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Figure 1.
Movement of Patients With Clostridium difficile Infection From the Emergency Department (ED) Throughout the Hospital
Movement of Patients With Clostridium difficile Infection From the Emergency Department (ED) Throughout the Hospital

Network graph displaying the hospital ward, procedural, and diagnostic areas in the hospital visited by a subset of patients with C difficile infection who were diagnosed in the ED during a single year. The size of each circle represents the number of C difficile–positive patients who passed through that location. Yellow denotes bed census areas (eg, nursing units), and gray denotes procedural and diagnostic common areas. Patients with C difficile infection visited a mean (SD) of 4.2 (4.0) locations while hospitalized, including procedural and diagnostic common areas, representing multiple sites of potential contamination of surfaces and disease transmission. BMT indicates bone-marrow transplant unit; CT-1 and -2, computed tomographic scanner suites 1 and 2; CT-ED, CT scanner suite in the emergency department; ICU, intensive care unit; IR, interventional radiology; Med, medical nursing unit; MRI-1, magnetic resonance imaging suite 1; Msk, musculoskeletal unit; Neuro-1 and -2, neurology units 1 and 2; OR, operating room; and TCU, transitional care unit.

Figure 2.
Clostridium difficile Exposure Across the Hospital and the Risk of C difficile Infection (CDI)
Clostridium difficile Exposure Across the Hospital and the Risk of C difficile Infection (CDI)

The number of exposures in a given area of the hospital is represented by the size of each box. The risk of developing CDI if exposed is indicated by (1) the color of each box; (2) the percentage reported; and (3) explicitly as the fraction of CDI occurrences among exposed patients for each location. While room-level data were collected, outcomes for individual patient rooms were analyzed at the level of the nursing unit. Analyses for each area exclude individuals who were exposed elsewhere in the hospital. Compared with the hospitalwide overall CDI incidence of 1.3%, shaded locations had increased risk of disease among exposed patients. Only the emergency department (ED) computed tomographic (CT) scanner (CT-ED) met statistical significance in adjusted analyses (incidence, 4%; OR, 2.5; 95% CI, 1.2-5.2). BMT indicates bone-marrow transplant area; Card cath, cardiac catheterization suite; CT-1 and CT-2, CT scanner suites; Echo, echocardiogram area; ICU, intensive care unit; IR, interventional radiology; Med, any of multiple specified medicine units; MRI-1 and MRI-2, magnetic resonance imaging suites; Msk, musculoskeletal unit; Neuro-1 and Neuro-2, neurology units; OR, operating rooms; TCU, transitional care unit.

1.
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
2.
Brown  K, Valenta  K, Fisman  D, Simor  A, Daneman  N.  Hospital ward antibiotic prescribing and the risks of Clostridium difficile infection.  JAMA Intern Med. 2015;175(4):626-633. doi:10.1001/jamainternmed.2014.8273PubMedGoogle ScholarCrossref
3.
Freedberg  DE, Salmasian  H, Cohen  B, Abrams  JA, Larson  EL.  Receipt of antibiotics in hospitalized patients and risk for Clostridium difficile infection in subsequent patients who occupy the same bed.  JAMA Intern Med. 2016;176(12):1801-1808. doi:10.1001/jamainternmed.2016.6193PubMedGoogle ScholarCrossref
4.
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. doi:10.1056/NEJMoa1216064PubMedGoogle ScholarCrossref
5.
Longtin  Y, Paquet-Bolduc  B, Gilca  R,  et al.  Effect of detecting and isolating Clostridium difficile carriers at hospital admission on the incidence of C difficile infections: a quasi-experimental controlled study.  JAMA Intern Med. 2016;176(6):796-804. doi:10.1001/jamainternmed.2016.0177PubMedGoogle ScholarCrossref
6.
Echaiz  JF, Veras  L, Zervos  M, Dubberke  E, Johnson  L.  Hospital roommates and development of health care-onset Clostridium difficile infection.  Am J Infect Control. 2014;42(10):1109-1111. doi:10.1016/j.ajic.2014.06.023PubMedGoogle ScholarCrossref
Research Letter
December 2017

