prevention and treatment interventions.
Objective
To quantify the incidence of CDI and its associated hospital length of stay (LOS) in the United States using a systematic literature review and meta-analysis.
Data Sources
MEDLINE via Ovid, Cochrane Library Databases via Wiley, Cumulative Index of Nursing and Allied Health Complete via EBSCO Information Services, Scopus, and Web of Science were searched for studies published in the United States between 2000 and 2019 that evaluated CDI and its associated LOS.
Study Selection
Incidence data were collected only from multicenter studies that had at least 5 sites. The LOS studies were included only if they assessed postinfection LOS or used methods accounting for time to infection using a multistate model or compared propensity score–matched patients with CDI with control patients without CDI. Long-term-care facility studies were excluded. Of the 119 full-text articles, 86 studies (72.3%) met the selection criteria.
Data Extraction and Synthesis
Two independent reviewers performed the data abstraction and quality assessment. Incidence data were pooled only when the denominators used the same units (eg, patient-days). These data were pooled by summing the number of hospital-onset CDI incident cases and the denominators across studies. Random-effects models were used to obtain pooled mean differences. Heterogeneity was assessed using the I2 value. Data analysis was performed in February 2019.
Main Outcomes and Measures
Incidence of CDI and CDI-associated hospital LOS in the United States.
Results
When the 13 studies that evaluated incidence data in patient-days due to hospital-onset CDI were pooled, the CDI incidence rate was 8.3 cases per 10 000 patient-days. Among propensity score–matched studies (16 of 20 studies), the CDI-associated mean difference in LOS (in days) between patients with and without CDI varied from 3.0 days (95% CI, 1.44-4.63 days) to 21.6 days (95% CI, 19.29-23.90 days).
Conclusions and Relevance
Pooled estimates from currently available literature suggest that CDI is associated with a large burden on the health care system. However, these estimates should be interpreted with caution because higher-quality studies should be completed to guide future evaluations of CDI prevention and treatment interventions.
Clostridium difficile (also known as Clostridioides difficile) is the most common pathogen causing health care–associated infections in the United States, accounting for 15% of all such infections.1 A Centers for Disease Control and Prevention report on antibiotic resistance threats categorized C difficile as an urgent threat.2 Antibiotic treatment for C difficile infection (CDI) is often followed by recurrent infection, leading to nontraditional treatments, such as fecal transplant and oral administration of nontoxigenic C difficile spores.3,4
Information about the burden of CDI in the United States could inform investments in prevention and treatment interventions. This information should include the incidence of CDI, how this incidence has changed over time, and poor outcomes associated with CDI. Although prior studies have shown that CDI is associated with poor outcomes, such as recurrence, long hospital length of stay (LOS), mortality, and high treatment costs, these results vary by study location and patient population.2,5 In addition, many current estimates of the poor outcomes and costs associated with CDI do not take into account the underlying severity of illness among patients who develop CDI and may overestimate the true attributable outcomes.6
To address gaps in our understanding of the current burden associated with CDI in the United States, we conducted a systematic literature review of studies conducted in the United States and published after 2000 that evaluated the incidence of CDI and associated LOS. The goals were to describe the recent incidence of CDI and to evaluate LOS attributable to CDI.
This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)7 and Meta-analysis of Observational Studies in Epidemiology (MOOSE)8 reporting guidelines. An experienced health sciences librarian (A.B.) conducted systematic searches in MEDLINE via Ovid, Cochrane Library Databases via Wiley, Cumulative Index of Nursing and Allied Health Complete via EBSCO Information Services, Scopus, and Web of Science to identify articles published from the inception of the database to February 2019. Citations published before 2000 were excluded. A combination of keywords and subject headings were used for “Clostridium difficile,” “length of stay,” and “incidence.” The full search strategies can be found in eAppendix 1 in the Supplement.
Inclusion and Exclusion Criteria
Publications were included if they evaluated the incidence of CDI or LOS associated with CDI. Studies were excluded if they did not contain original data, did not have a control group, were published outside the United States, were published in a language other than English, or were published before 2000. The year 2000 was chosen as the beginning of this systematic literature review because that was when the epidemic BI/NAP1/027 strain of C difficile emerged, after which CDI increased in prevalence and became less responsive to treatment.4 We excluded studies if they assessed only a specific subset of patients, unless that population could be categorized as 1 of the following subsets: immunocompromised patients, patients in the intensive care unit, patients with cancer, patients with end-stage renal disease, patients undergoing hemodialysis, surgical patients, solid-organ transplant recipients, patients with high-risk gastrointestinal conditions, or peripartum women. We excluded studies with a study period of less than 1 year. We also excluded studies of long-term care facilities. Incidence data were collected only from multicenter studies that had at least 5 sites, because single-site or small studies may be biased by outbreaks or other local conditions. We included incidence studies with denominators of patient-days or person-years, known timing of the CDI such as after surgery or after admission (ie, hospital onset [HO]), or exclusion of patients with a history of CDI.
