Managing Our Microbial Mark

2016 ◽  
Vol 42 (2-3) ◽  
pp. 393-428
Author(s):  
Ann Marie Marciarille
Keyword(s):  
United States ◽  
Infection Control ◽  
The United States ◽  
West African ◽  
Healthcare Associated Infections ◽  
Healthcare Facilities ◽  
Quality Reporting ◽  
Reporting Systems ◽  
Control Procedures ◽  
Healthcare Associated

The narrative of Ebola's arrival in the United States has been overwhelmed by our fear of a West African-style epidemic. The real story of Ebola's arrival is about our healthcare system's failure to identify, treat, and contain healthcare associated infections. Having long been willfully ignorant of the path of fatal infectious diseases through our healthcare facilities, this paper considers why our reimbursement and quality reporting systems made it easy for this to be so. West Africa's challenges in controlling Ebola resonate with our own struggles to standardize, centralize, and enforce infection control procedures in American healthcare facilities.

scholarly journals Healthcare-Associated Infections among Patients in a Large Burn Intensive Care Unit: Incidence and Pathogens, 2008–2012

2014 ◽  
Vol 35 (10) ◽  
pp. 1304-1306 ◽  
Author(s):  
David J. Weber ◽  
David van Duin ◽  
Lauren M. DiBiase ◽  
Charles Scott Hultman ◽  
Samuel W. Jones ◽  
...  
Keyword(s):  
United States ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
The United States ◽  
Burn Injuries ◽  
Morbidity And Mortality ◽  
Common Source ◽  
Healthcare Associated Infections ◽  
Burn Patients ◽  
Healthcare Associated

Burn injuries are a common source of morbidity and mortality in the United States, with an estimated 450,000 burn injuries requiring medical treatment, 40,000 requiring hospitalization, and 3,400 deaths from burns annually in the United States. Patients with severe burns are at high risk for local and systemic infections. Furthermore, burn patients are immunosuppressed, as thermal injury results in less phagocytic activity and lymphokine production by macrophages. In recent years, multidrug-resistant (MDR) pathogens have become major contributors to morbidity and mortality in burn patients.Since only limited data are available on the incidence of both device- and nondevice-associated healthcare-associated infections (HAIs) in burn patients, we undertook this retrospective cohort analysis of patients admitted to our burn intensive care unit (ICU) from 2008 to 2012.

scholarly journals 2467. Inferring Strain Type Attribution from Antibiotic Resistance Profiles among E. coli Causing Healthcare-Associated Infections in the United States, 2013–2017

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S854-S854
Author(s):  
Athena P Kourtis ◽  
Joseph D Lutgring ◽  
Edward Sheriff ◽  
Alison L Halpin ◽  
James Rasheed ◽  
...  
Keyword(s):  
United States ◽  
Laboratory Data ◽  
The United States ◽  
Targeted Prevention ◽  
Healthcare Associated Infections ◽  
E Coli ◽  
Laboratory Surveillance ◽  
Strain Type ◽  
Tract Infections ◽  
Healthcare Associated

Abstract Background E. coli is a leading cause of healthcare-associated infections; clonal group ST131, which has expanded worldwide with notable increased severity of infections, is commonly resistant to extended-spectrum cephalosporins (ESC) and fluoroquinolones (FQ). Herein, we relate ESC and FQ resistance profiles from CDC’s National Healthcare Safety Network (NHSN) with specific strain types from CDC laboratory surveillance collections. Methods NHSN isolate and antibiotic susceptibility testing data were collected from all E. coli associated with central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated events, or surgical site infections from 2013–2017. Resistance was scored as non-susceptibility to at least one drug per class [susceptible (S); resistant (R)]. ESC and FQ susceptibilities and multilocus sequence types (ST) using the Achtman 7 loci scheme were determined for a contemporaneous set of E. coli isolates collected through CDC laboratory surveillance. Results Of 96,672 E. coli infections reported to NHSN, 13% were ESC-R/FQ-R, 23% ESC-S/FQ-R, 4% ESC-R/FQ-S, and 60% were ESC-S/FQ-S. Among 105 ESC-R/FQ-R and 21 ESC-S/FQ-R laboratory isolates, the majority (67.6% and 52.4%, respectively) were ST131, whereas of 38 ESC-R/FQ-S and 53 ESC-S/FQ-S isolates, ST131 was a minority (18.4% and 7.5%, respectively). The odds of an isolate being ST131 were 10.5 if FQ-R (P < 0.001), 3.4 if ESC-R (P < 0.001), and 6.0 if ESC-R/FQ-R (P < 0.001). Using the national distribution of resistance combinations from NHSN, and assuming static ST-resistance distribution, we can infer that ST131 was responsible for 25.8% (95% CI, 23.9%-27.6%) of all E.coli healthcare-associated infections in the United States in 2013–2017. Conclusion Molecular inferences generated by applying laboratory data to resistance signature data in reportable datasets may make national E. coli ST burden estimates possible. Further characterization of resistance combinations with strain type, infection rates, and clinical outcomes may inform targeted prevention strategies at the local/regional level. Disclosures All authors: No reported disclosures.