Using Spatial and Temporal Mapping to Identify Nosocomial Disease Transmission of Clostridium difficile

Author Affiliations
  • 1Division of Hospital Medicine, Department of Medicine, University of California, San Francisco
  • 2Health Informatics, UCSF Health, University of California, San Francisco
  • 3Division of Rheumatology, Department of Medicine, University of California, San Francisco
  • 4San Francisco VA Medical Center, San Francisco, California
  • 5Center for Digital Health Innovation, University of California, San Francisco
JAMA Intern Med. 2017;177(12):1863-1865. doi:10.1001/jamainternmed.2017.5506

Hospital-acquired Clostridium difficile infection (CDI) is associated with significant morbidity and mortality.1 While risk factors like antibiotic exposure modulate susceptibility, infection control efforts aimed at reducing contact with infectious spores are critical to prevent nosocomial transmission.2-5 During hospitalization, patients visit many procedural and diagnostic common areas, presenting opportunities for contact with contaminated surfaces. However, these potential exposures are not typically captured in analyses evaluating disease transmission.6 Electronic health record (EHR) data allow us to track patients in time and space, but these data are not typically leveraged for infection control quality improvement efforts. We evaluated whether using a room within 24 hours of a patient with CDI was associated with increased risk of CDI in specific areas across our hospital.

Methods

We analyzed EHR data from all adult hospitalizations in a large university hospital between January 2013 and December 2015. Patients with CDI were considered C difficile positive from the time the positive test was ordered until hospital discharge. Spaces were considered potentially contaminated for 24 hours after a patient with CDI visited them. All hospitalized patients who had not yet tested positive for CDI and passed through a space while it was potentially contaminated were considered exposed to C difficile, while patients who occupied the same space at any other time served as the unexposed control group. Patients were followed for 60 days from the time they passed through a given space for development of CDI, after allowing a 24-hour incubation period during which time the occurrence of CDI was deemed unrelated to exposure. Clostridium difficile infection was defined as a positive laboratory test for C difficile (toxin immunoassay or glutamate dehydrogenase antigen plus confirmatory polymerase chain reaction), obtained when the patient was either an inpatient or outpatient in our system.

Logistic regression with clustering by patient was used to calculate odds ratios (ORs) for developing CDI of exposed vs unexposed individuals who passed through the same space. Patients with CDI during the prior year were excluded. Covariates included age, sex, antibiotic use, number of location changes, length of stay, proton pump inhibitor use, inflammatory bowel disease, and prior hospitalization (within 90 days). This study was approved by the University of California institutional review board, waiving written informed consent.

Results

There were 86 648 adult hospitalizations and 434 745 patient location changes within the hospital during the study period. There were 1152 cases of laboratory-documented CDI (overall incidence, 1.3%). The CDI-positive patients moved through a mean (SD) 4.2 (4.0) hospital locations, potentially contaminating those spaces (Figure 1).

Exposure and risk of subsequent CDI varied across locations (Figure 2). Being exposed to CDI in the computed tomography scanner in the emergency department (CT-ED) was significantly associated with the development of CDI (incidence, 4%; OR, 2.5; 95% CI, 1.2-5.2). This effect remained significant (OR, 2.7; 95% CI, 1.3-5.7) after adjustment for covariates and in sensitivity analyses extending the incubation period to 72 hours (OR, 2.8; 95% CI, 1.2-6.3). Trends in other areas did not reach statistical significance, and the effect was not significant in an adjusted hospitalwide analysis. Subsequent investigation revealed that cleaning practices for the scanner table of the CT-ED had not yet been updated to match the standardized methods applied in other radiology suites.

Discussion

In a large university hospital, passing through the CT-ED within 24 hours after a patient with C difficile had been there was associated with increased risk of developing CDI. Our novel analytic methods identified a previously undiscovered opportunity for real-world practice change. We continue to monitor the success of improved cleaning practices implemented as a result of this study. Leveraging EHR data for spatial and temporal analytics may be a widely applicable strategy for infection control and quality improvement at other institutions and for other infectious diseases.