Studies were included in the LOS analysis only if they provided data on postinfection LOS, if they used methods accounting for time to infection using a multistate model, or if propensity score–matched patients with CDI were compared with uninfected controls.5,9 Studies were excluded if they did not have an uninfected control group or a denominator that included patients without CDI.
Data Extraction and Quality Assessment
Titles and abstracts of all articles were screened to assess inclusion criteria. Two of 9 independent reviewers (M.L.S., M.A.W., M.F.K., H.-Y.C., M.L.C., L.A.H., D.J.D., A.R.M., and E.N.P.) abstracted data for each article. Reviewers resolved disagreements by consensus.
The reviewers abstracted data on study design, study population, setting and years, inclusion and exclusion criteria, number of patients included, description of control group, definition of CDI, outcomes (eg, incidence and LOS), and an assessment of the potential risk of bias. Risk of bias was assessed using the Downs and Black scale.10 Reviewers followed all questions from this scale as written except for question 27 (a single item on the Power subscale, which was scored 0-5), which was changed to a yes or no. Two of us (A.R.M. and M.L.S.) performed component quality analysis independently, reviewed all inconsistent assessments, and resolved disagreements by consensus.11
Data analysis was performed in February 2019. Excel spreadsheet software version 2007 (Microsoft Corp) and RevMan statistical software version 5.3 (Cochrane Community) were used for statistical analysis. Incidence data were pooled only when the denominators used the same units (eg, patient-days). These data were pooled by summing the number of HO-CDI incident cases and the denominators across studies. Pooled incidence was reported as the number of incident cases per the given denominator (eg, 10 000 patient-days).12 No P values were calculated.
Of the 34 775 articles identified (Figure), 119 were full-text articles, and 86 (72.3%) of those articles met the selection criteria and were included in the systematic literature review.13-93 Among these, 66 articles evaluated incidence,13-78 and 20 articles evaluated LOS.16,54,66,79-95 One-fifth of the studies that assessed LOS (4 studies)84,87,91,94 scored 18 or more points of the 28 points possible on the Downs and Black scale10 and, thus, were considered to be of higher quality.
Incidence of CDI Calculated Using Patient-Days (13 Studies)
Sixty-six studies13-78 measured CDI incidence. Thirteen of those 66 studies13-25 used patient-days as the denominator (Table 1). Among these studies, the CDI incidence varied from 2.8 CDI cases per 10 000 patient-days22 to 15.8 CDI cases per 10 000 patient-days.20 Three studies13,17,23 were conducted by the Centers for Disease Control and Prevention. Three studies17,18,21 were done in New York State. One study24 from Southern California found that the incidence of community-onset, health care facility (HCF)–associated CDI (11.1 cases per 10 000 patient-days) was almost 2-fold higher than that for HO, HCF-associated CDI (6.8 cases per 10 000 patient-days). The pooled incidence of HO-CDI among the 13 studies13-25 (Table 1) that used patient-days as the denominator was 8.3 CDI cases per 10 000 patient-days. Four studies13,15,18,21 included more than 100 facilities.
The definitions of C difficile used to identify cases varied. Three studies17,18,21 used clinical findings and results of laboratory tests for C difficile, 3 studies13,14,23 used the Centers for Disease Control and Prevention surveillance definition to identify C difficile, 2 studies20,22 applied infection preventionist evaluations for C difficile surveillance, and 2 studies24,25 used only results of laboratory tests for C difficile. The remaining studies used a variety of ways to identify CDI, including International Classification of Diseases, Ninth Revision (ICD-9) codes or other billing codes,15,16,19 laboratory test results,15,16,20,23 clinical findings,15,23 and initial doses of C difficile antibiotic therapy.19 When we examined incidence by time period, we found that the early studies from 2000 to 2008 had a range from 2.8 to 12.2 CDI cases per 10 000 patient-days, studies from 2008 to 2009 had a range from 6.3 to 9.6 CDI cases per 10 000 patient-days, and the later studies after 2010 reported a range from 6.8 to 15.8 CDI cases per 10 000 patient-days (Table 1).