scholarly journals Strategies to Prevent Clostridium difficile Infections in Acute Care Hospitals

2008 ◽  
Vol 29 (S1) ◽  
pp. S81-S92 ◽  
Author(s):  
Erik R. Dubberke ◽  
Dale N. Gerding ◽  
David Classen ◽  
Kathleen M. Arias ◽  
Kelly Podgorny ◽  
...  
Keyword(s):  
United States ◽  
Clostridium Difficile ◽  
Acute Care ◽  
Acute Care Hospitals ◽  
The United States ◽  
Binary Toxin ◽  
Healthcare Associated Infections ◽  
Executive Summary ◽  
Healthcare Associated

Previously published guidelines are available that provide comprehensive recommendations for detecting and preventing healthcare-associated infections. The intent of this document is to highlight practical recommendations in a concise format designed to assist acute care hospitals in implementing and prioritizing their Clostridium difficile infection (CDI) prevention efforts. Refer to the Society for Healthcare Epidemiology of America/Infectious Diseases Society of America “Compendium of Strategies to Prevent Healthcare-Associated Infections” Executive Summary and Introduction and accompanying editorial for additional discussion.1. Increasing rates of CDIC. difficile now rivals methicillin-resistant Staphylococcus aureus (MRSA) as the most common organism to cause healthcare-associated infections in the United States.a. In the United States, the proportion of hospital discharges in which the patient received the International Classification of Diseases, Ninth Revision discharge diagnosis code for CDI more than doubled between 2000 and 2003, and CDI rates continued to increase in 2004 and 2005 (L. C. McDonald, MD, personal communication, July 2007). These increases have been seen in pediatric and adult populations, but elderly individuals have been disproportionately affected. CDI incidence has also increased in Canada and Europe.b. There have been numerous reports of an increase in CDI severity.c. Most reports of increases in the incidence and severity of CDI have been associated with the BI/NAP1/027 strain of C. difficile. This strain produces more toxins A and B in vitro than do many other strains of C. difficile, produces a third toxin (binary toxin), and is highly resistant to fluoroquinolones.

scholarly journals 2450. A data-driven model of the economic burden of healthcare-associated infections as impacted by use of comprehensive genomic analysis of bacteria

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S847-S847
Author(s):  
Brian E Wong ◽  
Juan J Carmona ◽  
Mary M Fortunato-habib ◽  
Helen C van Aggelen ◽  
Alan J Doty ◽  
...  
Keyword(s):  
United States ◽  
Economic Burden ◽  
Genomic Analysis ◽  
Cost Savings ◽  
The United States ◽  
Vital Statistics ◽  
Economic Modeling ◽  
Whole Genome Sequence ◽  
Healthcare Associated Infections ◽  
Healthcare Associated

Abstract Background Each year, nearly 2 million patients contract and are affected by healthcare-associated infections (HAIs) in the United States alone, resulting in nearly 100K deaths. According to the Centers for Disease Control and Prevention (CDC), more patients die from HAIs in the United States per year than all breast and prostate cancer cases combined (National Vital Statistics Report, 2016). In addition to the mortality burden, the financial impact of HAIs within the hospital ecosystem is estimated to total between $28–45 billion. However, no economic model has demonstrated how early effective identification and mitigation of infection clusters can result in cost savings for hospitals until now. Methods As there is no publicly available data for infection cluster rates, we based our analysis on anonymized real-world retrospective data spanning 18 months (November 2016 to June 2018) from two US-based academic tertiary hospitals with a combined total of about 1,700 beds, then normalized to 800 beds. A cloud-computing platform (Philips IntelliSpace Epidemiology) was used for whole-genome sequence analysis and cluster identification. We determined that an average 800-bed facility would have an occurrence of 46 genetically related infectious clusters involving 2 or more patients (mean of 7.9, median of 3), affecting 180 patients in total. Results Given the average HAI treatment cost of $24,512 (average costs rescaled from literature to 2019 USD using PPI data), this represents a total cost of $4,412,160. If these clusters could have been limited to 2 patients, an additional 96 infections might have been prevented, representing a potentially avoidable economic burden of $2,353,152 for this 800-bed institution. Our data show that a 20% reduction in transmissions would drive a 3% overall reduction in HAIs, but results in savings of over $450,000. Conclusion Active, genomic-based surveillance can inform timely and precise preventative steps to help lower the size of infectious clusters. This health economic modeling shows that such measures can result in significant cost savings. As such, it recommends that prompt, dynamic detection of infectious clusters via genomics and active surveillance offers a relevant and timely strategy for cost savings within the healthcare ecosystem. Disclosures All authors: No reported disclosures.

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