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Article Information

Corresponding Author: Sara G. Murray, MD, MAS, 533 Parnassus Ave, Room U125, Box 0131, San Francisco, CA 94143-0131 (sara.murray@ucsf.edu).

Accepted for Publication: August 12, 2017.

Published Online: October 23, 2017. doi:10.1001/jamainternmed.2017.5506

Author Contributions: Drs Murray and Yim 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.

Concept and design: Murray, Yim, Croci, Rajkomar, Schmajuk, Cucina.

Acquisition, analysis, or interpretation of data: Murray, Yim, Rajkomar, Schmajuk, Khanna, Cucina.

Drafting of the manuscript: Murray, Yim, Rajkomar, Schmajuk, Khanna, Cucina.

Critical revision of the manuscript for important intellectual content: Murray, Yim, Croci, Rajkomar, Schmajuk, Cucina.

Statistical analysis: Murray, Yim, Rajkomar.

Obtained funding: Cucina.

Administrative, technical, or material support: Croci, Cucina.

Supervision: Murray, Schmajuk, Cucina.

Conflict of Interest Disclosures: Dr Rajkomar reports serving as a research advisor to Google unrelated to the present work. Dr Khanna reports being the principal inventor of CareWeb and will benefit financially from its commercialization, also unrelated to the present work. No other disclosures are reported.

Additional Contributions: We thank Stephen Shiboski, PhD, Department of Epidemiology & Biostatistics, University of California, San Francisco; Maria E. Byron, MD, Division of General Internal Medicine, Department of Medicine, University of California, San Francisco; Health Informatics, UCSF Health, University of California, San Francisco; Michelle Mourad, MD, Division of Hospital Medicine, Department of Medicine, University of California, San Francisco; Andrew D. Auerbach, MD, MPH, Division of Hospital Medicine, Department of Medicine, University of California, San Francisco; Health Informatics, UCSF Health, University of California, San Francisco; and Margaret C. Fang, MD, MPH, Division of Hospital Medicine, Department of Medicine, University of California, San Francisco, for their input and collaboration on this manuscript. We also thank the Quality Improvement Executive Committee and the Department of Hospital Epidemiology and Infection Control (HEIC) at our institution for their collaboration on improving infection control practices in our hospital based on the findings of this study. They received no compensation for their contributions beyond that of their employment.

References
1.
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
2.
Brown  K, Valenta  K, Fisman  D, Simor  A, Daneman  N.  Hospital ward antibiotic prescribing and the risks of Clostridium difficile infection.  JAMA Intern Med. 2015;175(4):626-633. doi:10.1001/jamainternmed.2014.8273PubMedGoogle ScholarCrossref
3.
Freedberg  DE, Salmasian  H, Cohen  B, Abrams  JA, Larson  EL.  Receipt of antibiotics in hospitalized patients and risk for Clostridium difficile infection in subsequent patients who occupy the same bed.  JAMA Intern Med. 2016;176(12):1801-1808. doi:10.1001/jamainternmed.2016.6193PubMedGoogle ScholarCrossref
4.
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. doi:10.1056/NEJMoa1216064PubMedGoogle ScholarCrossref
5.
Longtin  Y, Paquet-Bolduc  B, Gilca  R,  et al.  Effect of detecting and isolating Clostridium difficile carriers at hospital admission on the incidence of C difficile infections: a quasi-experimental controlled study.  JAMA Intern Med. 2016;176(6):796-804. doi:10.1001/jamainternmed.2016.0177PubMedGoogle ScholarCrossref
6.
Echaiz  JF, Veras  L, Zervos  M, Dubberke  E, Johnson  L.  Hospital roommates and development of health care-onset Clostridium difficile infection.  Am J Infect Control. 2014;42(10):1109-1111. doi:10.1016/j.ajic.2014.06.023PubMedGoogle ScholarCrossref
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