Incidence of CDI Calculated Using Person-Years (17 Studies)
Fourteen studies26-39 included both inpatients and outpatients (Table 2), reflected in a denominator of person-years in 8 studies.27-30,32,34,36,39 Seven of those 14 studies27-30,32,34,39 used only ICD-9 codes to define CDI. In a study36 of adult and adolescent patients with HIV/AIDS that included more than 100 hospitals, during 10 years of study, the peak incidence of CDI was 9.59 cases per 1000 person-years among patients with clinical AIDS. A study28 of the Armed Forces Health Surveillance Center in Maryland over the course of 12 years found the incidence of community-associated CDI to be 5.5 cases per 100 000 person-years. In a study29 evaluating the annual incidence of CDI and multiply recurrent CDI per 1000 person-years, the incidences increased by 42.7% and 188.8%, respectively, during a decade (2001-2012) in the United States. In another study30 with 12 years of data from 5 administrative databases, elderly people (ie, aged >65 years) had a CDI rate of 677 cases per 100 000 person-years. In contrast, a managed-care organization in Colorado found that the CDI incidence in 2007 was 14.9 CDI cases per 10 000 patient-years.32 These studies were too diverse to pool together into 1 estimate.
Three studies40-42 included only inpatients (Table 2). Two of these studies41,42 assessed the Agency for Healthcare Research and Quality (AHRQ) National Inpatient Sample (NIS). One evaluated infant patients from the AHRQ NIS cohort,41 and the other study evaluated adult patients from the AHRQ NIS cohort.42 Both studies documented substantial increases in CDI incidence between 2000 and 2005, from 2.8 to 5.1 cases per 10 000 hospitalizations, and from 5.5 to 11.2 cases per 10 000 hospitalizations, respectively.41,42 The third study,40 which was from the US National Hospital Discharge Survey between 2001 and 2010, found that the incidence of CDI in the pediatric population was 1.2 CDI discharges per 1000 total discharges.
Incident Cases of CDI (36 Studies)
Twenty-six studies43-68 documented HO-CDIs, which we assumed were incident cases (Table 3). Of these studies, the AHRQ NIS was the main data set, represented by 10 included studies.43,45,47,50,51,56,58,59,61,68 These studies assessed diverse patient populations with different comorbidities, including peripartum women68 and patients with inflammatory bowel disease,43 lymphoma,45 leukemia,58 subarachnoid hemorrhage treated with microsurgical or endovascular aneurysm repair,47 chronic liver disease,50 hematopoietic stem cell transplant,51 megacolon,56 or heart failure.59 Thus, the results of these studies were also too diverse to pool together. One study68 found that the CDI incidence among peripartum women increased from 0.36 cases per 10 000 in 1998 to 0.70 cases per 10 000 in 2006. The US National Hospital Discharge Survey database was represented in 6 included studies.49,52,53,55,64,65 These studies also assessed diverse patient populations, including children52 and adults with different comorbidities, such as cancer49,52 and inflammatory bowel disease.65 In 1 of these studies,65 the overall incidence of HO-CDI was 369.8 cases per 10 000 hospitalizations for inflammatory bowel disease. In that same study,65 the HO-CDI incidence was 445.6 cases per 10 000 hospitalizations for ulcerative colitis and 220.3 cases per 10 000 hospitalizations for Crohn disease.
Ten studies69-78 evaluated surgical patients (Table 3), and, thus, we assumed that the CDI cases were incident cases. Five studies73,75-78 used data from AHRQ NIS. These AHRQ NIS studies analyzed a variety of surgical procedures, including spine surgery76; hip,73 knee,77 or lower-extremity78 arthroplasty; and elective colon resections.75 One of them had CDI occurring in 1.4% of patients, for a rate of 144.99 cases of C difficile colitis per 10 000 elective colon resections, and the incidence increased from 1.31% in 2004 to 1.67% in 2006.75
LOS Associated With CDI (20 Studies)
Twenty studies16,54,66,79-94 (Table 4) evaluated CDI-associated LOS. Sixteen studies54,66,79-89,92,94,95 used propensity score matching to evaluate LOS associated with CDI, 2 studies16,93 used postinfection LOS, 1 study90 matched on LOS from admission until either positive C difficile test results or discharge, and 1 study91 accounted for time to infection using a multistate model. Also, one of the propensity score matched–studies applied multistate modeling to account for timing of infection.88 Pediatric patients were included in 3 of these studies.66,86,87
Among the 13 propensity score–matched studies of adults, the CDI-associated mean difference in LOS (in days) between patients with CDI and patients who did not have CDI varied greatly from 3.0 days (95% CI, 1.44-4.63 days)79 to 10.3 days.54 Among the 3 pediatric propensity score–matched studies,66,86,87 the highest CDI-associated mean difference in LOS (in days) was 21.6 days (95% CI, 19.29-23.90 days).66
Among the studies that used multistate models to account for timing of infection, a study91 performed in the Veterans Affairs health care system found that the magnitude of its estimated impact was smaller when methods were used to account for the time-varying nature of infection. That study estimated a CDI-attributable LOS of only 2.27 days (95% CI, 2.14-2.40 days).91 The other study88 that performed propensity score matching and used a multistate model converged on similar excess LOS estimates of 3.1 days (95% CI, 1.7-4.4 days) and 3.3 days (95% CI, 2.6-4.0 days), respectively.
Four studies84,87,91,94 that evaluated LOS earned 18 or more points on the Downs and Black scale.10 One study91 also used multistate modeling. Another was also performed in the Veterans Affairs health care system84,91 and found a mean difference between patients with and without CDI of 7.5 days.84 One study87 of pediatric patients found that those with CDI had a longer LOS (adjusted odds ratio, 4.34; 95% CI, 3.97-4.83). Another study94 of adult patients in Pennsylvania hospitals showed an attributable hospital LOS difference of 2.4 days (95% CI, 0.7-4.4 days; P < .01) between patients with and without CDI.
National epidemiological investigations have demonstrated recent marked increases in CDI in the United States.34 Thus, a national public health response to this increase requires current estimates of the CDI incidence.96-98 Our systematic review of the literature found that the CDI incidence varied by study and that the investigators used different denominators when they calculated the incidence for specific study populations. In our meta-analysis of studies that used patient-days as the denominator, we estimated the incidence of CDI in the United States to be 8.3 CDI cases per 10 000 patient-days.
Variation in CDI incidence may be due, in part, to advances in diagnostic technology and variations in diagnostic practices.99-101 Nucleic acid amplification tests are more sensitive than traditional C difficile stool tests (eg, toxin enzyme immunoassay). Nucleic acid amplification tests have been used more frequently in clinical practice since 2009, when the first commercial polymerase chain reaction was approved by the US Food and Drug Administration.102 The topic of CDI testing methods and risk adjustment is complex.103,104 Concerns have been expressed about the adequacy of risk adjustment to account for different CDI testing methods (toxin enzyme immunoassay alone, polymerase chain reaction alone, toxin enzyme immunoassay plus glutamate dehydrogenase followed by polymerase chain reaction for discrepancies, polymerase chain reaction followed by toxin enzyme immunoassay, and other diagnostic options) across HCFs. The choice of testing methods substantially affects the performance of these testing algorithms.99-101
In addition, the CDI incidence found by these studies likely varied because of the different database structures adopted by the various hospitals.13-78 Some analyses were based on health care systems databases, but most used large infection control surveillance, state, or national discharge databases.13-25 Beginning in January 2013, the Centers for Medicare & Medicaid Services began requiring public reporting of CDI rates via the National Healthcare Safety Network for those hospitals participating in the Inpatient Prospective Payment System.64 Specifically, 1 study29 demonstrated an increase in the annual incidence of CDI and multiply recurrent CDI per 1000 person-years by 42.7% and 188.8%, respectively, between 2001 and 2012. Another CDI surveillance study33 in 7 US states reported an increase not only in community-associated CDI incidence rates but also an increase in health care–associated CDI incidence rates. Furthermore, CDI can complicate comorbid conditions and result in the need for additional hospital resources.34 Included studies detected an increase in the CDI incidence in patients with inflammatory bowel disease,43 patients with cancer,52 those undergoing surgery,75,76 and even infants.41 The results of our systematic review of literature and meta-analysis emphasize the need to perform C difficile surveillance and direct resources to the prevention of CDI in order to reduce the incidence across the United States.
This systematic literature review has some limitations. First, the results of systematic literature reviews and meta-analyses are only as valid as the results of the studies evaluated. Most studies included in this systematic literature review were of moderate-to-low quality and may have overestimated the outcomes. We need more high-quality studies so that we can accurately determine postinfection LOS, because LOS before the infection should not be attributed to C difficile.5 Second, we included studies that used ICD-9 codes to define CDI. The ICD-9 codes are used for billing purposes and are not ideal for surveillance. However, a prior meta-analysis105 found that the ICD-9 code for C difficile had good sensitivity, specificity, positive predictive value, and negative predictive value compared with clinical definitions. Third, we only included studies conducted in the United States and published in English, which limits the external validity of this research. We used these inclusion criteria because our goal was to evaluate the burden of CDI in the United States. Future systematic literature reviews should be performed to evaluate this burden in other countries. Fourth, we found heterogeneity in all LOS-stratified analyses (eAppendix 2 and eTable in the Supplement). We found that the higher-quality studies that used advanced statistical methods to attempt to account for time-dependent bias found lower CDI-attributable LOS compared with other studies that did not use advanced methods. In addition, our incidence estimates were derived from multicenter studies only. Incidence rates in small studies may be variable and subject to bias; thus, this criterion was established a priori to determine representative incidence rates. From incident cases of CDI (36 studies), we were unable to exclude recurrent and multiply recurrent CDI cases if the study did not exclude those cases. For this meta-analysis, we decided to calculate the incidence rate with studies with a similar denominator (patient-days), with a result of 8.3 CDI cases per 10 000 patient-days.
Pooled estimates from the currently available literature suggest that C difficile is associated with a large burden on the US health care system. However, these estimates should be used with caution, and higher-quality studies should be completed to guide future evaluations of C difficile prevention and treatment interventions.
Accepted for Publication: October 26, 2019.
Published: January 8, 2020. doi:10.1001/jamanetworkopen.2019.17597
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Marra AR et al. JAMA Network Open.
Corresponding Author: Marin L. Schweizer, PhD, Carver College of Medicine, Department of Internal Medicine, University of Iowa, 200 Hawkins Dr, Iowa City, IA 52242-1071 (marin-schweizer@uiowa.edu).
Author Contributions: Drs Marra and Schweizer had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Marra, Perencevich, Nelson, Samore, Diekema, Schweizer.
Acquisition, analysis, or interpretation of data: Marra, Perencevich, Samore, Khader, Chiang, Chorazy, Herwaldt, Kuxhausen, Blevins, Ward, McDanel, Nair, Balkenende, Schweizer.
Drafting of the manuscript: Marra, Chiang, Ward.
Critical revision of the manuscript for important intellectual content: Marra, Perencevich, Nelson, Samore, Khader, Chorazy, Herwaldt, Diekema, Kuxhausen, Blevins, McDanel, Nair, Balkenende, Schweizer.
Statistical analysis: Marra, Kuxhausen, Nair, Schweizer.
Obtained funding: Perencevich, Nelson, Samore, Schweizer.
Administrative, technical, or material support: Samore, Chiang, Chorazy, Diekema, Kuxhausen, Blevins, Ward, Balkenende, Schweizer.
Supervision: Herwaldt, Diekema, Schweizer.
Conflict of Interest Disclosures: Dr Samore reported receiving an Epicenter grant from the Centers for Disease Control and Prevention (CDC) and grants from the Department of Veterans Affairs (VA), Agency for Healthcare Research and Quality, National Institutes of Health, Western Institute for Biomedical Research, and Pfizer outside the submitted work. Ms Ward and Dr Nair reported receiving Epicenter grants from the CDC during the conduct of the study. No other disclosures were reported.
Funding/Support: This work was funded by the CDC’s Safe Healthcare, Epidemiology, and Prevention Research Development Program under contract 200-2011-42039 (principal investigator: Dr Samore). This work was also supported in part by Center of Innovation funding grant CIN 13-412 (principal investigator: Dr Perencevich) from the VA Health Services Research and Development Service. Dr Nelson was supported by VA Health Services Research and Development Career Development Award 11-210. Dr Schweizer was supported by VA Health Services Research and Development Career Development Award 11-215.
Role of the Funder/Sponsor: The funders 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 decision to submit the manuscript for publication.
Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the VA or the US government. Dr Perencevich, a JAMA Network Open associate editor, was not involved in the editorial review of or the decision to publish this article.
1.Magill
SS, O’Leary
E, Janelle
SJ,
et al; Emerging Infections Program Hospital Prevalence Survey Team. Changes in prevalence of health care-associated infections in U.S. hospitals.
N Engl J Med. 2018;379(18):1732-1744. doi:
10.1056/NEJMoa1801550PubMedGoogle ScholarCrossref 3.Gerding
DN, Meyer
T, Lee
C,
et al. Administration of spores of nontoxigenic
Clostridium difficile strain M3 for prevention of recurrent
C. difficile infection: a randomized clinical trial.
JAMA. 2015;313(17):1719-1727. doi:
10.1001/jama.2015.3725PubMedGoogle ScholarCrossref 5.Zhang
S, Palazuelos-Munoz
S, Balsells
EM, Nair
H, Chit
A, Kyaw
MH. Cost of hospital management of
Clostridium difficile infection in United States: a meta-analysis and modelling study.
BMC Infect Dis. 2016;16(1):447. doi:
10.1186/s12879-016-1786-6PubMedGoogle ScholarCrossref 6.Nelson
RE, Nelson
SD, Khader
K,
et al. The magnitude of time-dependent bias in the estimation of excess length of stay attributable to healthcare-associated infections.
Infect Control Hosp Epidemiol. 2015;36(9):1089-1094. doi:
10.1017/ice.2015.129PubMedGoogle ScholarCrossref 7.Liberati
A, Altman
DG, Tetzlaff
J,
et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration.
BMJ. 2009;339:b2700. doi:
10.1136/bmj.b2700PubMedGoogle ScholarCrossref 8.Stroup
DF, Berlin
JA, Morton
SC,
et al; Meta-analysis of Observational Studies in Epidemiology (MOOSE) Group. Meta-analysis of observational studies in epidemiology: a proposal for reporting.
JAMA. 2000;283(15):2008-2012. doi:
10.1001/jama.283.15.2008PubMedGoogle ScholarCrossref 10.Downs
SH, Black
N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions.
J Epidemiol Community Health. 1998;52(6):377-384. doi:
10.1136/jech.52.6.377PubMedGoogle ScholarCrossref 11.Alderson
PGS, Higgins
JPT. Assessment of Study Quality: Cochrane Reviewer’s Handbook 4.2.3. Chichester, UK: John Wiley & Sons, Ltd; 2004.
12.Chan
KY, Wang
W, Wu
JJ,
et al; Global Health Epidemiology Reference Group (GHERG). Epidemiology of Alzheimer’s disease and other forms of dementia in China, 1990-2010: a systematic review and analysis.
Lancet. 2013;381(9882):2016-2023. doi:
10.1016/S0140-6736(13)60221-4PubMedGoogle ScholarCrossref 14.Burger
T, Fry
D, Fusco
R,
et al. Multihospital surveillance of nosocomial methicillin-resistant
Staphylococcus aureus, vancomycin-resistant enterococcus, and
Clostridium difficile: analysis of a 4-year data-sharing project, 1999-2002.
Am J Infect Control. 2006;34(7):458-464. doi:
10.1016/j.ajic.2005.08.010PubMedGoogle ScholarCrossref 17.Gase
KA, Haley
VB, Xiong
K, Van Antwerpen
C, Stricof
RL. Comparison of 2
Clostridium difficile surveillance methods: National Healthcare Safety Network’s laboratory-identified event reporting module versus clinical infection surveillance.
Infect Control Hosp Epidemiol. 2013;34(3):284-290. doi:
10.1086/669509PubMedGoogle ScholarCrossref 18.Haley
VB, DiRienzo
AG, Lutterloh
EC, Stricof
RL. Quantifying sources of bias in National Healthcare Safety Network laboratory-identified
Clostridium difficile infection rates.
Infect Control Hosp Epidemiol. 2014;35(1):1-7. doi:
10.1086/674389PubMedGoogle ScholarCrossref 19.Kim
J, Smathers
SA, Prasad
P, Leckerman
KH, Coffin
S, Zaoutis
T. Epidemiological features of
Clostridium difficile-associated disease among inpatients at children’s hospitals in the United States, 2001-2006.
Pediatrics. 2008;122(6):1266-1270. doi:
10.1542/peds.2008-0469PubMedGoogle ScholarCrossref 20.Kamboj
M, Son
C, Cantu
S,
et al. Hospital-onset
Clostridium difficile infection rates in persons with cancer or hematopoietic stem cell transplant: a C3IC network report.
Infect Control Hosp Epidemiol. 2012;33(11):1162-1165. doi:
10.1086/668023PubMedGoogle ScholarCrossref 21.McDonald
LC, Lessa
F, Sievert
D,
et al; Centers for Disease Control and Prevention (CDC). Vital signs: preventing
Clostridium difficile infections.
MMWR Morb Mortal Wkly Rep. 2012;61(9):157-162.
PubMedGoogle Scholar 22.Miller
BA, Chen
LF, Sexton
DJ, Anderson
DJ. Comparison of the burdens of hospital-onset, healthcare facility-associated
Clostridium difficile infection and of healthcare-associated infection due to methicillin-resistant
Staphylococcus aureus in community hospitals.
Infect Control Hosp Epidemiol. 2011;32(4):387-390. doi:
10.1086/659156PubMedGoogle ScholarCrossref 23.Sohn
S, Climo
M, Diekema
D,
et al; Prevention Epicenter Hospitals. Varying rates of
Clostridium difficile-associated diarrhea at prevention epicenter hospitals.
Infect Control Hosp Epidemiol. 2005;26(8):676-679. doi:
10.1086/502601PubMedGoogle ScholarCrossref 25.Zilberberg
MD, Tabak
YP, Sievert
DM,
et al. Using electronic health information to risk-stratify rates of
Clostridium difficile infection in US hospitals.
Infect Control Hosp Epidemiol. 2011;32(7):649-655. doi:
10.1086/660360PubMedGoogle ScholarCrossref 27.Dubberke
ER, Olsen
MA, Stwalley
D,
et al. Identification of Medicare recipients at highest risk for
Clostridium difficile infection in the US by population attributable risk analysis.
PLoS One. 2016;11(2):e0146822. doi:
10.1371/journal.pone.0146822PubMedGoogle Scholar 29.Ma
GK, Brensinger
CM, Wu
Q, Lewis
JD. Increasing incidence of multiply recurrent
Clostridium difficile infection in the United States: a cohort study.
Ann Intern Med. 2017;167(3):152-158. doi:
10.7326/M16-2733PubMedGoogle ScholarCrossref 33.Lessa
FC, Mu
Y, Winston
LG,
et al. Determinants of
Clostridium difficile infection incidence across diverse United States geographic locations.
Open Forum Infect Dis. 2014;1(2):ofu048. doi:
10.1093/ofid/ofu048PubMedGoogle Scholar 34.Reveles
KR, Lawson
KA, Mortensen
EM,
et al. National epidemiology of initial and recurrent
Clostridium difficile infection in the Veterans Health Administration from 2003 to 2014.
PLoS One. 2017;12(12):e0189227. doi:
10.1371/journal.pone.0189227PubMedGoogle Scholar 36.Sanchez
TH, Brooks
JT, Sullivan
PS,
et al; Adult/Adolescent Spectrum of HIV Disease Study Group. Bacterial diarrhea in persons with HIV infection, United States, 1992-2002.
Clin Infect Dis. 2005;41(11):1621-1627. doi:
10.1086/498027PubMedGoogle ScholarCrossref 43.Barber
GE, Hendler
S, Okafor
P, Limsui
D, Limketkai
BN. Rising incidence of intestinal infections in inflammatory bowel disease: a nationwide analysis.
Inflamm Bowel Dis. 2018;24(8):1849-1856. doi:
10.1093/ibd/izy086PubMedGoogle ScholarCrossref 44.Barlam
TF, Soria-Saucedo
R, Ameli
O, Cabral
HJ, Kaplan
WA, Kazis
LE. Retrospective analysis of long-term gastrointestinal symptoms after
Clostridium difficile infection in a nonelderly cohort.
PLoS One. 2018;13(12):e0209152. doi:
10.1371/journal.pone.0209152PubMedGoogle Scholar 46.Brown
KA, Daneman
N, Jones
M,
et al. The drivers of acute and long-term care
Clostridium difficile infection rates: a retrospective multilevel cohort study of 251 facilities.
Clin Infect Dis. 2017;65(8):1282-1288. doi:
10.1093/cid/cix532PubMedGoogle ScholarCrossref 48.Davis
ML, Sparrow
HG, Ikwuagwu
JO, Musick
WL, Garey
KW, Perez
KK. Multicentre derivation and validation of a simple predictive index for healthcare-associated
Clostridium difficile infection.
Clin Microbiol Infect. 2018;24(11):1190-1194. doi:
10.1016/j.cmi.2018.02.013PubMedGoogle ScholarCrossref 52.Gupta
A, Pardi
DS, Baddour
LM, Khanna
S. Outcomes in children with
Clostridium difficile infection: results from a nationwide survey.
Gastroenterol Rep (Oxf). 2016;4(4):293-298.
PubMedGoogle Scholar 54.Jiang
Y, Viner-Brown
S, Baier
R. Burden of hospital-onset
Clostridium difficile infection in patients discharged from Rhode Island hospitals, 2010-2011: application of present on admission indicators.
Infect Control Hosp Epidemiol. 2013;34(7):700-708. doi:
10.1086/670993PubMedGoogle ScholarCrossref 59.Mamic
P, Heidenreich
PA, Hedlin
H, Tennakoon
L, Staudenmayer
KL. Hospitalized patients with heart failure and common bacterial infections: a nationwide analysis of concomitant
Clostridium difficile infection rates and in-hospital mortality.
J Card Fail. 2016;22(11):891-900. doi:
10.1016/j.cardfail.2016.06.005PubMedGoogle ScholarCrossref 61.Miller
AC, Polgreen
LA, Cavanaugh
JE, Polgreen
PM. Hospital
Clostridium difficile infection rates and prediction of length of stay in patients without
C. difficile infection.
Infect Control Hosp Epidemiol. 2016;37(4):404-410. doi:
10.1017/ice.2015.340PubMedGoogle ScholarCrossref 62.Pant
C, Deshpande
A, Gilroy
R, Olyaee
M, Donskey
CJ. Rising incidence of
Clostridium difficile related discharges among hospitalized children in the United States.
Infect Control Hosp Epidemiol. 2016;37(1):104-106. doi:
10.1017/ice.2015.234PubMedGoogle ScholarCrossref 66.Sammons
JS, Localio
R, Xiao
R, Coffin
SE, Zaoutis
T.
Clostridium difficile infection is associated with increased risk of death and prolonged hospitalization in children.
Clin Infect Dis. 2013;57(1):1-8. doi:
10.1093/cid/cit155PubMedGoogle ScholarCrossref 67.Murphy
CR, Avery
TR, Dubberke
ER, Huang
SS. Frequent hospital readmissions for
Clostridium difficile infection and the impact on estimates of hospital-associated
C. difficile burden.
Infect Control Hosp Epidemiol. 2012;33(1):20-28. doi:
10.1086/663209PubMedGoogle ScholarCrossref 72.Bovonratwet
P, Bohl
DD, Malpani
R, Nam
D, Della Valle
CJ, Grauer
JN. Incidence, risk factors, and impact of
Clostridium difficile colitis following primary total hip and knee arthroplasty.
J Arthroplasty. 2018;33(1):205-210. doi:
10.1016/j.arth.2017.08.004PubMedGoogle ScholarCrossref 73.Delanois
RE, George
NE, Etcheson
JI, Gwam
CU, Mistry
JB, Mont
MA. Risk factors and costs associated with
Clostridium difficile colitis in patients with prosthetic joint infection undergoing revision total hip arthroplasty.
J Arthroplasty. 2018;33(5):1534-1538. doi:
10.1016/j.arth.2017.11.035PubMedGoogle ScholarCrossref 80.Drozd
EM, Inocencio
TJ, Braithwaite
S,
et al. Mortality, hospital costs, payments, and readmissions associated with
Clostridium difficile infection among Medicare beneficiaries.
Infect Dis Clin Pract (Baltim Md). 2015;23(6):318-323. doi:
10.1097/IPC.0000000000000299PubMedGoogle ScholarCrossref 85.Magee
G, Strauss
ME, Thomas
SM, Brown
H, Baumer
D, Broderick
KC. Impact of
Clostridium difficile-associated diarrhea on acute care length of stay, hospital costs, and readmission: a multicenter retrospective study of inpatients, 2009-2011.
Am J Infect Control. 2015;43(11):1148-1153. doi:
10.1016/j.ajic.2015.06.004PubMedGoogle ScholarCrossref 88.Pak
TR, Chacko
KI, O’Donnell
T,
et al. Estimating local costs associated with
Clostridium difficile infection using machine learning and electronic medical records.
Infect Control Hosp Epidemiol. 2017;38(12):1478-1486. doi:
10.1017/ice.2017.214PubMedGoogle ScholarCrossref 90.Song
X, Bartlett
JG, Speck
K, Naegeli
A, Carroll
K, Perl
TM. Rising economic impact of
Clostridium difficile-associated disease in adult hospitalized patient population.
Infect Control Hosp Epidemiol. 2008;29(9):823-828. doi:
10.1086/588756PubMedGoogle ScholarCrossref 91.Stevens
VW, Khader
K, Nelson
RE,
et al. Excess length of stay attributable to
Clostridium difficile infection (CDI) in the acute care setting: a multistate model.
Infect Control Hosp Epidemiol. 2015;36(9):1024-1030. doi:
10.1017/ice.2015.132PubMedGoogle ScholarCrossref 94.Tabak
YP, Zilberberg
MD, Johannes
RS, Sun
X, McDonald
LC. Attributable burden of hospital-onset
Clostridium difficile infection: a propensity score matching study.
Infect Control Hosp Epidemiol. 2013;34(6):588-596. doi:
10.1086/670621PubMedGoogle ScholarCrossref 95.Zilberberg
MD, Nathanson
BH, Sadigov
S, Higgins
TL, Kollef
MH, Shorr
AF. Epidemiology and outcomes of
Clostridium difficile-associated disease among patients on prolonged acute mechanical ventilation.
Chest. 2009;136(3):752-758. doi:
10.1378/chest.09-0596PubMedGoogle ScholarCrossref 100.Moehring
RW, Lofgren
ET, Anderson
DJ. Impact of change to molecular testing for
Clostridium difficile infection on healthcare facility-associated incidence rates.
Infect Control Hosp Epidemiol. 2013;34(10):1055-1061. doi:
10.1086/673144PubMedGoogle ScholarCrossref 104.Thompson
ND, Edwards
JR, Dudeck
MA, Fridkin
SK, Magill
SS. Evaluating the use of the case mix index for risk adjustment of healthcare-associated infection data: an illustration using
Clostridium difficile infection data from the National Healthcare Safety Network.
Infect Control Hosp Epidemiol. 2016;37(1):19-25. doi:
10.1017/ice.2015.252PubMedGoogle ScholarCrossref 105.Goto
M, Ohl
ME, Schweizer
ML, Perencevich
EN. Accuracy of administrative code data for the surveillance of healthcare-associated infections: a systematic review and meta-analysis.
Clin Infect Dis. 2014;58(5):688-696. doi:
10.1093/cid/cit737PubMedGoogle ScholarCrossref